U.S. patent application number 17/563987 was filed with the patent office on 2022-06-23 for generating optimal strategy for providing offers.
The applicant listed for this patent is FAIR ISAAC CORPORATION. Invention is credited to Gerald Fahner.
Application Number | 20220198555 17/563987 |
Document ID | / |
Family ID | 1000006185743 |
Filed Date | 2022-06-23 |
United States Patent
Application |
20220198555 |
Kind Code |
A1 |
Fahner; Gerald |
June 23, 2022 |
GENERATING OPTIMAL STRATEGY FOR PROVIDING OFFERS
Abstract
Generating optimal strategies for providing offers to a
plurality of customers is described. A plurality of categorical
attributes (for example, gender and residential status) and ordinal
attributes (for example, risk score and credit line utilization)
can be determined. Values of one of more categorical attributes can
be changed as per a transition probability table. Some
probabilities can be varied to determine a first tradeoff, based on
which a first updated strategy can be generated. Further, noise can
be added to one or more ordinal attributes. Standard deviation of a
noise distribution associated with the noise can be varied so as to
determine a second tradeoff, based on which a second updated
strategy can be generated. The second updated strategy can be an
update of the first updated strategy. Offers can be provided to the
plurality of customers in accordance with the second updated
strategy.
Inventors: |
Fahner; Gerald; (Austin,
TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FAIR ISAAC CORPORATION |
Roseville |
MN |
US |
|
|
Family ID: |
1000006185743 |
Appl. No.: |
17/563987 |
Filed: |
December 28, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14808765 |
Jul 24, 2015 |
11250499 |
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17563987 |
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13562230 |
Jul 30, 2012 |
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14808765 |
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61513392 |
Jul 29, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 40/02 20130101;
G06Q 30/02 20130101; G06Q 30/0254 20130101 |
International
Class: |
G06Q 40/02 20060101
G06Q040/02; G06Q 30/02 20060101 G06Q030/02 |
Claims
1-23. (canceled)
24. A computing system connected to a communications network for
exchanging data, the computing system having at least one
processor, a non-transitory data storage medium, and a plurality of
data structures and executable code stored in the non-transitory
storage medium, the data structures including a first decision tree
and a second decision tree, the execution of the executable code by
the at least one processor causing the computing system to: obtain
data stored in at least one data storage medium, the data
corresponding to a plurality of entities associated with a
plurality of attributes; determine a first offer for a first
attribute from among the plurality of attributes based on the
obtain data; form the first decision tree and the second decision
tree for characterizing one or more offers corresponding to the
first attribute; compare performance of the first decision tree
with performance of a second decision tree in relation to the first
attribute; provide a first offer to a first entity in accordance
with the first decision tree, in response to determining that the
first decision tree is superior to the second decision tree in view
of a performance threshold; and provide a second offer to the first
entity in accordance with the second decision tree, in response to
determining that the second decision tree is superior to the first
decision tree in view of the performance threshold.
25. The system of claim 24, wherein a plurality of causal models
are utilized to form at least one of the first decision model and
the second decision model to evaluate one or more objectives of an
entity, the causal model being used to determine a best offer for
the first attribute.
26. The system of claim 25, wherein the causal models characterize
a response of an entity to a historical offer.
27. The system of claim 25, wherein the determining of the best
offer is based on evaluation of at least one of a global maximum
value and a local maximum value by the first decision model and the
second decision model.
28. The system of claim 24, wherein the second decision tree is
obtained by changing a value of one or more attributes associated
with the first decision tree.
29. The system of claim 24, wherein the performance of the first
decision tree is characterized by business efficacy provided by
implementing a strategy associated with the first decision tree,
the business efficacy associated with the first decision tree being
based on a plurality of iterations of strategy evolution, the
business efficacy characterizes a profit of an entity providing the
offers to the plurality of entities.
30. The system of claim 24, wherein the performance of the second
decision tree is characterized by business efficacy provided by
implementing a strategy associated with the second decision tree,
the business efficacy associated with the second decision tree
being based on a plurality of iterations of strategy evolution, the
business efficacy characterizes a profit of an entity providing the
offers to the plurality of entities.
31. The system of claim 24, wherein the plurality of attributes are
represented by a graph having a plurality of dots with at least one
of a corresponding color and a corresponding intensity to
characterize a value of at least a first attribute for a first
entity.
32. The system of claim 33, wherein the first attribute is modified
by adding noise to the first attribute by varying a standard
deviation of a noise distribution to determine the noise.
33. The system of claim 32, wherein the adding of the noise to the
first attribute provides a first profit to a first entity providing
the first offer, wherein the first profit is more than a second
profit obtained without the addition of the noise to the first
attribute.
34. The system of claim 33, wherein the adding of the noise to the
first attribute comprises adding Gaussian noise to the first
attribute, wherein a standard deviation of the Gaussian noise is
varied to determine a tradeoff.
35. The system of claim 34, wherein an updated strategy associated
with the tradeoff is generated and a second offer is provided based
on the updated strategy being determined based on a first tradeoff
characterizing a balance between cost of a business entity and a
rate of updating the strategy.
36. A computing system connected to a communications network for
exchanging data, the computing system having at least one
processor, a non-transitory data storage medium, and a plurality of
data structures and executable code stored in the non-transitory
storage medium, the data structures including transition
probability table, the execution of the executable code by the at
least one processor causing the computing system to: determine a
plurality of attributes associated with a strategy; change values
of a first set of one or more attributes in accordance with a
transition probability table; vary one or more probabilities to
determine a first tradeoff; and generate, based on the first
tradeoff, a first updated strategy.
37. The system of claim 36, wherein the execution of the executable
code by the at least one processor causes the computing system to:
add noise to a second set of one or more attributes; vary standard
deviation of a noise distribution associated with the noise to
determine a second tradeoff; and generate, based on the second
tradeoff, a second updated strategy, the second updated strategy
characterizing an update of the first updated strategy.
38. The system of claim 37, wherein the execution of the executable
code by the at least one processor causes the computing system to
provide, based on the second updated strategy, one or more offers
to a plurality of entities.
39. The system of claim 38, wherein the first set of one or more
attributes include gender and residential status.
40. The system of claim 37, wherein the second set of one or more
attributes include risk score and credit line utilization.
41. The system of claim 40, wherein based on the transition
probability table, eligibility constraints for providing the one or
more offers to the plurality of entities are determined and at
least one of the first updated strategy and the second updated
strategy are based on the eligibility constraints to exclude some
offers to corresponding ineligible entities.
42. The system of claim 41, wherein at least one of the first
tradeoff and the second tradeoff are determined using corresponding
tradeoff curves, at least one of the first tradeoff and the second
tradeoff being characterized by a sweet-spot on a corresponding
tradeoff curve, the sweet-spot characterizing a position where
generated strategy data is more than a first threshold while profit
is more than a second threshold.
43. The system of claim 42, wherein the plurality of attributes
comprise observed variables known at time of providing the offers,
derived variables, predictive variables, and a score.
44. The system of claim 43, wherein the observed variables comprise
data filled by entities in applications, data associated with
financial-accounts, demographics data, transaction data, credit
bureau data, credit card score, credit card usage data, risk score,
revenue score, credit line utilization data, social network data,
conversations of one or more entities with one of other entities
and third parties, and third party data.
45. The system of claim 43, wherein the derived variables comprise
text keywords, n-grams, merger and acquisition transaction data,
and parameters of social networks.
46. The system of claim 43, wherein the predictive variables
comprise likelihood to default over a predetermined period of time
in future and expected entity lifetime value and the score is
calculated based on the observed variables.
Description
RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 61/513,392, filed on Jul. 29, 2011, and
entitled "Data-Driven Learning Of Utility-Maximizing Customer
Treatment Policies," content of which is incorporated herein by
reference in entirety.
TECHNICAL FIELD
[0002] The subject matter described herein relates to generating
optimal strategies for providing offers to a plurality of
customers.
BACKGROUND
[0003] Conventionally, business entities provide offers to
customers. For example, banks provide credit line increases to
existing credit card customers. Typically, a fixed (that is, not
changing with time) strategy, such as a fixed decision tree, is
used to decide a credit line increase to a particular customer.
Formation of such fixed strategies (for example, fixed credit line
increases) can require significant manual expertise from analysts
and domain experts, thereby requiring a lot of time, effort, and
associated costs.
SUMMARY
[0004] The current subject matter describes generating optimal
strategies for providing offers to a plurality of customers. In one
aspect, data associated with a plurality of individuals can be
obtained. Each individual can be associated with a plurality of
attributes. Using the obtained data, a best offer for each
attribute can be determined. A decision tree characterizing best
offers for corresponding attributes can be formed. Performance of
the decision tree can be compared with performance of a challenger
decision tree to obtain a best performing decision tree. Offers can
be provided to the plurality of individuals in accordance with the
best performing decision tree.
[0005] In some variations, a plurality of possible offers for the
plurality of individuals can be determined based on the obtained
data. The best offers for each attribute can be selected from the
plurality of possible offers.
[0006] Using the obtained data, a plurality of causal models can be
determined. By joining two or more causal models, a decision model
that evaluates one or more objectives of an entity can be formed.
The causal model can be used to determine the best offer for each
attribute. The causal models can characterize a response of an
individual to a historical offer. The determining of the best offer
can be based on evaluation of at least one of a global maximum
value and a local maximum value by the decision model.
[0007] The challenger decision trees can be obtained by changing
values of some attributes associated with the decision tree. The
performance of the decision tree can be characterized by business
efficacy provided by implementing a strategy associated with the
decision tree. The business efficacy associated with the decision
tree can be based on a plurality of iterations of strategy
evolution. The performance of the challenger decision tree can be
characterized by business efficacy provided by implementing a
strategy associated with the challenger decision tree. The business
efficacy associated with the challenger decision tree can be based
on a plurality of iterations of strategy evolution. The business
efficacy can characterize a profit of an entity providing the
offers to the plurality of individuals. The performance of the
decision tree can be characterized prior to implementation by
expected future business efficacy based on the decision model, and
the performance of the challenger decision tree can be
characterized prior to implementation by expected future business
efficacy based on the decision model. The expected future business
efficacy can be simulated based on multiple iterations of future
strategy evolution.
[0008] In another aspect, a graph characterizing a strategy for
providing offers can be obtained. One or more attributes associated
with a plurality of individuals can be modified. The one or
attributes can be represented by the graph. Based on the modified
one or more attributes, an updated strategy for providing offers
can be generated.
[0009] In some variations, the graph can include a plurality of
dots having at least one of a corresponding color and a
corresponding intensity. The at least one of the corresponding
color and the corresponding intensity can characterize a value of
at least one attribute for an associated individual. The offers can
be provided to the plurality of individuals. The modifying of the
one or more attributes can comprise adding noise to the attributes.
The adding of the noise to the attributes can comprise varying a
standard deviation of a noise distribution to determine the noise.
The adding of the noise to the attributes can provide an optimal
profit to an entity providing the offers. The optimal profit can be
more than a profit obtained without the addition of the noise.
[0010] In yet another aspect, attributes associated with a strategy
can be determined. Gaussian noise can be added to one or more
attributes. Standard deviation of the Gaussian noise can be varied
to determine a tradeoff .DELTA.n updated strategy associated with
the tradeoff can be generated.
[0011] In some variations, offers can be provided based on the
updated strategy. The updated strategy can be determined based on
the tradeoff. The tradeoff can characterize a balance between cost
of a business entity and rate of update of strategies.
[0012] In a further aspect, a plurality of attributes associated
with a strategy can be determined. Values of a first set of one or
more attributes can be changed in accordance with a transition
probability table. One or more probabilities can be varied to
determine a first tradeoff. Based on the first tradeoff, a first
updated strategy can be generated.
[0013] In some variations, noise can be added to a second set of
one or more attributes. Standard deviation of a noise distribution
associated with the noise can be varied to determine a second
tradeoff. Based on the second tradeoff, a second updated strategy
can be generated. The second updated strategy can characterize an
update of the first updated strategy. Based on the second updated
strategy, offers can be provided to a plurality of individuals. The
first set of one or more attributes can include gender and
residential status. The second set of one or more attributes can
include risk score and credit line utilization.
[0014] Further, from a table, eligibility constraints can be
determined for provision of one or more offers to one or more
customers. The first updated strategy and the second updated
strategy can be based on the eligibility constraints to exclude
provision of some offers to corresponding ineligible customers.
[0015] The first tradeoff and the second tradeoff can be determined
using corresponding tradeoff curves. Each tradeoff can be
characterized by a sweet-spot on a corresponding tradeoff curve.
The sweet-spot can characterize a position where generated strategy
data can be more than a first threshold while profit can be more
than a second threshold.
[0016] Non-transitory computer program products are also described
that comprise instructions, which, when executed by one or more
data processors, causes at least one data processor to perform
operations herein. Similarly, computer systems are also described
that may include a processor and a memory coupled to the processor.
The memory may temporarily or permanently store one or more
programs that cause the processor to perform one or more of the
operations described herein. Methods can be implemented by one or
more data processors forming part of one or more computing
systems.
[0017] The subject matter described herein provides many
advantages. For example, using the current subject matter,
generating strategies to provide offers to customers can require
minimal, negligible or no manual expertise by analysts and domain
experts, thereby requiring less time, effort, and associated costs.
Further, the effectiveness of a strategy can be shown, for a better
visual analysis of the strategy, on a graph (or other model)
displayed on a graphic user interface. This effectiveness of the
strategy can be changed by varying at least one statistical
parameter, such as standard deviation, so as to generate a most
optimal strategy that can provide maximum business efficacy.
Champion-challenger techniques can also be implemented in some
other implementations.
[0018] The details of one or more variations of the subject matter
described herein are set forth in the accompanying drawings and the
description below. Other features and advantages of the subject
matter described herein will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0019] FIG. 1A is a first flowchart illustrating an ongoing
provision of optimal offers to a plurality of customers;
[0020] FIG. 1B is a second flowchart illustrating an ongoing
provision of optimal offers to a plurality of customers;
[0021] FIG. 1C is a third flowchart illustrating an ongoing
provision of optimal offers to a plurality of customers;
[0022] FIG. 1D is a fourth flowchart illustrating an ongoing
provision of optimal offers to a plurality of customers;
[0023] FIG. 2 is a diagram illustrating a decision tree;
[0024] FIG. 3 is a plot illustrating an offer provided to each
customer associated with attributes risk score and credit line
utilization, in accordance with a champion strategy of providing
offers;
[0025] FIG. 4 is a plot illustrating an offer provided to each
customer associated with attributes risk score and credit line
utilization, in accordance with a challenger strategy of providing
offers;
[0026] FIG. 5 is a plot illustrating an offer provided to each
customer associated with attributes risk score and credit line
utilization, in accordance with a champion-challenger test that can
provide more common-support regions;
[0027] FIG. 6 is a diagram illustrating a system for varying
attributes in a controlled manner so as to control the area of
common support regions in a plot associated with those
attributes;
[0028] FIG. 7 is a plot illustrating an offer provided to each
customer associated with attributes modified risk score and
modified credit line utilization that can provide even more and
better controlled common support regions than those with respect to
FIG. 5;
[0029] FIG. 8 is a table illustrating offer eligibilities of a
plurality of customers;
[0030] FIG. 9 is a plot illustrating an offer provided to each
customer associated with attributes risk score and credit line
utilization, after the offer eligibilities have been imposed to
either provide or deny offers to corresponding customers;
[0031] FIG. 10 is a plot illustrating an offer provided to each
customer associated with attributes risk score and credit line
utilization, in accordance with a true optimal strategy;
[0032] FIG. 11 is a diagram illustrating changes in strategies for
providing offers and associated changes in business efficacy with
different iterations of strategy optimization, in accordance with a
timid testing/modeling technique;
[0033] FIG. 12 is a diagram illustrating changes in strategies for
providing offers and associated changes in business efficacy with
different iterations of strategy optimization, in accordance with
an aggressive testing/modeling technique;
[0034] FIG. 13 is a diagram illustrating an evaluation system that
can determine the exploration-versus-exploitation-tradeoff between
aggressiveness and timidness; and
[0035] FIG. 14 is a diagram illustrating a graph that can be
displayed to determine the
exploration-versus-exploitation-tradeoff.
[0036] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION
[0037] The current subject matter relates to generating optimal
strategies for providing offers to a plurality of customers. The
strategies can be illustrated using strategy tools, such as
decision trees. While decision trees have been described herein,
other strategy tools can also be used, such as one or more of the
following: decision tables, flow-charts, if-then-else analyses,
switch-case analyses, what-if analyses, influence diagrams, Markov
chains, odds algorithms, truth tables, and any other tool.
[0038] In an example of credit card limit management, an offer can
be an increase in credit card limit associated with a customer. A
first customer, which has a very low credit bureau score (that is,
high likelihood of defaulting in the near future), may not be
provided an offer or increase in credit limit. A second customer,
which has a higher credit score and uses the credit score once in a
while for minor purchases, can be provided a small (for example,
$100-$500) increase in credit card limit. A third customer, which
has a very high risk score and has account balance close to current
limit, can be provided a large (for example, $1000-$10000) increase
in credit card limit. Such a differentiation in provision of offers
can be advantageous for a lender that may want to maximize revenue
(for example, through card interest or interchange fees) while
limiting exposure and delinquencies.
[0039] Furthermore, in an example of retail coupon marketing, an
offer can be a discount coupon associated with a purchase of one or
more items, such as retail products. A loyal customer that may be
expected to be price-sensitive can be offered a discount coupon for
purchasing a higher-priced brand, whereas another customer that is
expected to be less price-sensitive may not be offered the
discount. Price-sensitivity can be inferred from historic purchase
transactions (of associated customer) that can reveal strong
preferences for discounted products. Such a differentiation in
provision of offers can be advantageous for a retailer that may
want to reward loyalty and increase sales revenue, or can be
advantageous for a manufacturer that may want to increase market
share.
[0040] FIG. 1A is a first flowchart 100 illustrating an ongoing
provision of optimal offers to a plurality of customers. The
provision can result in a currently most optimal utilization of
resources of an entity. The most optimal utilization of resources
can include less manual work, less effort, less time, less cost,
higher profit, less computing requirements, less storage
requirements, and optimal use of other business resources.
[0041] Data can be obtained, at 102, for a plurality of customers.
The data can include historic information associated with various
attributes of customers. The attributes can include observed
variables that can be known at the time of provision of offers,
such observed variables including: information filled by customers
in applications, information regarding financial-accounts,
demographics, transaction data, credit bureau data, credit card
score, credit card usage data, risk score, revenue score, credit
line utilization data, social network data, conversations/messages
of one or more customers with other customers or third parties,
third party data, and any other observed variable. Further, the
attributes can include derived or inferred variables, such derived
variables including: text keywords, n-grams, merger and acquisition
transaction data, parameters of social networks such as
connectedness (for example, roll-up activity), and any other
derived variable. Furthermore, the attributes can include
forward-looking predictive variables and score that can be computed
based on the observed variables, the predictive variables
including: likelihood to default over a particular (for example,
predetermined) period of time in future (for example, two years),
expected customer lifetime value, and any other predictive
variable.
[0042] The obtained data can be used to determine, at 104, various
offers that can be provided to a customer. The various offers can
include various increases in credit limit for a customer. For
example, different credit line increases can be at least some of:
$0 (that is, no increase), $500, $1000, $2500, $5000, $10000,
$50000, and any other credit line increase. The determining of
offers can be determined automatically by performing regression
analyses on the obtained data. Although an automatic determining of
offers is described, in other implementations, manual determining
of offers can also be possible.
[0043] Using the obtained data, causal models can be determined, at
106. Using the causal models, effects of actions on outcomes can be
inferred from the obtained data. Thus, the causal models are also
referred to herein as action-effect models. A causal model can
characterize a response function, such as: "function (attribute,
historical-offer)=Outcome subsequent to the historical-offer." In
one example, the historical-offer can be an offer, and the outcome
can be either an acceptance or denial of the offer by the customer.
In another example, the historical-offer can be an offer for a $500
increase in credit limit, and the outcome can be one or more of
subsequent credit card balances, delinquencies, losses, and any
other outcome. The causal models can be developed either for each
outcome or for selective outcomes. Such a development of causal
models can be performed using regression modeling. Extrapolation
risk can be removed-from or minimized-in the causal models, as
described further below.
[0044] Two or more causal models can be joined/tied together to
form, at 108, a decision model. The decision model can evaluate
business objectives of an entity, such as a business entity. The
business objectives can characterize business metrics, such as
cost, profits, loss, sales volume, revenue, and any other business
metric. The decision model can characterize business efficacy,
which can be defined by the following exemplary equation:
"Efficacy(attribute, offer)=a*M1(attribute, offer)+b*M2(attribute,
offer)," wherein Efficacy can be business efficacy, M1 can be a
first business metric, M2 can be a second business metric, and a
and b can characterize constraints associated with corresponding
business metrics. While the business efficacy equation has been
described as being dependent on two business metrics M1 and M2, in
other implementations, more than two (for example, three, five,
ten, twenty, hundred, five hundred, or any other finite number, as
appropriate) metrics can be used.
[0045] A best offer for each attribute can be determined, at 110,
by using the decision model. More specifically, the best offer can
be determined using the business efficacy equation, which is
described above as: "Efficacy(attribute, offer)=a*M1(attribute,
offer)+b*M2(attribute, offer)," wherein Efficacy can be business
efficacy, M1 can be a first business metric, M2 can be a second
business metric, and a and b can characterize constraints
associated with corresponding business metrics. The best offer for
an attribute can be one that maximizes business efficacy. That is,
the best offer for a particular attribute can be the value of offer
that can maximize the business efficacy equation:
"Efficacy(attribute, offer)=a*M1(attribute, offer)+b*M2(attribute,
offer)." The obtained maximum can be a global maximum. Although use
of a global maximum is described, in some other implementations,
the obtained maximum can be a local maximum. Accordingly, the best
offer can be characterized by the following mathematical equation:
"Offer.sup.Best (Attribute)=argmax {Efficacy(attribute, offer)},"
wherein Offer.sup.Best (Attribute) can be the best offer for a
particular attribute, and argmax can characterize argument of the
maximum.
[0046] The determined best offers for corresponding attributes can
be used to form, at 112, a decision tree. This decision tree can be
called a champion decision tree. A decision tree can be a graph or
a model that can characterize decisions and their possible
consequences, such as chance outcomes, resource costs, efficacy,
and the like. A decision tree can characterize an algorithm
associated with a model.
[0047] Challenger decision trees can be obtained, at 114. The
challenger decision trees can be obtained by changing some values
in a champion decision tree. For example, a challenger tree can be
constructed by changing some of the actions (offers) in the leaf
nodes as determined by a user. Alternately, a challenger can be
constructed by changing some of the split values to different
values as determined by a user.
[0048] Performances of the champion decision tree and the
challenger decision trees are compared, at 116. More specifically,
the decision tree, provision of offers according to which can
provide maximum business efficacy/advantages, can be
determined.
[0049] Offers can be provided, at 118, in accordance with the best
performing tree. In a first iteration, the champion decision tree
can usually have the best performance. That is, in the first
iteration, the provision of offers in accordance with the champion
decision tree can provide most business efficacy than provision of
offers in accordance with any challenger decision tree. In
subsequent iterations, if a challenger decision tree challenges the
champion decision tree and becomes the champion decision tree, the
new champion decision tree (previously challenger decision tree)
can provide more business efficacy.
[0050] The offers can be provided to virtual customers in
iterations until a most optimal strategy (for example, the most
optimal decision tree that can provide optimal business efficacy)
is obtained. After the most optimal strategy is obtained, the
offers can be provided to real customers.
[0051] It can be determined, at 120, whether a threshold time has
elapsed/passed since the provision of offers according to a best
performing decision tree. The particular (for example,
predetermined) time can be 5 minutes, 10 minutes, 1 hour, 2 hours,
10 hours, 1 day, 2 days, 10 days, 20 days, 3 months, or any other
time. A new action may not be performed until the particular (for
example, predetermined) time has elapsed/passed. When the
particular (for example, predetermined) time has elapsed, one or
more (or all) of 102, 104, 106, 108, 110, 112, 114, 116, 118, and
120 can be re-performed such that the most current data can be used
to provide optimal offers to customers.
[0052] FIG. 1B is a second flowchart 130 illustrating an ongoing
provision of optimal offers to a plurality of customers. A graph
can be obtained at 130. The graph can characterize a strategy of
providing offers to a plurality of customers. The graph can show
values of different attributes, such as risk score and credit line
utilization. One or more attributes (for example, risk score and
credit line utilization) can be modified at 134. The modification
can include addition of noise to each attribute. The noise can be
Gaussian noise. Standard deviation of the Gaussian noise can be
varied to determine a most optimal standard deviation that can be
used to provide most business efficacy (for example, profit of an
entity providing the offers). An updated strategy to provide offers
can be generated, at 136, based on the modified one or more
attributes. Offers can be provided, at 138, based on the updated
strategy. The offers can be provided to virtual customers in
iterations until a most optimal strategy (for example, the most
optimal decision tree that can provide optimal business efficacy)
is obtained. After the most optimal strategy is obtained, the
offers can be provided to real customers. It can be determined, at
140, if a threshold time has elapsed. If the threshold time has
elapsed, 132, 134, 136, 138, and 140 can be re-performed. If the
threshold time has not elapsed, new action may not be
performed.
[0053] FIG. 1C is a third flowchart 160 illustrating an ongoing
provision of optimal offers to a plurality of customers. Attributes
associated with a strategy for providing offers to a plurality of
customers can be determined at 162. The attributes can be risk
score, credit line utilization, and the like. Gaussian noise with
an associated Gaussian distribution can be added, at 164, to one or
more attributes (in some implementations, all attributes). Standard
deviation of the Gaussian noise associated with each attribute can
be varied, at 166, to determine an exploration-exploitation
tradeoff. An updated strategy can be generated, at 168, based on
the exploration-exploitation tradeoff. The exploration-exploitation
tradeoff can maintain a balance between aggressiveness (so as to
learn as much and as fast as possible) and timidness (so as to keep
testing costs under control). Based on the updated strategy, offers
can be provided to customers. The offers can be provided to virtual
customers in iterations until a most optimal strategy (for example,
the most optimal decision tree that can provide optimal business
efficacy) is obtained. After the most optimal strategy is obtained,
the offers can be provided to real customers.
[0054] FIG. 1D is a fourth flowchart 180 illustrating an ongoing
provision of optimal offers to a plurality of customers. Attributes
associated with a strategy for providing offers to a plurality of
customers can be determined at 182. The attributes can be
categorical attributes, such as gender, residential status, and the
like. Values of some attributes can be changed, at 184, with some
probability in accordance with a transition probability table. The
transition probability table can be accessed either via wires or
wirelessly over a network. One or more probabilities in the
transition probability table can be varied, at 186, to determine a
tradeoff. The tradeoff can be used to generate, at 188, an updated
strategy that can be more optimal than the strategy in the previous
iteration. The tradeoff can maintain a balance between a fast and
adequate learning and a high cost. Based on the updated strategy,
offers can be provided to customers. The offers can be provided to
virtual customers in iterations until a most optimal strategy (for
example, the most optimal decision tree that can provide optimal
business efficacy) is obtained. After the most optimal strategy is
obtained, the offers can be provided to real customers.
[0055] FIG. 2 is a diagram illustrating a decision tree 200 in
accordance with some implementations of the current subject matter.
The decision tree 200 can characterize a strategy for providing
offers 202 to a plurality of customers. The offers 202 can be
increases in credit limit for selective (for example, all eligible)
accounts 204 of one or more customers of the plurality of
customers. The offers 202 can be increases in credit limit of $
zero (that is, no increase) 206, $ zero (that is, no increase) 208,
$ two-thousand 210, and $ three-thousand 212. Each customer can be
associated with at least the following attributes: risk score 214,
revenue score 216, and credit line utilization 218.
[0056] The risk score 214 can be one of low 220, medium 222, and
high 224. The differentiation between low 220, medium 222, and high
224 risk scores can be based on corresponding split values. For
example, the risk score 214 can be from 1 to 100, and the split
values can be thirty-three and sixty-six so as to classify a
particular risk score 214 as one of low 220, medium 222, and high
224. If the risk score 214 is low, no offer may be made regardless
of values of revenue score 216 and credit line utilization 218.
[0057] If the risk score 214 is medium, the revenue score 216 can
be classified into low 226, medium 228, and high 230. The revenue
score 216 can be classified into low 226, medium 228, and high 230
based on corresponding split values. When the risk score 214 is
medium 222 and the revenue score is high 230, the offer can be $
two-thousand 210 increase in credit limit. If the risk score 214 is
high 224, the revenue score 216 can be classified into low 232,
medium 234, and high 236. This classification of the revenue score
216 can be based on corresponding split values.
[0058] In one implementation, the split values associated with low
226, medium 228, and high 230 revenue scores can be same as split
values associated with low 232, medium 234, and high 236 revenue
scores. In other implementations, the split values associated with
low 226, medium 228, and high 230 revenue scores can be different
from split values associated with low 232, medium 234, and high 236
revenue scores. Similar splits and corresponding offers can be made
in the decision tree 200, as shown.
[0059] All (or most) of the attributes 214, 216, and 218, and
associated split values can be determined from the historic data
obtained at 102. In other implementations, the attributes 214, 216,
and 218, and/or the split values can be specified by a
designer.
[0060] FIG. 3 is a plot 300 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes risk score 302 and credit line
utilization 304, in accordance with a champion strategy of
providing offers. Each dot in the plot 300 can characterize a
customer. At least one of color and intensity of the dot can
characterize offer (for example, credit line increase) provided to
the customer. The offer can be provided in accordance with the
strategy characterized by a champion decision tree, such as the
decision tree 200. A scale 306 can characterize the quantitative
numerical value of the offer associated with a corresponding color
or intensity. The scale 306 can display a continuous range of
colors and/or intensities. Thus, the scale 306 can be used to infer
the numerical value of the offer provided to a customer associated
with a particular color or intensity.
[0061] The strategy associated with plot 300 can provide, among
other offers, offers of $ zero (that is, no increase in credit
limit), $ two-thousand, and $ five-thousand. The highest offers can
be provided to customers with moderate to high risk scores 302, and
with moderate credit line utilizations 304. For example, the
highest offers (for example, increases of credit limits by $
five-thousand or about $ five-thousand) can be provided to
customers with risk scores 302 being more than
seven-hundred-and-fifty, and with credit utilizations 304 between
twenty and hundred. Medium offers can be provided to customers with
medium risk scores 302 and high credit line utilizations 304. For
example, medium offers (for example, increases of credit limits by
$ two-thousand or about $ two-thousand) can be provided to
customers with risk scores 302 between six-hundred-and-eighty and
seven-hundred-and-fifty, and with credit line utilizations 304
between seventy and hundred. No offers may be provided (that is,
credit limit is not increased) to most of the other customers, as
shown.
[0062] The plot 300 includes some common regions where some
customers receive a first offer (for example, increases of credit
limits by $ five-thousand) whereas other customers receive a second
offer (for example, increases of credit limits by $ two-thousand).
For example, consider the regions in plot 300 where credit
utilization 304 is fifty and risk score 302 is between
seven-hundred-and-fifty and eight-hundred. Within these common
regions, customers with similar values of attributes can receive
different offers.
[0063] The common regions are also referred to, herein, as common
support regions.
[0064] FIG. 4 is a plot 400 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes risk score 302 and credit line
utilization 304, in accordance with a challenger strategy of
providing offers. As shown, the regions associated with different
offers can be defined differently in plot 400 from those in plot
300. For example, the challenger strategy can require a risk score
302 of at least seven-hundred-and-eighty for a customer to receive
the highest offer, whereas the champion strategy of plot 300
requires a risk score 302 of seven-hundred-and-fifty for a customer
to receive the highest offer.
[0065] The plot 400 can also include common support regions, as
shown.
[0066] Common support regions in plots 300 and 400 can be common
regions where some customers receive a first offer (for example,
increases of credit limits by $ five-thousand) whereas other
customers receive a second offer (for example, increases of credit
limits by $ two-thousand). Thus, alternate offers can be tested on
some customers in these common regions so as to maintain or
increase the business efficacy of the business entity providing
these offers. In non common-support regions (that is, regions that
may not have common support), the outcomes from alternative offers
can be unknown, and therefore extending alternative offers to
customers in these non common-support regions can carry higher
risks, which a risk-averse business may want to avoid. Thus, it can
be advantageous to have/implement strategies with more (for
example, more in area/size) common-support regions.
[0067] FIG. 5 is a plot 500 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes risk score 302 and credit line
utilization 304, in accordance with a champion-challenger test that
can provide more (for example, more in area/size) common-support
regions than those in plots 300 and 400. In the champion-challenger
test, each customer can be assigned to either the champion strategy
of plot 300 or the challenger strategy of plot 400. The probability
for assignment to the champion strategy can be same as the
probability for assignment to the challenger strategy. Such an
assignment of customers can produce the plot 500, where the common
support regions can be more in area/size than area/size of those in
plots 300 and 400. Such common support regions are illustrated by
enclosing those regions by corresponding ellipses 502, 504, and
506. Non common-support regions (that is, regions that may not have
common support) are illustrated by showing question-marks in those
regions.
[0068] As noted above, alternate offers can be tested on some
customers in the common regions enclosed by ellipses 502, 504, and
506 so as to maintain or increase the business efficacy of the
business entity providing offers. In non common-support regions
(for example, regions that may not have common support), extending
alternative offers to customers in these non common-support regions
can carry higher risks, which a risk-averse business may want to
avoid. Such a testing of alternate offers in selective regions (for
example, common support regions) of a plot can advantageously
maintain or improve/increase business efficacy by reducing or
eliminating the risk of manual judgments associated with
extrapolating effect of alternate offers in the non common-support
regions.
[0069] FIG. 6 is a diagram illustrating a system 600 (and
associated technique) for varying attributes in a controlled manner
so as to control the area of common support regions in a plot
associated with those attributes. The system 600 can receive
attributes risk score 602 and credit line utilization 604 from
obtained historic data, as in 102. A noise generator 606 can add
noise to the attribute risk score 602 so as to obtain a modified
risk score 608. A noise generator 610 can add noise to the
attribute credit line utilization 604 so as to obtain a modified
credit line utilization 612. The modified risk score 608 and the
modified credit line utilization 604 can be used to form a decision
tree 614. Offers can be provided, at 616, to a plurality of
customers according to the decision tree 614.
[0070] The noise added to the attributes risk score 602 and credit
line utilization 604 can be Gaussian noise that can have a Gaussian
distribution associated with a mean and a standard distribution. To
vary the noise added, the mean and the standard distribution can be
varied by a designer in a controlled manner. The standard deviation
can be a parameter that can control the spread of noise. Also, the
area of common-support region can be directly proportional to the
standard deviation (that is, higher the standard deviation, more
the common-support region). So, in some implementations, the mean
can be set to zero, and the standard deviation can be varied until
a decision-tree and associated plot with sufficient common support
are obtained. Increasing the standard deviation by a significant
amount (for example, more than a predetermined threshold) can
reduce business efficacy--so, a balance can be obtained to provide
high (for example, more than a threshold) common-support area
paired with high business efficacy.
[0071] While examples of Gaussian noise have been described, other
noises that can have a distribution can also be used, such as
Bayesian noise, Poisson noise, Cauchy noise, Brownian noise, and
any other noise that can have a distribution. The distributions of
these noises can be controlled using one or more statistical
parameters, such as standard deviation.
[0072] FIG. 7 is a plot 700 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes modified risk score 702 and modified
credit line utilization 704 that can provide even more and better
controlled common support regions than those with respect to FIG.
5. The modified risk score 702 can be obtained by adding, by noise
generator 606, noise in a controlled manner (for example, by
varying standard deviation of the noise distribution) to the risk
score 302. The modified credit line utilization 704 can be obtained
by adding, by noise generator 610, noise in a controlled manner
(for example, by varying standard deviation of the noise
distribution) to the credit line utilization 304. As shown, area of
the common support region in plot 700 can be significantly more
than the common support regions in plots 300 and 400, or even
500.
[0073] In some other implementations, the attributes can be such
that addition of noise may not be appropriate. For example, when
the attributes can be categorical attributes such as gender and
residential status, the addition of noise to these attributes may
not be appropriate. In such cases, a methodology that can be an
alternate of the noise-addition methodology can be implemented. In
this alternate methodology, values of such categorical attributes
can be randomly changed/flipped according to a table, such as a
transition probability table. For example, if value of attribute
residential status is "lives with parents," then this value can be
changed/flipped with 20% probability to the value "renter," and
vice versa, in accordance with the transition probability table
provided by the designer. Further, if value of the attribute gender
is "male," then this value can be changed/flipped with 10%
probability to "female," and vice versa, in accordance with the
transition probability table provided by the designer. Thus, while
the test designer can provide a value of standard deviation to
control addition of noise in some other implementations described
herein, here, the test designer can provide the transition
probability table.
[0074] In some implementations, the probabilities associated with
the transition probability table can be uniform so that each value
of each categorical attribute can be equally likely be flipped to
any other value. In other implementations, the probabilities can be
chosen such that certain value flips can be more likely than
others, especially when there may be a sense of closeness of values
of the attributes. For example, customers that live with their
parents can be regarded more similar to renters and less similar to
home owners. In this case, the transition probability between
values "renter" and "lives with parents" can be higher than
transition probability between values "lives with parents" and
"owner." Accordingly, this can result in a decision tree that can
more often consider a customer with value "lives with parents" as
having a value "renter," and vice versa, and less often consider
the customer as having the value "owner." Further, the transition
probabilities may not need to be symmetric--for example, the value
"lives with parents" can be considered similar to the value
"renter" treatments, but the value "renter" may be considered
completely different (that is, not similar) to the value "lives
with parents."
[0075] Above, (1) addition of noise, and (2) random flipping of
values of attributes using a transition probability table are
described as separate implementations. However, when customers may
be associated with both ordinal attributes (for example, risk score
and credit line utilization) and categorical attributes (for
example, gender and residential status) are used, the above-noted
two methods (that is, (1) addition of noise, and (2) random
flipping of values of attributes using a transition probability
table) can be used in combination. The combination of these two
methods can be either in any sequence or in parallel.
[0076] FIG. 8 is a table 800 illustrating offer eligibilities of a
plurality of customers. These offer eligibilities can be used to
impose limits/constraints during provision of offers to the
customers so further improve/increase the business efficacy. The
table 800 includes offers 802, such as first offer (A) 804, second
offer (B) 806, and third offer (C) 808. The offers 800 can be for
customers 810, such as X1, X2, X3, X4, and so on. The table 800
further includes treatment eligibility sets 812. The entries "1"
can characterize eligible customer-offer combinations, and the
entries "0" can characterize ineligible customer-offer
combinations. The values of offer eligibility sets 812 can
characterize offers 802 for which an associated customer may be
eligible.
[0077] The offer eligibilities of table 800 can be determined as
follows. For each value of an offer, customers can be determined
that fall into a region, which can be associated with attributes
"X" and where this value can be well represented. A conditional
multinomial regression model can be implemented to predict the
probability for each offer "k" as a function of the "X." That is,
pk(X)=Probability{Offer=k|X}, wherein k=0, 1, 2, . . . . A customer
with attributes "X" can be eligible for offer "k" if pk(X)>c,
where "c" can be a probability threshold, such as zero point two
(that is, 0.2). Otherwise, the customer can be ineligible for offer
"k."
[0078] FIG. 9 is a plot 900 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes risk score 902 and credit line
utilization 904, after the offer eligibilities have been imposed to
either provide or deny offers to corresponding customers. The
associated probability threshold "c" can be zero point two (that
is, 0.2). The offer eligibilities can be obtained from offer
eligibility tables, such as table 800. The risk score 902 can be
same as either risk score 302 or modified risk score 702. The
credit line utilization 904 can be same as either credit line
utilization 304 or modified credit line utilization 704. Each
customer can be associated with one of seven non-empty offer
eligibility sets, which can be differentiated by at least one of
color and intensity of the illustrated dots, wherein each dot can
characterizes a customer.
[0079] FIG. 10 is a plot 1000 illustrating an offer (for example,
credit line increase (CLI)) provided to each customer that is
associated with attributes risk score 1002 and credit line
utilization 1004, in accordance with a true optimal strategy. This
true optimal strategy can be based on a simulation model, and is
shown here for comparison and benchmarking. The true optimal
strategy can be even more optimal (for example, provide more
business efficacy) than the initial champion strategy. The champion
strategy can be obtained in a first iteration of flowchart 100, and
in subsequent iterations, increasingly optimal strategies for
providing offers can be obtained. After a threshold number of
iterations, a most optimal strategy can be obtained. The risk score
1002 can be same as either risk score 302 or modified risk score
702. The credit line utilization 1004 can be same as either credit
line utilization 304 or modified credit line utilization 704.
[0080] FIG. 11 is a diagram illustrating changes in strategies for
providing offers and associated changes in business efficacy (for
example, profit of an entity providing offers) with different
iterations of strategy optimization, in accordance with a timid
testing/modeling technique. In the timid modeling technique,
standard deviation of noise added (for example, addition of noise
is described with respect to system 600) to attributes can be
small. The initial strategy 1102 resembles the champion strategy,
and subsequent changes from plot to plot (e.g. 1002 to 1004, 1004
to 1006, etc.) can be all small, because only a small fraction of
customers located close to decision boundaries can receive
alternate treatments, as there is a small common-support
region.
[0081] The standard deviations can be fixed for all iterations. In
other implementations, the standard deviation can vary in each
iteration. For example, the standard deviation can either increase
or decrease, either linearly or non-linearly, in a particular (for
example, predetermined) fashion.
[0082] Further, a graph 1150 illustrates business efficacy over
five iterations. The business efficacy can be characterized by
profits and associated costs. Line 1152 illustrates profits (in
units of dollars per customer account per year (p.p.a)) made by the
test designs over five iterations. Line 1154 illustrates costs of
testing, wherein cost of testing can characterize a comparison
between expected profit from a champion strategy and expected
profit from a current test design (for example, scattered plots
1102, 1104, 1106, 1108, and 1110) around the champion strategy.
Line 1156 illustrates benchmark profits generated by the true
optimal strategy. Lines 1152, 1154, and 1156 can have different
corresponding colors and/or intensities for a clear
visualization.
[0083] FIG. 12 is a diagram illustrating changes in strategies for
providing offers and associated changes in business efficacy (for
example, profit of an entity providing offers) with different
iterations of strategy optimization, in accordance with an
aggressive testing/modeling technique. In the aggressive modeling
technique, standard deviation of noise added (for example, addition
of noise is described with respect to system 600) to attributes can
be increased by large values. The initial plot 1202 in the first
iteration can be significantly different from the plot 1102 and
from the champion strategy, because a large fraction of customers
located close to decision boundaries can receive alternate
treatments, as there is a large common-support region.
[0084] The standard deviations can be fixed for all iterations. In
other implementations, the standard deviation can vary in each
iteration. For example, the standard deviation can either increase
or decrease, either linearly or non-linearly, in a particular (for
example, predetermined) fashion.
[0085] Further, a graph 1250 illustrates business efficacy (for
example, profit of an entity providing offers) over five
iterations.
[0086] The graphs 1150 and 1250 can be compared as follows.
Initially, the timid test design can make a profit of $40 per
account per customer account per annum (p.a.a.), which can be
higher than the $30 p.a.a. profit made by the moderately aggressive
test design. But after a small number of iterations, the moderately
aggressive testing can make the higher profit. After five
iterations, the moderately aggressive testing can makes a profit of
$88 p.a.a. versus $66 p.a.a. for timid testing.
[0087] Cost of testing can characterize a comparison between
expected profit from a champion strategy and expected profit from a
current test design (for example, scattered plots 1102, 1104, 1106,
1108, 1110, 1202, 1204, 1206, 1208, and 1210, whichever is current
and appropriate) around the champion strategy. For each iteration,
costs of testing can also be evaluated by comparing the profit
performance of the current champion strategy with the profit
performance of the current test design. Costs of testing can be
very small (for example, $1 p.p.a.) for timid testing, whereas the
costs of testing can remain moderate (for example, $10 p.p.a. or
less) for the moderately aggressive testing. Further, the cost of
testing can shrink over time for the moderately aggressive test
design, which can be partly due to the decreasing value of standard
deviation over time. The profit figures shown for the test designs
have been already accounted-for in the testing costs.
[0088] Thus, as noted above, standard deviation of the noise
distribution associated with noise added to attributes can be
increased to follow a more aggressive strategy. However, when the
standard deviation is increased, the cost can increase, thereby
decreasing the profit. Therefore, an optimal standard deviation is
used such that the profit increases, as desired, in the iterations.
Accordingly, very timid test designs can be rejected due to
associated slow learning, and very aggressive test designs can be
rejected due to associated high costs. To obtain a tradeoff (also
referred to herein as an exploration-versus-exploitation-tradeoff)
between aggressiveness (so as to learn as much and as fast as
possible) and timidness (so as to keep testing costs under
control), an evaluation system can be used that can display and
determine the tradeoff.
[0089] FIG. 13 is a diagram illustrating an evaluation system 1300
that can determine the exploration-versus-exploitation-tradeoff
between aggressiveness (so as to learn as much and as fast as
possible) and timidness (so as to keep testing costs under
control).
[0090] The evaluation system 1300 can include a business objectives
evaluator 1302 that can determine an effectiveness of exploitation.
To determine the effectiveness of exploitation, the business
objective evaluator 1302 can determine metrics for exploitation,
such as profit per customer as expected from a test design (for
example, one of scatter plots 1102, 1104, 1106, 1108, 1110, 1202,
1204, 1206, 1208, and 1210, whichever is appropriate). While profit
is described as a metric, other metrics can also be used, such as
loss, cost, revenue, volume, and any other business exploitation
metric.
[0091] The evaluation system 1300 can further include a common
support evaluator 1304 that can determine an effectiveness of
exploration. To determine the effectiveness of exploration, the
common support evaluator 1304 can determine an exploration index
(EI), which can characterize an extent of common support regions in
scattered plots (for example, scatter plots 1102, 1104, 1106, 1108,
1110, 1202, 1204, 1206, 1208, 1210, and the like) of test designs.
The exploration index and the common support regions can
characterize amount of information captured in the test data for
developing a causal model for determining effects of treatments on
customers. Although an exploration index has been described, other
information regarding data support for causal model development can
also be used.
[0092] The exploration index can include a portion of customers
associated with offer eligibility sets of cardinality greater than
or equal to two. The offer eligibility sets can be offer
eligibility sets 812 in table 800. In one variation, the
exploration index can calculate a sum of cardinalities of treatment
eligibility sets for all customers. Such a calculation can be
advantageous for customers with larger eligibility sets, as more
causal effects can be determined and used, due to presence of a
larger dataset.
[0093] The exploration index can also be formed using different
optimization criteria. For example, a parametric structure for
quadratic causal models with first order interactions can be
assumed. The parametric structure can specify a design matrix for a
regression model. Then, a design optimality criterion, such as
D-optimality, can be used. For the design optimality criterion, a
determinant of the information matrix of the design can be
calculated. The value of the determinant can be directly
proportional to accuracy of estimation of the model parameters
characterized by the exploration index.
[0094] The business metrics, as determined by the business
objective evaluator 1302, and the exploration index, as determined
by the common support evaluator 1304, can be used to find a balance
(for example, exploration-versus-exploitation-tradeoff) between
exploitation and exploration. Information associated with
exploration-versus-exploitation-tradeoff can be used to determine
offers that can be provided to customers. These offers can be used
by the business objectives evaluator 1302 and the common support
evaluator 1304 to re-determine the effectiveness of exploitation
and exploration, and determine new offers that can be more optimal
than previous offers.
[0095] FIG. 14 is a diagram illustrating a graph 1400 that can be
displayed to determine the
exploration-versus-exploitation-tradeoff. The graph 1400 can
include a plot of profit 1402 versus exploration index (EI) 1404.
The profit 1402 can characterize exploitation, and the exploration
index 1404 can characterize exploration. The graph 1400 can include
a tradeoff curve 1406 that can be formed by varying standard
deviation of noise added to attributes of a plot associated with a
decision tree or strategy of providing offers.
[0096] A sweet spot 1408 can be determined on the tradeoff curve
1406. The sweet spot 1408 can characterize the
exploration-versus-exploitation-tradeoff. The location of the sweet
spot 1408 can be a position on the tradeoff curve 1406 where a
substantial amount of information (as measured by the exploration
index 1404) can be generated, while profit 1402 may not be
substantially decreased. Thus, the sweet spot 1408 can characterize
a position where generated strategy data can be more than a first
threshold while profit can be more than a second threshold.
[0097] The term customer, as referred herein, can include a
customer, an individual, an entity, a person, personnel, and/or the
like. The term offer, as used herein, can include a discount, an
allowance, a commission, a concession, an exemption, a coupon, a
present, a time-share, a proposal, a presentation, cash, and/or any
other offer. The term strategy, as used herein, can include a
policy, a plan, an arrangement, intelligence, or any other
strategy.
[0098] While two-dimensional graphs/plots have been described to
show values for two attributes, other models can exist when there
are more than two attributes. For example, when three attributes
are used, three-dimensional models can be used; and when "n"
attributes are used, n-dimensional models can be used.
[0099] Various implementations of the subject matter described
herein may be realized in digital electronic circuitry, integrated
circuitry, specially designed ASICs (application specific
integrated circuits), computer hardware, firmware, software, and/or
combinations thereof. These various implementations may include
implementation in one or more computer programs that are executable
and/or interpretable on a programmable system including at least
one programmable processor, which may be special or general
purpose, coupled to receive data and instructions from, and to
transmit data and instructions to, a storage system, at least one
input device, and at least one output device.
[0100] These computer programs (also known as programs, software,
software applications or code) include machine instructions for a
programmable processor, and may be implemented in a high-level
procedural and/or object-oriented programming language, and/or in
assembly/machine language. As used herein, the term
"machine-readable medium" refers to any computer program product,
apparatus and/or device (e.g., magnetic discs, optical disks,
memory, Programmable Logic Devices (PLDs)) used to provide machine
instructions and/or data to a programmable processor, including a
machine-readable medium that receives machine instructions as a
machine-readable signal. The term "machine-readable signal" refers
to any signal used to provide machine instructions and/or data to a
programmable processor.
[0101] To provide for interaction with a user, the subject matter
described herein may be implemented on a computer having a display
device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal
display) monitor) for displaying information to the user and a
keyboard and a pointing device (e.g., a mouse or a trackball) by
which the user may provide input to the computer. Other kinds of
devices may be used to provide for interaction with a user as well;
for example, feedback provided to the user may be any form of
sensory feedback (e.g., visual feedback, auditory feedback, or
tactile feedback); and input from the user may be received in any
form, including acoustic, speech, or tactile input.
[0102] The subject matter described herein may be implemented in a
computing system that includes a back-end component (e.g., as a
data server), or that includes a middleware component (e.g., an
application server), or that includes a front-end component (e.g.,
a client computer having a graphical user interface or a Web
browser through which a user may interact with an implementation of
the subject matter described herein), or any combination of such
back-end, middleware, or front-end components. The components of
the system may be interconnected by any form or medium of digital
data communication (e.g., a communication network). Examples of
communication networks include a local area network ("LAN"), a wide
area network ("WAN"), and the Internet.
[0103] The computing system may include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other.
[0104] Although a few variations have been described in detail
above, other modifications are possible. For example, the logic
flow depicted in the accompanying figures and described herein do
not require the particular order shown, or sequential order, to
achieve desirable results. Other embodiments may be within the
scope of the following claims.
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