U.S. patent application number 14/794776 was filed with the patent office on 2015-10-29 for generating and displaying customer commitment framework data.
The applicant listed for this patent is SDL Enterprise Technologies Inc.. Invention is credited to Joseph Hyeon Chang, Michelle Amanda Evans, Elizabeth Ann High, Yi Lu.
Application Number | 20150310464 14/794776 |
Document ID | / |
Family ID | 48611094 |
Filed Date | 2015-10-29 |
United States Patent
Application |
20150310464 |
Kind Code |
A1 |
Evans; Michelle Amanda ; et
al. |
October 29, 2015 |
Generating and Displaying Customer Commitment Framework Data
Abstract
Systems and methods for generating and displaying customer
commitment framework data. Exemplary methods for determining the
shareability of online content may include obtaining, via a digital
intelligence system, customer experience data regarding any of a
product, a brand, and customer responses for a first entity, as
well as periodically calculating, via the digital intelligence
system, customer commitment framework data from the customer
experience data, and generating a customer commitment dashboard
that comprises a graphical representation of the customer
commitment framework data.
Inventors: |
Evans; Michelle Amanda;
(Seattle, WA) ; High; Elizabeth Ann; (Seattle,
WA) ; Lu; Yi; (Seattle, WA) ; Chang; Joseph
Hyeon; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SDL Enterprise Technologies Inc. |
Amsterdam |
|
NL |
|
|
Family ID: |
48611094 |
Appl. No.: |
14/794776 |
Filed: |
July 8, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13767832 |
Feb 14, 2013 |
9123055 |
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14794776 |
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13587789 |
Aug 16, 2012 |
8793154 |
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13767832 |
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61525041 |
Aug 18, 2011 |
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Current U.S.
Class: |
705/7.33 ;
705/7.29 |
Current CPC
Class: |
G06F 16/24578 20190101;
G06Q 30/0201 20130101; G06Q 30/0204 20130101; G06Q 30/0202
20130101; G06Q 30/01 20130101; G06Q 50/01 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method, comprising: obtaining, via a digital intelligence
system, customer experience data regarding any of a product, a
brand, and customer relevance for a first entity; periodically
calculating, via the digital intelligence system, customer
commitment framework data from the customer experience data; and
generating a customer commitment dashboard that comprises a
graphical representation of the customer commitment framework
data.
2. The method according to claim 1, wherein the customer commitment
framework data comprises any of a product commitment score, a brand
commitment score, and a customer relevance score.
3. The method according to claim 2, wherein the product commitment
score, the brand commitment score, and the customer relevance score
are calculated for a plurality of market segments.
4. The method according to claim 2, wherein the product commitment
score, the brand commitment score, and the customer relevance score
are calculated for a plurality of languages.
5. The method according to claim 1, wherein the customer commitment
dashboard comprises a corresponding graphical representation for
customer experience data regarding any of a product, a brand, and
customer relevance for a second entity.
6. The method according to claim 5, wherein the second entity is a
competitor of the first entity and the corresponding graphical
representation of customer experience data for the second entity is
utilized as a benchmark.
7. The method according to claim 1, further comprising calculating
average score for a plurality of industry verticals, wherein an
average score includes any of an average product commitment score,
an average brand commitment score, and an average customer
relevance score, which represents an average of scores for a
plurality of entities within an industry vertical.
8. The method according to claim 1, further comprising generating a
graphical representation of segmentation that is a function of an
influence vertical and any of product commitment score, brand
commitment score, and customer relevance score horizontal.
9. A system, comprising: one or more processors; and logic encoded
in one or more tangible media for execution by the one or more
processors and when executed operable to perform operations
comprising: obtaining, via a digital intelligence system, customer
experience data regarding any of a product, a brand, and customer
relevance for a first entity; periodically calculating, via the
digital intelligence system, customer commitment framework data
from the customer experience data; and generating a customer
commitment dashboard that comprises a graphical representation of
the customer commitment framework data.
10. The system according to claim 9, wherein the logic when
executed is further operable to perform operations comprising
generating a table of segmentation for any of a product, a brand,
and customer relevance, wherein journey phases are displayed in
rows and segments are displayed in columns.
11. The system according to claim 10, wherein cells of the table
include at least one of a product commitment score, a brand
commitment score, and a customer relevance score.
12. The system according to claim 9, wherein the logic when
executed is further operable to perform operations comprising
generating a table of segment characteristics for one or more of
the segmentations.
13. The system according to claim 9, wherein the logic when
executed is further operable to perform operations comprising
generating a graphical representation of any of product commitment
scores, brand commitment scores, and customer relevance scores for
the first entity, over a period of time.
14. The system according to claim 13, wherein the logic when
executed is further operable to perform operations comprising
overlaying any of product commitment scores, brand commitment
scores, and customer relevance scores for a plurality of
competitors onto the graphical representation.
15. The system according to claim 13, wherein the logic when
executed is further operable to perform operations comprising
overlaying product commitment scores for a plurality of competing
products onto the graphical representation in such a way that the
product commitment scores, brand commitment scores, and customer
relevance scores of the first entity are distinguishable from the
product commitment scores, brand commitment scores, and customer
relevance scores of the plurality of competing products.
16. The system according to claim 9, wherein the customer
commitment dashboard comprises a link that exposes customer
commitment framework data utilized to generate the graphical
representation of the customer commitment framework data.
17. The system according to claim 9, wherein the customer
commitment framework data comprises any of a product commitment
score, a brand commitment score, and a customer relevance
score.
18. The system according to claim 17, wherein the product
commitment score, the brand commitment score, and the customer
relevance score are calculated for any of a plurality of market
segments and a plurality of languages.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This Non-Provisional U.S. patent application is a
continuation of U.S. patent application Ser. No. 13/767,832, filed
on Feb. 14, 2013 and titled Generating and Displaying Customer
Commitment Framework Data, which is a continuation-in-part of U.S.
patent application Ser. No. 13/587,789, filed on Aug. 16, 2012 and
titled Customer Relevance Scores and Methods of Use, which claims
the priority benefit of U.S. Provisional Application No.
61/525,041, filed on Aug. 18, 2011 and titled New Sharing and
Recommendation Tracking Method. This application also relates to
U.S. Provisional Patent Application No. 61/675,784, filed on Jul.
25, 2012 and titled Product Cycle Analysis Using Social Media Data,
as well as U.S. Provisional Patent Application No. 61/606,326,
filed on Mar. 2, 2012 and titled Product Cycle Analysis Using
Social Media Data. All of the aforementioned disclosures are all
hereby incorporated by reference herein in their entireties
including all references cited therein.
FIELD OF THE PRESENT TECHNOLOGY
[0002] The present technology relates generally to generating and
displaying customer commitment framework (CCF) data. Dashboards may
be provided, which include representations of CCF data, such as
graphs, tables, or other visual formats that provide users with
views of CCF data in a manner that allows for real-time
course-correction of programs and processes. Exemplary types of CCF
data include product commitment scores (PCS), brand commitment
scores (BCS), and customer response scores (CRS), which are each
indicative of various aspects of a customer experience and journey
analysis.
BACKGROUND
[0003] Social media communications provide a wealth of information
regarding the purchasing behaviors and interests of consumers.
While this information is voluminous, it is often difficult to
categorize and translate this information into meaningful and
actionable information that may be utilized by a company to improve
their products, advertising, customer service, and the like.
SUMMARY OF THE DISCLOSURE
[0004] According to some embodiments, the present technology may be
directed to a method for determining the shareability of online
content. The method may comprise: (a) obtaining, via a digital
intelligence system, customer experience data regarding any of a
product, a brand, and customer relevance for a first entity; (b)
periodically calculating, via the digital intelligence system,
customer commitment framework data from the customer experience
data; and (c) generating a customer commitment dashboard that
comprises a graphical representation of the customer commitment
framework data.
[0005] According to other embodiments, the present technology may
be directed to systems for determining the shareability of online
content. These systems may comprise: (a) one or more processors;
and (b) logic encoded in one or more tangible media for execution
by the one or more processors and when executed operable to perform
operations comprising: (i) obtaining, via a digital intelligence
system, customer experience data regarding any of a product, a
brand, and customer relevance for a first entity; (ii) periodically
calculating, via the digital intelligence system, customer
commitment framework data from the customer experience data; and
(iii) generating a customer commitment dashboard that comprises a
graphical representation of the customer commitment framework
data.
[0006] According to additional embodiments, the present technology
may be directed to non-transitory computer readable storage mediums
having a computer program embodied thereon. The computer program is
executable by a processor in a computing system to perform a method
that includes the steps of: (a) obtaining, via a digital
intelligence system, customer experience data regarding a first
entity; (b) periodically calculating, via the digital intelligence
system, customer commitment framework data from the customer
experience data; and (c) generating a customer commitment dashboard
that comprises a graphical representation of the customer
commitment framework data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Certain embodiments of the present technology are
illustrated by the accompanying figures. It will be understood that
the figures are not necessarily to scale and that details not
necessary for an understanding of the technology or that render
other details difficult to perceive may be omitted. It will be
understood that the technology is not necessarily limited to the
particular embodiments illustrated herein.
[0008] FIG. 1 is a block diagram of an exemplary digital
intelligence system.
[0009] FIG. 2 is a block diagram of an exemplary digital
intelligence application for use in accordance with the present
technology;
[0010] FIG. 3 illustrates exemplary keyword matrices for
categorizing social media conversations;
[0011] FIG. 4 is an exemplary customer commitment dashboard
illustrating product commitment score data;
[0012] FIG. 5 is an exemplary customer commitment dashboard
illustrating product commitment score data as well as brand
commitment score data;
[0013] FIG. 6 is an exemplary customer commitment dashboard
illustrating product commitment score data as well as brand
commitment score data and customer relevancy score data;
[0014] FIG. 7 is an exemplary customer commitment dashboard
illustrating product commitment score (PCS) data displayed
according to segmentation;
[0015] FIG. 8 is an exemplary customer commitment dashboard
illustrating PCS segmentation and customer journey data;
[0016] FIG. 9 is an exemplary customer commitment dashboard
illustrating PCS segmentation and segmentation characteristics;
[0017] FIG. 10 is an exemplary customer commitment dashboard
illustrating a time-based and graphical PCS data analysis;
[0018] FIG. 11 is an exemplary customer commitment dashboard
illustrating a time-based and graphical analysis of customer
journey data;
[0019] FIG. 12 is an exemplary customer commitment dashboard
illustrating a time-based and graphical analysis of experience
scores relative to product awareness; and
[0020] FIG. 13 is a block diagram of an exemplary computing system
for implementing embodiments of the present technology.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0021] While this technology is susceptible of embodiment in many
different forms, there is shown in the drawings and will herein be
described in detail several specific embodiments with the
understanding that the present disclosure is to be considered as an
exemplification of the principles of the technology and is not
intended to limit the technology to the embodiments
illustrated.
[0022] It will be understood that like or analogous elements and/or
components, referred to herein, may be identified throughout the
drawings with like reference characters. It will be further
understood that several of the figures are merely schematic
representations of the present technology. As such, some of the
components may have been distorted from their actual scale for
pictorial clarity.
[0023] Generally speaking, the present technology is directed to
systems, methods, and media to generate and display customer
commitment framework data. Broadly, the customer commitment
framework (CCF) empowers organizations to optimize their customer
experience with a data-driven approach to decision-making. CCF
provides real-time predictive measures and robust insights to
strengthen customer commitment around the three exemplary customer
journeys, including Shopping, Sharing, and Advocacy.
[0024] The present technology provides intuitive dashboards, known
as customer commitment dashboards (CCDs), which may advantageously
be used to drive real-time course-correction of programs and
processes. Because CCD models CCF data, which includes customer
experience and customer journey data, product and brand scores may
be benchmarked and compared to competitors, as well as an industry
average, providing the ability to leverage best practices in any
given industry vertical.
[0025] The present technology may calculate the aforementioned
product and brand scores that indicate how well received a product
or brand is amongst consumers. These scores may also be used to
predict buying behaviors and target consumers based upon their
position within a product cycle. That is, scores may be calculated
that represent consumer experiences across many phases of a product
cycle (e.g., development, launch, updating, phase out, and the
like).
[0026] The terms "customer experience data" as used throughout this
document refers to customer experience and/or customer journey data
gathered from a wide variety of source including, but not limited
to data gathered from social networking platforms, as well as news
sources, forums, blogs, and so forth. One of ordinary skill in the
art will appreciate that any digital data source that provides
customer experience or customer journey data, as described herein,
may likewise be utilized in accordance with the present disclosure.
An "author" may include any customer or potential customer that has
generated content that expresses an opinion about a product or
service, especially content that relates to a customer experience
or a customer journey.
[0027] Many of the examples provided herein reference the analysis
of conversations and content occurring via social media platforms.
It will be understood that these examples are non-limiting and the
CCF analysis methods described herein may be equally applied to
content occurring via other content sources, such as those
mentioned above.
[0028] Additionally, the terms "a product" may be claimed or
referred to as both products and/or services provide by an
entity.
[0029] Generally, CCF data may be determined from the gathered
customer experience data. In some instances, the CCF data may
include various scores, such as PCS, BCS, and/or CRS, which are
real-time predictive measures data, which may be used as a means to
strengthen customer commitment.
[0030] Prior to calculating various scores that indicate how well
received a product, customer experience, and/or customer journey is
amongst consumers, the present technology may evaluate customer
experience data from authors and categorize conversations or other
content. In some instances, conversations may be categorized as
falling within a product commitment score domain, a brand
commitment score domain, and/or a customer relevance score domain.
Generally speaking, conversations or content may be categorized by
evaluating keywords included in the conversations, and more
specifically based upon a frequency of keywords included in these
conversations.
[0031] In some instances, BCS may be utilized to determine a
consumer's emotional connection to a brand. Exemplary emotions
associated with the BCS may comprise, but are not limited to,
hopefulness, attraction, and/or devotion. These emotions may be
tied to segments of the product cycle, such as understand, learn,
and commit--customer engagement levels with a particular brand.
[0032] The CRS allows marketers to determine the shareability of
content. By making content more shareable, marketers can increase
website traffic, which may in turn, drive consumer behaviors within
the product cycle, such as buying. The CRS may quantify the
shareability of content and may be used as a benchmark for
comparing the effectiveness of content in driving commercial
activity.
[0033] Customer relevance scores (CRS) may quantify how likely it
is that a customer or social network user will share a particular
piece of content, such as a video, an article, a website, or other
online content within the context of a social network. The CRS
gauges the shareability of content. Because it can be demonstrated
with empirical evidence that shared content increases revenue more
than passive or unshared content, increasing the CRS for content
may result in a corresponding increase in revenue attributable to
the content. In sum, the CRS may be used to quantify the value of
the content, based upon its shareability.
[0034] Generally speaking, the present technology may be utilized
to determine the shareability of online content. Additionally, the
present technology may be utilized to evaluate how and why content
is shared. Content that is more frequently shared may be analyzed
to determine various elements that make the content shareable, such
as narrative, thematic, and underlying message elements.
Additionally, the present technology may be used to create code
frames from frequently shared content. These code frames (similar
to templates) may be applied to other content to increase the
shareability of the content. Shareable content may be created from
scratch using these code frames.
[0035] Advantageously, the present technology may track customer
experience data for online content, such as sharing of the content,
downloading of the content, uploading of the content, conversations
relating to the content, and so forth. Using this customer
experience data, the present technology may determine the
shareability of the content. When content sharing is quantified,
the metrics gathered may be utilized to increase key brand metrics,
understand and quantify what makes certain content shareable,
increase advertising recall, increase correct branding of
advertising, increase brand consideration, increase brand
recommendation, increase brand favorability, and increase enhanced
resonance and acceptance of campaign messages.
[0036] In some instances, the present technology may mathematically
quantify consumer sentiment relative to a product. Moreover, the
consumer sentiment may be extracted from an analysis of content
included in social media messages and conversations. Additionally,
the portion of the product cycle in which the consumer is currently
participating may be determined by an analysis of the words
included in their customer experience data. Therefore, consumer
sentiment regarding a product may be determined relative to a time
frame associated with at least one phase of a product cycle for the
product.
[0037] In some exemplary embodiments, the scores calculated by the
present technology may be based upon data included in social media
messages of authors (e.g., consumers posting messages on social
networks). Thus, customer experience data obtained from various
social media sources may provide valuable and actionable
information when transformed by the present technology into various
metrics. Each of the metrics/scores/values calculated by the
present technology is described in greater detail herein.
[0038] Referring to FIG. 1, the present technology may be
implemented to collect and evaluate customer experience data for
analysis as customer commitment framework data. The present
technology may be facilitated by a digital intelligence system 100,
hereinafter "system 100" as shown in FIG. 1. The system 100 may be
described as generally including one or more web servers that may
communicatively couple with client devices such as end user
computing systems. For the purposes of clarity, the system 100 is
depicted as showing only one web server 105 and one client device
110 that are communicatively coupled with one another via a network
115. Additionally, customer experience data gathered from various
sources (not depicted in FIG. 1) may be stored in database 120,
along with various scores, values, and the corresponding data
generated by the web server 105, as will be discussed in greater
detail below.
[0039] It is noteworthy to mention that the network 115 may include
any one (or combination) of private or public communications
networks such as the Internet. The client device 110 may interact
with the web server 105 via a web based interface, or an
application resident on the client device 110, as will be discussed
in greater detail herein.
[0040] According to some embodiments, the system 100 may include a
cloud based computing environment that collects, analyzes, and
publishes datasets. In general, a cloud-based computing environment
is a resource that typically combines the computational power of a
large grouping of processors and/or that combines the storage
capacity of a large grouping of computer memories or storage
devices. For example, systems that provide a cloud resource may be
utilized exclusively by their owners, such as Google.TM. or
Yahoo!.TM.; or such systems may be accessible to outside users who
deploy applications within the computing infrastructure to obtain
the benefit of large computational or storage resources.
[0041] In some embodiments according to the present technology, the
cloud may be formed, for example, by a network of web servers such
as web servers 105 with each web server (or at least a plurality
thereof) providing processor and/or storage resources. These
servers may manage workloads provided by multiple users (e.g.,
cloud resource consumers or other users). Typically, each user
places workload demands upon the cloud that vary in real-time,
sometimes dramatically. The nature and extent of these variations
typically depend on the type of business associated with the
user.
[0042] The system 100 may be generally described as a particular
purpose computing environment that includes executable instructions
that are configured to generate and display customer commitment
framework data.
[0043] In some embodiments, the web server 105 may include
executable instructions in the form of a digital intelligence
application, hereinafter referred to as "application 200" that
collects and evaluates customer experience data used in various
customer commitment framework data analyses. FIG. 2 illustrates and
exemplary schematic diagram of the application 200.
[0044] The application 200 is shown as generally comprising an
interface module 205, a data gathering module 210, a Product
Commitment Score (PCS) module 215, a consumer experience module
220, a segmentation module 225, a CRS module 235, and a code
framing module 240. It is noteworthy that the application 200 may
include additional modules, engines, or components, and still fall
within the scope of the present technology. As used herein, the
term "module" may also refer to any of an application-specific
integrated circuit (ASIC), an electronic circuit, a processor
(shared, dedicated, or group) that executes one or more software or
firmware programs, a combinational logic circuit, and/or other
suitable components that provide the described functionality. In
other embodiments, individual modules of the application 200 may
include separately configured web servers.
[0045] Generally speaking, the user interface module 205 may
generate a plurality of graphical user interfaces that allow end
users to interact with the application 200. These graphical user
interfaces may allow end users to input information that is
utilized by the system 100 to capture and analyze customer
experience data. The information input by end users may include
product information for products they desire to evaluate, the
product cycle or a portion of the product cycle of interest, the
type of consumers or messages they desire to analyze, and so
forth.
[0046] Initially, the data gathering module 210 may be executed to
obtain customer experience data from one or more digital mediums,
such as websites, blogs, social media platforms, and the like. End
users may establish profiles that define what types of customer
experience data are to be gathered by the data gathering module
210. For example, a software developer may desire to gather
customer experience data regarding consumer sentiment for a
particular application.
[0047] The data gathering module 210 may evaluate customer
experience data for keywords, groups of keywords, or search queries
that are utilized to search for conversations or messages that
include these keywords.
[0048] FIG. 3 illustrates various matrices that may be used to
semiotically evaluate conversations or other content. For example,
if a conversation has a predominate number of keywords that fall in
the customer relevance score (CRS) matrix, the conversation may be
categorized as falling within the CRS domain. Thus, a CRS equation
may be utilized to calculate a CRS for the social media
conversation, as will be discussed in greater detail.
[0049] Exemplary PCS core keywords are shown in matrix 305, while
exemplary BCS core keywords are included in matrix 310. Exemplary
CRS core keywords are included in matrix 315, which includes column
320 of Interested, column 325 of Connected, and column 330 of
Sharing. Each of these columns may be associated with a
shareability classification in some embodiments. Thus, keywords in
a conversation may place the conversation into one or more of these
classifications, namely Interested, Connected, and/or Sharing,
respectively.
[0050] For example, if a conversation included the words sharing
and endorsing, which are included in the Sharing column 330, the
conversation may be classified within the Sharing classification.
The conversation may be placed into more than one classification if
the system detects keywords present in the Interested or Connected
columns. In some instances, the conversation may be classified by a
predominance of classifying words in the conversation. Thus, if the
conversation includes a predominate number of Interested keywords,
the conversation may be classified as Interested. In some
embodiments, these classifications may also be weighted such that
the inclusion of a predetermined number of Sharing keywords
automatically causes the conversation to be classified with the
Sharing classification, regardless of how many other Interested or
Connected keywords are present in the conversation.
[0051] In accordance with the present disclosure, selection of
customer experience data may be influenced by the specific types of
behaviors that a merchant is attempting to quantify. In other
embodiments, the data gather module 210 may analyze the customer
experience data to determine where within the product cycle a
consumer currently resides--for example, in the awareness,
interest, desire, or action phases. Awareness may be inferred from
conversations that discuss any of the three key drivers of the
product cycle (e.g., learn, try, buy, and the like). Interest in a
product may be a strong indicator that a consumer has gone beyond
being simply aware of a product. When consumers expressing a desire
to purchase a product it may be inferred to be a strong indicator
that consumers are considering a product for purchase.
Additionally, when consumers indicate an active intent to purchase
a product, it may be inferred that the consumer is strongly
progressing along the buying cycle.
[0052] In some embodiments according to the present technology, the
data gathering module 210 may obtain customer experience data from
specific types of consumers, and in additional embodiments, based
upon where the consumers are positioned within the product cycle,
such as those within the research phase. That is, the customer
experience data for a set of consumers may be monitored because
they are actively researching products to purchase.
[0053] The data gathering module 210 may utilize a conversation
matrix to obtain relevant customer experience data. The data
gathering module 210 may employ the conversation matrix to search
and capture relevant customer experience data from various digital
platforms. The search terms and matrices utilized by the data
gathering module 210 may be updated if the data gathering module
210 fails to obtain sufficient data, or if the data that is
obtained is inaccurate.
[0054] Product Commitment Scores
[0055] The PCS module 215 may be executed to calculate various
types of PCS values that aid merchants in determining the
commitment level of consumers to a particular product.
Additionally, the PCS value may be utilized as a leading indicator
that may be utilized to predict consumer behavior relative to a
particular product or service. For example, the PCS value may be
used to predict how well a particular product will be received by
consumers. Moreover, the PCS value may be used to predict the
likelihood that a product will be purchased and if consumers will
remain committed to the product throughout the lifecycle of the
product. In some non-limiting examples, if the product includes
software, the product lifecycle may include conception, product
launch, and eventual upgrade of the software by consumers.
[0056] In some embodiments, the PCS value may represent the
difference between the number of positive research, trial, and
purchase messages and the number of negative research, trial and
purchase messages. Again, these messages may include social media
messages and may be obtained by the data gathering module 210 from
evaluating one or more social media platforms. More specifically,
these messages or conversations have been previously categorized as
belonging to, or being associated with, the PCS domain.
[0057] It is noteworthy that the PCS module 215 may calculate
individual PCS values at a specific consumer (e.g., author) level.
Adjustments and weighting of consumer level PCS values may also be
performed by the PCS module 215.
[0058] For example, each consumer may contribute to the overall PCS
value to the degree of their relative authority. That is, the PCS
module 215 may account for a consumer's influence relative to the
total influence of all consumers having at least one research,
trial, or purchase conversation relative to a particular
product.
[0059] The PCS module 215 may also adjust consumer level PCS values
to account for each consumer's influence relative to the influence
of all consumers having at least one research, trial, or purchase
message. That is, the more influential a consumer is, the more
weight is attributed to the consumer's conversations. Influence may
be inferred because the consumer has a large social network or
because the consumer is an expert in the product field.
[0060] The overall PCS value may generally comprise one or more
summation consumer level PCS value. In additional embodiments the
overall PCS value (and consumer level PCS values) may comprise a
summation of, for example, three different component values such as
a research value, a trial value, and a purchase value, where each
of these values may be calculated separately. These three values
represent the phases of the product cycle.
[0061] In general, each of the three component values may each
include a summation of seven different sentiment values.
Conceptually, the seven sentiment values exist on a continuum where
the first sentiment value indicates a very negative sentiment, and
the seventh sentiment value indicates a very positive sentiment.
The second through sixth sentiments fall somewhere in between. The
distributions of messages/conversations along the spectrum of
sentiment values may indicate the success of the product at the
different phases of the product cycle.
[0062] In some embodiments, messages that are the most positive
(sentiment score of seven, for example) may receive the most
points, whereas the least positive (sentiment score of five, for
example) may receive the least amount of positive points. The most
negative conversations (sentiment score of one) may receive the
greatest number of negative points. Conversations being the least
negative (sentiment score of four) may receive the fewest negative
points.
[0063] As mentioned briefly above, consumer level PCS may also be
weighted. For example, a consumer having 100% most positive
conversations in the research, trial and purchase categories should
get the maximum score of 100. As such, the weight for sentiment
seven=100/3=+33.33.
[0064] Likewise, a consumer having 100% most negative conversation
in the research, trial and purchase categories should get the
minimum score=-100. As such, the weight for sentiment
1=-100/3=-33.33.
[0065] Consumers that have less negative conversations than
sentiment one, a decrease in penalization points of -33.33 may be
seen, respecting the original weighting. As consumers have less
positive conversations their reward points may be reduced to
respect the original weighting.
[0066] In sum, the PCS module 215 may consider not only the
aggregate number of conversations in each phase of the product
cycle, but the sentiment level associated with each conversation.
Additionally, the sentiment for each conversation may be weighted
based upon consumer characteristics (e.g., mood, influence, etc.).
Moreover, the conversations may further be weighted by the
authority level of the consumers associated with the conversations.
The final PCS (either overall or consumer level) may then be
indexed from zero to 100, for example, where 100 indicates that the
product scores perfectly through the product cycle or at least one
phase of the product cycle.
[0067] The present technology may be adapted to adjust the consumer
level and overall PCS values based upon various factors. For
example, a value calculated for the sentiment of a message may be
adjusted for the consumer's general mood, such as when it is known
that the consumer is always positive or almost always skeptical
and/or negative. In other instances the PCS values may be adjusted
based upon the importance of a particular message to the sale of a
product or service.
[0068] While many methods for calculating and weighting PCS have
been disclosed one or ordinary skill in the art will appreciate
that other algorithms and weighting methodologies that may be
utilized to quantify and predict consumer sentiment and buying
behaviors for product cycles are likewise contemplated for use in
accordance with the present technology.
[0069] PCS values may also be utilized to benchmark a particular
product against a competing product. For example, a PCS value for a
navigation software application for a first merchant may be
compared against a PCS value for similar navigation software from a
competing merchant. The PCS value may provide actionable
information that allows the first merchant to modify their
marketing, consumer service, and/or product features to increase
their PCS value. It is noteworthy to mention that PCS values may be
generated for merchants at specific intervals, such as daily,
weekly, monthly, or quarterly.
[0070] According to some embodiments, the consumer experience
module 220 may be executed to evaluate portions of the consumer
journey (e.g., product cycle) relative to a product. Generally
speaking, consumer experience values may comprise mathematical
representations of customer experience data at specific point in
time (or a specific time period) along the product cycle. In some
instances, the consumer experience scores may include the three PCS
component values (e.g., research value, trial value, and purchase
value) described above, but analyzed relative to a particular time
frame. Therefore, the consumer experience values may be described
as more granular and temporally focused portion of the PCS (either
intermediate or overall).
[0071] Consumers may be previously identified by the data gathering
module 210, for example, by identifying consumers in certain types
of survey data. Various scores may be generated by the consumer
experience module 220 that represent different consumer
experiences. These scores/values may be utilized by merchants to
improve their products and/or marketing campaigns.
[0072] Using the consumer experiences scores, a merchant may
explore more detailed metrics regarding the touchpoints surrounding
a product. In some instances, the consumer experience scores may be
generated by conducting a more detailed evaluation of customer
experience data relative to the calculation of a PCS. Therefore,
the conversation matrices employed by the data gathering module 210
may be modified (e.g., may include greater detail) to capture more
specific portions of digital conversations/messages across phases
of the product cycle. The consumer experience module 220 may also
generate optimal consumer journey models that enable merchants to
plan effective product development and marketing strategies, while
also allowing for course correction when products or marketing fail
to produce acceptable consumer experiences.
[0073] According to some embodiments, the segmentation module 225
may be executed to determine and develop actionable priorities
tailored to specific consumer types. The segmentation module 225
may cluster consumers based on a variety of factors using a
segmentation model that considers product cycle components and
likelihood of purchasing a product. The segmentation module 225 may
utilize the social data gathered by the data gathering module 210.
Additionally, the segmentation module 225 may generate feedback for
consumer segments in near real-time, specifically for consumers
that are the most (and alternatively the least) likely to purchase
a particular product.
[0074] In some embodiments, utilizing the data gathering module
210, consumer customer experience data may be obtained from groups
of consumers engaged in traditional marketing or consumer research
activities. Consumers may be queried for a social networking
identifier (e.g., handle, profile, username, etc.) such that the
data gathering module 210 may collect customer experience data for
that consumer. When customer experience data is obtained, the
segmentation module 225 may link or correlate the customer
experience data with primary research data, such as data obtained
from traditional marketing or consumer research activities. The
segmentation module 225 may evaluate customer experience data of
the consumer to determine if the consumer is acting in
correspondence with the research data gathered about the consumer.
Moreover, the segmentation module 225 may also determine if the
consumer is influencing other consumers with their social media
conversations. The segmentation module 225 may also used the
combined data sets to generate models that allow the segmentation
module 225 to predict which social media conversations that should
be tracked to glean the most accurate and relevant information
about the consumer.
[0075] In other embodiments, the segmentation module 225 may
utilize the correlated group consumers into categories based upon
various factors. For example, very influential consumers who focus
on superior customer service may be clustered into a consumer
segment.
[0076] The segmentation module 225 may segment or cluster the
customer experience data based upon the content of the social media
conversations. For example, the segmentation module 225 may
evaluate a group of social media messages and determine that two
thirds of the consumer's desire superior consumer service, whereas
only five percent desire an aesthetically pleasing website. Again,
the clustering, as with sentiment analysis, may be conducted based
upon keywords included in the social data. As with PCS values and
consumer experience values, the segmentation module 225 may
determine the segmentation of customer experience data based upon
certain algorithms, mathematical, and/or statistical methodologies.
According to some embodiments, the segmentation module 225 may
employ statistical methodologies such as clustering ensembles. The
clustering of consumers allows the merchant to direct more
resources to consumer service efforts and away from website
development. As consumer sentiments change, so may the
segmentation, and thus the priorities of the merchant.
[0077] Brand Commitment Scores
[0078] Based upon the categorization of the social media
conversation as being within the BCS domain, the BCS module 230 may
be executed to calculate a BCS for a social media conversation.
According to some embodiments, the BCS quantifies brand affinity
for a particular consumer or group of consumers. The BCS may also
quantify the consumer's emotions regarding the brand. The BCS may
provide a metric, which allows merchants to build relationships
between customers and brands.
[0079] In some embodiments, the BCS is a composite calculation that
encompasses the understand, explore, and commit segments of the
brand affinity journey. The BCS relates to the brand affinity
journey inasmuch as the understand segment of the brand affinity
journey is associated with hopefulness, the explore segment of the
brand affinity journey is associated with attraction, and the
commit segment of the brand affinity journey is associated with
devotion. Keywords conveying these emotions may be used to
categorize a social media conversation as falling within the brand
commitment domain.
[0080] In greater detail, the hopefulness emotion attempts to
quantify what is important to a customer. Common keywords
associated with hopefulness may comprise, but are not limited to
hope, expect, optimistic, and so forth. Using this metric,
merchants may be able to align expectations of their consumers with
their brand. Merchants may tailor their branding and/or marketing
to set a level of expectation regarding their products and
long-term relationship with customers. The branding of an
organization or products may be utilized to build and maintain an
emotional connection with consumers which may be leveraged to drive
sustained purchasing behavior for products and advocacy in content
engagement.
[0081] The attraction emotion attempts to quantify if the brand
properly reflects who their customers are. Common keywords
associated with attraction may comprise, but are not limited to
excited, admire, appeal, and so forth. Using this metric, merchants
may be able to identify reconciliation when needed. Merchants may
tailor their branding and/or marketing to ensure that their
products are being advertised and/or branded in accordance with the
needs of their customers. These needs may comprise reputation,
quality, popularity, and so forth.
[0082] The devotion emotion attempts to quantify how deeply the
consumer is committed to the brand. Common keywords associated with
devotion may comprise, but are not limited to love, loyal, trust,
and so forth. Using this metric, merchants may be able to identify
a relationship status between a brand and a consumer. The more
devoted the customer is to the brand, the more committed the
customer will be to purchasing the product associated with the
brand. Merchants may wish to tailor their branding or marketing to
drive up customer devotion and identify consumers with lagging
commitment.
[0083] Because these metrics and resultant BCS may be tracked over
time and per author, the merchant may determine how changes in
marketing and/or branding strategies affect these different
consumer emotions. BCS may be calculated for groups or consumer
segments such as demographic, psychographic, or other common
consumer segmentations that would be known to one of ordinary skill
in the art with the present disclosure before them.
[0084] An exemplary algorithm (Equation A) for calculating a BCS
for a social media conversation is provided below:
.SIGMA.(Ar/.SIGMA.Ar)*Cw*Sa (Equation A)
[0085] where an author rank score Ar is first calculated for each
of a group of authors. The group of authors may include the known
customers or alternatively, a subgroup of customers. An author rank
may be calculated by determining an influence for an author. The
influence of an author may be determined, for example, by a number
of connections for the author (e.g., followers, contacts, etc.).
The social status of an author may also be considered. For example,
an influential celebrity may have their conversations ranked more
highly than an average consumer in some embodiments.
[0086] Once an author rank score has been calculated for each
author in the group of authors, the author rank score for the
author of the comment may be divided by a sum of the author rank
scores for each author in the author group to generate an adjusted
author rank score. The author rank scores and/or adjusted author
rank score may be calculated over a given period of time, relative
to a particular product. Thus, BCS may be calculated over time to
provide merchants with indices or metrics that quantify how well
their branding efforts are being received by consumers.
[0087] Next, a component weight Cw for the conversation may be
multiplied with the adjusted author rank score. The component
weight may comprise previously established scaling factors for each
stage of the product cycle. For example, the understand/hopefulness
scaling factor may be approximately 0.15, whereas the
explore/attraction scaling factor may be approximately 0.25.
Additionally, the commit/devotion scaling factor may be
approximately 0.6. Thus, in some embodiments, the most important
scaling factor for component weight relative to the BCS is the
commit/devotion scaling factor. Advantageously, the commit/devotion
scaling factor may be attributed more weight because the BCS
attempts to determined a brand commitment level for consumers.
Therefore, devotion may be strongly correlated to brand commitment,
whereas hopefulness and/or attraction are less likely to be
indicative of brand commitment, although they may be contributory
to some degree.
[0088] As mentioned previously, the component weighting for each of
these three scaling factors may be determined based upon empirical
evidence, such as the evaluation of social media conversations of
trustworthy authors. For example, a plurality of conversations
gathered from various trustworthy consumers may be utilized as the
basis for setting the weight of individual scaling factors.
[0089] Additionally, the BCS may be weighted based upon consumer
sentiment Sw as described in greater detail herein. Thus, the
system may calculate a sentiment weight for consumers who are
consistently positive, negative, neutral, or otherwise amenable to
sentiment categorization. This consumer sentiment Sw may be
utilized to alter the BCS for a particular conversation. For
example, an author who is consistently negative may have their BCS
adjusted based upon a negative conversation. In contrast, the
consistently negative author may have their BCS adjusted upwardly
if they express devotion within their conversation for a particular
brand or product, as it would be unexpected for the author to
provide a positive conversation.
[0090] Customer Relevance Scores
[0091] In some embodiments, based upon the categorization of the
social media conversation as being within the CRS domain, the CRS
module 235 may be executed to calculate a CRS for online content.
According to some embodiments, the CRS may quantify how likely it
is, or will be, that a customer or social network user will share a
particular piece of content, such as a video, an article, a
website, or other online content within the context of a social
network.
[0092] For example, end users such as marketers may desire to
obtain shareability metrics for online content. The data gathering
module 210 may be executed to gather customer experience data
regarding the online content. For example, the data gathering
module 210 may gather metrics regarding sharing, downloading,
uploading, posting, linking, referencing, liking, tagging, or other
similar actions for online content that would occur within the
context of a social network. The data gathering module 210 may also
gather conversations, commentary, blog posts, articles, or other
textual information associated with online content. For example,
the data gathering module 210 may capture a comment thread
associated with a video.
[0093] Next, the CRS module 235 may evaluate the customer
experience data to classify the conversations. Shareability
classification may be performed by determining if keywords included
in the conversations about the online content fall within any of
the columns of the CRS core matrix 315 of FIG. 3. Once the social
media conversations have been classified, a CRS may be calculated
for each conversation, a group of conversations for a particular
author, or of all conversations for all authors in regard to the
online content in question, and so forth.
[0094] An exemplary CRS may be calculated by the CRS module 235
using Equation A provided herein. That is, the adjusted author rank
score may be calculated in a similar manner described herein with
regard to the BCS. In contrast with the method for calculating the
BCS, the CRS may contemplate different component weights Cw for the
conversations. That is, the component weights for the CRS may be
associated with the shareability classifications described herein
such as interested, connected, and sharing. Whereas the BCS related
to the understand, explore, commit product cycle segments with
hopefulness, attraction, and devotion, the CRS relates to the
understand, explore, and commit product cycle segments with
interested, connected, and shared.
[0095] Thus, the component weight Cw for a conversation may
comprise previously established scaling factors for each stage of
the product cycle. For example, the understand/interested scaling
factor may be approximately 0.15, whereas the explore/connected
scaling factor may be approximately 0.25. Additionally, the
commit/shared scaling factor may be approximately 0.6. Thus, the
fact that the content has been shared is more relevant to the CRS
than the keywords that indicate that the consumer is interested in
the content. The connected shareability classification may consider
how often the consumer mentions their social media connectivity and
is therefore more related to shareability than words that simply
connote an interest in the content.
[0096] As the conversations are evaluated for keywords and
classified, the CRS module 235 may determine a shareability level
for the online content. For example, if a predominate number of
conversations include keywords that fall within the Interested
classification, the CRS module 235 may indicate that the online
content is likely to pique the interests of consumers.
[0097] In accordance with the weightings described herein,
conversations that predominately involve keywords in the Interested
classification will generate lower CRS relative to conversations
that predominately involve keywords in the Connected classification
in some embodiments. Therefore, conversations that predominately
involve keywords in the Shared classification will have the highest
relative CRS in some embodiments.
[0098] Again, the CRS may be calculated by the CRS module 235 at an
author level. Additionally, an average CRS may be calculated for a
plurality of authors having social media conversations about
selected online content. Moreover, as with the BCS, the CRS may be
corrected, weighted, adjusted, or otherwise modified based upon the
sentiment score for a particular author.
[0099] In sum, Equation A may be employed by the PCS module 215,
the BCS module 230, and/or the CRS module 235 to calculate the PCS,
BCS, and CRS, respectively. The score calculated by the use of
Equation A is dependent upon the initial classification of social
media conversations as falling within the PCS, BCS, or CRS domains
in some embodiments. Thus, Equation A may be used to calculate PCS
for social media conversations falling with the PCS domain, while
BCS and CRS may be calculated for conversations falling within the
BCS and CRS domains, respectively.
[0100] Although not shown in Equation A, the CRS for online content
may be augmented by web analytics regarding the online content. For
example, the CRS module 235 may determine click counts, share
counts, embeds, links, or other quantifiable ways online content
may be shared within social networks. These analytics may be used
to adjust the CRS. Social media conversations that generate
relatively low CRS may be offset by the fact that the content is,
in fact, shared more frequently than the conversations imply.
[0101] To increase the shareability of online content, the code
framing module 240 may be utilized to generate a code frame that
may be applied to online content having a relatively low CRS. For
example, a code frame may be generated to increase the CRS of
online content that falls within the Interested classification,
described herein.
[0102] The content that generated the relatively high CRS may be
evaluated by the code framing module 240 using a semiotic analysis
to determine categories that define the shareability of content.
With regard to semiotics utilized by the code framing module 240, a
code or "frame" may be built to understand how various elements of
online content work together to create meaning and trigger
subconscious sharing impulses in consumers. These elements may be
labeled as categories. Significant, but non-limiting categories
that trigger subconscious sharing impulses may comprise a Narrative
category may define the style of the content. A Theme category may
define the type of story that the content is attempting to convey.
An Underlying Message category may define the moral outcome or
other message that the content is attempting to convey. Highly
shareable content may engage the consumer on these various levels
and success in these various levels may be determined by evaluating
consumer response to content for these various categories.
[0103] Once the code frame has been generated by the code frame
module 240 (also referred to as code framing module 240), online
content having a relatively low CRS, which is similar to the type
of content used to generate the code frame, may be applied to the
content to increase the CRS for the content.
[0104] In some instances, a semiotic analysis may be performed on
the online content with the low CRS to determine the narrative,
theme, and underlying message categories for the online content. If
the code frame module 240 can determine discrepancies between the
code frame and the categories of the online content, the code frame
module 240 may identify what categories need improvement. In other
instances, marketers may utilize the code frame as a guideline for
correcting defective content (e.g., content that is not being
shared adequately). According to some embodiments, the code frame
may be used by marketers to generate online content from scratch.
Thus, the code frame may be used as a template to create content
that is highly likely to be shared.
[0105] According to various exemplary embodiments, the system 100
may be configured to generate and display customer commitment
framework data. Broadly, the customer commitment framework (CCF)
empowers organizations to optimize their customer experience with a
data-driven approach to decision-making. CCF provides real-time
predictive measures and robust insights to strengthen customer
commitment around the three exemplary customer journeys, including
Shopping, Sharing, and Advocacy. With regard to Shopping, the
system 100 may utilize CCF data informs and enables product and
service strategy which foster awareness, feed trends, convert
shopping into customers, and insure long-term commitment within
target markets.
[0106] Indeed, spending money may be considered the ultimate
commitment behavior, whether for the first time, buying more, or
upgrading. The CCF data allows users to make marketing investments
that lead customers along the product journey and closer to
purchase through understanding the enablers and barriers to
purchase. Responsive, intelligent marketing can influence the
drivers behind purchasing. To support the journey and closing of
shopping behaviors, the entire enterprise must be aligned with
effective sales channels and supply chain to create the optimal
customer experience. CCF data may be utilized by the system 100 to
provide the user with visually appropriate and intuitive dashboards
that present metrics (e.g., scores) in a manner that allows the
user to determine instances where the closing of shopping behaviors
may be increased.
[0107] With regard to Sharing, the system 100 may utilize and
display CCF data, allowing the user to develop a content strategy
for creating impactful and relevant content, leading to highly
engaged customers. Advantageously, these engaged customers are more
likely to share and amplify the impact of advertising content.
[0108] Indeed, effective content production and management are
critical to ensure that messages and customer experience across all
channels are relevant. Relevancy drives sharing and the CCF data
generated and displayed by the system 100 can increase the
likelihood of user endorsement of products and services.
[0109] With regard to Advocacy, the system 100 may utilize CCF data
to inform users of their brand strategies, which may be used to
create vocal advocates for products and services. In some
embodiments, the system 100 may generate Customer Commitment
Dashboards (CCDs), provide social data-derived KPIs and journey
mapping, as well as customer segmentation. When coupled with
human-led analysis and consulting, this system creates a deep and
real-time understanding of your customer's experiences along their
journey; as well as targets your organizational investments on what
is most important to your most valuable customers.
[0110] It will be understood that the CCDs generated by the present
technology may effectively model customer experience data, and
provide product and brand scores, which may be benchmarked and
compared to competitors, as well as an industry average, providing
the ability to leverage best practices in any given industry
vertical.
[0111] Generally, the system 100 may analyze CCF data to evaluate a
customer journey with predictive scores that provide an
understanding of the barriers and enablers in the customer journey
to shopping, advocacy and sharing. Additionally, the system 100 may
model social data to customer challenges and opportunities in
real-time, enabling users to plan and react quickly, by providing
predictive measures with appropriate diagnostics.
[0112] Additionally, the CCF data generated by the system 100 may
be used to pinpoint both challenges and opportunities for enhancing
the customer experience by applying CCF scores (PCS, CRS, BCS) to
specific stages of the customer journey, suggesting which areas of
an organization should be engage to increase customer response. In
addition, in some embodiments the system 100 selectively targets
CCF data and analyzes only those social media conversations that
are indicative of a key customer journey, turning the "big" social
dataset into targeted insights for effective course correction via
the use of dashboards. Again, the system 100 may also model CCF
data for brand and products against competitors CCF data to locate
areas of competitive opportunity and highlight a competitor's best
practices.
[0113] In some instances, the system 100 may provide statistical
links between CCF scores (such as PCS, CRS, and BCS) and
established scorecard metrics such as customer satisfaction (CSAT),
net satisfaction score (NSAT), and net promoter score (NPS)--just
to name a few. Such comparisons allow users to understand how these
measures move together and impact each other to enable smarter
decision-making and corporate scorecard improvement.
[0114] According to some embodiments, the system 100 may be
programmed to obtain customer experience data from one or more
social media platforms regarding a first entity, such as the user
who desires to view CCDs regarding their CCF data. In some
instances, periodic calculations of customer commitment framework
data from the customer experience data are performed. As mentioned
above, this CCF data may include, but is not limited to the PCS,
BCS, and CRS scores described above, but may also include ancillary
or complementary scores such as CSAT and the like. In some
instances, the system 100 may calculate these scores at a given
interval, such as every hour, or each day. These scores may be
stored in a database (such as database 120 of FIG. 1), also
referred to as a data laboratory.
[0115] Upon a user request, the system 100 may generate a customer
commitment dashboard that comprises a graphical representation of
the customer commitment framework data. While various types of
representations of customer commitment framework data will be
described in greater detail below, it will be mentioned that CCF
data may be calculated for a plurality of market segments and in a
plurality of languages. Thus, users that market their products in
various markets, and to consumers in various countries, may use CCF
data that may be used to evaluate and improve marketing within
these markets and languages.
[0116] In some instances, the system 100 may calculate average
scores for a plurality of industry verticals. It is noteworthy that
an average score may include any of an average product commitment
score, an average brand commitment score, and an average customer
relevance score. These "average" scores represent an average of
corresponding scores for competitors of the first entity. Thus, a
user can compare their data against an aggregate/average score
relative to their competition.
[0117] Generally, FIGS. 4-12 each illustrate exemplary graphical
user interfaces in the form of customer commitment dashboards.
These dashboards are merely exemplary and are representative of the
many types of GUIs that may be generated by the system 100. Thus,
one of ordinary skill in the art will appreciate that exact types
of CCF data, and their arrangement into a CCD may depend upon the
type of information that is relevant to the viewer. The system 100
may utilize benchmarking comparisons as the basis for determining
how the data is displayed to the user, or when data is displayed to
the end user. For example, if the system 100 detects highly
discrepant PCS score(s), relative to an average PCS score for a
company's nearest competitors, the system 100 may choose to display
such data first, or highlight such data to draw the attention of
the user.
[0118] FIG. 4 is an exemplary customer commitment dashboard
illustrating product commitment score data. The dashboard is shown
as comprising PCS quadrant graph 505, which includes data points
for a plurality of competing products. The graph 505 is a function
of PCS score values, which extend across the Y-axis of the graph,
while product volume values extend across the X-axis of the graph.
In this instances, the PCS number comprise year-to-date PCS scores
averages for each product.
[0119] The dashboard also includes a table representation 510
showing industry vertical CCF data, which includes average PCS
scores for each vertical, a highest PCS score for each vertical, as
well as a lowest PCS score for each vertical.
[0120] FIG. 5 is an exemplary customer commitment dashboard
illustrating product commitment score data as well as brand
commitment score data. The dashboard of FIG. 5 includes the same
data as provided in FIG. 4, and additionally includes similar
graphs and tables for BCS scores. FIG. 6 is an exemplary customer
commitment dashboard illustrating product commitment score data as
well as brand commitment score data and customer relevancy score
data.
[0121] FIG. 7 is an exemplary customer commitment dashboard
illustrating product commitment score (PCS) data displayed
according to segmentation. More specifically, the dashboard
includes a graphical representation 705 of PCS scores as a function
of influence, where the Y-axis includes a range of influence and
the X-axis is includes PCS scores which increase from left to
right. Thus, the CCF data for a product or service may be visually
represented, graphically, according to customer segments, showing
the influence of a product or a marketing campaign's effectiveness.
A summary paragraph may be provided that describes each particular
segment shown in the graph 705. A control panel 710 provides
controls which allow the user to select temporal ranges over which
data may be viewed. For example, users may select to view PCS
scores for segments over a period of a week, a month, a quarter, or
a year, or may utilize a pair of date input boxes to provide a
bounded range of dates.
[0122] FIG. 8 is an exemplary customer commitment dashboard
illustrating PCS segmentation and customer journey data. The
dashboard includes a table-based representation 805 of segment
data, broken down into journey phases. More specifically, each row
of the table includes a unique journey phase of a product. Each of
the columns of the table include a specific segment. The cells of
the table include PCS scores for each journey phase of each
segment. It will be understood that while the cells are described
as including PCS scores, the cells may be filled alternatively with
BCS scores or CRS scores, depending on the CCF data selected by the
user. The user may toggle through CCF data scores (e.g., PCS, BCS,
and CRS) using tabs 810a-c.
[0123] FIG. 9 is an exemplary customer commitment dashboard
illustrating PCS segmentation and segmentation characteristics. The
dashboard of FIG. 9 includes a table-based representation 905 of
CCF data, where each segment is displayed in row format, while
segment characteristics are displayed in column format. Exemplary
segment characteristics include influence, PCS score, experience
score, and experience change scores. The experience change scores
may assist a user in identifying net score changes over time.
[0124] FIG. 10 is an exemplary customer commitment dashboard
illustrating a time-based and graphical PCS data analysis. The
dashboard of FIG. 10 includes a graphical representation 1005 of
PCS scores for various products. PCS scores on the Y-axis are shown
over time on the X-axis. A band for highest and lowest PCS scores
is overlaid onto the graph, as well as individual PCS scores for
the first entity's product, which in this case includes SM2. A
trend line is also overlaid onto the graph. Similar graphical
representations for competitive position 1010, and driver
performance over time 1015 are also shown.
[0125] FIG. 11 is an exemplary customer commitment dashboard
illustrating a time-based and graphical analysis of customer
journey data. This dashboard includes a graphical representation of
experience scores versus average experience scores within each
phase of a customer journey. Exemplary phases include product
awareness, connection, evaluation, through to long term commitment.
A table representation of experience scores for a plurality of
products are provided, as well as a market level analysis of
experience scores.
[0126] FIG. 12 is an exemplary customer commitment dashboard
illustrating a time-based and graphical analysis of experience
scores relative to product awareness. This dashboard includes
similar CCF data relative to the dashboard of FIG. 11, with an
emphasis placed on scores over time. FIG. 13 illustrates an
exemplary computing system 1300 that may be used to implement an
embodiment of the present technology. The system 1300 of FIG. 13
may be implemented in the contexts of the likes of computing
systems, networks, servers, or combinations thereof disclosed
herein. The computing system 1300 of FIG. 13 includes one or more
processors 1310 and main memory 1320. Main memory 1320 stores, in
part, instructions and data for execution by processor 1310. Main
memory 1320 may store the executable code when in operation. The
system 1300 of FIG. 13 further includes a mass storage device 1330,
portable storage medium drive(s) 1340, output devices 1350, user
input devices 1360, a graphics display 1370, and peripheral devices
1380.
[0127] The components shown in FIG. 13 are depicted as being
connected via a single bus 1390. The components may be connected
through one or more data transport means. Processor unit 1310 and
main memory 1320 may be connected via a local microprocessor bus,
and the mass storage device 1330, peripheral device(s) 1380,
portable storage device 1340, and display system 1370 may be
connected via one or more input/output (I/O) buses.
[0128] Mass storage device 1330, which may be implemented with a
magnetic disk drive or an optical disk drive, is a non-volatile
storage device for storing data and instructions for use by
processor unit 1310. Mass storage device 1330 may store the system
software for implementing embodiments of the present technology for
purposes of loading that software into main memory 1320.
[0129] Portable storage device 1340 operates in conjunction with a
portable non-volatile storage medium, such as a floppy disk,
compact disk, digital video disc, or USB storage device, to input
and output data and code to and from the computer system 1300 of
FIG. 13. The system software for implementing embodiments of the
present technology may be stored on such a portable medium and
input to the computer system 1300 via the portable storage device
1340.
[0130] Input devices 1360 provide a portion of a user interface.
Input devices 1360 may include an alphanumeric keypad, such as a
keyboard, for inputting alpha-numeric and other information, or a
pointing device, such as a mouse, a trackball, stylus, or cursor
direction keys. Additionally, the system 1300 as shown in FIG. 13
includes output devices 1350. Suitable output devices include
speakers, printers, network interfaces, and monitors.
[0131] Display system 1370 may include a liquid crystal display
(LCD) or other suitable display device. Display system 1370
receives textual and graphical information, and processes the
information for output to the display device.
[0132] Peripherals 1380 may include any type of computer support
device to add additional functionality to the computer system.
Peripheral device(s) 1380 may include a modem or a router.
[0133] The components provided in the computer system 1300 of FIG.
13 are those typically found in computer systems that may be
suitable for use with embodiments of the present technology and are
intended to represent a broad category of such computer components
that are well known in the art. Thus, the computer system 1300 of
FIG. 13 may be a personal computer, hand held computing system,
telephone, mobile computing system, workstation, server,
minicomputer, mainframe computer, or any other computing system.
The computer may also include different bus configurations,
networked platforms, multi-processor platforms, etc. Various
operating systems may be used including Unix, Linux, Windows,
Macintosh OS, Palm OS, Android, iPhone OS and other suitable
operating systems.
[0134] It is noteworthy that any hardware platform suitable for
performing the processing described herein is suitable for use with
the technology. Computer-readable storage media refer to any medium
or media that participate in providing instructions to a central
processing unit (CPU), a processor, a microcontroller, or the like.
Such media may take forms including, but not limited to,
non-volatile and volatile media such as optical or magnetic disks
and dynamic memory, respectively. Common forms of computer-readable
storage media include a floppy disk, a flexible disk, a hard disk,
magnetic tape, any other magnetic storage medium, a CD-ROM disk,
digital video disk (DVD), any other optical storage medium, RAM,
PROM, EPROM, a FLASHEPROM, any other memory chip or cartridge.
[0135] While various embodiments have been described above, it
should be understood that they have been presented by way of
example only, and not limitation. The descriptions are not intended
to limit the scope of the technology to the particular forms set
forth herein. Thus, the breadth and scope of a preferred embodiment
should not be limited by any of the above-described exemplary
embodiments. It should be understood that the above description is
illustrative and not restrictive. To the contrary, the present
descriptions are intended to cover such alternatives,
modifications, and equivalents as may be included within the spirit
and scope of the technology as defined by the appended claims and
otherwise appreciated by one of ordinary skill in the art. The
scope of the technology should, therefore, be determined not with
reference to the above description, but instead should be
determined with reference to the appended claims along with their
full scope of equivalents.
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