U.S. patent application number 15/052831 was filed with the patent office on 2016-09-01 for system and method of analyzing social media to predict the churn propensity of an individual or community of customers.
The applicant listed for this patent is Ebrahim Bagheri, Mohammad-Amin Jashki, Aaron David Nielsen, Qiao Pang, Fattane Zarrinkalam. Invention is credited to Ebrahim Bagheri, Mohammad-Amin Jashki, Aaron David Nielsen, Qiao Pang, Fattane Zarrinkalam.
Application Number | 20160253688 15/052831 |
Document ID | / |
Family ID | 56741270 |
Filed Date | 2016-09-01 |
United States Patent
Application |
20160253688 |
Kind Code |
A1 |
Nielsen; Aaron David ; et
al. |
September 1, 2016 |
SYSTEM AND METHOD OF ANALYZING SOCIAL MEDIA TO PREDICT THE CHURN
PROPENSITY OF AN INDIVIDUAL OR COMMUNITY OF CUSTOMERS
Abstract
A system and method for mining social media signals and cues i)
created by a user (for example, a customer) and/or ii) to which the
user is exposed (the "data"), and for processing that data as it
relates to a service (including a fee or subscription-based
service), in order to predict the user's predisposition or
likelihood to either leave the subscription or the service or
reduce his/her engagement with the subscription or the service.
Inventors: |
Nielsen; Aaron David;
(Langley, CA) ; Pang; Qiao; (Surrey, CA) ;
Bagheri; Ebrahim; (Toronto, CA) ; Jashki;
Mohammad-Amin; (Toronto, CA) ; Zarrinkalam;
Fattane; (Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nielsen; Aaron David
Pang; Qiao
Bagheri; Ebrahim
Jashki; Mohammad-Amin
Zarrinkalam; Fattane |
Langley
Surrey
Toronto
Toronto
Toronto |
|
CA
CA
CA
CA
CA |
|
|
Family ID: |
56741270 |
Appl. No.: |
15/052831 |
Filed: |
February 24, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62120221 |
Feb 24, 2015 |
|
|
|
Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06F 16/337 20190101;
G06F 16/9535 20190101; G06Q 50/01 20130101; G06Q 30/0202
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/00 20060101 G06Q050/00; G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer implemented method of collecting/mining data relating
to social media influence around a customer, and analyzing said
data to predict a customer's predisposition to either leave a
subscription or a service or reduce his/her engagement with a
subscription or a service which comprises: a) receiving a plurality
of social media inputs associated with the customer; b) determining
a churn probability for the customer; and c) performing an action
based on the determined churn probability.
2. A computer-implemented method to characterise social influence
and to predict behavior of a user, said user being part of a social
network which comprises a) creating a dynamically updatable social
influence profile of the user, b) predicting future behavior of the
user based on influence given by the user and received by the user
from his social circles, and thereafter c) predicting the user's
predisposition to either leave a subscription or a service or
reduce his/her engagement with a subscription or a service.
3. A computer implemented method of collecting/mining data relating
to social media influence around a customer, and analyzing said
data to predict a customer's predisposition to either leave a
subscription or a service or reduce his/her engagement with a
subscription or a service comprises: a) identifying a social media
profile of the customer; b) comparing customer and his social media
profile to clusters of customers, based upon similar social media
profiles ("cohorts"); and c) calculating predicted churn behavior
of the customer, based upon known churn behavior of cohorts.
4. The method of claim 3 wherein cohorts are identified by a)
extracting a plurality of feature vectors of the customer; and b)
computing cohorts from the feature vectors.
5. The method of claim 3 wherein feature vectors are social network
inputs, cues and influences.
6. A system, comprising: an information module that is configured
to identify a user of a service; a probability module that is
configured to determine a churn probability for the user of the
service; and an action module that is configured to perform an
action based on the determined churn probability.
7. The system of claim 6 wherein the probability module includes a
churn calculator that is configured to analyze one or more
behaviors associated with the user within a plurality of social
networks and platforms (social media profile of the user), to
compare user and his social media profile to clusters of other
users, based upon similar social media profiles ("cohorts"); and to
calculate predicted churn behavior of the user, based upon known
churn behavior of cohorts.
8. A computer implemented method of designing an efficient customer
retention program for managing customer churn among customers of a
business, the customer retention program including an analysis of
the causes of customer churn and identifying customers who are most
likely to churn in the future, so that appropriate steps may be
taken to prevent customers who are likely to churn in the future
from churning, the method comprising: a) identifying a social media
profile of the customer; b) comparing customer and his social media
profile to clusters of customers, based upon similar social media
profiles ("cohorts"); c) calculating predicted churn behavior of
the customer, based upon known churn behavior of cohorts; and d)
performing an action based on the predicted churn behavior of the
customer.
9. A computer-implementable method for predicting and delivery of
churn signals for customers that are at risk of terminating their
subscription and/or service to the customer retention units at the
provider company, wherein the churn predictions are generated by
analysis of full social media profiles of customers.
10. The method of claim 9 wherein customer loyal/disloyal
characteristics towards services and subscriptions are used in the
churn prediction.
11. The method of claim 9 wherein customer engagement with rival
companies plays a factor in the prediction of churn signals.
12. The method of claim 9 wherein the influence of social networks
on customers is incorporated in the prediction of churn
signals.
13. The method of claim 9 wherein churn signals are utilized to
prevent customers from canceling their contracts and/or
subscriptions.
14. The method of claim 9 wherein a social media profile for a
customer is comprised of all of their historical posts, blogs,
status updated, communications, and general publicly available
material on their social media accounts from the group consisting
of, but not limited to, TWITTER, FACEBOOK, LINKEDIN, INSTAGRAM,
WORDPRESS, and GOOGLE+.
15. The method of claim 9 wherein a social media profile may
include private material on their social media accounts that is
available to the social media accounts of the companies they have
subscribed to but not the general public.
16. The method of claim 9 wherein life events are utilized in the
prediction of churn signals.
17. A machine implemented system that predicts and delivers churn
signals to customer relationship management (CRM) software of
service-based or subscription businesses for customers who are at
risk of cancelling their services which comprises: a) a processor
system that lives on the CRM software as a plug in; b) a second
processor that continuously monitors and processes SMPs of that
company's customers to find new churn signals; and c) a third
processor with live communication between the first and the second
processor and which delivers new signals from the first processor
to the second as soon as new churn signals are predicted.
18. The system of claim 17 wherein the second processor and the
third processor include a means to communicate with the first
processor and automatically submits new churn signals to processor
one in real time.
19. The system of claim 17 wherein an interface on the first
processor shows new churn signals for the customers and includes
reasons as to why such customers might churn and the probability of
the churn.
20. The system of claim 17 wherein the second and third processor
are the same.
21. The system of claim 17 wherein the second processor includes a
churn signal application management and interface.
22. The system of claim 17 wherein the social media profiles are
rendered from social media outlets selected from the group
consisting, but not limited to, FACEBOOK, TWITTER, INSTAGRAM,
LINKEDIN, and online blogs.
23. A non-transitory, tangible computer-readable medium storing
instructions adapted to be executed by a computer processor to
perform a method for generating a customer churn prediction, for an
entity in need of such prediction, said method comprising the steps
of: extracting and receiving, by a churn prediction program
executing on the computer processor, a variety of social media
inputs; pre-processing the social media inputs to identify relevant
social media posts, data trends and social network structures
(pre-processed data); extracting and engineering features of the
pre-processed data, such features comprising at least one of i)
assessed social media postings, ii) assessed life events, iii)
assessed engagement with the entity and competitors of said entity
iv) assessed trend predisposition of customers to the entity based
upon their prior churns, v) assessed one or more communities of
customers to the entity and predisposition of the customers to the
entity to churn based upon churn risk of the one or more
communities; create feature vectors based at least upon i) to v);
aggregating feature vectors into a database and creating churn
model in the processor (churn model of aggregated features);
determining, by the churn prediction program executing on the
computer processor, predicted churn behavior of any one customer to
the entity based upon, the comparison of at least one feature
vector of the any one customer to the churn model of aggregated
features.
Description
REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 62/120,221, filed Feb. 24, 2014, and titled
"SYSTEM AND METHOD OF ANALYZING SOCIAL MEDIA TO PREDICT THE CHURN
PROPENSITY OF AN INDIVIDUAL OR COMMUNITY OF CUSTOMERS," which is
hereby incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0002] This invention is related to the automatic prediction of
customer churn in service and subscription based businesses by
using intelligent and real-time analysis of customer's social media
profiles, engagement and community.
BACKGROUND OF THE INVENTION
[0003] The popularity of the subscription-based business model has
increased dramatically over the past 25 years, as more and more
service providers opt for the predictability and cash flow benefits
of monthly recurring revenue. Companies using subscription or
recurring billing models understand the potentially lucrative
lifetime value of each customer they sign up, especially if the
customer will commit to longer-term contracts, and so they are
willing to invest considerable amounts in new customer acquisition.
When customers prematurely leave a service provider, the recovery
of the acquisition costs can become a real problem. And so, service
providers devote substantial time and resources into customer
retention, by staffing "save teams" and offering loyalty incentives
such as price reductions, plan upgrades, and free devices in order
to maintain existing customers. However, the rate at which
customers cancel or opt for a competitor, called the "churn rate",
has really not abated that much, despite the efforts.
[0004] Two contemporary market attributes are contributing to an
increasing focus on solving the problem of consumer churn. First,
many of the new technology industries born during the past 30 years
or so are entering a period of relative maturity and near-total
market penetration. For example, the days of mass new customer
acquisition in the cable television, broadband internet, mobile
phone, retail banking, and insurance segments are all but over, and
now players in these markets have to rely on signing up their
competitors' customers in order to replace their own churning
customers. Secondly, with the boom in online connectivity, search
engines, mass digital media, and online consumer communities,
individuals are always only a mouse click away from being lured
away by a bigger, better deal. Trading existing customers for new
customers is a losing proposition, and so service providers are
investing heavily in churn solutions in order to retain their more
profitable existing customers.
[0005] The financial rewards that can be gained by a mere one or
two percent drop in annual churn rate are enormous for a large
service provider. Yet, to this point, churn reduction remains an
area of significant opportunity, because the current customer
retention solutions deployed by service providers can generally be
characterized as reactive, and driven by internal data. They are
reactive in the sense that most of the efforts made to "save" or
retain the customer are undertaken only after the customer
communicates their intent to cancel their subscription, at which
point it is unlikely any incentive will change their mind. And even
when some of the more innovative service providers deploy more
predictive churn indication technologies and processes, they are
almost exclusively driven by data collected from their own
management systems and customer databases, and pay little or no
attention to consumer data available in the external environment,
and specifically, the social web.
[0006] There is therefore, a great opportunity for a technical
innovation in customer churn--one that is predictive and proactive,
and that brings rich sources of social and web data and analysis
into the mix. Such an invention would complement rather than
displace current retention solutions, and would enable service
providers to treat churn with preventative measures. It would also
move retention efforts further upstream into the customer's
experience, leading to higher save rates and a reduction in the
cost of incentives.
[0007] The purpose of the present invention is therefore to help
service providers attack churn in a much more predictive and
proactive manner, which in turn will generate a financial return
for the service provider through higher retention rates, lower
loyalty incentive costs, and a superior customer experience.
SUMMARY OF THE INVENTION
[0008] It is an object of the present invention to mine social
media signals and cues i) created by a user and/or ii) to which the
user is exposed (the "data"), and to process that data as it
relates to a service or a subscription-based service, in order to
predict the user's predisposition to either leave the subscription
or the service or reduce his/her engagement with the subscription
or the service.
[0009] It is an object of the invention to provide methods, systems
and computer program products for reducing churn and/or improving
retention of customers of a service or a subscription, using social
media signals and cues i) created by a user and/or ii) to which the
user is exposed.
[0010] The present invention generally relates to a
computer-implemented method to characterize social influence and to
predict behavior of a user, said user being part of a social
network, and more particularly to a computer-implemented method
which comprises creating a dynamically updatable social influence
profile of the user, predicting future behavior of the user based
on influence given by the user and received by the user from his
social circles, and thereafter predicting the user's predisposition
to either leave a subscription or a service or reduce his/her
engagement with a subscription or a service.
[0011] The method and system of the invention depends upon the
characterization of influence among social media users, and in
assigning feature vector similar cohorts to a user in order to
predict future behavior of that user, using the proprietary "churn"
analysis of the invention.
[0012] It is believed that customer save rates can be increased by
increasing the sophistication and variety of ways to attempt to
save the customers from subscription or service cancellation. The
tools and techniques discussed herein relate to tailoring
computer-based customer saving procedures to a particular customer
who may not have yet explicitly signaled to the company/provider
the desire to cancel or reduce a service or a subscription. The
invention "catches" the user at least one step before such
conveyance.
[0013] The method and system of the present invention have
substantial benefits for service and subscription-based businesses,
which lose substantial revenue each year due to customer churn. To
mitigate these losses, companies have implemented customer
retention programs; however, these programs are limited in their
effectiveness. There are several reasons for this, including but
not limited to: [0014] Existing churn prediction programs are
limited to analyzing internally available business data such as
service usage patterns. [0015] Companies lack accurate
individual-level insights about their customers' personalities and
in particular their general propensity to change service providers.
[0016] Determining a customer's social community is a critical part
of churn prediction as a customer's network can greatly impact
their predisposition to switch. Existing programs attempt to assess
this via call patterns but fail to include the richness of social
media interactions. [0017] Existing social monitoring solutions are
designed to capture explicit brand mentions and hence fail to
capture non-explicit customer churn signals such as engagement with
competitors, general service dissatisfaction, and community
influence. [0018] It is very difficult to cost effectively
associate social profiles and signals to an individual's customer
record which is necessary for using social insights in a churn
prediction solution.
[0019] The present invention provides, in one aspect, a method for
the early prediction of communities of customers who are likely to
cancel their subscriptions or services. This is accomplished by
analyzing a customer's social media profile (SMP).
[0020] The present invention provides, in another aspect, a
computerized system for locating the social profiles for customers,
collecting their SMP, analyzing the information, and delivering the
results to businesses so they can proactively make efforts to
retain their customers before they decide to cancel their
products.
[0021] The present invention provides, in another aspect, a method
for finding individuals with low degrees of loyalty to their
service providers. These predictions are achieved by analyzing the
historical social media content of each individual and looking for
features indicating customer estrangement to service providers.
[0022] The present method and system is able to accurately and
proactively identify the churn risk of an individual customer or
community of customers. As a result, this method provides the
following benefits: [0023] Companies can proactively identify
customers that are at a high risk of churning and hence implement
an early retention strategy that will be more effective and less
costly than waiting for them to cancel [0024] Companies can modify
their retention offers based on the customer's overall propensity
to change providers. For example, a loyal customer that rarely
switches providers can be offered a smaller retention package.
[0025] Companies can examine the impact of social communities and
influence to improve their retention strategies. For example, a
company could shift the sentiment of a customer that is a high
influencer in their community. [0026] Companies can advance their
existing churn prediction systems with signals from the customer's
SMP.
[0027] The end result of implementing a churn prediction and
management program as outlined herein is to develop a better
understanding of the causes of churn and of the characteristics of
customers who will likely churn in the future and to generate a
target list of the most likely future churners. By attaining a
better understanding of the reasons for churn, and identifying the
most likely churners, the business/enterprise may implement a much
more efficient and much more effective customer retention
program.
[0028] The present invention provides, in one aspect, a computer
implemented method of collecting/mining data relating to social
media influence around a customer, and analyzing said data to
predict a customer's predisposition to either leave a subscription
or a service or reduce his/her engagement with a subscription or a
service which comprises: a) receiving a plurality of social media
inputs associated with the customer; b) determining a churn
probability for the customer; and c) performing an action based on
the determined churn probability.
[0029] The present invention provides, in another aspect a
computer-implemented method to characterize social influence and to
predict behavior of a user, said user being part of a social
network which comprises a) creating a dynamically updatable social
influence profile of the user, b) predicting future behavior of the
user based on influence given by user and received by the user from
his social circles, and thereafter c) predicting the user's
predisposition to either leave a subscription or a service or
reduce his/her engagement with a subscription or a service.
[0030] The present invention provides, in another aspect a computer
implemented method of collecting/mining data relating to social
media influence around a customer, and analyzing said data to
predict a customer's predisposition to either leave a subscription
or a service or reduce his/her engagement with a subscription or a
service comprises: [0031] a) identifying a social media profile of
the customer; [0032] b) comparing customer and his social media
profile to clusters of customers, based upon similar social media
profiles ("cohorts"); and [0033] c) calculating predicted churn
behavior of the customer, based upon known churn behavior of
cohorts.
[0034] The present invention provides, in another aspect a system,
comprising: an information module that is configured to identify a
user of a service; a probability module that is configured to
determine a churn probability for the user of the service; and an
action module that is configured to perform an action based on the
determined churn probability.
[0035] The present invention provides, in another aspect a computer
implemented method of designing an efficient customer retention
program for managing customer churn among customers of a business,
the customer retention program including an analysis of the causes
of customer churn and identifying customers who are most likely to
churn in the future, so that appropriate steps may be taken to
prevent customers who are likely to churn in the future from
churning, the method comprising: [0036] a) identifying a social
media profile of the customer; [0037] b) comparing customer and his
social media profile to clusters of customers, based upon similar
social media profiles ("cohorts"); [0038] c) calculating predicted
churn behavior of the customer, based upon known churn behavior of
cohorts; and [0039] d) performing an action based on the predicted
churn behavior of the customer.
[0040] The present invention provides, in another aspect a
computer-implementable method for predicting and delivery of churn
signals for customers that are at risk of terminating their
subscription and/or service to the customer retention units at the
provider company, wherein the churn predictions are generated by
analysis of full social media profiles of customers.
[0041] The present invention provides, in another aspect, a machine
implemented system that predicts and delivers churn signals to
customer relationship management (CRM) software of service-based or
subscription businesses for customers who are at risk of cancelling
their services which comprises: [0042] a) a processor system that
lives on the CRM software as a plug in; [0043] b) a second
processor that continuously monitors and processes SMPs of that
company's customers to find new churn signals; and [0044] c) a
third processor with live communication between the first and the
second processor and which delivers new signals from the first
processor to the second as soon as new churn signals are
predicted.
[0045] The present invention provides, in another aspect, a
non-transitory, tangible computer-readable medium storing
instructions adapted to be executed by a computer processor to
perform a method for generating customer churn prediction, for an
entity in need of such prediction, said method comprising the steps
of: extracting and receiving, by a churn prediction program
executing on the computer processor, a variety of social media
inputs; pre-processing the social media inputs to identify relevant
social media posts, data trends and social network structures
(pre-processed data); extracting and engineering features of the
pre-processed data, such features comprising at least one of i)
assessed social media postings, ii) assessed life events, iii)
assessed engagement with the entity and competitors of said entity
iv) assessed trend predisposition of customers to the entity based
upon their prior churns, v) assessed one or more communities of
customers to the entity and predisposition of the customers to the
entity to churn based upon churn risk of the one or more
communities; create feature vectors based at least upon i) to v);
aggregating feature vectors into a database and creating churn
model in the processor (churn model of aggregated features);
determining, by the churn prediction program executing on the
computer processor, predicted churn behavior of any one customer to
the entity based upon, the comparison of at least one feature
vector of the any one customer to the churn model of aggregated
features.
BRIEF DESCRIPTION OF THE FIGURES
[0046] FIG. 1 is a diagram depicting the complete computer system
100 used to identify communities with high likelihood of churn and
alert the customer retention departments.
[0047] FIG. 2 is a diagram of the Social Aggregator 200 component
of the present invention that creates a database of social media
profiles 240.
[0048] FIG. 3 is a diagram 300 of the Social Predictor
sub-component 121 that can predict the churn risk of patrons.
[0049] FIG. 4 is an example 400 of how cohorts of churned 411 and
not churned 421 customers will be utilized to predict the
likelihood of churn for new customers 401.
[0050] FIG. 5 is a flow chart 500 of how the Social Predictor
Scheduler 122 triggers a computation of churn risk on the condition
that the generated social media contents are relevant.
[0051] FIG. 6 is a flow chart 600 of how influential connections
641 in the social network of a customer 611 are determined.
[0052] FIG. 7 is a depiction 700 of a console where customer
retention agents would view a queue of churn risk signals.
[0053] FIG. 8 is a depiction 800 of a page where customer retention
agents would view details about a customer including the insights
that contributed to their churn risk assessment.
[0054] FIG. 9 is a flow chart of the general architecture of the
data integration, preprocessing, feature engineering and
extraction, feature vector generation and model generation.
[0055] FIG. 10 is a flow chart of "life event participation" in
churn prediction, in accordance with one aspect of method of the
invention.
[0056] FIG. 11 is a schematic of the "life event" prediction
component of the method of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0057] A detailed description of the one or more embodiments of the
present invention is provided below along with accompanying figures
that illustrate the principles of the invention. The invention is
described in connection with such embodiments, but the invention is
not limited to any embodiment. The scope of the invention is
limited only by the claims and the invention encompasses numerous
alternatives, modifications and equivalents. Numerous specific
details are set forth in the following description in order to
provide a thorough understanding of the invention. These details
are provided for the purpose of example and the invention may be
practiced according to the claims without some or all of these
specific details. For the purpose of clarity, technical material
that is known in the technical fields related to the invention has
not been described in detail so that the invention is not
necessarily obscured.
[0058] Unless specifically stated otherwise, it is appreciated that
throughout the description, discussions utilizing terms such as
"processing" or "computing" or "calculating" or "determining" or
"displaying" or the like, refer to the action of the processes of a
data processing system, or similar electronic computing device,
that manipulates and transforms data represented as physical
(electronic) quantities within the computer system's registers and
memories into other data similarly represented as physical
quantities within the computer system memories or registers or
other such information storage transmission, or display
devices.
[0059] The algorithms and displays with the applications described
herein are not inherently related to any particular computer or
other apparatus. Various general-purpose systems may be used with
programs in accordance with teachings herein, or it may prove
convenient to construct more specialized apparatus to perform the
required machine-implemented method operations. The required
structure for a variety of these systems will appear from the
description below. In addition, embodiments of the present
invention are not described with references to any particular
programming language. It will be appreciated that a variety of
programming languages may be used to implement the teachings of
embodiments of the invention as described herein.
[0060] An embodiment of the invention may be implemented as a
method of as a machine-readable non-transitory storage medium that
stores executable instructions that, when executed by a data
processing system, causes the system to perform a method. An
apparatus, such as a data processing system, can also be an
embodiment of the invention. Other features of the present
invention will be apparent from the accompanying drawings and from
the detailed description which follows.
[0061] Terms
[0062] The term "invention" and the like mean "the one or more
inventions disclosed in this application", unless expressly
specified otherwise.
[0063] The terms "an aspect", "an embodiment", "embodiment",
"embodiments", "the embodiment", "the embodiments", "one or more
embodiments", "some embodiments", "certain embodiments", "one
embodiment", "another embodiment" and the like mean "one or more
(but not all) embodiments of the disclosed invention(s)", unless
expressly specified otherwise.
[0064] The term "variation" of an invention means an embodiment of
the invention, unless expressly specified otherwise.
[0065] The term "device" and "mobile device" refer herein to any
personal digital assistants, Smart phones, other cell phones,
tablets and the like.
[0066] A reference to "another embodiment" or "another aspect" in
describing an embodiment does not imply that the referenced
embodiment is mutually exclusive with another embodiment (e.g., an
embodiment described before the referenced embodiment), unless
expressly specified otherwise.
[0067] The terms "including", "comprising", and variations thereof
mean "including but not limited to", unless expressly specified
otherwise.
[0068] The terms "a", "an", and "the" mean "one or more", unless
expressly specified otherwise.
[0069] The term "plurality" means "two or more", unless expressly
specified otherwise.
[0070] The term "herein" means in the present application,
including anything which may be incorporated by reference, unless
expressly specified otherwise.
[0071] The term "whereby" is used herein only to precede a clause
or other set of words that express only the intended result,
objective, or consequence of something that is previously and
explicitly recited. Thus, when the term "whereby" is used in a
claim, the clause or other words that the term "whereby" modified
do not establish specific further limits of the claim or otherwise
restricts the meaning or scope of the claim.
[0072] The term "e.g." and the like mean "for example", and thus
does not limit the term or phrase it explains. For example, in a
sentence "the computer sends data (e.g., instructions, a data
structure) over the Internet", the term "e.g." explains that
"instructions" are an example of "data" that the computer may send
over the Internet, and also explains that "a data structure" is an
example of "data" that the computer may send over the Internet.
However, both "instructions" and "a data structure" are merely
examples of "data", and other things besides "instructions" and "a
data structure" can be "data".
[0073] The term "respective" and the like mean "taken
individually". Thus if two or more things have "respective"
characteristics, then each such thing has its own characteristic,
and these characteristics can be different from each other but need
not be. For example, the phrase "each of two machines has a
respective function" means that the first such machine has a
function and the second such machine has a function as well. The
function of the first machine may or may not be the same as the
function of the second machine.
[0074] The term "i.e." and the like mean "that is", and thus limits
the term or phrase it explains. For example, in the sentence "the
computer sends data (i.e. instructions) over the Internet", the
term "i.e." explains that "instructions" are the "data" that the
computer sends over the Internet.
[0075] The term social media profile (abbreviated to "SMP")
includes, but is not limited to, social streams, follows (e.g.,
likes and follows), community influence, personality types and
social media engagement with peers, family members, the company and
competitors across social networks such as TWITTER.RTM.,
FACEBOOK.RTM., LINKEDIN.RTM. INSTAGRAM.RTM. GOOGLE+.RTM.
REDDIT.RTM. YELP.RTM. and WORDPRESS.RTM.. Within the scope of the
invention, analysis is carried out on the processed SMP to
intelligently infer the desired communities.
[0076] The term "user" may be interchanged with "customer" as
varying aspects of the invention are described. For example and in
the interest of clarity: a customer of a service or subscription
(whose social media behaviors and influences are mined and analyzed
in accordance with the present invention) is also a user of at
least one social media platform or network. A plurality of feature
vectors of the user are identified against which cohorts of the
user may be computed in order to predict the likelihood of the user
either leaving a subscription or a service (with which he/she is a
customer) or reduce his/her engagement with a subscription or a
service (with which he/she is a customer).
[0077] It is also to be understood that "user" need not necessarily
be an individual by a community of users. In other words, using the
method and system of the invention, predictions of user service
termination or reduction can be determined on a mass scale.
[0078] As used herein, "churn" or "churn rate" refers to a number
of individuals that leave a group or other collection over a
certain period of time, such as a number of customers that leave a
subscription-based service. Churn, therefore, is similar to
attrition, and may be the opposite of retention. For example, a
customer-based service model may succeed when customer churn is low
(and retention is high), and may fail when customer churn is high
(and retention is low), among other things. However, specifically
within the scope of the invention, churn may also refer to a user
reducing the type or nature of services from a
supplier/company/industry (while not fully leaving the service
entirely). In whichever form, churn is considered detrimental to a
supplier/company/industry. Industries that rely on
subscription-based service models, such as the cable television
industry, the cell phone industry, web-based services, and so on,
spend a considerable amount of time, money, and effort attempting
to identify reasons why their customer churn, in order to provide
retention incentives to customers that keep them from ending use of
provided services. However, prior to the present invention, their
efforts often lack insight or are driven by information received
directly from customers or from simple metrics, which may lead to
ineffective results and unsuccessful determinations as to why
customers are not being retained, among other problems.
[0079] Any given numerical range shall include whole and fractions
of numbers within the range. For example, the range "1 to 10" shall
be interpreted to specifically include whole numbers between 1 and
10 (e.g., 1, 2, 3, 4, . . . , 10) and non-whole numbers (e.g., 1.1,
1.2, . . . 1.9).
[0080] Where two or more terms or phrases are synonymous (e.g.,
because of an explicit statement that the terms or phrases are
synonymous), instances of one such term/phrase does not mean
instances of another such term/phrase must have a different
meaning. For example, where a statement renders the meaning of
"including" to be synonymous with "including but not limited to",
the mere usage of the phrase "including but not limited to" does
not mean that the term "including" means something other than
"including but not limited to".
[0081] As used herein, the terms "component" and "system" are
intended to encompass computer-readable data storage that is
configured with computer-executable instructions that cause certain
functionality to be performed when executed by a processor. The
computer-executable instructions may include a routine, a function,
or the like. It is also to be understood that a component or system
may be localized on a single device or machine or distributed
across several devices or machines.
[0082] Neither the Title (set forth at the beginning of the first
page of the present application) nor the Abstract (set forth at the
end of the present application) is to be taken as limiting in any
way as the scope of the disclosed invention(s). An Abstract has
been included in this application merely because an Abstract of not
more than 150 words is required under 37 C.F.R. Section 1.72(b).
The title of the present application and headings of the sections
provided in the present application are for convenience only, and
are not to be taken as limiting the disclosure in any way.
[0083] Why Care about Churn?
[0084] Consumers typically purchase products or subscribe to
services from businesses who they perceive to be offering the best
products or services at the lowest price. And while consumers are
often loyal to providers and brands they are familiar with, they
will surely shift allegiance if they believe they can obtain better
products or services or a better price somewhere else. Established
ongoing relationships with existing customers can be a significant
source of revenue for many businesses, while losing customers to
competitors can significantly cut into a company's revenue.
Managing this phenomenon, taking active steps to prevent customer
"churn" is a high priority for many businesses.
[0085] In many cases it is less expensive for a business to retain
existing customers than to acquire new ones. For this reason many
companies will go to great lengths to maintain their existing
customer base. In highly competitive industries it is common for
companies to implement elaborate customer loyalty programs or
aggressive customer retention programs to prevent or limit churn.
Such programs may offer incentives to customers to entice them to
continue buying the company's products or services or they may
simply provide some personalized contact or message to existing
customers to reinforce and strengthen the relationship.
[0086] Designing an efficient and effective customer retention
program can be difficult, especially when confronted with a large
diversified customer base. Companies may not know whether churning
is a significant problem or not. And if it is, which customer
groups are most likely affected. Furthermore, a company's tolerance
threshold for churn may be very low. Customer churn may be
considered a problem even though it may only affect a small
percentage of the overall customer base. Contacting all customers
during a customer retention program is too expensive and
inefficient. However, contacting too few customers could result in
a failure to contact many customers who are likely to churn and who
are the appropriate targets of the customer retention program.
Deciding who to contact, represents a significant obstacle to
preparing an effective customer retention program.
[0087] Despite the critical importance of customer churn, companies
have been challenged to accurately and cost effectively predict
customers that are at risk of leaving their services or
subscriptions. There are several key reasons for this, including
but not limited to:
[0088] Companies in high churn industries have begun to deploy
systems and processes that attempt to predict at-risk customers and
communicate proactively with them. They obtain data from their own
internal systems, and analyze for patterns and attributes which
they can correlate with probable customer outcomes. For example, a
wireless network may decide to reach out proactively to customers
in an area that has experienced an unusual outage, or a service
provider may identify customers placing three or more calls for
technical support to be at risk of leaving. However, the fact
remains that the great wealth of external, individual, attributable
customer preference and sentiment being expressed in real time on
social media channels is not yet effectively being added to the mix
by companies attempting to identify potential churners.
[0089] Greater emphasis is being placed by companies these days on
understanding their customers. Marketing teams are introducing
"Voice of the Customer" programs designed to elicit feedback and
measure the customer's willingness to recommend the product or
service, and building detailed psychographic and demographic
customer profiles in an attempt to understand the behaviors of
customer segments. However, the data and insights generated from
these exercises is generalized across groups of customers, and
still does not accurately capture the specific personality
attributes and attrition behaviors of individual consumers. So
while the general data is helpful in defining product
specifications and building marketing campaigns, the absence of
individual user-level insights prevents companies from taking
direct and specific action on individual cases.
[0090] When it comes to retention and attrition, the opinions and
actions of a customer's network of friends and family can be a
highly influential factor. One method that companies are using to
gauge that sort of influence is to map the customer's social
networks to gain a clearer understanding of the strongest social
forces acting upon them. For example, a wireless carrier could
analyze a list of frequently called phone numbers and determine who
a customer's closest social, family and professional contacts might
be. Currently, those analytical processes do not include social
media interactions, such as tweet frequency, lists of friends and
followers, and message content. However, having access to that
social media data would be of tremendous benefit to the carriers'
existing efforts, and perhaps even more valuable to other service
providers with no access to raw social node data such as phone
usage patterns.
[0091] Companies that are implementing social media listening or
monitoring tools have a large and growing number of options in the
marketplace. However, nearly all of the currently-available
listening technologies are configured to capture only explicit
brand and product name mentions, and filter out all non-explicit
messages and activity. Yet, the non-explicit signals are a rich
source of valuable insights, provided that the messages and
activity can be attributed to specific customers--something that
the conventional social listening solutions fail to do. By limiting
their analysis to explicit brand mentions, the current solutions
miss out on the opportunity to associate specific customers with
the more nuanced, but still very revealing messages they post about
dissatisfaction, engagement with competitors, and intent to
churn.
[0092] Any technology that claims to leverage social media profiles
and signals to predict churn must possess two important
characteristics, namely; attribution and cost-efficiency.
Attribution in this context refers to the technology's ability to
associate or connect a company's individual customers with their
social profiles in such a way that the past and future messages
they post are automatically attributed back to that specific
customer. Without an attribution component, it is nearly impossible
to predict individual churn situations. Further, the methods of
obtaining and analyzing social messages can be prohibitively
expensive if not done efficiently, and so the technology used to
perform these operations must be carefully designed and built with
cost efficiencies in mind. Currently available technologies are not
able to deliver against these two requirements.
[0093] The present invention addresses and resolves the
aforementioned challenges and provides a computer-implementation
method and system for predicting the customers that are highly
likely to cancel or change their service or subscription. With this
information, companies can proactively implement early retention
strategies that are lower cost and more effective.
[0094] One aspect of the present invention relates to the use of a
customer's "social media profile" (SMP) to predict whether they are
at risk of churning or not. The rise of social media outlets such
as TWITTER, FACEBOOK, Blogs, and INSTAGRAM has generated a wealth
of publicly available user information. Human subjects use these
social platforms to express their frustration, excitement, and
opinions. And their statements with regards to the services they
are subscribed to are no exception. While some people might not
have a social media presence most do and each customer of a company
can be characterized by his/her SMP. This profile includes a
history of their generated social content across all social media
outlets. SMPs can be leveraged to determine whether a particular
customer is at risk of cancelling their service/subscription. This
information can then be delivered to the businesses that are at
risk. Note that it makes a considerable difference to know about
this risk upfront, even before the customer knows they might churn,
than to wait until they contact the company and ask to terminate
their contract.
[0095] Examples of how the method in the present invention uses
real time and historical SMPs to help the businesses retain at risk
customers, include: [0096] 1. A telecommunications customer tweets
that they think their "internet subscription" is expensive compared
to others. Even though the customer didn't specifically mention the
company, the solution detects the concern and notifies the company
who proactively offers to reduce the customer's internet fees.
[0097] 2. An insurance customer asks his/her peers via FACEBOOK
posts if they have experienced the same excessive wait time when
trying to contact the company's customer service desk. The
insurance company calls the customer back to directly solicit the
customer's feedback and apologizes for long waits during the
customer's recent calls. [0098] 3. A cable customer writes a blog
post complaining that their premium cable package doesn't include
important hockey games. The cable company proactively offers the
customer a discount that includes additional channels which include
the important hockey games. [0099] 4. A wireless phone customer
sends several pictures of their data usage with angry comments
about being charged for extra data usage. The wireless company
sends an email to the customer notifying them that they have
doubled their limits for being a loyal customer.
[0100] In another embodiment, the present invention processes SMPs
of customers in real time and generates churn notifications if the
customers are engaged in conversations with their competitors. This
engagement can be a signal of their intention to explore the
opportunity of switching service/subscription providers. While a
single posting to a competitor does not necessarily indicate the
customer will churn, the cumulative engagement with rival companies
can significantly strengthen the signal and more definitively
indicate an upcoming switch.
[0101] In another aspect, the present invention analyzes the whole
history of the SMPs with a goal of determining how loyal each
customer generally is to services and subscriptions. As explained
earlier, not every individual has the same personality type when it
comes to dealing with their subscriptions. Some individuals
frequently change service providers while others only change if
absolutely required (e.g. they move to a new city and the old
provider is not available in the new geographical area). Customers
sometimes display this predisposition in social media outlets. By
analyzing the customer's history on social media platforms, it is
possible to determine the customer's predisposition to and
frequency of switching changing providers. This information can be
used in determining the customer's churn risk with a specific
provider.
[0102] Another embodiment of the present invention is to address
the influence of the social network connections on the company's
customers. If a customer's very close friend had an alarming
experience with the same company and shares it on a social media
platform, it is likely that the customer is going to reconsider
their relationship with the company.
[0103] More examples of how the above embodiments can be used to
help companies reduce customer churn are presented in the
following: [0104] 1. An insurance company's customer posts "Do you
offer special promotions for your most long term loyal customers?"
on a rival company FACEBOOK account. This, in combination with the
customer's SMP, triggers a churn notification and delivers it to
the current insurance company. The current insurance company
realizes that this customer might switch because she/he believes
that long term customers should be eligible for extra discounts.
They send a special promotion and thank you card to the customer
demonstrating they appreciate their business. [0105] 2. A cable
company's customer regularly posts on TWITTER. In January, 2014
they posted "Just signed up with cable company A. In June 2014,
they posted "Just switched to cable company B". In December 2014,
they posted "It's time to switch again. Happy to be a customer of
cable company C". The system finds this pattern and sends a
notification to cable company C that the customer has a propensity
to switch service providers every 6 months. The cable company
proactively reaches out after 4 months with a special promotion to
retain the customer. [0106] 3. A close friend of a bank's customer
posted "Bank X was offering a limited time extra 2% interest for
opening a new savings account. Got the deal!" on TWITTER. Also the
customer's sister retweeted a friend's message "Bank X is waiving
checking account fees if you open a new account in the next two
months!". These are processed as social network influencer features
and trigger a churn risk. The current bank is notified and can take
action to retain their customer.
[0107] Predicting Churn Using Similarity to Cohorts
[0108] As mentioned in the summary and title, one aspect of the
invention is to predict communities of customers with certain churn
behaviors. To accomplish this, clusters of patrons are created.
Individual customers are then compared to these clusters and the
individual's churn probability can be inferred from the community
that they belong to. The present invention, calls these groups of
customers with similar churn behaviors "cohorts". While the people
in each cohort may not necessarily share the same demographics,
they are similar in terms of their SMPs, engagement with rival
companies, degree of loyalty towards services/subscriptions, and
also influences from their social network. Therefore, their churn
risk scores are to be very close to each other based on those
constituents.
[0109] The present invention leverages existing clustering
algorithms to create these churn cohorts. Similar to any clustering
algorithm, a set of useful features are extracted from customers'
SMPs. Some of these features are natural language components while
others are numerical. Each of the natural language features goes
through a pipeline of natural language processing techniques and is
eventually transformed to a numerical feature. The clustering
algorithm will work with a vector of numeric features.
[0110] The initial set of cohorts is created from a manually
annotated set of customers. This is the training data set that the
cohorts are constructed from and used for answering churn
prediction questions. The customers in the training set are
annotated with churned and not churned labels. Features for each
customer are extracted and the clustering algorithm computes the
cohorts from these feature vectors. Most features are calculated
for three time periods: 1) short term 2) medium term, and, 3) long
term history. For the purposes of the present invention, 1 week, 1
month, and 6 months are used respectively for each time periods but
other alternatives can also be employed.
[0111] Below is the list of features that are used to train the
direct customer feedback cohorts. Other features may be included as
applicable.
v.sub.Customer
Cohort=(Neg.sub.Dir.sub.1w,Neg.sub.Dir.sub.1m,Neg.sub.Dir.sub.6m,Neg.sub.-
Ind.sub.1w,Neg.sub.Ind.sub.1m,Neg.sub.Ind.sub.6m,News.sub.1w,News.sub.1m,N-
ews.sub.6m,Pos.sub.Dir.sub.1w,Pos.sub.Dir.sub.1m,Pos.sub.Dir.sub.6m,Pos.su-
b.Ind.sub.1w,Pos.sub.Ind.sub.1m,Pos.sub.Ind.sub.6m,Comp.sub.Quest.sub.1w,C-
omp.sub.Quest.sub.1m,Comp.sub.Quest.sub.6m,Comp.sub.newa.sub.1w,Comp.sub.N-
ews.sub.1m,Comp.sub.News.sub.6m,Cancel.sub.1yCancel.sub.2y,Renew.sub.1y,Re-
new.sub.2y)
[0112] Neg_Dir_1w (Direct negative in the past week): Negative
sentiment score inferred from social contents that are directed at
the current company. This is a score that is calculated from the
SMP of the customer in the past week.
[0113] Neg_Dir_1m (Direct negative in the past month): Negative
sentiment score inferred from social contents of a specific
customer that are directed at the current company. This is a score
that is calculated from the SMP of the customer in the past
month.
[0114] Neg_Dir_6m (Direct Negative in the past 6 months): Negative
sentiment score inferred from social contents of a specific
customer that are directed at the current company. This is a score
that is calculated from the SMP of the customer in the past 6
months.
[0115] Neg_Indir_1w (Indirect negative in the past week): Negative
sentiment score inferred from social contents of a specific
customer that are indirectly mentioned about the current company.
These include contents about the industrial expertise of the
company. This is a score that is calculated from the SMP of the
customer in the past week.
[0116] Neg_Indir_1m (Indirect negative in the past month): Negative
sentiment score inferred from social contents of a specific
customer that are indirectly mentioned about the current company.
These include contents about the industrial expertise of the
company. This is a score that is calculated from the SMP of the
customer in the past month.
[0117] Neg_Indir_6m (Indirect Negative in the past 6 months):
Negative sentiment score inferred from social contents of a
specific customer that are indirectly mentioned about the current
company. These include contents about the industrial expertise of
the company. This is a score that is calculated from the SMP of the
customer in the past 6 months.
[0118] Pos_Dir_1w (Direct positive in the past week): Positive
sentiment score inferred from social contents of a specific
customer that are directed at the current company. This is a score
that is calculated from the SMP of the customer in the past
week.
[0119] Pos_Dir_1m (Direct positive in the past month): Positive
sentiment score inferred from social contents of a specific
customer that are directed at the current company. This is a score
that is calculated from the SMP of the customer in the past
month.
[0120] Pos_Dir_6m (Direct positive in the past 6 months): Positive
sentiment score inferred from social contents of a specific
customer that are directed at the current company. This is a score
that is calculated from the SMP of the customer in the past 6
months.
[0121] Pos_Indir_1w (Indirect positive in the past week): Positive
sentiment score inferred from social contents of a specific
customer that are indirectly mentioned about the current company.
These include contents about the industrial expertise of the
company. This is a score that is calculated from the SMP of the
customer in the past week.
[0122] Pos_Indir_1m (Indirect positive in the past month): Positive
sentiment score inferred from social contents of a specific
customer that are indirectly mentioned about the current company.
These include contents about the industrial expertise of the
company. This is a score that is calculated from the SMP of the
customer in the past month.
[0123] Pos_Indir_6m (Indirect positive in the past 6 months):
Positive sentiment score inferred from social contents of a
specific customer that are indirectly mentioned about the current
company. These include contents about the industrial expertise of
the company. This is a score that is calculated from the SMP of the
customer in the past 6 months.
[0124] News_1w (News about current company in the past week):
Neutral news announcement score inferred from social contents of a
specific customer about the current company. This is a score that
is calculated from the SMP of the customer in the past week.
[0125] News_1m (News about current company in the past month):
Neutral news announcement score inferred from social contents of a
specific customer about the current company. This is a score that
is calculated from the SMP of the customer in the past week.
[0126] News_6m (News about current company in the past 6 months):
Neutral news announcement score inferred from social contents of a
specific customer about the current company. This is a score that
is calculated from the SMP of the customer in the past week.
[0127] Comp_Quest_1w (Asking questions from a competitor in the
past week): Engaging in questions score inferred from questions
posed on competitor company's social media platforms by a specific
customer. This is a score that is calculated from questions posted
on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) of rival companies in the past week.
[0128] Comp_Quest_1m (Asking questions from a competitor in the
past week): Engaging in questions score inferred from questions
posed on competitor company's social media platforms by a specific
customer. This is a score that is calculated from questions posted
on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) of rival companies in the past week.
[0129] Comp_Quest_6m (Asking questions from a competitor in the
past week): Engaging in questions score inferred from questions
posed on competitor company's social media platforms by a specific
customer. This is a score that is calculated from questions posted
on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) of rival companies in the past week.
[0130] Comp_news_1w (News about a competitor in the past week):
Neutral news announcement score inferred from social contents about
the rival company by a specific customer. This is a score that is
calculated from the social media contents (e.g. Tweets, FACEBOOK
posts, GOOGLE+ posts, etc.) in the past week.
[0131] Comp_news_1m (News about a competitor in the past month):
Neutral news announcement score inferred from social contents about
the rival company by a specific customer. This is a score that is
calculated from the social media contents (e.g. Tweets, FACEBOOK
posts, GOOGLE+ posts, etc.) in the past month.
[0132] Comp_news_6m (News about a competitor in the past 6 months):
Neutral news announcement score inferred from social contents about
the rival company by a specific customer. This is a score that is
calculated from the social media contents (e.g. Tweets, FACEBOOK
posts, GOOGLE+ posts, etc.) in the past 6 months.
[0133] Cancel_1y (Services canceled in the past year): Service
cancellation score inferred from the number of service/subscription
cancellation announcements by a specific customer. This is a score
that is calculated from the SMP of the customer in the past
year.
[0134] Cancel_2y (Services canceled in the past 2 years): Service
cancellation score inferred from the number of service/subscription
cancellation announcements by a specific customer. This is a score
that is calculated from the SMP of the customer in the past 2
years.
[0135] Renew_1y (Services renewed in the past year): Service
renewal score inferred from the number of service/subscription
renewal announcements by a specific customer. This is a score that
is calculated from the SMP of the customer in the past year.
[0136] Renew_2y (Services renewed in the past two years): Service
renewal score inferred from the number of service/subscription
renewal announcements by a specific customer. This is a score that
is calculated from the SMP of the customer in the past year.
[0137] The social network features that are used to train the
social network influenced cohorts are presented in the following.
Other features may be included as applicable.
v.sub.Network
Cohort=(Net1.sub.Neg.sub.1w,Net1.sub.Neg.sub.1m,Net1.sub.Neg.sub.6m,Net2.-
sub.Neg,Net2.sub.Neg.sub.1m,Net2.sub.Neg.sub.6m,Net1.sub.Pos.sub.1w,Net1.s-
ub.Pos.sub.1m,Net1.sub.Pos .sub.6m,Net2.sub.Pos.sub.1w,Net2.sub.Pos
.sub.1m,Net2.sub.Pos.sub.6m,Net1.sub.Disc.sub.1w,Net1.sub.Disc.sub.1m,Net-
1.sub.Disc.sub.6m,Net2.sub.Disc.sub.1w,Net2.sub.Disc.sub.1m,Net2.sub.Disc6-
m,Net1.sub.Enc1w,Net1.sub.Enc.sub.1m,Net1.sub.Enc.sub.6m,Net2.sub.Enc.sub.-
1w,Net2.sub.Enc.sub.1m,Net2.sub.Enc.sub.6m,Net1.sub.Churn.sub.1w,Net1.sub.-
Churn.sub.1m,Net1.sub.Churn.sub.6m,Net2.sub.Churn.sub.1w,Net2.sub.Churn.su-
b.1m,Net2.sub.Churn.sub.6m)
[0138] Net1_Neg_1w (Negative in close social network in the past
week): Negative sentiment score inferred from social contents that
are generated from the customer's first circle of social network
about the current company. First circle includes acquaintances that
are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past week.
[0139] Net1_Neg_1m (Negative in close social network in the past
month): Negative sentiment score inferred from social contents that
are generated from the customer's first circle of social network
about the current company. First circle includes acquaintances that
are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past month.
[0140] Net1_Neg_6m (Negative in close social network in the past 6
months): Negative sentiment score inferred from social contents
that are generated from the customer's first circle of social
network about the current company. First circle includes
acquaintances that are direct connections and also have high
influence on the customer. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past 6 months.
[0141] Net1_Pos_1w (Positive in close social network in the past
week): Positive sentiment score inferred from social contents that
are generated from the customer's first circle of social network
about the current company. First circle includes acquaintances that
are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past week.
[0142] Net1_Pos_1m (Positive in close social network in the past
month): Positive sentiment score inferred from social contents that
are generated from the customer's first circle of social network
about the current company. First circle includes acquaintances that
are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past month.
[0143] Net1_Pos_6m (Positive in close social network in the past 6
months): Positive sentiment score inferred from social contents
that are generated from the customer's first circle of social
network about the current company. First circle includes
acquaintances that are direct connections and also have high
influence on the customer. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past 6 months.
[0144] Net2_Neg_1w (Negative in distant social network in the past
week): Negative sentiment score inferred from social contents that
are generated from the customer's second circle of social network
about the current company. Second circle includes acquaintances
that are direct connections and but are not in the first circle.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
week.
[0145] Net2_Neg_1m (Negative in distant social network in the past
month): Negative sentiment score inferred from social contents that
are generated from the customer's second circle of social network
about the current company. Second circle includes acquaintances
that are direct connections and but are not in the first circle.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
month.
[0146] Net2_Neg_6m (Negative in distant social network in the past
6 months): Negative sentiment score inferred from social contents
that are generated from the customer's second circle of social
network about the current company. Second circle includes
acquaintances that are direct connections and but are not in the
first circle. This is a score that is calculated from the social
media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.)
in the past 6 months.
[0147] Net2_Pos_1w (Positive in distant social network in the past
week): Positive sentiment score inferred from social contents that
are generated from the customer's second circle of social network
about the current company. Second circle includes acquaintances
that are direct connections and but are not in the first circle.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
week.
[0148] Net2_Pos_1m (Positive in distant social network in the past
month): Positive sentiment score inferred from social contents that
are generated from the customer's second circle of social network
about the current company. Second circle includes acquaintances
that are direct connections and but are not in the first circle.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
month.
[0149] Net2_Pos_6m (Positive in distant social network in the past
6 months): Positive sentiment score inferred from social contents
that are generated from the customer's second circle of social
network about the current company. Second circle includes
acquaintances that are direct connections and but are not in the
first circle. This is a score that is calculated from the social
media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.)
in the past week.
[0150] Net1_Disc_1w (Discourage current service in close social
network in the past week): Discouragement sentiment score inferred
from social contents that are generated from the customer's first
circle of social network and discouraging the use of current
company services. First circle includes acquaintances that are
direct connections and also have high influence on the customer.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
week.
[0151] Net1_Disc_1m (Discourage current service in close social
network in the past month): Discouragement sentiment score inferred
from social contents that are generated from the customer's first
circle of social network and discouraging the use of current
company services. First circle includes acquaintances that are
direct connections and also have high influence on the customer.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
month.
[0152] Net1_Disc_6m (Discourage current service in close social
network in the past 6 months): Discouragement sentiment score
inferred from social contents that are generated from the
customer's first circle of social network and discouraging the use
of current company services. First circle includes acquaintances
that are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past 6 months.
[0153] Net1_Enc_1w (Encourage joining competitor in close social
network in the past week): Encouragement sentiment score inferred
from social contents that are generated from the customer's first
circle of social network and encouraging the use of rival
companies' services. First circle includes acquaintances that are
direct connections and also have high influence on the customer.
This is a score that is calculated from the social media contents
(e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past
week.
[0154] Net1_Enc_1m (Encourage joining competitor in close social
network in the past month): Encouragement sentiment score inferred
from social contents that are generated from the customer's first
circle of social network and encourage the use of rival companies
services. First circle includes acquaintances that are direct
connections and also have high influence on the customer. This is a
score that is calculated from the social media contents (e.g.
Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
[0155] Net1_Enc_6m (Encourage joining competitor in close social
network in the past 6 months): Encouragement sentiment score
inferred from social contents that are generated from the
customer's first circle of social network and encourage the use of
rival companies services. First circle includes acquaintances that
are direct connections and also have high influence on the
customer. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past 6 months.
[0156] Net2_Disc_1w (Negative in distant social network in the past
week): Discouragement sentiment score inferred from social contents
that are generated from the customer's second circle of social
network and discourage the use of current company services. Second
circle includes acquaintances that are direct connections and but
not in the first circle. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past week.
[0157] Net2_Disc_1m (Negative in distant social network in the past
month): Discouragement sentiment score inferred from social
contents that are generated from the customer's second circle of
social network and discourage the use of current company services.
Second circle includes acquaintances that are direct connections
and but not in the first circle. This is a score that is calculated
from the social media contents (e.g. Tweets, FACEBOOK posts,
GOOGLE+ posts, etc.) in the past month.
[0158] Net2_Disc_6m (Negative in distant social network in the past
6 months): Discouragement sentiment score inferred from social
contents that are generated from the customer's second circle of
social network and discourage the use of current company services.
Second circle includes acquaintances that are direct connections
and but not in the first circle. This is a score that is calculated
from the social media contents (e.g. Tweets, FACEBOOK posts,
GOOGLE+ posts, etc.) in the past 5 month.
[0159] Net2_Enc_1w (Encourage joining competitor in distant social
network in the past week): Encouragement sentiment score inferred
from social contents that are generated from the customer's second
circle of social network and encourage the use of rival companies
services. Second circle includes acquaintances that are direct
connections and but not in the first circle. This is a score that
is calculated from the social media contents (e.g. Tweets, FACEBOOK
posts, GOOGLE+ posts, etc.) in the past week.
[0160] Net2_Enc_1m (Encourage joining competitor in distant social
network in the past month): Encouragement sentiment score inferred
from social contents that are generated from the customer's second
circle of social network and encourage the use of rival companies
services. Second circle includes acquaintances that are direct
connections and but not in the first circle. This is a score that
is calculated from the social media contents (e.g. Tweets, FACEBOOK
posts, GOOGLE+ posts, etc.) in the past month.
[0161] Net2_Enc_6m (Encourage joining competitor in distant social
network in the past 6 months): Encouragement sentiment score
inferred from social contents that are generated from the
customer's second circle of social network and encourage the use of
rival companies services. Second circle includes acquaintances that
are direct connections and but not in the first circle. This is a
score that is calculated from the social media contents (e.g.
Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6
months.
[0162] Net1_Churn_1w (Churn in close social network in the past
week): Churn announcement score inferred from social contents that
are generated from the customer's first circle of social network
and indicates that they churned. First circle includes
acquaintances that are direct connections and also have high
influence on the customer. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past week.
[0163] Net1_Churn_1m (Churn in close social network in the past
month): Churn announcement score inferred from social contents that
are generated from the customer's first circle of social network
and indicates that they churned. First circle includes
acquaintances that are direct connections and also have high
influence on the customer. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past week.
[0164] Net1_Churn_6m (Churn in close social network in the past 6
months): Churn announcement score inferred from social contents
that are generated from the customer's first circle of social
network and indicates that they churned. First circle includes
acquaintances that are direct connections and also have high
influence on the customer. This is a score that is calculated from
the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+
posts, etc.) in the past week.
[0165] Net2_Churn_1w (Churn in distant social network in the past
week): Churn announcement score inferred from social contents that
are generated from the customer's second circle of social network
and indicates that they churned. Second circle includes
acquaintances that are direct connections and but not in the first
circle. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past week.
[0166] Net2_Churn_1m (Churn in distant social network in the past
month): Churn announcement score inferred from social contents that
are generated from the customer's second circle of social network
and indicates that they churned. Second circle includes
acquaintances that are direct connections and but not in the first
circle. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past month.
[0167] Net2_Churn_6m (Churn in distant social network in the past 6
months): Churn announcement score inferred from social contents
that are generated from the customer's second circle of social
network and indicates that they churned. Second circle includes
acquaintances that are direct connections and but not in the first
circle. This is a score that is calculated from the social media
contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the
past 6 months.
[0168] First and Second Circle in Customers Social Network
[0169] The present invention utilizes several features in churn
risk prediction as provided herein. Cohorts are selected/composed
based on social network influence. For greater detailed
descriptions, these are named with Net1 or Net2 prefixes
herein.
[0170] An aspect of the invention employs "first circle versus
second circle" in a user's social network(s). The first and second
circles are assumed to be direct friends in a customer social
network, but the difference is that the first circle has a greater
influence on the customer behavior than the second circle. It is
worth noting, that even though first and second circle are
expressed as direct friends in a social network, within preferred
aspects of the present invention, the understood scope/definition
of first and second circle is more generic and is applicable to nth
order connections (e.g. friend of a friend, or friend of a friend
of a friend) and direct connections are only used as one
example.
[0171] A connection has various terminologies in different social
networks. A connection is equivalent to "friend",
"follower/followee", and "connection" in FACEBOOK, TWITTER, and
LINKEDIN respectively. In the present invention, "connection" is
used as the generic terminology that represents that two accounts
are related to each other directly on a social network.
[0172] In order to determine the first and second circles, the
following metrics are calculated for each connection of a customer.
These metrics are then added together to create a general
"influence" score. All connections with an influence score above a
threshold are considered to be in the first circle and the rest of
the connections are defined as the second circle. [0173] Favoriting
metric 621: This metric captures the actions that can be translated
as favoriting particular contents from a connection. For example,
"liking" a photo or a post in FACEBOOK is a favoriting action. A
higher number of favoriting of a connection leads to a higher
favoriting metric for that connection. [0174] Mentions metric 622:
This metric captures the mentions of a connection on the social
media platform from the customer. For example, tagging a connection
in a photo is a mention, tagging someone in a tweet is also a
mention. A higher number of mentions of a connection leads to a
higher mention metric for that connection. [0175] Explicit
relationship metric 623: This metric captures the explicit
relationships that some social media platforms provide. For
example, a "sister" relationship or "married" relationship in
FACEBOOK is a form of explicit relationship. Each kind of
relationship has its own weight in the present invention. For
example, "married" leads to a higher score than "cousin". [0176]
Shared interest metric 624: This metric captures the commons
interests between a patron and its connection. Following the same
company on LINKEDIN is an example of a shared interest. Liking the
same FACEBOOK page is also another example. A higher number of
commons interests leads to a higher shared interest metric. [0177]
Engagement metric 625: This metric captures the level of engagement
between a user and its connection. Commenting on a connection's
photo is considered a single engagement for instance. Being tagged
in the same photo is another example. A higher number of
engagements leads to higher engagement metric.
[0178] Prediction of Churn Probability from Cohorts
[0179] In the previous section, a number of features were
described. Namely, two different vectors of features and their
feature elements were discussed. v.sub.Customer Cohort is the set
of vectors that are used for creating one category of clusters that
are mainly based on features that represent the individual
customer. The second set of vectors, v.sub.Network Cohort is the
vector set that are mainly composed of features that represent the
social network of the customer and hence incorporate the social
network influence for a particular patron.
[0180] For the training set C of customers with labeled each patron
c a pair of vectors (c.sub.v.sub.Customer Cohort,
C.sub.v.sub.Network Cohort) is constructed. Let
V={c.sub.v.sub.Customer Cohort: c.epsilon.C} and
N={c.sub.v.sub.Network Cohort: c.epsilon.C}. Three clustering
algorithms Alg1, Alg2, and Alg3 are defined below: [0181] ALG1:
k-means using Manhattan distance [0182] ALG2: k-medoids using
euclidean distance [0183] ALG3: Agglomerative using group average
All three of these algorithms are processed on the two sets of
vectors V and N defined above. The result is 6 groups of clusters
that represent cohorts. The present invention uses an ensemble
method comprised of, in one preferred aspect, the above six cohort
groups to predict the final churn probability of a customer. Once
the six cluster groups are created from the training model, the
churn probability of new patron can be calculated as follows:
Let
[0184]
Closest.sub.i(S,v)={distance(s.sub.j,v).ltoreq.distance(s.sub.k,v)-
:j.epsilon.{1 . . . i},s.sub.k=kth vector from v,s.epsilon.S}
Let
P(ALG,S,v)={.SIGMA.churn/.SIGMA.nochurn:churn,nochurn.epsilon.C={cCloses-
t.sub.i(ALG(S),v)}
Let p.sub.Customer
Cohort=1/3.SIGMA..sub.i=1.sup.3P(ALG.sub.i,V,c.sub.v.sub.Customer
Cohort)
Let P.sub.Network
Cohort=1/3.SIGMA..sub.i=1.sup.3P(ALG.sub.i,N,c.sub.v.sub.Network
Cohort)
Let P.sub.churn=.alpha.P.sub.Customer Cohort+.beta.P.sub.Network
Cohort w where .alpha.+.beta.=1
[0185] In order to give more weight to customer cohorts which are
direct representative of customer behavior versus network cohorts,
the present invention uses .alpha.=0.7, .beta.=0.3 in its current
form but all other variations of these weight assignments are
considered embodiments of the present invention.
[0186] In summary, within the method of the invention, multiple
clustering analyses generate "cohorts" of customers. A voting
mechanism computes a probability from both customer cohorts and
network cohorts, and eventually a weighting scheme calculates the
final churn possibility of a customer.
[0187] Further Comments on Preferred Aspects of the Invention
[0188] The present invention uses, in a preferred aspect, one or
more of the following components to effectively predict churn risk:
[0189] 1. Automatic identification of the social profiles for each
customer's contact record. For example, given an email address, a
step in the process of the invention will find the customer's
social media profiles on networks such as TWITTER, FACEBOOK,
INSTAGRAM and LINKEDIN. This enables the gathering of all signals
publically shared by the customer (as compared to only explicit
mentions of a brand which are detected by social listening
solutions). [0190] 2. In addition to identifying the customer's
SMP, a step in the process of the invention also identifies other
members of an account that the company may not be directly aware of
and listens for their social signals as well. For example, a
telecommunications account may be registered under a father's name,
but his live at home daughter may vocally share their family's
frustrations about the telecommunications company. A step in the
process of the invention determines these relationships using
strategies such as name similarity, the content and frequency of
social engagements and geo specific information. [0191] 3. The
invention also identifies other social media users that are
influential to the individual customer and other account holders.
For example, if an influencer tries to get a customer to switch it
greatly increases their churn risk. Similarly, a step in the
process of the invention determines the relative value of the
influencer to the organization based on their influence. [0192] 4.
A step in the process of the invention identifies communities of
social media users based on implicit relationships instead of
relationships explicitly observable in the network structure. For
example, by identifying communities of users with common interests
the process of the invention can then use the churn history of the
community members to calculate the churn probability of a given
individual community member. [0193] 5. As part of the churn
analysis, a step in the process of the invention detects a wide
variety of social signals including but not limited to: customers
explicitly or implicitly mentioning a company's or competitor's
brand or service and interacting with a company or competitor by
following their social media account and/or favoriting/retweeting
their content. It is important to note that the process not only
looks at individual signals, but also the totality or stream of
signals. [0194] 6. As part of the churn analysis, a step in the
process of the invention detects how a customer interacts with the
company. In particular, the process detects and assesses the
sentiment of explicit or implicit conversations and/or mentions of
the company's brands and services, interactions such as following
social media accounts, and signals such as favoriting or retweeting
content. It is important to note that the process not only looks at
individual signals, but also the totality or stream of signals.
[0195] 7. In addition to listening to company related signals, a
step in the process of the invention also detects the same signals
as related to competitors and their services. [0196] 8. The
invention also uses a broader array of content to assess the
customer's overall personality type. In particular, the customer's
general propensity to be loyal to their service providers or
regularly switch providers. As well, the frequency with which a
customer typically switches providers.
DETAILED DESCRIPTION OF THE FIGURES
[0197] FIG. 1 100 summarized the system of the present invention.
The invention starts from a collection of inputs for each
service-based or subscription company and finishes with a
continuous stream of churn signal deliveries. The system is
comprised of the following components: [0198] 1. Input 111: The
input of the system is a collection of whatever information the
company has from their customers. Most notably, email, name, phone
number and address. [0199] 2. Social Media Finder 114: This is a
third party service that accepts people's information and produces
all available social handles for them. [0200] 3. Social Proact 112:
This is a web interface for managing users to be monitored and
viewing churn signals. [0201] 4. Customer Database 113: The
database of customers with their information such as name, email,
etc. plus all their social media account handles. [0202] 5. Social
Aggregator 115: This is a service that monitors the social media
streams of all users that are being monitored for each company.
This uses the social media handles generated from Social Media
Finder component. New posts, tweets, and images persist as they are
generated. [0203] 6. Social Prediction Scheduler 122: This
component runs periodically and finds users that have generated new
social content. If the new content could be helpful in churn
prediction, a prediction task is scheduled for the Social Predictor
component. [0204] 7. Social Predictor 121: Every time a prediction
task is assigned to this component, the full social media profile
of that customer is analyzed, different features are computed and
if the customer has a churn risk, this component asks the Churn
Delivery Service to notify the appropriate companies. The churn
signals include the customer id, a probability of churn, and also a
brief summary of why the system has flagged this customer as a
churn risk. [0205] 8. Churn Delivery Service 131: This is a service
that delivers new churn signals to companies as soon as the Social
Predictor finds new customers that are at risk of leaving their
current company. [0206] 9. Output 141: The output of the system is
a collection of churn signals that are delivered to companies in
real time. Since taking the proper measures on time is very
important, these signals are delivered in real time as soon as they
are generated by the Social Predictor. Churn Delivery Service is
responsible for the delivery of the output to the proper
destination (usually companies' CRM solutions).
[0207] FIG. 2 200 depicts the details of how the Social Aggregator
works. This component is responsible for creating and maintaining
"social media profiles" for all customers. This component is
comprised of the following: [0208] 1. Customer Handles Database
113: This is a database of user account handles that have been
found by the Social Media Finder. These are used to monitor all
social media accounts of all customers in real time. [0209] 2.
TWITTER Stream Monitor 221: This component subscribes to TWITTER
streams for all the customer handles that exist in the system. As
soon as new tweets are generated for those users, they are stored
in a database server. [0210] 3. FACEBOOK Stream Monitor 222: This
component subscribes to FACEBOOK streams for all the customer
handles that exist in the system. As soon as new posts are
generated for those users, they are stored in a database server.
[0211] 4. LINKEDIN Stream Monitor 223: This component subscribes to
LINKEDIN streams for all the customer handles that exist in the
system. As soon as new posts are generated for those users, they
are stored in a database server. [0212] 5. GOOGLE Plus Stream
Monitor 224: This component subscribes to GOOGLE Plus streams for
all the customer handles that exist in the system. As soon as new
posts are generated for those users, they are stored in a database
server. [0213] 6. INSTAGRAM stream monitor 225: This component
subscribes to INSTAGRAM streams for all the customer handles that
exist in the system. As soon as new posts are generated for those
users, they are stored in a database server. [0214] 7. TWITTER API
211: The web API endpoint to pull new TWITTER 521 contents for each
customer account. [0215] 8. FACEBOOK API 212: The web API endpoint
to pull new FACEBOOK contents 522 for each customer account. [0216]
9. LINKEDIN API 213: The web API endpoint to pull new LINKEDIN
contents 523 for each customer account. [0217] 10. GOOGLE Plus API
214: The web API endpoint to pull new GOOGLE Plus contents 524 for
each customer account. [0218] 11. INSTAGRAM API 215: The web API
endpoint to pull new INSTAGRAM contents 525 for each customer
account. [0219] 12. Social Media Profiles Database 240: This is a
database that stores all the history of social media contents for
the users in a central location to be used by the Social Predictor
component.
[0220] FIG. 3 300 illustrates the sub-components of the Social
Predictor component. This part is responsible for generating the
churn signals for a community of users from their SMP history. The
sub-components are: [0221] 1. Input: The input to this component is
a local user id that can be used to retrieve all the social media
material generated by that user which is already populated in a
database server by Social Media Aggregator. [0222] 2. Social Media
Profile 312: This is a history of all social media material
generated by a customer. [0223] 3. Influencers Related Contents
311: These are the social media contents that are generated by
someone in the social network of the particular customer but are
also relevant to the services/subscriptions he/she currently has.
[0224] 4. Loyalty Analyzer 323: This sub-component goes through the
historical data and determines if the customer is a loyal patron to
his/her providers or not. [0225] 5. Social Content Analyzer 322:
This sub-component analyzes all of the historical data to find
patterns that indicate the customer is likely to churn. [0226] 6.
Influencers Analyzer 321: This sub-component analyzes the social
media materials generated by peers in the social network of the
customer to measure how likely he/she will be to be influenced by
others and leave their current contract/service. [0227] 7. Social
Media Learner 330: This is not a real time sub-component. But
instead an offline process that tries to find patterns for
customers with a high likeliness of churn. Once patterns are found,
they are stored as prediction models to be used by each of the
above analyzers. [0228] 8. Churn Signal Producer 340: Once all
levels of analysis are done for a customer, this sub-component
determines if a churn signal needs to be delivered to the relevant
companies. If a churn signal is bound to be delivered, a
probability measure is attached to the signal. Besides the
probability, a brief description of the indications that led to the
signal is also generated. This can be used by customer retention
employees to take the optimal action to keep the customer's
business.
[0229] FIG. 4 400 shows an example of a new customer whose
proximity with multiple cohorts, and hence his similarity to each
customer within those cohorts, determines his churn risk. The
circle around the customer illustrates an example of how the
neighbors of the customer across all clusters will help to
determine his churn risk. His characteristics are closer to that of
the churned group in this example and he will likely be flagged as
high risk. [0230] 1. Churned cohort 411: This is a pre-existing
cohort of customers that have been clustered in a group and have
also churned (as evident from training data) by one of the
clustering algorithms. Note that not all of the customers within a
cluster belong to the churned group. [0231] 2. Not Churned cohort
421: This is a pre-existing cohort of customers that have been
clustered in a group and have also not churned (as evident from
training data) by one of the clustering algorithms. Note that not
all of the customers within a cluster belong to the not churned
group. [0232] 3. New Customer 401: A patron who is not part of the
original training set and whose churn risk is to be determined
based on his Social Media Profile 312 and his Influencers Related
Contents 311.
[0233] FIG. 5 500 demonstrates how the Social Prediction Scheduler
subcomponent 112 works with Social Aggregator 115 and Social
Predictor 121 to filter out the contents that are not going to
affect churn risk but trigger a re-prediction when relevant social
media content is generated. [0234] 1. New Tweet 521: Including but
not limited to new tweets, or other TWITTER actions such as
following, being followed, and retweeting. [0235] 2. New FACEBOOK
Content 522: Including but not limited to new or updated posts,
friending, commenting, and liking. [0236] 3. New LINKEDIN Content
523: Including but not limited to new or updated posts, following,
commenting, and liking. [0237] 4. New GOOGLE+ Content 524:
Including but not limited to new or updated posts, adding to
circles, commenting, and liking. [0238] 5. New INSTAGRAM Content
525: Including but not limited to new or updated photos, friending,
commenting, and liking. [0239] 6. Is New Tweet Relevant? 511:
Determines if New Tweet 521 is relevant for computing churn risk.
If it is, a request is sent to Social Predictor 121 to recompute
the churn risk for that particular customer. Otherwise, no action
531 is taken. [0240] 7. Is New FACEBOOK Content Relevant? 512:
Determines if the new FACEBOOK content 522 is relevant for
computing churn risk. If it is, a request is sent to Social
Predictor 121 to recompute the churn risk for that particular
customer. Otherwise, no action 531 is taken. [0241] 8. Is New
LINKEDIN Content Relevant? 513: Determines if the new LINKEDIN
content 523 is relevant for computing churn risk. If it is, a
request is sent to Social Predictor 121 to recompute the churn risk
for that particular customer. Otherwise, no action 531 is taken.
[0242] 9. Is New GOOGLE+ Content Relevant? 514: Determines if the
new GOOGLE+ content 524 is relevant for computing churn risk. If it
is, a request is sent to Social Predictor 121 to recompute the
churn risk for that particular customer. Otherwise, no action 531
is taken. [0243] 10. Is New INSTAGRAM Content Relevant? 515:
Determines if the new INSTAGRAM content 525 is relevant for
computing churn risk. If it is, a request is sent to Social
Predictor 121 to recompute the churn risk for that particular
customer. Otherwise, no action 531 is taken.
[0244] FIG. 6 600 is a flow chart of how influential connections
641 in the social network of a customer 611 are determined. The
influential connections are also called "first circle" and
non-influential connections are named "second circle" in parts of
the present invention. Multiple metrics are computed for each pair
of customer and customer connection. The metrics participate in a
weighted addition and the result of that addition goes through a
threshold to decide if that connection is in first circle or not.
[0245] 1. Patron 611: This represents the SMP of the customer whose
connections need to be partitioned into influential and not
influential groups. [0246] 2. Connection 612: This is the
individual connection who is to be placed in either a first or
second circle. [0247] 3. Favoriting Metric 621: This metric
captures the actions that can be translated as favoriting
particular contents from a connection. For example, "liking" a
photo or a post in FACEBOOK is a favoriting action. A higher number
of favoriting of a connection leads to a higher favoriting metric
for that connection. [0248] 4. Mentions Metric 622: This metric
captures the mentions of a connection on the social media platform
from the customer. For example, tagging a connection in a photo is
a mention, tagging someone in a tweet is also a mention. A higher
number of mentions of a connection leads to a higher mention metric
for that connection. [0249] 5. Explicit Relationship Metric 623:
This metric captures the explicit relationships that some social
media platforms provide. For example, a "sister" relationship or
"married" relationship in FACEBOOK is a form of explicit
relationship. Each kind of relationship has its own weight in the
present invention. For example, "married" leads to a higher score
than "cousin". [0250] 6. Shared Interest Metric 624: This metric
captures the commons interests between a customer and his
connection. Following the same company on LINKEDIN is an example of
a shared interest. Liking the same FACEBOOK page is another
example. A higher number of commons interests leads to a higher
shared interest metric. [0251] 7. Engagement Metric 625: This
metric captures the level of engagement between a customer and his
connection. Commenting on a connection's photo is considered a
single engagement for instance. Being tagged in the same photo is
another example. A higher number of engagements leads to a higher
engagement metric. [0252] 8. Sum Threshold 630: All the above
metrics participate in a weighted sum. All sums above a certain
threshold will place the connection 612 in the influential circle
641 and the rest will belong to not influential 642. [0253] 9.
Influential 641: This is the influential group (i.e. first circle).
All connections above the threshold 630 will be placed in this
group. [0254] 10. Not Influential 642: This is the not influential
group (i.e. second circle). All connections below or equal to the
threshold 630 will be placed in this group.
[0255] FIG. 7 700 is a depiction of a console where customer
retention agents would be able to view a queue of churn risk
signals.
[0256] FIG. 8 800 is a depiction of a page where customer retention
agents would be able to view additional details about a customer
including the insights that contributed to their churn risk
assessment.
[0257] The present invention provides a non-transitory, tangible
computer-readable medium storing instructions adapted to be
executed by a computer processor to perform a method for generating
a customer churn prediction, for an entity in need of such
prediction, said method comprising the steps of: extracting and
receiving, by a churn prediction program executing on the computer
processor, a variety of social media inputs; pre-processing the
social media inputs to identify relevant social media posts, data
trends and social network structures (pre-processed data);
extracting and engineering features of the pre-processed data, such
features comprising at least one of i) assessed social media
postings, ii) assessed life events, iii) assessed engagement with
the entity and competitors of said entity iv) assessed trend
predisposition of customers to the entity based upon their prior
churns, v) assessed one or more communities of customers to the
entity and predisposition of the customers to the entity to churn
based upon churn risk of the one or more communities; create
feature vectors based at least upon i) to v); aggregating feature
vectors into a database and creating churn model in the processor
(churn model of aggregated features); determining, by the churn
prediction program executing on the computer processor, predicted
churn behavior of any one customer to the entity based upon, the
comparison of at least one feature vector of the any one customer
to the churn model of aggregated features.
[0258] The present invention further provides computer architecture
and system to support the implementation of the methods described
and claimed herein. In regards to the system, one or more described
webpages may be associated with a networking system or networking
service. However, alternate embodiments may have application to the
retrieval and rendering of structured documents hosted by any type
of network addressable resource or web site. Additionally, as used
herein, a user may be an individual, a group, or an entity (such as
a business or third party application).
[0259] Particular embodiments may operate in a wide area network
environment, such as the Internet, including multiple network
addressable systems. The system of the invention may operate in a
network environment, in which various example embodiments may
operate. For example, a network cloud generally represents one or
more interconnected networks, over which the systems and hosts
described herein can communicate. Network cloud may include
packet-based wide area networks (such as the Internet), private
networks, wireless networks, satellite networks, cellular networks,
paging networks, and the like.
[0260] Networking system is a network addressable system that, in
various example embodiments, comprises one or more physical servers
and data stores. The one or more physical servers may be operably
connected to computer network via, by way of example, a set of
routers and/or networking switches. In an example embodiment, the
functionality hosted by the one or more physical servers may
include web or HTTP servers, FTP servers, as well as, without
limitation, webpages and applications implemented using Common
Gateway Interface (CGI) script, PHP Hypertext Preprocessor (PHP),
Active Server Pages (ASP), Hyper Text Markup Language (HTML),
Extensible Markup Language (XML), Java, JavaScript, Asynchronous
JavaScript and XML (AJAX), Flash, ActionScript, and the like.
[0261] Physical servers may host functionality directed to the
operations of the networking system. The data store may store
content and data relating to, and enabling, operation of networking
system as digital data objects. A data object, in particular
embodiments, is an item of digital information typically stored or
embodied in a data file, database, or record. Content objects may
take many forms, including: text (e.g., ASCII, SGML, HTML), images
(e.g., jpeg, tif and gif), graphics (vector-based or bitmap),
audio, video (e.g., mpeg), or other multimedia, and combinations
thereof. Content object data may also include executable code
objects, podcasts, etc. Logically, the data store corresponds to
one or more of a variety of separate and integrated databases, such
as relational databases and object-oriented databases, that
maintain information as an integrated collection of logically
related records or files stored on one or more physical systems.
Structurally, the data store may generally include one or more of a
large class of data storage and management systems. In particular
embodiments, the data store may be implemented by any suitable
physical system(s) including components, such as one or more
database servers, mass storage media, media library systems,
storage area networks, data storage clouds, and the like. In one
example embodiment, the data store includes one or more servers,
databases (e.g., MySQL), and/or data warehouses. As is readily
apparent herein, the data store may include data associated with
different networking systems, users and/or commercial entity
(client) systems.
[0262] It is to be understood that churn rate measures a number of
individuals (for example, customer, clients, subscribers) that
leave a group or other collection over a certain period of time. A
relevant example is the number of customers that leave a
subscription-based service. Churn, therefore, is similar to
attrition, and may be the opposite of retention. For example, a
subscriber-based service model may succeed when subscriber churn is
low (and retention is high), and may fail when subscriber churn is
high (and retention is low), among other things.
[0263] Industries that rely on subscription-based service models,
such as the cable television industry, the cell phone industry,
web-based services, retail banking and insurance and so on, spend a
considerable amount of time, money, and effort attempting to
identify reasons why their customers churn, in order to provide
retention incentives to customers that keep them from ending use of
provided services. However, their efforts often lack insight or are
driven by information received directly from customers or from
simple metrics, which may lead to ineffective results and
unsuccessful determinations as to why customers are not being
retained, among other problems. Within the scope of this
disclosure, entities and commercial clients, service providers and
clients may be used interchangeably. Subscribers/customers are
generally referring to those individuals who use the services of
the entities, service providers and commercial clients and are the
targets of the churn tracking methods described herein. So, the
present disclosure describes methods, systems, and computer program
products, which individually provide functionality for reducing a
churn rate for service providers, such as by determining churn
probabilities for subscribers/customers and/or other members of
those service providers.
[0264] Churn prediction and tracking system of the invention is
generally a computer or computing device including functionality
for communicating (e.g., remotely) over a computer network. Churn
prediction and tracking system may be a desktop computer, laptop
computer, personal digital assistant (PDA), in- or out-of-car
navigation system, smart phone or other cellular or mobile phone,
or mobile gaming device, among other suitable computing devices.
Churn prediction and tracking system may execute one or more
applications, such as a web browser (e.g., Microsoft Internet
Explorer, Mozilla Firefox, Apple Safari, GOOGLE CHROME, and Opera),
to access and view content over a computer network.
[0265] A webpage or resource embedded within a webpage, which may
itself include multiple embedded resources, may include data
records, such as plain textual information, or more complex
digitally encoded multimedia content, such as software programs or
other code objects, graphics, images, audio signals, videos, and so
forth. One prevalent markup language for creating webpages is the
Hypertext Markup Language (HTML). Other common web
browser-supported languages and technologies include the Extensible
Markup Language (XML), the Extensible Hypertext Markup Language
(XHTML), JavaScript, Flash, ActionScript, Cascading Style Sheet
(CSS), and, frequently, Java. By way of example, HTML enables a
page developer to create a structured document by denoting
structural semantics for text and links, as well as images, web
applications, and other objects that can be embedded within the
page. Generally, a webpage may be delivered to entities/commercial
clients/service providers/clients as a static document; however,
through the use of web elements embedded in the page, an
interactive experience may be achieved with the page or a sequence
of pages.
[0266] The elements of one hardware system for use within the
method of the invention is described in greater detail below. In
particular, network interface provides communication between
hardware system and any of a wide range of networks, such as an
Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Mass storage
provides permanent storage for the data and programming
instructions to perform the above-described functions implemented
in servers, whereas system memory (e.g., DRAM) provides temporary
storage for the data and programming instructions when executed by
processor. I/O ports are one or more serial and/or parallel
communication ports that provide communication between additional
peripheral devices, which may be coupled to hardware system.
[0267] Hardware system may include a variety of system
architectures and various components of hardware system may be
rearranged. For example, cache may be on-chip with processor.
Alternatively, cache and processor may be packed together as a
"processor module," with processor being referred to as the
"processor core." Furthermore, certain embodiments of the present
disclosure may not require nor include all of the above components.
For example, the peripheral devices shown coupled to standard I/O
bus may couple to high performance I/O bus. In addition, in some
embodiments, only a single bus may exist, with the components of
hardware system being coupled to the single bus. Furthermore,
hardware system may include additional components, such as
additional processors, storage devices, or memories.
[0268] An operating system manages and controls the operation of
hardware system, including the input and output of data to and from
software applications. The operating system provides an interface
between the software applications being executed on the system and
the hardware components of the system. Any suitable operating
system may be used, such as the LINUX Operating System, the Apple
Macintosh Operating System, available from Apple Computer Inc. of
Cupertino, Calif., UNIX operating systems, Microsoft.RTM.
Windows.RTM. operating systems, BSD operating systems, and the
like. Of course, other embodiments are possible. For example, the
functions described herein may be implemented in firmware or on an
application-specific integrated circuit.
[0269] General Architecture of the Social Predictor Engine
[0270] In FIG. 9, the general architecture of the data pipeline of
a preferred aspect of the social predictor engine in this invention
is shown. The social predictor engine extracts patterns that are
used for predicting the churn probability of customers, from
different sources of social data, for example, TWITTER. The
integrated data is provided to the social predictor engine by the
social aggregator. This data is raw and is preprocessed to be used
by the other components in the higher levels of the data pipeline.
The extracted patterns are based on selected cues or features which
are calculated based on data per customer. These features may be
based on both the behavior of the user or the network. For example
the sentiment based features calculate some features that are
indicated by each user's behavior, while network based features
capture these indications from the network and calculate their
impact on the churn probability of each user. Finally a machine
learning model, which is essentially an estimation of real behavior
of users, is trained based on the different features, calculated to
capture different aspects of user's behavior. In this aspect, there
are preferably five layers in this pipeline as follows:
[0271] 1.1 Data Integration
[0272] This is the layer responsible for streaming, requesting and
extracting data from different social network APIs. This layer
stores data and exposes data through a REST API. The main
components of this layer are the Social Aggregator and the Social
Provider. The Social Aggregator contains multiple long running
processes which tap into the corresponding social network's
streaming APIs in a real-time manner. As some social networks only
provide REST APIs, the Social Aggregator is also capable of polling
data from a REST API. Data then is stored in highly available
databases, and exposed to the other parts of the system via load
balanced REST APIs.
[0273] 1.2 Data Preprocessing
[0274] This layer is responsible for pre-processing the data
received from the Social Data Aggregator. This pre-processing is
mainly to extract relevant data from the raw data received from
Data Integration Layer and to properly format this data for passing
to the next layer. Different types of pre-processing may be
employed based on the features that are going to be extracted on
the next layer. Three main pre-processing components in this layer
may be: Relevant Post Extraction, Trend Data Extraction, and Social
Network Structure Extraction.
[0275] 1.2.1 Relevant Post Extraction
[0276] A major role of this step is to extract relevant social
media inputs or posts (for example, Tweets) from raw data. This
step uses a manually designed lexicon to estimate relevancy of each
single post to telecom domain. In this lexion there are a set of
words and a relevancy score it attributed to each term. The
relevancy of each post is calculated based on below formula:
relevancy ( T ) = t .di-elect cons. T L score ( t ) / N
##EQU00001##
T is the set of terms in each Post and L is the set of terms in the
lexicon and N is the length of the post. The score function returns
the relevancy scores based on the lexicon. If the relevancy score
of a post is greater than a selected threshold, it is considered as
a "relevant" one. The threshold varies and can be adapted, case by
case, to achieve the best results in a cross-validation method.
[0277] Data may be appended to each post to tag the existence of
keywords which relate that post to a target company or its
competitors. The BrandTagger component tags the relevant terms in a
post. BrandTagger essentially looks for the terms related to the
target company identities and their variants. Target companies are
both the current company of a user and its competitive companies.
For example if the current company of a user is `wind` it will look
for `windmobile`, `Wind`, `Wind company` and so on. This component
has an API to check if an input text contains a specific company or
its competitor brands, social media IDs, and other similar company
related identities. This information will be used subsequently in
calculating features. It is preferred that only essential metadata,
like the userID or createdTime and retained and the other
irrelevant metadata will be removed prior to passing to the next
level. The preprocessed data is written to file (or can be passed
directly) for being used by next layer.
[0278] 1.2.2 Trend Data Extraction
[0279] In this step, data is reformatted to be used for trend
analysis over the social media data. Relevant pieces of information
that are changing during time are extracted. Examples include
changes in membership in groups, changes in following/unfollowing
friends or companies, changes in number of followers, changes in
posting behavior, changes in user's activity in a social media and
so on. The changes could be related to a specific user or the
behavioral changes in the whole network that can indicate current
user behavioral trends. The formatted data is passed to the next
layer for feature extraction.
[0280] 1.2.3 Social Network Structure Extraction
[0281] Information is extracted regarding the structure of social
network for a customer/user/subscriber. Namely in this step,
relevant data is extracted for recreating the surrounding network
of the customer/subscriber/user.
[0282] 1.3 Feature Engineering and Extraction
[0283] This layer comprises a plurality of steps, each of which
serves to extract the features based on the previously preprocessed
data. Each step is designed to extract a different type of
feature.
[0284] 1.3.1 Tweet Sentiment Assessment:
[0285] An important set of features, usable in the churn analysis
of the present invention, are based on a "sentiment" of each
company's customers tweets. This component uses, preferably, three
sources for assessing the sentiment of related tweets. Using a
third package is preferred for tie breaking and for fusion of the
three sentiments.
[0286] A feature extraction module the assessed data and extracts
proper features based on the sentiments. Table 1 shows a list of
such features and their descriptions.
TABLE-US-00001 TABLE 1 Feature Name Description
num_sign_mean_sentiment_pos_B1_C1 number of tweets containing
positive sentiments towards both company's brand and the competitor
brands num_sign_mean_sentiment_pos_B1_C0 number of tweets
containing positive sentiments towards company's brand and not the
competitor brands num_sign_mean_sentiment_pos_B0_C1 number of
tweets containing positive sentiments towards not the company's
brand but the competitor brands num_sign_mean_sentiment_pos_B0_C0
number of tweets containing positive sentiments towards neither of
company's brand nor the competitor brands
num_sign_mean_sentiment_neg_B1_C1 number of tweets containing
negative sentiments towards both company's brand and the competitor
brands num_sign_mean_sentiment_neg_B1_C0 number of tweets
containing negative sentiments towards company's brand and not the
competitor brands num_sign_mean_sentiment_neg_B0_C1 number of
tweets containing negative sentiments towards not the company's
brand but the competitor brands num_sign_mean_sentiment_neg_B0_C0
number of tweets containing negative sentiments towards neither of
company's brand nor the competitor brands
num_sign_mean_sentiment_nutral_B1_C1 number of tweets containing
neutral sentiments towards both company's brand and the competitor
brands num_sign_mean_sentiment_nutral_B1_C0 number of tweets
containing neutral sentiments towards company's brand and not the
competitor brands num_sign_mean_sentiment_nutral_B0_C1 number of
tweets containing neutral sentiments towards not the company's
brand but the competitor brands
num_sign_mean_sentiment_nutral_B0_C0 number of tweets containing
neutral sentiments towards neither of company's brand nor the
competitor brands max_sentiment_mean_B1_C0 max of tweet sentiment
values (each sentiment value is the mean of three sentiment
analysers) toward company and not the competitors brands
min_sentiment_mean_B1_C0 min of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
company and not the competitors brands mean_sentiment_mean_B1_C0
mean of tweet sentiment values (each sentiment value is the mean of
three sentiment analysers) toward company and not the competitors
brands max_sentiment_mean_B0_C1 max of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
not the company and but the competitors brands
min_sentiment_mean_B0_C1 min of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
not the company and but the competitors brands
mean_sentiment_mean_B0_C1 mean of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
not the company and but the competitors brands
max_sentiment_mean_B1_C1 max of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
both the company and the competitors brands
min_sentiment_mean_B1_C1 min of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
both the company and the competitors brands
mean_sentiment_mean_B1_C1 mean of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
both the company and the competitors brands
max_sentiment_mean_B0_C0 max of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
neither the company nor the competitors brands
min_sentiment_mean_B0_C0 min of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
neither the company nor the competitors brands
mean_sentiment_mean_B0_C0 mean of tweet sentiment values (each
sentiment value is the mean of three sentiment analysers) toward
neither the company nor the competitors brands
max_sentiment_max_B1_C0 max of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward
company and not the competitors brands min_sentiment_max_B1_C0 min
of tweet sentiment values (each sentiment value is the max of three
sentiment analysers) toward company and not the competitors brands
mean_sentiment_max_B1_C0 mean of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward
company and not the competitors brands max_sentiment_max_B0_C1 max
of tweet sentiment values (each sentiment value is the max of three
sentiment analysers) toward not the company and but the competitors
brands min_sentiment_max_B0_C1 min of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward not
the company and but the competitors brands mean_sentiment_max_B0_C1
mean of tweet sentiment values (each sentiment value is the max of
three sentiment analysers) toward not the company and but the
competitors brands max_sentiment_max_B1_C1 max of tweet sentiment
values (each sentiment value is the max of three sentiment
analysers) toward both the company and the competitors brands
min_sentiment_max_B1_C1 min of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward
both the company and the competitors brands
mean_sentiment_max_B1_C1 mean of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward
both the company and the competitors brands max_sentiment_max_B0_C0
max of tweet sentiment values (each sentiment value is the max of
three sentiment analysers) toward neither the company nor the
competitors brands min_sentiment_max_B0_C0 min of tweet sentiment
values (each sentiment value is the max of three sentiment
analysers) toward neither the company nor the competitors brands
mean_sentiment_max_B0_C0 mean of tweet sentiment values (each
sentiment value is the max of three sentiment analysers) toward
neither the company nor the competitors brands
max_sentiment_min_B1_C0 max of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward
company and not the competitors brands min_sentiment_min_B1_C0 min
of tweet sentiment values (each sentiment value is the min of three
sentiment analysers) toward company and not the competitors brands
mean_sentiment_min_B1_C0 mean of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward
company and not the competitors brands max_sentiment_min_B0_C1 max
of tweet sentiment values (each sentiment value is the min of three
sentiment analysers) toward not the company and but the competitors
brands min_sentiment_min_B0_C1 min of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward not
the company and but the competitors brands mean_sentiment_min_B0_C1
mean of tweet sentiment values (each sentiment value is the min of
three sentiment analysers) toward not the company and but the
competitors brands max_sentiment_min_B1_C1 max of tweet sentiment
values (each sentiment value is the min of three sentiment
analysers) toward both the company and the competitors brands
min_sentiment_min_B1_C1 min of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward
both the company and the competitors brands
mean_sentiment_min_B1_C1 mean of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward
both the company and the competitors brands max_sentiment_min_B0_C0
max of tweet sentiment values (each sentiment value is the min of
three sentiment analysers) toward neither the company nor the
competitors brands min_sentiment_min_B0_C0 min of tweet sentiment
values (each sentiment value is the min of three sentiment
analysers) toward neither the company nor the competitors brands
mean_sentiment_min_B0_C0 mean of tweet sentiment values (each
sentiment value is the min of three sentiment analysers) toward
neither the company nor the competitors brands
[0287] 1.3.2 Tweet Churn Level Assessment:
[0288] Another feature is based on tweet level churn assessment.
This feature may be considered as a subclass of sentiment based
features, with a goal being the detection of signals of churn at a
tweet level. TweetChurnLevelAssessment is a proxy script that uses
a package for identifying these signals based on bag of words
model.
[0289] 1.3.3 Related People Sentiment Assessment
[0290] From the extracted relevant posts, the sentiment of "related
people" is assessed for aggregation. For example, in one aspect,
the sentiments of 1-10 related people are assessed using the
features listed in Table 1.
[0291] 1.3.4 FACEBOOK Sentiment Assessment
[0292] This step is similar to tweet sentiment assessment
component, described above. The only difference is that it uses the
FACEBOOK data for assessment and extracting features.
[0293] 1.3.5 Life Event Assessment
[0294] Another set of features which may be extracted and
aggregated relates to life events. Events are selected and
extracted from user posts in social media using, for example, a
machine learning model. A plurality of life events may be searched
for and used, including, but not limited to moving, going to
college, getting married, leaving a job and starting a new job. The
selected extracted events may be used to calculate a set of
features given in Table 2.
TABLE-US-00002 TABLE 2 Feature Name Description mv_evnt|1 number of
moving events in the first most recent month mv_evnt|2 number of
moving events in the second most recent month mv_evnt|3 number of
moving events in the third most recent month mv_evnt|4 number of
moving events in the fourth most recent month mv_evnt|5 number of
moving events in the fifth most recent month co_evnt|1 number of
college events in the first most recent month co_evnt|2 number of
college events in the second most recent month co_evnt|3 number of
college events in the third most recent month co_evnt|4 number of
college events in the fourth most recent month co_evnt|5 number of
college events in the fifth most recent month wd_evnt|1 number of
wedding events in the first most recent month wd_evnt|2 number of
wedding events in the second most recent month wd_evnt|3 number of
wedding events in the third most recent month wd_evnt|4 number of
wedding events in the fourth most recent month wd_evnt|5 number of
wedding events in the fifth most recent month ej_evnt|1 number of
exit from job events in the first most recent month ej_evnt|2
number of exit from job events in the second most recent month
ej_evnt|3 number of exit from job events in the third most recent
month ej_evnt|4 number of exit from job events in the fourth most
recent month ej_evnt|5 number of exit from job events in the fifth
most recent month nj_evnt|1 number of new job events in the first
most recent month nj_evnt|2 number of new job events in the second
most recent month nj_evnt|3 number of new job events in the third
most recent month nj_evnt|4 number of new job events in the fourth
most recent month nj_evnt|5 number of new job events in the fifth
most recent month ratio_mv_evnt|1 ratio to all tweets in
corresponding month for moving events in the first most recent
month ratio_mv_evnt|2 ratio to all tweets in corresponding month
for moving events in the second most recent month ratio_mv_evnt|3
ratio to all tweets in corresponding month for moving events in the
third most recent month ratio_mv_evnt|4 ratio to all tweets in
corresponding month for moving events in the fourth most recent
month ratio_mv_evnt|5 ratio to all tweets in corresponding month
for moving events in the fifth most recent month ratio_co_evnt|1
ratio to all tweets in corresponding month for college events in
the first most recent month ratio_co_evnt|2 ratio to all tweets in
corresponding month for college events in the second most recent
month ratio_co_evnt|3 ratio to all tweets in corresponding month
for college events in the third most recent month ratio_co_evnt|4
ratio to all tweets in corresponding month for college events in
the fourth most recent month ratio_co_evnt|5 ratio to all tweets in
corresponding month for college events in the fifth most recent
month ratio_wd_evnt|1 ratio to all tweets in corresponding month
for wedding events in the first most recent month ratio_wd_evnt|2
ratio to all tweets in corresponding month for wedding events in
the second most recent month ratio_wd_evnt|3 ratio to all tweets in
corresponding month for wedding events in the third most recent
month ratio_wd_evnt|4 ratio to all tweets in corresponding month
for wedding events in the fourth most recent month ratio_wd_evnt|5
ratio to all tweets in corresponding month for wedding events in
the fifth most recent month ratio_ej_evnt|1 ratio to all tweets in
corresponding month for exit from job events in the first most
recent month ratio_ej_evnt|2 ratio to all tweets in corresponding
month for exit from job events in the second most recent month
ratio_ej_evnt|3 ratio to all tweets in corresponding month for exit
from job events in the third most recent month ratio_ej_evnt|4
ratio to all tweets in corresponding month for exit from job events
in the fourth most recent month ratio_ej_evnt|5 ratio to all tweets
in corresponding month for exit from job events in the fifth most
recent month ratio_nj_evnt|1 ratio to all tweets in corresponding
month for new job events in the first most recent month
ratio_nj_evnt|2 ratio to all tweets in corresponding month for new
job events in the second most recent month ratio_nj_evnt|3 ratio to
all tweets in corresponding month for new job events in the third
most recent month ratio_nj_evnt|4 ratio to all tweets in
corresponding month for new job events in the fourth most recent
month ratio_nj_evnt|5 ratio to all tweets in corresponding month
for new job events in the fifth most recent month
[0295] 1.3.6 Company/Competitor Engagement Assessment
[0296] This step collects, analyzes and aggregates posts on a
variety of social media platforms pages of a competitor or a target
company (to the business or entity) to find evidence and
frequencies of churn by the customer.
TABLE-US-00003 Feature Name Description CompanyEnagementCount How
many times the company has reach/response to the customer in the
past 6 months CompetitorEnagementCount How many times the
competitors have reach/response to the customer in the past 6
months
[0297] 1.3.7 Trend Analysis and Assessment
[0298] In this step, temporal features which are necessary for
analysing the trend of churn for a single user based upon his/her
previous churn scores and also based on the overall trend of churn
from the company are collected and assessed. Examples, of such
features are presented in Table 3 below. These features are used
for trend analysis and predicting the trend in a
pre-determined/pre-selected time increment, for example in the
following month.
TABLE-US-00004 TABLE 3 Feature Name Description
TemporalSingleChurnScore The churn scores in past 12 months for
each user for each month TemporalOveralChurnScore The mean churn
score of users during past 12 month TemporalGroupMemberShip Changes
in Membership of a user in groups during time
TemporalConnectionCount changes in following/unfollowing friends or
companies each month TemporalPostCount Changes in number of posts
during each month (will reflect internet usage behavior)
[0299] 1.3.8 Community Assessment
[0300] A significant body of data relates to and can be extracted
from the various communities to which a customer belongs. For
example friends who work in the same place as a customer will be
more likely to use or not use a specific subscription or service.
Changes in the preferences and behaviors of members in a community
directly affects others in that same community and the aggregation
and analysis of that data is used in churn prediction. Based on the
structural properties of a social network among the users,
communities are detected and scored based on their churn risk. Such
score is calculated based on the ratio of the number of users of a
target company to the count of all members in this community.
Features are then calculated based on membership of users to such
communities.
TABLE-US-00005 Feature Name Description CommunityMembershipCount
How many communities a person is a member of.
MeanCommunityMembershioChurnRisk Calculated based on the mean of
churn risk of the communities a users is a member.
[0301] 1.4 Feature Vector Generation
[0302] The different feature types for each user are combined
together to create a single feature vector for each user. The
outcome is a single feature vector per user as follow:
TABLE-US-00006 Feature Type: Sentiment Based userID fs1 fs2 fs3 fs4
fs5 1 .5 .3 0 1 .2 2 .3 .2 -.2 1 0 . . . . . . . . . . . . . . . .
. . Feature Type: Life Event Based userID ft1 ft2 ft3 tf 1 .3 .1 .2
0 2 .4 0 .1 1
The Combined Feature Vectors:
TABLE-US-00007 [0303] userID fs1 fs2 fs3 fs4 fs5 ft1 ft2 ft3 ft4 1
.5 .3 0 1 .2 .3 .1 .2 0 2 .3 .2 -.2 1 0 .4 0 .1 1
[0304] By way of example, fs1 to fs5 could be related to semantic
based features and ft1 to ft4 to life event based features. These
features are calculated separately by different components in the
assessment layer. However for training a machine learning model one
feature vector per user, at least, is required. Therefore these
separate feature vectors are combined to create a single feature
vector. There is unique identifier key field like user ID which is
considered to be preserved in the different featured vectors
(coming from different assessment components). This key field is
used for joining the feature vectors.
[0305] 1.5 Model
[0306] Finally the model is built over the extracted features given
by the below layer. Various different machine learning algorithms
may be used for model building such as, for example, xgboost and
random forest. Applying random forest, the implementation that is
provided by sklearn package in python was used. Preferably, about
5000 trees are used in the model, preferably as deep as 50 levels.
The height of trees shows the amount of interactions between
features which can be captured by a tree. As it is preferred to
have a relatively large set of features, comparing to sample size,
using this interaction level, prevents side effect of co related
features.
[0307] Life Event Components
[0308] One of the correlated concepts to subscriber/customer churn
relates to life events, as noted above. Further detail on this
aspect is provided herein and in FIG. 11. Life events are
preferably significant trackable events that occur in individual's
life. They could be something that happens in the personal life of
someone such as getting married or they could be a professional
circumstance such as starting a new job.
[0309] Capturing data relating to life event is an aspect of the
churn prediction method of the invention as such life events can
affect the customer behavior and might encourage or motivate
him/her to switch their service providers. These events, properly
tracked and engineered, can provide valuable insight into the churn
prediction solution and hence life events metrics comprise an
important part in a preferred method of the present invention. The
life event workflow starts from collecting social data for each
chosen life event, curating those events for each event type, using
a machine learning solution to generate both probabilities of a
life event for a single social content and also a binary result.
These generated life event results are used to predict customer
churn.
[0310] Table 4 is a list of non-limiting life events and a brief
description of each used in the current invention.
TABLE-US-00008 TABLE 4 Life Event Prediction Sub- component
Description College A component that predicts if a customer is
going to start attending college in the near future by analyzing a
single social content. Marriage A component that predicts if a
customer is going to get married in the near future by analyzing a
single social content. Move A component that predicts if a customer
is going to start move to a new location in the near future by
analyzing a single social content. Travel A component that predicts
if a customer is going to travel in the near future by analyzing a
single social content. New Child A component that predicts if a
customer is going to have a child in the near future by analyzing a
single social content. New job A component that predicts if a
customer is going to start a new job in the near future by
analyzing a single social content.
[0311] FIGS. 10 and 11 depict how the individual life prediction
components create features that are used in the churn prediction
component in the current invention.
[0312] Specifically, each life prediction component is built in a
multi-step process: [0313] 1. Collect relevant social media content
for a specific life event. For example, for new job life event
component collect a large data set of relevant tweets, FACEBOOK
posts, GOOGLE+ posts, etc. and store them in a database repository.
[0314] 2. Collect randomized social content and store them in a
database repository. [0315] 3. Use a manual curation tool to have
multiple human subjects review each of the social contents
collected for a life event and annotate that content either as
positive (indicating the content implies the corresponding life
event will occur) or negative (indicating the content implies the
corresponding life event will not occur) [0316] 4. Update the
curated social content collection in a database repository. [0317]
5. Using the curated social content to build and train a predictor
model (for example, using a machine learning trainer component).
[0318] 6. Serializing and storing the predictor model for future
predictions.
[0319] "Cloud computing" services provide shared resources,
software, and information to computers and other devices upon
request. In cloud computing environments, software can be
accessible over the Internet rather than installed locally on
in-house computer systems. Cloud computing typically involves
over-the-Internet provision of dynamically scalable and often
virtualized resources. The database resources aggregated and
collected and arranged within the scope of the invention may be
stored and provided in a cloud computing context.
[0320] It is to be understood that the implementation of the method
of the invention may be via CRM or via the exchange and conveyance
of data and information via an API to the company/client.
[0321] So, in one embodiment of the invention, an churn prediction
platform integrates a contact center, agent stations, and,
optionally, a customer relation management (CRM) server. Typically,
the contact center, the agent station(s), and the customer relation
management server are coupled over one or more networks, which may
be the Internet, a private network, or a telephone network. The
customer relation management server may be physically located
within the contact center and maintained by a third party, or
located remote from the contact center and still operated thereby.
The elements of this agent state model are dynamic and updated in
real-time as an agent (delivering a service) seeks to acquire
information in regards to the likelihood of a customer of that
service "churning".
[0322] In this type of embodiment, the system of the invention
implements a web-based customer relationship management (CRM)
system. For example, in one embodiment, the system includes
application servers configured to implement and execute CRM
software applications as well as provide related data, code, forms,
webpages and other information and retrieve from, a database system
related data and content, related to the delivery of the method of
the invention.
[0323] Alternatively, the implementation of the method of the
invention may be via an application programming interface (API).
Application Programming Interface ("API") can accept the directives
from, for example, an admin console. API can be a
standards-compliant Internet protocol following Simple Object
Access Protocol ("SOAP") or Representational State Transfer
("REST") patterns. Alternatively, API can be a body of industry
standard or proprietary Remote Procedure Call ("RPC")
technologies.
[0324] An API may use a directive dispatcher to dispatch
device-neutral directives to one or more directive processors.
Directive processors can be included for any number of features. An
API can ease the work of programming GUI components. For example,
an API can facilitate integration of new features into existing
applications (a so-called "plug-in API"). An API can also assist
otherwise distinct applications with sharing data, which can help
to integrate and enhance the functionalities of the
applications.
[0325] APIs often come in the form of a library that includes
specifications for routines, data structures, object classes, and
variables. In other cases, notably SOAP and REST services, an API
is simply a specification of remote calls exposed to the API
consumers.
[0326] By way of example, an admin user of a computer architecture
within the scope of the invention can utilize administrator console
software program to implement the method of the invention. Such a
software program can be a Web browser application, software
installed on a computer system, or an application ("app") installed
on a tablet or smart phone. The admin user can supply "directives"
to via an admin console. "Directives" can be commands, scripts,
software packages, configuration manifests, configuration policies,
software licensing keys, workflows, user and hardware catalogs, and
other such inputs that the admin desires to implement over a
population of devices and external systems.
* * * * *