U.S. patent application number 13/961586 was filed with the patent office on 2014-03-06 for predictive and profile learning sales automation analytics system and method.
This patent application is currently assigned to ORACLE OTC SUBSIDIARY LLC. The applicant listed for this patent is ORACLE OTC SUBSIDIARY LLC. Invention is credited to JAMES NEAL RICHTER.
Application Number | 20140067470 13/961586 |
Document ID | / |
Family ID | 39585432 |
Filed Date | 2014-03-06 |
United States Patent
Application |
20140067470 |
Kind Code |
A1 |
RICHTER; JAMES NEAL |
March 6, 2014 |
PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM
AND METHOD
Abstract
A sales automation system and method, namely a system and method
for scoring sales representative performance and forecasting future
sales representative performance. These scoring and forecasting
techniques can apply to a sales representative monitoring his own
performance, comparing himself to others within the organization
(or even between organizations using methods described in
application), contemplating which job duties are falling behind and
which are ahead of schedule, and numerous other related activities.
Similarly, with the sales representative providing a full set of
performance data, the system is in a position to aid a sales
manager identify which sales representatives are behind others and
why, as well as help with resource planning should requirements,
such as quotas or staffing, change.
Inventors: |
RICHTER; JAMES NEAL;
(BELGRADE, MT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ORACLE OTC SUBSIDIARY LLC |
REDWOOD SHORES |
CA |
US |
|
|
Assignee: |
ORACLE OTC SUBSIDIARY LLC
REDWOOD SHORES
CA
|
Family ID: |
39585432 |
Appl. No.: |
13/961586 |
Filed: |
August 7, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
11617437 |
Dec 28, 2006 |
8527324 |
|
|
13961586 |
|
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06Q 10/00 20130101;
G06Q 30/0202 20130101; G06Q 10/06398 20130101; G06Q 10/06395
20130101 |
Class at
Publication: |
705/7.31 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A sales automation method comprising: providing a repository of
ideal and learned sales data profiles and a repository of raw sales
data; extracting raw sales data and extracting ideal and learned
sales data; defining a sales pipeline having a construct of a
series of stages thereby defining a sales strategy where each stage
is assigned a conversion rate and assumed value potential;
correlating the raw sales data among the series of stages based on
the respective task; correlating the ideal and learned data among
the series of stages based on respective task; developing a
predictive model of a conversion rate of the sales strategy base on
the correlating of the ideal and learned data; and applying the
predictive model to the correlated raw sales data and developing a
raw predictive model to a row conversion rate.
2. A method comprising: determining at least one first revenue
forecasting parameter based on at least one performance
characteristic of at least one person supporting generation of
revenue for the organization; and determining at least one second
revenue forecasting parameter to forecast the revenue of an
organization, wherein the at least second forecasting parameter is
determined at least in part from sales opportunities in a sales
pipeline; and forecasting revenue of the organization using at
least in part the first and second forecasting parameters.
3. The method according to claim 2 further wherein the at least one
performance characteristic is based on historical performance of
the person.
4. The method according to claim 3 further wherein the performance
characteristic is based on the classification of the person's
capability to generate revenue for the organization.
5. The method according to claim 2 further including maintaining a
profile for at least one of the persons, wherein the profile
includes at least one parameter indicative of the capability to
generate revenue.
6. The method according to claim 2 further comprising: maintaining
a central data repository including time stamped cached and sales
records, idealized and learned sales data and performance metrics;
maintaining a repository of raw sales records including raw sales
data; retrieving data from said central repository and said
repository of raw sales records; producing metric scores, derived
sales data, and learned sales data; and storing said produced data
in the central data repository.
7. The method according to claim 6 further comprising: receiving a
user initiated query; querying based on the user initiated query
the central data repository to transmit the cached sales records,
idealized and learned sales data, and performance metrics; and
applying transforms, idealized and learned functions, and data
models to the transmitted data.
8. A system comprising: a processor; and a memory coupled with and
readable by the processor and storing therein a set of instructions
which, when executed by the processor, causes the processor to
perform a method comprising: determining at least one first revenue
forecasting parameter based on at least one performance
characteristic of at least one person supporting generation of
revenue for the organization; and determining at least one second
revenue forecasting parameter to forecast the revenue of an
organization, wherein the at least second forecasting parameter is
determined at least in part from sales opportunities in a sales
pipeline; and forecasting revenue of the organization using at
least in part the first and second forecasting parameters.
9. The system according to claim 8 further wherein the at least one
performance characteristic is based on historical performance of
the person.
10. The system according to claim 9 further wherein the performance
characteristic is based on the classification of the person's
capability to generate revenue for the organization.
11. The system according to claim 8 further including maintaining a
profile for at least one of the persons, wherein the profile
includes at least one parameter indicative of the capability to
generate revenue.
12. The system according to claim 8 further comprising: maintaining
a central data repository including time stamped cached and sales
records, idealized and learned sales data and performance metrics;
maintaining a repository of raw sales records including raw sales
data; retrieving data from said central repository and said
repository of raw sales records; producing metric scores, derived
sales data, and learned sales data; and storing said produced data
in the central data repository.
13. The system according to claim 12 further comprising: receiving
a user initiated query; querying based on the user initiated query
the central data repository to transmit the cached sales records,
idealized and learned sales data, and performance metrics; and
applying transforms, idealized and learned functions, and data
models to the transmitted data.
14. A computer-readable memory comprising a set of instructions
stored therein which, when executed by a processor, causes the
processor to perform a method comprising: determining at least one
first revenue forecasting parameter based on at least one
performance characteristic of at least one person supporting
generation of revenue for the organization; and determining at
least one second revenue forecasting parameter to forecast the
revenue of an organization, wherein the at least second forecasting
parameter is determined at least in part from sales opportunities
in a sales pipeline; and forecasting revenue of the organization
using at least in part the first and second forecasting
parameters.
15. The computer-readable memory according to claim 14 further
wherein the at least one performance characteristic is based on
historical performance of the person.
16. The computer-readable memory according to claim 15 further
wherein the performance characteristic is based on the
classification of the person's capability to generate revenue for
the organization.
17. The computer-readable memory according to claim 14 further
including maintaining a profile for at least one of the persons,
wherein the profile includes at least one parameter indicative of
the capability to generate revenue.
18. The computer-readable memory according to claim 14 further
comprising: maintaining a central data repository including time
stamped cached and sales records, idealized and learned sales data
and performance metrics; maintaining a repository of raw sales
records including raw sales data; retrieving data from said central
repository and said repository of raw sales records; producing
metric scores, derived sales data, and learned sales data; and
storing said produced data in the central data repository.
19. The computer-readable memory according to claim 18 further
comprising: receiving a user initiated query; querying based on the
user initiated query the central data repository to transmit the
cached sales records, idealized and learned sales data, and
performance metrics; and applying transforms, idealized and learned
functions, and data models to the transmitted data.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] The present application is a Divisional of U.S. application
Ser. No. 13/617,437, filed on Dec. 28, 2006, entitled "PREDICTIVE
AND PROFILE LEARNING SALESPERSON PERFORMANCE SYSTEM AND METHOD," of
which the entire disclosure is incorporated herein by reference for
all purposes.
[0002] The present application is also related to the following
co-pending and commonly assigned U.S. patent applications:
U.S. patent application Ser. No. ______ (Attorney Docket Number
88325-883762(125810US)) filed concurrent herewith and entitled
"PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM
AND METHOD," and which claims priority to U.S. application Ser. No.
13/617,437, filed on Dec. 28, 2006, entitled "PREDICTIVE AND
PROFILE LEARNING SALESPERSON PERFORMANCE SYSTEM AND METHOD;" U.S.
patent application Ser. No. ______ (Attorney Docket Number
88325-883766(125820US)) filed concurrent herewith and entitled
"PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM
AND METHOD," and which claims priority to U.S. application Ser. No.
13/617,437, filed on Dec. 28, 2006, entitled "PREDICTIVE AND
PROFILE LEARNING SALESPERSON PERFORMANCE SYSTEM AND METHOD;" U.S.
patent application Ser. No. ______ (Attorney Docket Number
88325-883769(125840US)) filed concurrent herewith and entitled
"PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM
AND METHOD," and which claims priority to U.S. application Ser. No.
13/617,437, filed on Dec. 28, 2006, entitled "PREDICTIVE AND
PROFILE LEARNING SALESPERSON PERFORMANCE SYSTEM AND METHOD;" and
U.S. patent application Ser. No. ______ (Attorney Docket Number
88325-883768(125850US)) filed concurrent herewith and entitled
"PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM
AND METHOD," and which claims priority to U.S. application Ser. No.
13/617,437, filed on Dec. 28, 2006, entitled "PREDICTIVE AND
PROFILE LEARNING SALESPERSON PERFORMANCE SYSTEM AND METHOD."
BACKGROUND
[0003] 1. Field
[0004] This application relates generally to sales automation and
forecasting systems and, more particularly, to tracking and storing
sales data for performance prediction.
[0005] 2. Background
[0006] Sales automation systems are common in the art. In standard
usage, these systems aid a sales representative, a sales manager,
or both, to be more efficient at selling a product or service.
Sales forecasting analytics are commonly a component of a sales
automation systems, and are largely focused towards the sales
manager to help forecast and manage the sales process.
[0007] A sales automation system is generally a tool that allows
sales representatives and managers to organize contact records, as
well as manage records associated with sales quotes and invoices.
It can also work in the context of a `Sales Methodology` where the
sales process is structured around the representative working
though a set of stages in an attempt to complete a sale. Sales
automation tools typically allow the tracking of such records in
terms of time periods associated with fiscal or financial
accounting. During these `sale periods` it is important for sales
representatives and managers to have analytical reports defining
progress towards various goals.
[0008] Typically the sales representative recognizes the benefit of
using a sales automation system for maintaining their list of
contacts, but most other tasks are considered overhead with little
benefit. Thus, sales automation systems commonly suffer from a lack
of full acceptance by the sales staff, which limits their
usefulness to the sales managers as well--if the sales
representative doesn't utilize the system then the manager does not
have a full picture of the sales process. As the sales
representative is responsible for using the system to track
individual deal progress through the sales stage, should they fail
to log the sales progress the system is left without the valuable
data necessary for the sales manager to forecast and manage the
sales goals. A system that provides incentive to a sales
representative to use features beyond a contact management system
is needed. Additionally, in part because of the lack of sales data
and in part because of the prior state of the art, many analytics
used by the sales manager are quite simple models with manual
parameters based upon the sales manager's intuitions about prior
performance.
[0009] Many algorithms in the general field of data mining provide
resources to a knowledgeable individual for extracting relevant
information from large amounts of data. There exist data mining
applications to aid in this process. In some instances there are
data mining approaches incorporated into other systems, such as
sales automation systems. These incorporated techniques are usually
quite rudimentary compared to the full suite of techniques
available in a complete data mining system, yet still require some
level of sophistication on the part of the user (in sales
automation systems the user would be the sales manager). Some of
the more advanced techniques available would be standard
statistical approaches for assigning error bars or applying a
linear regression analysis. These statistical approaches are often
guided by or overridden with ad-hoc scaling factors based upon the
sales manager's intuition, such as: "Bob usually over promises his
amount sold by about 25%, yet Sue is more conservative and usually
under promises by 15%. Therefore I will adjust Bob's sales
predictions down by 25%, but increase Sue's predictions by
15%."
[0010] There exist new data mining and machine learning techniques
which can go beyond the traditional analyses, above, but they
require data to work accurately to overcome the ad-hoc manual
scaling factors. To collect this data the sales representative must
be motivated to provide the information. Stereotypically, sales
people are motivated by two goals: meeting personal monetary
targets, and out performing their peers. Methods that target these
motivations are needed and will increase the acceptance of a sales
automation system by the sales people, and hence provide a richer
set of useful data to the sales manager. Most prior systems have
failed to adequately provide features found compelling to the sales
representative, and have overlooked the connection that the sales
manager's job is best done with the full data available from an
engaged sales representative. Some prior systems have recognized
the benefits of catering to the interests of a sales
representative, but have neglected to use the data naturally
collected by the sales automation system to reflect back and help
provide the necessary feedback to keep the system accurately
tuned--commonly the systems relied on a manual configuration of the
various parameters.
[0011] A better sales automation and forecasting management system
is needed to address the above noted shortcomings in the prior
art.
SUMMARY
[0012] One aspect of the inventive subject matter includes a sales
automation system and a method for scoring sales representative
performance and forecasting future sales representative
performance. These scoring and forecasting techniques can apply to
a sales representative monitoring his own performance, comparing
himself to others within the organization (or even between
organizations using methods described in application),
contemplating which job duties are falling behind and which are
ahead of schedule, and numerous other related activities.
Similarly, with the sales representative providing a full set of
performance data, the system is in a position to aid a sales
manager identify which sales representatives are behind others and
why, as well as help with resource planning should requirements,
such as quotas or staffing, change.
[0013] Further aspects include a central repository of logged sales
related data including data representative of the progress made
through the various sales stages of the sale cycle. The logged
sales data is extracted to support the learning forecasting and
prediction functions of the present invention. Numerical and
quantitative data records can be retrieved based on user request
for data. Still further aspects include an incentive because of the
ability to provide a sales representative through a user interface
current and predictive analysis of their performance relative to
their peers and groups of other sales representatives. Some
embodiments also provide a tool that allows the sales
representative and manager to perform a predictive analysis of
their conversion rate to monitor monetary performance.
[0014] For example, all calculations can be done to score the
performance of individual people and groups with respect to learned
data models of top performing sales people as well as with respect
to user-entered ideal models of performance. An example of this
would be an ideal revenue growth of a sales person over time. A
second example is a relative analysis of the person's ongoing sales
`pipeline` with respect to learned and ideal models of good
performance. In either case the system can learn the historical
sales models for an individual or high performing sales
representative using any of a variety of machine learning
techniques. In addition, an idealized model can be entered by the
sales manager. The resulting comparison can aid in the
understanding of how individuals relate to one another and to an
idealized individual. This helps sales representatives understand
their relative performance, and it helps sales managers understand
if there is a general deviation between their idealized model and
the actual performance of an individual or group.
[0015] Another example can be evaluating an entire sales process
sales methodology with respect to the ideal model of the process as
well as compared to other good performing processes or
methodologies. Note that this analysis can be independent of
individuals looking at the process in isolation. It can also be
used to identify weak individuals or groups that bring the overall
performance of the process or methodology down. One aspect of the
embodiments is the ability to use the systems and methods in CRM
software applied to sales process analysis and its combination with
iterative machine learning methods.
[0016] With the historical performance of each sales representative
stored by the sales automation system, the system can more
accurately update the forecasting portion of the sales
analytics.
[0017] These and other advantageous features of the various
embodiments will be in part apparent and in part pointed out herein
below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] For a better understanding of the present invention,
reference may be made to the accompanying drawings in which:
[0019] FIG. 1 is a top level diagram of the system
architecture;
[0020] FIG. 2 is an illustration of the data agglomeration of the
respective elements of the central repository;
[0021] FIG. 3 is an illustration of a data query module according
to embodiments of the invention;
[0022] FIG. 4 is a machine learning and forecasting functional flow
according to embodiments of the invention;
[0023] FIG. 5 is a future planning module functional flow according
to embodiments of the invention;
[0024] FIGS. 6 and 7 are diagrams illustrating the revenue growth
MLF examples;
[0025] FIG. 7 is an illustration of the Ideal Growth Analysis;
[0026] FIG. 8 is an illustration of the Ideal Pipeline or Strategic
analysis;
[0027] FIG. 9 is an illustration of Query, Analytic Comparison and
display method;
[0028] FIG. 10 is an illustration of the graphical output of Ideal
Revenue Growth Analysis;
[0029] FIG. 11 is an illustration of the graphical output of Ideal
Pipeline or Strategy Analysis;
[0030] FIG. 12 is a diagram of the Data Manipulation Algorithm
(DMA);
[0031] FIG. 13 is a diagram of the DMF flow;
[0032] FIG. 14 is a diagram of the Post-DMF Heuristics
algorithm;
[0033] FIGS. 15A and 15B are a flowchart illustrating a Pipeline
Analysis method according to embodiments of the invention;
[0034] FIGS. 16A and 16B are flowcharts illustrating a revenue
forecasting method according to embodiments of the invention;
[0035] FIGS. 17A and 17B are a flowchart illustrating a method for
scoring performance according to embodiments of the invention;
[0036] FIG. 18 is a flowchart illustrating a method for allocation
of quotas by periods according to embodiments of the invention;
and
[0037] FIG. 19 is a flowchart illustrating a method for allocation
of quotas by territories according to embodiments of the
invention.
INCORPORATION BY REFERENCE
[0038] An addition to the disclosure herein relative to the subject
matters A-D reference is to be made to the publications listed
below:
[0039] (A) Queuing Models/Systems: [0040] An Introduction to
Stochastic Modeling by Samuel Karlin, Howard M. Taylor Academic
Press; 3 edition (February 1998) (hereby incorporated by reference
for all purposes)
[0041] (B) Machine Learning & Data Mining [0042] Pattern
Recognition, Third Edition by Sergios Theodoridis, Konstantinos
Koutroumbas; Academic Press; 3 edition (Feb. 24, 2006) (hereby
incorporated by reference for all purposes) [0043] Pattern
Classification (2nd Edition) by Richard O. Duda, Peter E. Hart,
David G. Stork; Wiley-Interscience; 2nd edition (October 2000)
(hereby incorporated by reference for all purposes) [0044] Machine
Learning by Tom M. Mitchell; McGraw-Hill Science/Engineering/Math;
1 edition (Mar. 1, 1997) (hereby incorporated by reference for all
purposes)
[0045] (C) Forecasting and Prediction: [0046] Forecasting: Methods
and Applications by Spyros G. Makridakis, Steven C. Wheelwright,
Rob J Hyndman; Wiley; 3 edition (December 1997) (hereby
incorporated by reference for all purposes)
[0047] (D) Sales/Business: [0048] Selling and Sales Management by
Robert D. Hirsch, Ralph Jackson; Barron's Educational Series (Sep.
29, 1993) (hereby incorporated by reference for all purposes)
DEFINITION OF TERMS
[0049] Sales Strategy: A sales methodology where the process of
selling is organized around a set of abstract `stages`, where at
each stage the sales person and/or potential customer perform a set
of tasks.
[0050] Sales Pipeline: A set of potential sales transactions
(deals) for a sales person or group in various stages of
completion.
[0051] Sales Funnel: A standard visual picture of a sales
pipeline.
[0052] Sales Period: A specific time period when sales activity is
conducted and measured.
[0053] Sales Quota: A revenue goal for a particular sales person or
group of people for a specific `sales period`.
[0054] Top Performer: A sales person who meets a set of user/system
defined performance metrics for a set of sales periods.
[0055] Data Agglomeration: The process of grouping a set of raw
data points by a particular attribute along with a particular
mathematical operator.
DETAILED DESCRIPTION OF INVENTION
[0056] In the following detailed description of exemplary
embodiments of the invention, reference is made to the accompanying
drawings that form a part hereof, and in which is shown by way of
illustration specific exemplary embodiments in which the invention
may be practiced. These embodiments are described in sufficient
detail to enable those skilled in the art to practice the inventive
subject matter, and it is to be understood that other embodiments
may be utilized and that logical, mechanical, electrical and other
changes may be made without departing from the scope of the
inventive subject matter.
[0057] Some portions of the detailed descriptions which follow are
presented in terms of algorithms and symbolic representations of
operations on data bits within a computer memory. These algorithmic
descriptions and representations are the ways used by those skilled
in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of steps leading to a desired result. The steps are those requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to these
signals as bits, values, elements, symbols, characters, terms,
numbers, or the like. It should be borne in mind, however, that all
of these and similar terms are to be associated with the
appropriate physical quantities and are merely convenient labels
applied to these quantities. Unless specifically stated otherwise
as apparent from the following discussions, terms such as
"processing" or "computing" or "calculating" or "determining" or
"displaying" or the like, refer to the action and processes of a
computer system, or similar computing device, that manipulates and
transforms data represented as physical (e.g., 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.
[0058] In the Figures, the same reference number is used throughout
to refer to an identical component that appears in multiple
Figures. Signals and connections may be referred to by the same
reference number or label, and the actual meaning will be clear
from its use in the context of the description. Also, please note
that the first digit(s) of the reference number for a given item or
part of the invention should correspond to the Fig. number in which
the item or part is first identified.
[0059] The description of the various embodiments is to be
construed as exemplary only and does not describe every possible
instance of the inventive subject matter. Numerous alternatives
could be implemented, using combinations of current or future
technologies, which would still fall within the scope of the
claims. The following detailed description is, therefore, not to be
taken in a limiting sense, and the scope of the inventive subject
matter is defined only by the appended claims.
[0060] One embodiment of the present invention comprising a central
repository of logged sales related data; a machine learning module
and a prediction module teaches a novel system and method for sales
performance and forecasting.
[0061] For simplicity and illustrative purposes, the principles of
the present invention are described by referring mainly to an
exemplar embodiment, particularly, with references to the Internet
and the World Wide Web (WWW) as the exemplary databases of
informational items. However, one of ordinary skill in the art
would readily recognize that the same principles are equally
applicable to, and can be implemented in, other informational
database, and that any such variation would be within such
modifications that do not depart from the true spirit and scope of
the present invention. Similarly, simplicity of description for the
present invention makes use of a data warehouse. However, one of
ordinary skill in the art would readily recognize that the same
functionality can be gained in the absence of a data warehouse
through summarized data, simulated data, data models or the like
and not depart from the true spirit and scope of the present
invention.
[0062] FIG. 1 illustrates a representative architecture 100
according to embodiments of the invention. The details of the
various embodiments can be better understood by referring to the
figures of the drawings. The data warehouse (DW) 102 is the central
repository for the supporting the machine learning and forecasting
(MLF) 108 and future planning (FP) 110 modules. While those of
ordinary skill in the art could readily identify different sets or
subsets of elements, for explanatory purposes in the embodiments of
the present invention the following elements are described as part
of a data warehouse [0063] Data Transforms--data [0064] Heuristics
{Idealized and Learned}--using an Idealized or Learned set of rules
or replicable method or approach for directing attention to data
key to prediction and forecasting [0065] Data Models {Idealized and
Learned}--using data models using idealized or learning information
[0066] Derived Data Store [0067] Data Cache [0068] Metrics and
Scores--related to sales goods [0069] Stored Scenarios--fabricated
sales conditions [0070] Configuration Settings--
[0071] One aspect of the DW 102 of the various embodiments is that
nearly all elements may have an associated timestamp. One of
ordinary skill in the art can clearly see that the DW 102 can be
maintained in a database, in working memory, on disk, or by
innumerable other means. Similarly, the DW 102 may contain original
data, summarized data, machine learned data, or manually configured
data. Referring to FIG. 1, the DW 102 is shown having the various
elements listed above. The Data Agglomeration 104 is shown
communicably linked to the DW 102 for retrieving and caching
records. The Query Module 106 for initiating user queries and
displaying is shown communicably linked to the DW. The machine
learning module 108 and future planning module 110 is shown
communicably linked to DW 102 and Data agglomeration 104. The DW
102 includes sales data generated from raw sales data manipulated
by the MLF 108 and FP 110 to generate learned data based on
historical data, and includes idealized data based on benchmarked
models or user input or idealized data formulated from data
operated on by heuristic rules/algorithms. A display and user
interface 116 may be used to access DW 102. Data provided through
user interface 116 may be transformed using run time heuristics and
data transforms 118. Additionally a user interface 114 may be used
to provide input and receive output from FP 110.
[0072] FIG. 2 is an illustration of the operation of data
agglomeration module 104 according to embodiments of the invention.
The Data Agglomeration Module 104 associated with the DW 102
retrieves snapshots of raw sales records/transactions 112 and
transcribes the numerical and quantitative data of the records with
an attached timestamp at regular intervals. In addition, the
records 112 may be accumulated per sales representative and per
that representative's management hierarchy. During this process a
number of calculations can be made, including but not limited to,
standard `sales metrics` like the percentage of a representative's
(or group of representatives') sales quota they have attained as of
a given date. A data cache and metric score calculated by the Data
Agglomeration can be stored back on the DW 102.
[0073] One task of data agglomeration module 104 is to retrieve
sales records (sales transactions) 112 and cache a subset of those
records, the quantitative data, with a timestamp. The module also
can cache data using data transformations as well as using data
models, and heuristics in combination with previous data caches to
produce various sales metrics. One of ordinary skill in the art can
easily determine that the Data Agglomeration module 104 could be
performed in a number of different ways and similarly that the
scope of the inventive subject matter would not be diminished by
not using this module. Rather the inclusion of this module is an
efficiency enhancement to the novel aspects of the current
invention. Similarly, the caching and agglomeration can occur in
memory, on disk, or in any other method which should be obvious to
one of ordinary skill in the art.
[0074] FIG. 3 is an illustration of a data query module 106
according to embodiments of the invention. The Query Module 106,
also referred to as a Data Query and Display Module (DQ-DM)
associated with the DW 102 allows the user initiation of queries
and displays the quantitative data graphically in combination with
a user's group and other peer groups. Each metric has a
customizable graphical display and custom metrics can be created
for display. These graphical metrics can be combined with other
graphical and tabular data to create a `dashboard` for sales
people.
[0075] The basic data flow is driven by a user request for data via
display and user interface 116. A query is generated, and the
response data is sent to the query module 106 in combinations with
various other elements of the DW 102. Next, the data and elements
move to the run-time engine where transforms, heuristics and data
models 118 are applied. The resulting retrieved and calculated data
is then provided to display components of display and user
interface 116 for viewing by the user.
[0076] FIG. 4 is a machine learning and forecasting functional flow
according to embodiments of the invention. The machine learning and
forecasting (MLF) module 108 associated with the DW 102 is a data
mining module used within embodiments of the present invention. It
takes input from the various sales data tables 112, user input
heuristics, and user input idealized data models. The output is
generally various scores on each metric or combinations of metric
as well as learned data models.
[0077] The MLF module 108 is comprised of a variety of components.
Each component contains a collection of algorithms that can be
applied both in parallel and sequentially to produce output within
a component. The following components are useful in the preferred
embodiment, but one with ordinary skill in the art can easily see
that not all of the components are necessary, for example, either
or both of the heuristics engines 404 and 408 could be eliminated
without altering the intended functionality of the current
invention.
MLF:
[0078] Data Manipulation Algorithms (DMA) 402 (further described in
FIG. 12) [0079] Pre-DMF Heuristics Engine (HE1) 404 (further
described in FIG. 14) [0080] Data Mining and Forecasting Algorithms
(DMF) 406 (further described in FIG. 13) [0081] Post-DMF Heuristics
Engine (HE2) 408 (further described in FIG. 14)
[0082] The process flow illustrated in FIG. 4 is as follows: [0083]
Sales records 112 and elements of the DW 102 are retrieved and
input to the DMA 402 which is a component of MLF 108. The
configuration settings specify what algorithms are applied in
combination with DW 102 elements. [0084] The output of the DMA 402
is passed to HE1 404 which is also a component of MLF 108 along
with retrieved heuristics from the DW. Various heuristics specified
by the configuration settings are applied to the input. [0085] The
output of HE1 404 is passed to DMF 406 where configuration settings
govern the algorithms used in what combination with DW 102
elements. [0086] The output of the DMF 406 is passed to the HE2
408. Internals proceed similar to step 2. [0087] The output of the
HE2 408 is sent to the DW 102. The output may be some combination
metric-scores, derived data store, learned data models and learned
heuristics.
[0088] FIG. 5 is a future planning module functional flow according
to embodiments of the invention. The Future Planning and Analysis
module (FPA) 110 illustrated in FIG. 5 allows users to play out
future scenarios and use learned and idealized models as well as
current and past data to execute these scenarios. The FPA 110
module builds upon previous modules capabilities and includes a
scenario analysis engine 502 that adds the ability to store and
retrieve `scenarios` and execute these scenarios in isolation in
addition to changing base data and model values for a particular
scenario. Scenario analysis engine 502 interacts with MLF 108 based
on input received from a user interface 114. The results of the
scenario may be displayed 504 via user interface 114.
[0089] Examples 6 and 7 discussed below provide example use cases
of the FPA 110.
[0090] FIGS. 6 and 7 are diagrams illustrating the revenue growth
MLF examples. The MLF module 108 is used to do the main data
analysis of the FPA 110. The module is supplied with previous
inputs from the data warehouse 102 and sales records 112 as well as
any `stored scenarios` in the DW. The User interface 116 of the FPA
module 110 allows the user to manipulate any data point that has
been loaded from the DW 102 as well as change parameters of the
various `models` used in the MLF module 108. The changes entered by
the UI are updated into the MLF via the `Scenario Analysis Engine`
502, a sub-component of the FPA 110 that is shown in FIG. 5.
[0091] In addition to accepting data and model changes from the UI,
the `Scenario Analysis Engine` 502 contains a specific script, or
algorithm, that specifies what components of the MLF 108 are used
and in what sequence to produce a desired output analysis 604.
Various analytical methods 602, such as time series analysis or
smoothing methods may be applied. Output analysis 604 may include
an idealized average performance profile or and idealized
top-performance profile.
[0092] FIGS. 1-9 have illustrated various aspects of the system and
system operation. Further details on the operation of the system
are provided below in FIGS. 15-19. As noted above, MLF 108 may use
various algorithms during its operation. Further details on these
algorithms are provided in FIGS. 12-14.
[0093] FIG. 12 provides further details regarding data manipulation
algorithms 402. The Transforms section 1206 lists several example
transforms which may apply in some embodiments. One of ordinary
skill in the art can recognize that this set of examples is
representative and not exhaustive. Similarly, this step might use
any number or combination of these transforms without altering the
scope or intent of this patent.
[0094] FIG. 13 is a flow chart of the Data Mining and Forecasting
Algorithms. Example Algorithms 1306 for FIG. 13 include, but are
not limited to the following: [0095] Bayesian Classifier [0096]
Perceptron Algorithm [0097] Expectation Maximization [0098] K
Nearest Neighbor [0099] Radial Basis Function Networks [0100]
Relaxation Discriminate Classification [0101] Divisive Clustering
Algorithms [0102] Agglomerative Clustering Algorithms [0103]
Ho-Kashyap Procedures [0104] Linear Programming [0105] Moving
Average Forecasting (Family of Algorithms) [0106] Box-Jenkins
Forecasting [0107] Holt-Winters Seasonality Forecasting [0108]
Wavelet Forecasting [0109] Iterative Optimization Algorithms [0110]
Evolutionary Optimization Algorithms [0111] Theoretical Queuing
Models
[0112] In FIG. 13, the list of Algorithms 1304 (1 through N)
represents any combination of data mining and forecasting
algorithms. The system may use a single algorithm or any number of
multiple algorithms in conjunction. These algorithms could include
any commonly known data mining algorithms such as clustering,
outlier detection, linear regression, or any other technique known
to someone with ordinary skill in the art. The section labeled
"Ensemble Methods" 1306 lists several techniques used to resolve
the combination of the data mining algorithms. One of ordinary
skill in the art can recognize that this list is non-exhaustive and
that any of the listed approaches alone or in combination could be
used to achieve the desired purpose.
[0113] In FIG. 14, the section labeled "Apply Rule" 1404 represents
any type of rule that would be available in a rules engine or
expert system, as known by one of ordinary skill in the art.
Examples include, "Deal takes longer than 30 days AND deal size
>$10,000" or "Quota attainment >50%" or "Sales funnel stage
<3" or "Sales person progress score >B+" or any other
construction that proves useful to aiding the sales process.
[0114] FIGS. 15-19 are flowcharts illustrating methods according to
various embodiments of the invention. The methods to be performed
by the embodiments may constitute computer programs made up of
computer-executable instructions. Describing the methods by
reference to a flowchart enables one skilled in the art to develop
such programs including such instructions to carry out the method
on suitable processors (the processor or processors of the computer
executing the instructions from computer-readable media such as
ROM, RAM, CD-ROM, DVD-ROM, flash memories, hard drives and other
media). The methods illustrated in FIGS. 15-19 are inclusive of
acts that may be taken by an operating environment executing one or
more exemplary embodiments of the invention.
[0115] FIGS. 15A and 15B are a flowchart illustrating a Pipeline
Analysis method according to embodiments of the invention. Although
the example method includes details regarding how the modules
interact with each other to provide a specific analysis in
particular embodiments, it should be noted that operations in the
method may be performed by different modules in various alternative
embodiments.
[0116] In some embodiments, the method used input data comprising a
list of sales periods to analyze and a sales strategy to analyze.
The method at begins at block 1502 by extracting stage descriptive
information from data warehouse 102 for the input sales strategy.
At block 1504, a goal model for a given sales-strategy,
sales-period pair is extracted. At block 1506, fuzzy logic quality
and priority heuristics for the given sales-strategy and period are
extracted.
[0117] One or more of the actions in blocks 1508-1514 may be
executed for each sales period. At block 1508, revenue goals for
the given sales period are extracted. At block 1510, previous
learned sales period models for the given sales strategy, if any,
are extracted from data warehouse 102. At block 1512, the data
warehouse 102 is queried for sales transaction data for the given
sales period. In some embodiments, the sales transaction data may
include ongoing, closed, lost or deferred transactions. At block
1514, a seasonality model for the given sales period is extracted
from the data warehouse.
[0118] The actions in blocks 1520-1528 may be executed for each
sales transaction obtained at block 1512 in order to perform data
cleaning, data smoothing and/or heuristic classification. At block
1520, a smoothing method may be used to fill in missing data. At
block 1522, erroneous or biasing data points may be determined and
excluded. At block 1524, sales strategy stage transition times are
calculated for each stage. In some embodiments, the DMA module
performs the smoothing, exclusion of erroneous or biasing data
points and/or calculation of the sales strategy transition
times.
[0119] At block 1526, a quality score is classified according to a
quality heuristic. At block 1528, a priority score is classified
according to a priority heuristic. In some embodiments, the quality
score and the priority score may be classified by a Pre-HE
module.
[0120] The actions in blocks 1530-1538 may be part of a loop that
is executed for each sales period in the input sales period list.
Further, block 1530-1536 may be part of a loop that is executed for
each sales strategy stage. At block 1530, weighted transition times
for all sales transaction are computed. At block 1532, weighted
abandonment times for all sales transactions are computed. At block
1534, the transition and abandonment times are classified according
to the current goal model. At block 1536, a M/M/s or other Queuing
Model is built for the current stage.
[0121] At block 1538, the M/M/s Queuing Models for each stage is
assembled into a multi-level queuing network.
[0122] At block 1540, a M/M/s Queuing Network goal is built using
the goal model. At block 1542, Queuing Network `Previous Data` is
built from a previous learned model.
[0123] Blocks 1544-1550 are repeated for each learning algorithm,
and for `n` time steps within each learning algorithm. At block
1544, a random sales transaction is selected. At block 1546, if the
randomly selected sales transaction fits the selected `Goal` model,
then the `Goal` model weight is reinforced. At block 1548, if the
randomly selected sales transaction fits the selected `Previous
Data` model, then a `Previous Data` model weight is reinforced. At
block 1550, if the randomly selected sales transaction fits the
`Current Data` model, the a `Current Data` model weight is
reinforced.
[0124] At block 1552, an aggregate model is formed by combining
each level of the `Goal`, `Previous Data` and `Current Data` models
using the learned weights.
[0125] Blocks 1554-1558 may be executed for each sales transaction.
If the aggregate model accurately predicts the outcome of the sales
transaction (block 1554) and the transaction has a high quality
score (block 1556), then a positive metric score is assigned for
the transaction (block 1558).
[0126] After the sales transactions have been evaluated, a post-HE
module may optionally be executed at block 1560.
[0127] At block 1562 the learned metric scores are stored in data
warehouse 102. At block 1564, the aggregate module is stored as the
`Previous Data` in data warehouse 102.
[0128] See Example 3a below for a description of an example
application of the Pipeline Analysis method illustrated in FIGS.
15A and 15B.
[0129] FIGS. 16A and 16B are flowcharts illustrating a revenue
forecasting method according to embodiments of the invention. In
some embodiments, a list of sales periods to analyze and a list of
sales people to analyze may be supplied as input for the method. A
pseudo code description of the method is as follows:
TABLE-US-00001 Extract `Goal Model` for given Sales-Periods (block
1602) Extract Fuzzy Logic Quality and Priority Heuristics for given
Sales-Period (block 1604) For Each Sales-Period (blocks 1606 1612)
Extract `Revenue Goals` for given Sales-Period (block 1606) Extract
previous learned Sales-Period models for given Sales-Strategy, if
any. (block 1068) Query Sales Transaction Data for Sales-Period
{Ongoing, Closed, Lost, Deferred} (block 1610) Extract Seasonality
Model for given Sales-Period (block 1612) For Each Sales
Transaction (blocks 1620-1628) Use smoothing method to fill in
missing data (DMA Module, block 1620) Exclude erroneous or biasing
data points (DMA Module, block 1622) Calculate Revenue Goal
Attainment (DMA Module, block 1624) Classify Quality Score
according to Quality Heuristic (Pre-HE, block 1628) Accumulate data
and Scores to the sales-person-subject of the transaction (block
1630) For Each Sales-Person (block 1630) For Each Forecasting
Algorithm Calculate Forecast of remaining time units in current
Sales period given past data (Block 1630) Quality score the
forecasts according to Fuzzy Logic Quality Heuristics (block 1640)
Exclude erroneous or biasing forecasted data(block 1642) Record
Consensus, High and Low forecasts for each time-division-unit.
(block 1644) Store all Forecasted values and Scores in Data
Warehouse(block 1650)
[0130] FIGS. 17A and 17B are a flowchart illustrating a method for
scoring performance according to embodiments of the invention. A
list of sales periods to analyze, a list of sales people to
analyze, and/or a list of sales groups to analyze may be provided
as input to the method in some embodiments. A pseudo code
description of the method is as follows:
TABLE-US-00002 Extract `Goal Model` for given Sales-Periods (Block
1702) Extract Fuzzy Logic Quality and Priority Heuristics for given
Sales-Periods(Block 1704) For Each Sales-Period (Blocks 1706-1718)
Extract `Revenue Goals` for given Sales-Period(Block 1706) Extract
Learned Revenue Profiles for given Sales-Period and all
Persons/Groups(Block 1708) Extract Idealized Revenue Profiles for
given Sales-Period and all Persons/Groups(Block 1710) Extract
Learned Pipeline Analysis data for given People/Groups(Block 1712)
Extract previous learned Sales-Pipeline models for given
Sales-Strategy, if any(Block 1714) Query Sales Transaction Data for
Sales-Period {Ongoing, Closed, Lost, Deferred}(Block 1716) Extract
Seasonality Model for given Sales-Period(Block 1718) //Data
Cleaning, Data Smoothing and Heuristic Classification For Each
Sales Transaction (Blocks 1720-1726) Use smoothing method to fill
in missing data (Block 1720) Exclude erroneous or biasing data
points (Block 1722) Classify Quality Score according to Quality
Heuristic (Block 1724) Accumulate data and Scores to the
sales-person-subject of the transaction (Block 1726) //Data
Analysis (DMA Module) For Each Sales-Person (Blocks 1730-1738) For
Each Sales-Period (Blocks 1730-1738) Calculate Revenue Goal
Attainment (Block 1730) Classify as {Top, Average, Low} Performer
(Block 1732) Calculate Revenue Variance (Block 1734) Calculate
difference between Learned Model and Actual data (Block 1736)
Calculate difference between Idealized Model and Actual data (Block
1738) //(DMF Module) For Each Learning Algorithm (Blocks 1740-1746)
For n time steps (Blocks 1740-144) Select Random Sales Person/Group
(Block 1740) If Transaction Fits Idealized Model Then reinforce
Idealized Model weight (Block 1742) If Transaction Fits Learned
Model Then reinforce Learned Model weight (Block 1744) Form
Aggregate Model based upon Idealized and Learned Models (and
weights) (Block 1746) //Post-HE module For Each Sales-Person and
Group Reclassify Person/Group according to difference between
Learned and Idealized Model as {Top Average, Low} Performer (Block
1750) Store all Learned Models, Scores and Classifications in Data
Warehouse. (Block 1760)
[0131] FIG. 18 is a flowchart illustrating a method for allocation
of quotas by sales periods according to embodiments of the
invention. A list of sales periods to analyze, a list of sales
people to analyze, and/or a list of sales groups to analyze may be
provided as input to the method in some embodiments. A pseudo code
description of the method is as follows:
TABLE-US-00003 1. // Query Module 2. Extract Revenue Models for
given Sales-Periods (Block 1802) 3. Extract Pipeline Models for
given Sales-Periods (Block 1804) 4. Extract Fuzzy Logic Quality
Heuristics for given Sales-Periods (Block 1806) 5. For Each
Sales-Period 6. Extract `Revenue Goals` for given Sales-Period
(Block 1808) 7. 8. 9. For Each Sales Period (Blocks 1810-1844) 10.
// Heuristic Classification 11. Initialize Quotas Set for Each
Person/Group Based upon current Sales Period (Block 1810) 12.
Accumulate Quotas Set to calculate a Total Quota (Block 1812) 13.
Classify Quality Score for each Quota according to Quality
Heuristic (Block 1814) 14. Classify Quality Score for Total Quota
vs. Goal Quota according to Quality Heuristic (Block 1816) 15. 16.
Best-Quotas = Quotas //Each of these is a set of individual Quotas
(Block 1818) 17. 18. For n time steps (Blocks 1822 36) 19.
Quotas-New = Quotas (Block 1822) 20. Select Random Person/Group
Quota from Quotas-New (Block 1824) 21. Randomly adjust selected
Person/Groups Quota a moderate amount via White-noise function
(Block 1826) 22. Classify Quality Score for this new Quota
according to Quality Heuristic (Block 1828) 23. Classify Quality
Score for Total Quota vs. Quotas-New according to Quality Heuristic
(Block 1830) 24. If Quality Score of Quotas-New .gtoreq.
Best-Quotas (Block 1832) 25. Best-Quotas = Quotas-New (Block 1834)
26. Else If Best-Quotas is unchanged for m time-steps (Block 1836)
27. Add Best-Quotas and its Quality Score to Result Queue (Block
1838) 28. n = 1 (Block 1840) 29. Goto 11 (go to Block 1810) 30. 31.
Select a Quotas Set from Result Queue with the best Quality Score
(Block 1842) 32. Store this Quotas Set For the given Sales Period
in Data Warehouse (Block 1844) 33. 34. //Quality Score of a Quota
is a function of its Forecasted Revenue and Pipeline Performance
vs. the 35. // overall Goals
[0132] FIG. 19 is a flowchart illustrating a method for allocation
of quotas by territories according to embodiments of the invention.
A list of sales periods to analyze, a list of sales people to
analyze, and/or a list of sales groups to analyze may be provided
as input to the method in some embodiments. A pseudo code
description of the method is as
TABLE-US-00004 1. Extract Revenue Models for given
Sales-Territories (Block 1902) 2. Extract Pipeline Models for given
Sales-Territories (Block 1904) 3. Extract Fuzzy Logic Quality
Heuristics for given Sales-Territories (Block 1906) 4. For Each
Sales-Territories 5. Extract `Revenue Goals` for given
Sales-Territories (Block 1908) 6. 7. 8. For Each Sales Territories
(Blocks 1910-1942) 9. // Heuristic Classification 10. Initialize
Quotas Set for Each Person/Group Based upon current Sales
Territories (Block 1910) 11. Accumulate Quotas Set to calculate a
Total Quota (Block 1912) 12. Classify Quality Score for each Quota
according to Quality Heuristic (Block 1914) 13. Classify Quality
Score for Total Quota vs. Goal Quota according to Quality Heuristic
(Block 1916) 14. 15. Best-Quotas = Quotas //Each of these is a set
of individual Quotas (Block 1918) 16. 17. For n time steps (Blocks
1920 1934) 18. Quotas-New = Quotas (Block 1920) 19. Select Random
Person/Group Quota from Quotas-New (Block 1922) 20. Randomly adjust
selected Person/Groups Quota a moderate amount via White-noise
function (Block 1924) 21. Classify Quality Score for this new Quota
according to Quality Heuristic (Block 1926) 22. Classify Quality
Score for Total Quota vs. Quotas-New according to Quality Heuristic
(Block 1928) 23. If Quality Score of Quotas-New .gtoreq.
Best-Quotas (Block 1930) 24. Best-Quotas = Quotas-New 25. Else If
Best-Quotas is unchanged for m time-steps (Block 1932) 26. Add
Best-Quotas and its Quality Score to Result Queue (Block 1934) 27.
n = 1 (Block 1936) 28. Goto 11 (go to Block 1910 at Block 1938) 29.
30. Select a Quotas Set from Result Queue with the best Quality
Score (Block 1940) 31. Store this Quotas Set For the given Sales
Territories in Data Warehouse (Block 1942) 32. 33. //Quality Score
of a Quota is a function of its Forecasted Revenue and Pipeline
Performance vs. the 34. // overall Goals
EXAMPLES
[0133] A series of examples illustrating the operation of the
above-described systems and methods will now be provided.
Example 1
Forecasting Revenue
[0134] Using the data query and display module, sales data is
retrieved representing some window-in-time of sales revenue as well
as ongoing in-process sales records. From the DW 102 a
configuration is retrieved defining the data flow and data
transformed to be used. Also from the DW 102 a cache of data from
the previous run(s) of the data caching and agglomeration module,
metric scores and derived data from previous runs of this module is
fetched.
[0135] The Data Manipulation Algorithm (DMA) module 402 (FIGS. 4
and 12) is applied to the raw sales data to align it with records
from the `data cache` using data transforms. Supplementary
calculations are also made to the metric scores. An example output
data stream from the module would be a large list of quantitative
data associated with sales-people, sales-periods, products sold and
performance metrics for each of these units.
[0136] The output of this module is fed to the HE1 404. One example
function of the HE1 module 404 is to clean the data of statistical
outliers before passing to the DMF module 406. Another example is
to create additional quantitative metrics. An example of this is to
assign a heuristic category to individual sales people. Sales
people who have consistently met or exceeded goals (as defined by
evaluating any number of factors) for a large percentage of sales
periods would be marked as `top performers`. People who meet most
goals for the same time period would be marked as `average
performers`, the rest as `under-performing`. A third example would
flag particular metrics as having an anomaly without necessarily
excluding it, such as a sales person who had his or her performance
enhanced or degraded by an anomalous event and this should not
unduly influence later calculations.
[0137] The next sub-module in the chain is the code DMF 406 (FIGS.
4 and 13). This module uses the data and metric stream as an
attribute set in the combination of data models with a suite of
forecasting algorithms to establish a set of revenue forecasts. An
example data model would be a user-entered ideal profile of how a
sales person or group's various metrics should be shaped
numerically. It may contain various weights for how individual
metrics would be used in forecasting algorithms. In addition other
parts of the DMF module (FIGS. 4 and 13) could be applied to learn
a data model, or profile of how attribute values progress in time
associated with performance levels, sales groups and products. This
engine also is able to learn new data models with various
algorithms by examining previous data in a posteriori plus feedback
fashion.
[0138] The output of the DMF module 406 flows to the HE2 sub-module
408 (FIGS. 4 and 14). In this scenario HE2 408 would be used to
take the suite of revenue forecasts and assign quality scores based
upon both user entered heuristics and learned heuristics. This
engine also is able to learn new heuristics with various algorithms
by examining previous data in a posteriori plus feedback fashion.
The final output of the example scenario is a set of revenue
forecasts for each person, group and aggregate forecasts for sales.
In addition accuracy scores for each forecast, as well as learned
data models and heuristics are stored.
Example 2
Scoring the Performance of Sales People and Groups
[0139] Similar to Example 1 with the modification that all
calculations are done to score the performance of individual people
and groups with respect to learned data models of top-performers as
well as with respect to user-entered ideal models of performance.
An example of this would be an ideal revenue growth of a sales
person over time. A second example is a relative analysis of the
person's ongoing sales `pipeline` with respect to learned and ideal
models of good performance. In either case the system can learn the
historical sales models for an individual or high performing sales
representative using any of a variety of machine learning
techniques. In addition, an idealized model can be entered by the
sales manager. The resulting comparison can aid in the
understanding of how individuals relate to one another and to an
idealized individual. This helps sales representatives understand
their relative performance, and it helps sales managers understand
if there is a general deviation between their idealized model and
the actual performance of an individual or group.
Example 2 Details
Ideal Revenue Growth or Progression of a Sales Person Over Past
Time and into the Future
[0140] This analysis occurs in the MLF and DQ-DM Modules. See FIGS.
6 & 7 for diagrammatic details. The method's purpose is to
extract raw data and learn a central pattern for revenue
progression in time of a sales person or group that is relative to
a combination of an idealized pattern and the Revenue Progression
pattern of the group of Top Performers.
[0141] MLF Module Operation for Example 2:
[0142] The sales data for all persons groups is queried from the
`Sales Records` and from the Data Warehouse. Also from the Data
Warehouse the various heuristics are retrieved. The Sales records
consist of the `Closed, Lost and Deferred` Sales Revenue for a
range of hierarchical sales periods over the past X periods in
time. This data is extracted per person and per group (aggregate).
Individuals in the Top-Performer group are included in this query
with their membership in this group tagged as such. The following
heuristics are retrieved from the DW. Note that these examples are
not exhaustive and one of ordinary skill in the art can easily add
to this list. [0143] MLF-Module Pre-Heuristic-Engine Examples
[0144] (Learned) Sales Revenue Variance: Number of sales periods
where the revenue variance of an individual customer (or class or
group of customers) have variance below some percentage threshold.
Variance is the percentage drop or gain compared to last period.
[0145] (Idealized) Sales Revenue Variance Profile: [0146] The ideal
profile of revenue variance. This is characterized by a range of
growth or decrease percentages period-over-period. An example would
be that ideal growth or expected decrease in sales
period-over-period should vary between -10% and +15% in the
3.sup.rd month of a given year vs. the 2.sup.nd month. [0147]
(Learned) Sales Revenue Profile of Top Performers: [0148] Form an
aggregate profile of the progression of the revenue profile for all
`Top Performers`. This profile would be normalized to be a
numerical curve of percentage increases starting from the initial
revenue goal of the starting sales period of the analysis. The
profile would be weighted by the overall `performance score` of the
individual. The basic computation is a smoothed weighted average
using any combination of classical methods (iterative exponential
smoothing) or newer time-series analysis methods with or without
seasonality adjustments. [0149] (Learned) Sales Revenue Profile of
Sales Group: [0150] Same as the previous analysis but using the
subject-salesperson's peer-group. [0151] (Learned) Sales Revenue
Profile of Sales Person: [0152] Same as previous analysis except
done for each individual in isolation. [0153] (Idealized) Sales
Revenue Profile: [0154] This is an idealized profile established
& input by sales managers or executives that gives an ideal
average and spread of a revenue curve over time.
[0155] Main DMF Module Details:
[0156] In this step previous learned profiles and new sales records
are used to learn updated profiles. This may be accomplished by
running a suite of classical forecasting methods, time series
analysis methods, smoothing methods and seasonality adjustments.
The outputs of the individual analysis methods are aggregated via a
weighted scheme. Note that the weights of each method are learned
as well. After a run is performed and forecasts are stored, they
are used in the next run and compared to the actual reported
revenue gained, lost, or otherwise deferred. The aggregation
weights of the different methods are adjusted based upon the
success or failure of the particular method's predicted data vs.
actual data.
[0157] The output of the DMF module (FIG. 13) is processed by the
MLF Post-Heuristics engine 408 (FIG. 14). Here each learned profile
is given a quality score that is a function of the accuracy of the
predictions from the DMF module and it's similarity to the
idealized models. In particular embodiments, the heuristic analysis
is a set of fuzzy logic rules.
[0158] Output of MLF Module: [0159] Quality score for each person
or group [0160] Updated `Learned` profiles. [0161] Updated metadata
for Idealized model-profiles.
[0162] DQ-DM Module for Example 2:
[0163] Given a `subject` (sales representative or group) of
analysis, the following is performed to display to the user a
graded analysis of the subject's revenue growth or progression. See
FIGS. 7 and 10.
[0164] From the DW the following can be retrieved: [0165] Run-Time
profile comparison and analysis methods [0166] Learned Revenue
Profile for the Subject [0167] Learned Revenue Profile for the
Subject's Group [0168] Learned Revenue Profile for the Top
Performers [0169] Idealized Revenue Profile. [0170] Learned Revenue
Variance Profile for the Subject [0171] Learned Revenue Variance
Profile for the Subject's Group [0172] Learned Revenue Variance
Profile for the Top Performers [0173] Idealized Revenue Variance
Profile.
[0174] The run-time heuristics are performed on the above data. The
Heuristics produce two types of output, comparative grading and
absolute grading. The comparative grading assigns a quality score
for the subjects profile relative to a comparison-group. Typically
this is the subjects peer group in the organization and the group
of top-performers. The absolute grading is similar except that a
score is assigned assessing how well the subjects profile fits the
idealized profiles. FIG. 10 has an example chart and assigned grade
for an absolute revenue trend and profile analysis. One of average
skill in the art can easily see other variations of this
example.
Example 3
Scoring Sales Processes
[0175] Similar to examples 1 & 2. The module is applied to the
problem of evaluating an entire sales processor sales methodology
with respect to the ideal model of the process as well as compared
to other good performing processes or methodologies. Note that this
analysis can be independent of individuals looking at the process
in isolation. It can also be used to identify weak individuals or
groups that bring the overall performance of the process or
methodology down. The novelty of this approach is it's usage in CRM
software applied to sales process analysis and its combination with
iterative machine learning methods.
Example 3a
Ideal Pipeline Analysis of Sales Pipeline & Strategy
[0176] Background for Example 3a:
[0177] A sales pipeline is an abstract construct consisting of
series of `stages`. At each stage a set of tasks is to be performed
by the sales representative. For example, stage 1 might be called
`Leads` where potential deals are placed in this first stage upon
initial contact with the sales representative. To progress to Stage
2 (Demo Product) the deal's primary contact must exchange
preliminary information with the sales representative and agree to
schedule a product demonstration. Stage 7, for example, could be
the `Negotiation` phase where representative and client agree to
financial terms of a deal. The Set of stages is generally referred
to as a Sales Strategy. Representatives may be working on potential
clients using a plurality of Sales Strategies.
[0178] The active deals of a given representative can be assigned
to a stage in a particular strategy. A representative's `raw
pipeline` is a set of monetary values that are the sum of the
assumed value potential deals in a given stage. A representative's
`forecasted pipeline` is a set of values as above except that each
stage is assigned a conversion rate percentage. The raw values in
each stage are multiplied by the conversion rate percentage to form
a set of monetary values that a representative terms his or her
expected forecast. Ordinarily these percentages are manually
assigned by users.
[0179] Sales Strategy and Pipeline Analysis `Learning Method`:
[0180] Various data is requested from the DW 102 and Sales Records
112. A representative outline of this data is shown below: [0181]
Closed or Lost or Deferred Revenue History [0182] Date and time of
each stage transition for all deals [0183] Sales Process stages and
stage-forecast-percentages [0184] Sales Goals [0185] Ongoing Active
Deals [0186] Quality Heuristics [0187] Revenue Model (see Example
2) [0188] Strategy Quality Rules [0189] Data Cleaning
Procedures
[0190] The data above is loaded and a set of algorithms is applied
to them to extract statistical information on the stage transition
timings, stage abandonment rates (lost deals) and deferred or
backward stage moves. All sales strategies are modeled with a
multi-level queuing model such as M/M/s queuing network (for
further details, see An Introduction to Stochastic Modeling, by
Samuel Karlin, Howard M. Taylor, Academic Press, 3 edition
(February 1998), which is hereby incorporated by reference herein
for all purposes).
[0191] This analysis allows the predictive modeling of the sales
strategy and compares it to the user entered expected conversion
rates from one stage to the next. This analysis also allows the
modeling of how changing human resources allocated to specific
stage will affect the behavior of the strategy as a whole.
[0192] This queuing network method is combined with a set of fuzzy
logic rule sets, for example, that classify deals based upon their
characteristics. Specifically the attributes of a deal are used to
perform a supervised machine learning algorithm based upon the
outputs of the queuing model and the system learns a classification
system for assigning quality scores to deals. These scores are used
to prioritize attention to deals of various types. Deals that the
classifier system and queuing model predict will transition quickly
between stages are given an increased `forecast percentage`. While
deals with some attribute that the ML algorithm has identified as
correlated with slow progress will be assigned a lower forecast
percentage.
[0193] Note also that this analysis allows prediction of deal close
times and assigning scoring metrics to individual sales
representatives and groups based upon how a particular deal is
progressing (for example--being 2 days past typical transition
times in stage 2 indicates that the predicted close date will be 10
days behind typical). Another algorithm in this suite is the
forecasting, smoothing and time series analysis algorithms
mentioned in example 2. Here these techniques can be used to both
repair missing data and to provide compatible sales strategy
forecasts for comparative analysis and decisioning. It is also used
to provide seasonality adjustments to the output of the queuing
network.
[0194] This system can be run at a frequency relevant to the
typical business cycle of a sales strategy. For example, if a
typical deal is closed in 30 days and some component of a deal is
likely to change every day, then the system would be run at least
twice a day. The data and models built from the last run are loaded
and their predictions checked. The various quality weights on the
output of each algorithm are adjusted by a reinforcement learning
algorithm.
[0195] Sales Strategy and Pipeline Analysis `Query, Analytic
Comparison and Display Method`:
[0196] FIG. 9 is an illustration of this process. It is similar to
FIG. 7. The aggregate outputs of the MLF module for this task are
loaded along with the subject sales process. An example output is
given on FIG. 11.
[0197] The basic method is shared with Example 2's DQ-DM section
along with FIG. 7.
[0198] This following data can be loaded:
[0199] From the DW the following can be retrieved: [0200] Run-Time
profile of the current sales strategy. [0201] Learned forecast
stage percentages [0202] Quality Metrics for poor, average and
excellent performing deals [0203] Seasonality Model [0204] Data
associated with the subject sales strategy [0205] Sales records for
open deals within this strategy
[0206] The output of this model is an aggregated summary of the
open deals in the current sales period for a given sales period.
Each stage in the strategy is assigned a score for overall
execution and a forecasted overall conversion rate. FIG. 15
described above provides an example algorithm that details how the
modules interact with each other to provide this (Example 3a)
analysis.
Example 3b
Ideal Pipeline Analysis of Sales Representative or Group
[0207] Sales Strategy and Pipeline Analysis `Learning Method`:
[0208] Example 3b is similar to Example 3a, except that the
analysis is done for a specific sales representative or group
across the range of sales strategies they work on.
[0209] Sales Strategy or Pipeline Analysis `Query, Analytic
Comparison and Display Method`: [0210] See Example 3a except that
this analysis is done for a specific sales representative or group
across the range of sales strategies they work on.
Example 4
Allocation of Quotas
[0211] The system can be used to allocate or re-allocate sales
goals or quotas. Using a suite of machine learning or optimization
algorithm one can learn optimal allocation of quotas such that some
fitness function can be maximized. The fitness function would be
comprised of a weighted sum of factors and use the outputs of
Example 2a as primary inputs to the fitness function to reallocate
quotas.
Example 5
Allocation of Territories
[0212] The system can be used to allocate or re-allocate sales
territories. For example, using a suite of machine learning or
optimization algorithm one can learn optimal allocation of quotas
such that some fitness function is maximized. The fitness function
may be comprised of a weighted sum of factors and use the outputs
of Example 2a as primary inputs to the fitness function to
reallocate territories.
[0213] The FP module 110 as supported by the DW 102 allows sales
people to execute `what if` scenarios for planning various
organization attributes for the future. The basic idea is to solve
(by isolated analysis, approximation, or by a direct technique)(in
spirit, not necessarily literal solving) for a particular variable.
In basic algebra when one solves equations (s), some number of
variables are chosen as `unknown` while the rest of the variables
that are known are used to identify values and ranges of values for
the unknown variables that fulfill the equations.
[0214] The input to the FP 110 can be made up of the raw sales
record data 112 as well as the DW 102 using all previously
mentioned elements and an additional `scenarios` element. The
processing sub-modules are the MLF 108, a scenario analysis engine
(SAE) 502 and a display and user interface element 504. A display
and user interface components allow the user to see the output of
the FP 110 as well as interact with some number of quantitative
attributes. Additionally, the SAE 502 may contain specific
processes as described herein.
[0215] The display 504 and user interface element 116 can
communicate bi-directionally with the SAE 502 & MLF 108. The FP
110 can also store scenarios that users have created during the use
of the FP 110. One purpose of the SAE 502 and MLF 108 is to use
`conditional` data to create new MLF analysis outputs given this
conditional data. The outputs, conditional data, and associated
elements of the DW 102 are termed a `scenario` and also stored in
an area of the DW 102. The scenario engine 502 contains a number of
algorithms as described herein.
Example 6
Goal Based Revenue Planning
[0216] A manager has in mind a particular goal for a given metric,
in this case goal-revenue for some number of time periods into the
future. However, after looking at revenue forecasts from the MLF it
is apparent that revenue forecasts are short of the goal. At this
point a manager has a number of business factors that can be
adjusted. Sales people's individual goals can be altered, product
pricing can be adjusted and new sales people can be hired. As an
example, a manager may use the system to determine which of these
factors should be altered to best meet the desired goal.
[0217] For example, using the FP 110, a manager can input the
desired goal-revenue and look at how factors can be altered to meet
this goal. Taking one factor, as an example the individual revenue
goals, the system can be asked to meet the new revenue goal by
assigning new individual goals. The output here would be a
breakdown of goal changes, as well as a likelihood score of the
users meeting the new goals given past history and current status.
It would also output an overall likelihood score of meeting the new
goal-revenue.
Example 7
Hiring and Firing of Individuals
[0218] Building on Example 1 of the FP 110, the manager can also
attempt to meet some new goal by examining the effect of hiring
additional individuals plus firing and/or replacing existing
individuals. The manger is presented with a list of individuals,
their past metrics, future goals and future forecasts. Each
individual is also assigned various performance scores. The manger
can in the simplest case eliminate individuals from the list and
look at the effect on the future goals, forecasts and likelihood
numbers. Another action would be to add an individual to the list
of some performance level, presumable either `average` or `top` and
look at the effect on forecasts, likelihood and goals. The third
action would be the combination of the two, replacing an individual
with a new individual.
[0219] A slightly more complex embodiment is to set up a schedule
of hiring of new people at a given performance level to see how the
forecasts are affected over longer time scales. This staged hiring
model would benefit from the accurate historical models of how long
it took existing sales people to come up to their current level of
performance and similarly indicate the growth rate for individual
performance as they learn the particulars of the current product
and sales environment.
[0220] An embodiment of the heuristics would be both simple
rule-sets as well as fuzzy rule sets to assign grades or scores to
individual metrics. An embodiment of a data-model is a set of
values describing how given metrics change over some other variable
(an example would be a metric over time).
[0221] Thus as is evident from the above, the various embodiments
may provide a Revenue Forecasting system to learn to forecast
revenue as a combination of standard approaches as well as ones
based upon learning a profile of the members of the organization
and their historical attributes, performance classification
etc.
[0222] Further, the various embodiments may provide for scoring the
performance of Sales People and Groups, including: [0223] Scoring a
person/groups learned profile against known-good profiles and
entered ideal-profiles. Give detailed breakdown of areas of
difference [0224] Scoring projected revenues vs. goals
[0225] Still further, the various embodiments may provide analysis
of performance of Sales Strategy vs. Ideal/Goal, including: [0226]
Learning/building a model of Strategy-Pipeline based upon real data
[0227] Comparing to Idealized model of Strategy-Pipeline [0228]
Allowing prediction of pipeline throughput per person group and as
a whole [0229] Providing real-time scores per person group of
deviation from Ideal
[0230] Yet further, the various embodiments provide the ability to
perform Future Planning and What-If Scenarios, including: [0231]
Goal based Revenue Planning [0232] Given learned profiles of
individuals and processes what is the likelihood of meeting a
particular Revenue Goal? [0233] Breakdown reallocation of Quota
increases based upon above learned profiles [0234] Breakdown
reallocation of Territories based upon above learned profiles
[0235] Hiring/Firing Planning--Similar to above with the addition
of allowing user to ask the system to evaluate the effect of adding
new people, replacing people, firing people and its effect on
future revenue and sales strategy performance
[0236] The various sales performance and forecasting system
examples shown above illustrate a novel predictive and profile
learning sales automation analytics system. A user of the present
invention may choose any of the above sales performance and
forecasting system embodiments, or an equivalent thereof, depending
upon the desired application. In this regard, it is recognized that
various forms of the subject invention could be utilized without
departing from the spirit and scope of the present invention.
[0237] As is evident from the foregoing description, certain
aspects of the present invention are not limited by the particular
details of the examples illustrated herein, and it is therefore
contemplated that other modifications and applications, or
equivalents thereof, will occur to those skilled in the art. It is
accordingly intended that the claims shall cover all such
modifications and applications that do not depart from the spirit
and scope of the present invention. Therefore, it is manifestly
intended that this inventive subject matter be limited only by the
following claims and equivalents thereof.
[0238] The Abstract is provided to comply with 37 C.F.R.
.sctn.1.72(b) to allow the reader to quickly ascertain the nature
and gist of the technical disclosure. The Abstract is submitted
with the understanding that it will not be used to limit the scope
of the claims.
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