Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
SYSTEMS AND METHODS FOR GENERATING AND ADJUSTING
RECOMMENDATIONS PROVIDED ON A USER INTERFACE
FIELD
[0001] The present disclosure relates to user interfaces and more particularly
to
systems and methods for generating and adjusting recommendations.
BACKGROUND
[0002] Significant amounts of information can be considered to provide a user
with a more desirable margin rate or interest rate when engaged in a financial
relationship with an entity. Many user relationships can be improved by
contacting users with improved rates for the user's account, whether the
account
supplies the entity with a certain margin rate, commission rate, loan rate,
etc.
Further, considering all information related to the user's account is
important to
the entity to avoid offering or accepting unreasonable rates. However,
considering
all the necessary information is time consuming when engaged in a conversation
with a user as well as when attempting to improve a user relationship by
periodically reviewing user accounts.
[0003] The background description provided here is for the purpose of
generally
presenting the context of the disclosure. Work of the presently named
inventors,
to the extent it is described in this background section, as well as aspects
of the
description that may not otherwise qualify as prior art at the time of filing,
are
neither expressly nor impliedly admitted as prior art against the present
disclosure.
SUMMARY
[0004] A recommendation system includes at least one processor and a memory
coupled to the at least one processor. The memory stores a profile database
including a plurality of profiles corresponding to a plurality of user
identifiers.
Each profile of the plurality of profiles includes a set of parameters for a
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corresponding user identifier. The memory stores a historical recommendation
database including previously generated recommendations transmitted to a web
portal. The memory stores a calculator database including a plurality of
calculator
versions. The historical recommendation database includes, for each user
identifier, a corresponding calculator version identifier used to generate an
initial
recommendation.
[0005] The memory stores instructions that, upon execution, cause the at least
one processor to, in response to receiving a recommendation request for a
requesting user identifier via the web portal, obtain a first profile of the
requesting
user identifier from the profile database. The instructions further cause the
at least
one processor to, from a set of groups, identify a first group of the set of
groups
corresponding to the requesting user identifier based on the first profile.
Each
profile of the plurality of profiles is associated with a corresponding group
of the
set of groups based on a similarity.
[0006] The instructions further cause the at least one processor to determine
a
bounded range based on (i) the first group and (ii) the first profile and
select a
calculator version from the plurality of calculator versions. The instructions
further cause the at least one processor to, based on the bounded range,
estimate
the initial recommendation using the selected calculator version and transmit
the
initial recommendation to the web portal for display.
[0007] In other features, the instructions, upon execution, cause the at least
one
processor to, in response to receiving a response for a responding user
identifier
via the web portal, update the first profile with a most recent recommendation
in
response to the response indicating an acceptance. The instructions further
cause
the at least one processor to generate a subsequent recommendation in response
to
the response indicating a denial. In other features, the instructions, upon
execution, cause the at least one processor to, in response to the response
indicating the denial, obtain a first user entry from the historical
recommendation
database. The instructions further cause the at least one processor to
generate the
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subsequent recommendation based on parameters of the most recent
recommendation stored in the first user entry.
[0008] In other features, the instructions, upon execution, cause the at least
one
processor to, prior to transmitting the initial recommendation, adjust the
initial
recommendation in response to the recommendation request including a desired
recommendation. In other features, the instructions, upon execution, cause the
at
least one processor to, prior to transmitting the initial recommendation, for
each
user of the first group, obtain a corresponding entry from the historical
recommendation database. The instructions further cause the at least one
processor to determine a likelihood of acceptance of the initial
recommendation
based on the obtained entries and adjust the initial recommendation according
to
the likelihood of acceptance.
[0009] In other features, the set of parameters for each user profile includes
(i) a
present margin rate, (ii) a margin, (iii) a profitability score, (iv) a risk
score, (v) an
amount of revenue, (vi) a margin percentage of revenue, and (vii) a risk
relative to
revenue. In other features, the similarity is determined using a clustering
algorithm to identity and group similar profiles. In other features, each
group of
the set of groups is associated with a respective calculator version. In other
features, each calculator version of the plurality of calculator versions is
marked
as testing or validated.
[0010] In other features, the instructions, upon execution, cause the at least
one
processor to, after a predetermined testing period has elapsed, select each
calculator version of the plurality of calculator versions marked as testing.
The
instructions further cause the at least one processor to calculate, for each
selected
calculator version, a performance metric over the predetermined testing period
and identify a highest performance metric of the calculated performance
metrics.
The instructions further cause the at least one processor to mark a
corresponding
calculator version that corresponds to the highest performance metric as
validated.
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[0011] A recommendation method includes, in response to receiving a
recommendation request for a requesting user identifier via a web portal,
obtaining a first profile of the requesting user identifier from a profile
database.
The profile database stores a plurality of profiles corresponding to a
plurality of
user identifiers. Each profile of the plurality of profiles includes a set of
parameters for a corresponding user identifier. The method further includes,
from
a set of groups, identifying a first group of the set of groups corresponding
to the
requesting user identifier based on the first profile. Each profile of the
plurality of
profiles is associated with a corresponding group of the set of groups based
on a
similarity.
[0012] The method further includes determining a bounded range based on
(i) the first group and (ii) the first profile. The method further includes
selecting a
calculator version from a plurality of calculator versions. A calculator
database
stores the plurality of calculator versions. A historical recommendation
database
stores, for each user identifier, a corresponding calculator version
identifier used
to generate an initial recommendation. The method further includes, based on
the
bounded range, estimating the initial recommendation using the selected
calculator version and transmitting the initial recommendation to the web
portal
for display.
[0013] In other features, the method includes, in response to receiving a
response for a responding user identifier via the web portal, updating the
first
profile with a most recent recommendation in response to the response
indicating
an acceptance. The method further includes generating a subsequent
recommendation in response to the response indicating a denial. In other
features,
the method includes, in response to the response indicating the denial,
obtaining a
first user entry from the historical recommendation database. The method
further
includes generating the subsequent recommendation based on parameters of the
most recent recommendation stored in the first user entry.
[0014] In other features, the method includes, prior to transmitting the
initial
recommendation, adjusting the initial recommendation in response to the
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recommendation request including a desired recommendation. In other features,
the method includes, prior to transmitting the initial recommendation, for
each
user of the first group, obtaining a corresponding entry from the historical
recommendation database. The method further includes determining a likelihood
of acceptance of the initial recommendation based on the obtained entries. The
method further includes adjusting the initial recommendation according to the
likelihood of acceptance.
[0015] In other features, the set of parameters for each user profile includes
(i) a
present margin rate, (ii) a margin, (iii) a profitability score, (iv) a risk
score, (v) an
amount of revenue, (vi) a margin percentage of revenue, and (vii) a risk
relative to
revenue. In other features, the similarity is determined using a clustering
algorithm to identity and group similar profiles. In other features, each
group of
the set of groups is associated with a respective calculator version. In other
features, each calculator version of the plurality of calculator versions is
marked
as testing or validated.
[0016] In other features, the method includes, after a predetermined testing
period has elapsed, selecting each calculator version of the plurality of
calculator
versions marked as testing. The method further includes calculating, for each
selected calculator version, a performance metric over the predetermined
testing
period. The method further includes identifying a highest performance metric
of
the calculated performance metrics. The method further includes marking a
corresponding calculator version that corresponds to the highest performance
metric as validated.
[0017] Further areas of applicability of the present disclosure will become
apparent from the detailed description, the claims, and the drawings. The
detailed
description and specific examples are intended for purposes of illustration
only
and are not intended to limit the scope of the disclosure.
Date Recue/Date Received 2020-05-11
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The present disclosure will become more fully understood from the
detailed description and the accompanying drawings.
[0019] FIG. 1 is a high-level block diagram of an example network
communication system including a recommendation system according to the
principles of the present disclosure.
[0020] FIG. 2 is an example user interface displaying input to be received for
generating and adjusting recommendations.
[0021] FIG. 3 is an example user interface displaying generated
recommendations.
[0022] FIG. 4 is a functional block diagram of an example recommendation
device.
[0023] FIG. 5 is a flowchart depicting example operation of generating and
adjusting recommendations based on user input.
[0024] FIG. 6 is a flowchart depicting example operation of comparison testing
of calculator versions.
[0025] In the drawings, reference numbers may be reused to identify similar
and/or identical elements.
DETAILED DESCRIPTION
[0026] A recommendation system generates recommendation metrics for users
based on account information and user input to provide a more desirable rate
to
the user and improve a relationship with the user. The recommendations system
is
an automated system that generates the recommendation metrics for the user in
response to the user requesting an updated recommendation metric. In various
implementations, the recommendation system may intermittently generate
recommendation metrics for users to determine if improved recommendation
metrics can be offered to the user, allowing the user to be approached by an
entity
with a recommendation metric as opposed to the user requesting the
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recommendation metric. The more desirable rate may pertain to a rate
associated
with an account the user holds with the entity. For example, the more
desirable
rate may be a margin rate, commission rate, loan rate, etc. In various
implementations, the recommendation metrics provided more desirable metrics
pertaining to a relationship of the user and entity other than the rate.
[0027] As described in further detail below, recommendation metrics may
include an initial recommendation and subsequent recommendations that are both
included within a bounded range. The bounded range may be determined based
on a set of parameters that correspond to the first user. The recommendation
system accesses a plurality of databases that maintain the set of parameters
to
calculate the recommendations. These calculations may be conducted in real-
time
to assist an entity in negotiating with an individual during a conversation.
In this
context, real-time means that the calculations are conducted quickly enough to
be
used in a single interaction (such as a phone call) between the entity and the
individual. In other words, real-time in this context means that calculations
are
conducted in less than 5 minutes, in less than 1 minute, or in less than 10
seconds.
[0028] One database maintains account information including a present margin
rate, a margin, a profitability score, a risk score, an amount of revenue, a
margin
percentage of revenue, a risk relative to revenue, etc. The recommendation
system
also considers user input when the user is requesting updated recommendation
metrics. For example, the recommendation system may consider information
provided by the user that indicate a retention likelihood based on a mention
of a
competitor. The recommendation system generates the recommendations based
on the above listed factors.
[0029] Referring now to FIG. 1, a high-level block diagram of an example
network communication system including a recommendation system 100
according to the principles of the present disclosure is shown. An analyst
device 104 communicates via the Internet 108 with a recommendation device 112.
In various implementations, the analyst device 104 may interact directly with
the
recommendation device 112 using an internal network connection. In response to
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the analyst device 104 receiving a user request via a web portal from the
analyst,
the recommendation device 112 accesses multiple databases, internal or via the
Internet 108, to generate an initial or subsequent recommendation for a
requesting
user submitting the user request.
[0030] The recommendation device 112 can access a profile database 116 via
the Internet 108 or via the internal network connection. The profile database
116
stores a profile for users maintaining an account with an entity associated
with the
profile database 116. In various implementations, the entity may operate a
financial trading platform, a loan providing platform, or another financial
platform. A profile stored in the profile database 116 includes demographic
information of the corresponding user, such as age, account duration,
location,
etc. The profile also stores account information including the present margin
rate,
the margin, the profitability score, the risk score, the amount of revenue,
the
margin percentage of revenue, the risk relative to revenue, etc.
[0031] The recommendation device 112 can also access a historical
recommendation database 120. Similar to the profile database 116, the
historical
recommendation database 120 may also be accessed via the internal network
connection to generate recommendations on a user interface of the analyst
device 104. In various implementations, the user may be communicating with an
analyst via phone or in-person. For the purpose of the present disclosure, it
is
assumed that the user is communicating with the analyst using a chat system
via
the web portal. In various implementations, the chat system is operated by a
bot
(which may be implemented using artificial intelligence) instead of by an
analyst.
The historical recommendation database 120 is indexed by user and stores
information generated from each interaction between the user and the
recommendation device 112.
[0032] For example, the historical recommendation database 120 will create an
entry in response to the user submitting user input. The user input may
include a
request for a recommendation, a response to a previously provided
recommendation, an indication of a switch to a competitor, etc. Additionally,
as
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will be discussed in more detail below, when the recommendation device 112
generates the initial recommendation for the user, the historical
recommendation
database 120 will store the initial recommendation as well as the user's
response
to the initial recommendation, which may include acceptance or denial.
Further,
any subsequent recommendations that the recommendation device 112 generates
will also be stored in the historical recommendation database 120 with the
parameters of the recommendation as well as the parameters of the user's
response.
[0033] The recommendation device 112 can also access a grouping
database 124 that stores a group to which each user belongs. For example, a
group
may be based on user parameters, such as demographic information, account
information, etc. The recommendation system 100 has recognized a set of groups
of generally similar users based on the user parameters described above. In
one
example, the users may be grouped according to their risk score, each group
corresponding to a different risk score range.
[0034] In various implementations, a similarity amongst the users may be
determined. To identify similar users, a grouping system categorizes each user
into a group based on features listed above. In various implementations, the
recommendation system 100 may use unsupervised learning, such as K-means
clustering, to cluster users into the set of groups. In various
implementations,
lower or upper limits on the number of clusters may be set. These limits may
be
adaptive based on, for example, the total number of users. Additionally or
alternatively, the number of clusters may be determined based on upper and/or
lower limits placed on how many users can be in each cluster.
[0035] The recommendation device 112 determines the grouping of the user to
influence or adjust the initial and subsequent recommendations. For example,
the
recommendation device 112 can obtain a likelihood that a member of a
particular
group would accept a particular recommendation. Similarly, based on the
grouping of the user, the recommendation device 112 can assess if a different
user
of the same group that has obtained a competitor's recommendation would accept
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a particular recommendation that the recommendation device 112 generates, for
example, based on a distance metric between a requested rate included in the
user
request and a historical distance metric between a similar user's request and
accepted rate. The grouping database 124 may also be accessed via the
Internet 108 or internal connection.
[0036] The recommendation device 112 can also access a calculator
database 128 via the Internet 108 or internal connection. The calculator
database 128 stores a variety of versions of calculators. Each calculator
version
may have a different set of operating constraints. The operating constraints
may
include a preferred revenue to rate ratio, a preferred margin percentage of
revenue, etc. These operating constraints affect the bounded range for the
particular user as well as the initial and subsequent recommendations
described
above. The operating constraints of each calculator version vary so that the
calculator versions may be compared to one another using automated AB testing.
The historical recommendation database 120 stores which calculator version
from
the calculator database 128 is used in each recommendation generation of each
entry for comparison and continuity of user recommendations.
[0037] In various implementations, when generating an initial recommendation
for a user, the recommendation device 112 may randomly select a calculator
version from the calculator database 128. In another implementation, the
calculator version stored in the calculator database 128 may be determined
based
on the requesting user's profile selected from the profile database 116 and/or
the
group of the requesting user. When the recommendation device 112 is generating
subsequent recommendations, the recommendation device 112 determines which
calculator version was used to calculate the initial recommendation from the
historical recommendation database 120 and continues to use the same
calculator
version to further improve the automated AB testing calculator comparison and
continuity.
[0038] Referring now to FIG. 2, an example user interface 200 displaying input
to be received for generating and adjusting recommendations is shown. As shown
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in the example user interface 200, a calculator version 7.6 of the
recommendation
device may generate recommendations using an account number 204 of a user,
the trailing 12 month revenue 208 of the user's account, the margin percentage
of
revenue 212 of the user's account, and a current margin rate 216 of the user's
account. In various implementations, when a user requests a recommendation,
the
user only needs to provide their account number and the recommendation device
automatically populates the remaining information. The recommendation device
may automatically generate a recommendation once the input information is
received by the recommendation generator shown in the example user
interface 200. In various limitations, the analyst may be accessing the
recommendation device using a web portal on the analyst device and may select
a
generate button 220 to generate the recommendation.
[0039] FIG. 3 is an example user interface 300 displaying generated
recommendations. Once the generate button is selected, the recommendation
device will generate at least one recommendation. In various implementations,
the
recommendation device may generate an initial recommendation 304, providing
the initial recommendation to suggest to the user based on the information
previously provided. The recommendation device may simultaneously generate a
subsequent recommendation 308 that may be subsequently provided to the user in
the event that the initial recommendation is not accepted. The analyst may
also be
presented with a floor value 312, which is an absolute minimum of the bounded
range that the recommendation device calculates to determine the range of
recommendations that may be provided to the user. When an analyst is using the
recommendation device, a return button 316 may be present to return to the
user
interface shown in FIG. 2. In an instance when the recommendation device is
automatic, the recommendation device may select a set of users, calculate
recommendations, and store the recommendations in a database for an analyst to
access in the future. In various implementations, the automatic recommendation
device may generate alerts when an initial recommendation is beyond a
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predetermined threshold difference from a present rate, indicating to an
analyst to
approach the user to improve the relationship.
[0040] In FIG. 4, a functional block diagram of the recommendation device 112
is shown. The recommendation device 112 receives user input. The user input
may be received directly from a user, for example, when the recommendation
device 112 is operable by users. In various implementations, the user input is
submitted by an analyst using an analyst device communicating with a user
requesting a recommendation. In an automated version, the recommendation
device 112 may select profiles of users from the profile database 116 to
compare
present user rates to generated recommendations.
[0041] As described in more detail below, each recommendation interaction is
stored and learned from by selecting particular calculator versions to
generate
recommendations. In the automated version, the recommendation device 112
selects a set of users that are classified into groups based on stored profile
data,
such as account information, demographic information, portfolio composition
(for
example, stocks, mutual funds, stock sector, etc.), trading activity, risk
score,
profitability, attrition probability, a similarity metric, a desired retention
value,
etc. Then, in a scenario where a member of one of the groups has a successful
negotiation, the recommendation device 112 may cycle through the remaining
users in the group to identify whether another user can be recommended a
reduced rate. Approaching the user with a lower rate without the user having
to
prompt any recommendation interaction improves the user relationship and may
increase profitability.
[0042] The user input includes identifying information of the user, for
example,
an account number as well as a recommendation request. The user input may
further include additional information that may be used to adjust a generated
recommendation. The additional information may include a desired
recommendation, an indication of switching to a competitor, a name of the
competitor, a retention likelihood, an indication of intent (i.e., an
indication in the
recommendation request that suggests the user desires a recommendation that
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significantly changes their present circumstances, such as salient terms used
to
indicate aggression), etc.
[0043] The recommendation device 112 includes an input type determination
module 404. The input type determination module 404 receives the user input,
as
described above, from the analyst, from the corresponding user, or from the
profile database, and determines whether the user input is a request for a
recommendation or a response to a transmitted recommendation. If the user
request includes an initial request for initial recommendation, the
recommendation device 112 obtains user parameters to determine the initial
recommendation. The user input of the requesting user is forwarded to a
recommendation request module 408 and a grouping module 412. The
recommendation request module 408 obtains a profile of the requesting user
from
the profile database 116. The grouping module 412 receives the user input
including the identification information identifying the requesting user and
similarly obtains the profile of the requesting user from the profile database
116.
The grouping module 412 also determines a group of the requesting user as
indexed in the grouping database 124. In various implementations, the grouping
module 412 may determine a group of the requesting user based on the grouping
of users stored in the grouping database 124. Alternatively, the grouping
module 412 may obtain which group the requesting user belongs to from the
grouping database 124.
[0044] In various implementations, the grouping module 412 may group the
user based on the user input indicating that the user is looking to switch to
a
competitor. Optionally, the profile of the user may include website
interactions of
the user with their present account. The website interactions may indicate
whether
the user has recently downloaded a statement, viewed performance, rates, etc.,
and determine whether the website interactions indicate the user may be
switching
to another entity or competitor. Based on the website interactions or user
input
indicating a switch, the grouping module 412 may group the user based on a
retention likelihood.
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[0045] A range determination module 416 receives the recommendation request
including parameters of the user input from the recommendation request
module 408 as well as the group of the requesting user from the grouping
module 412. The range determination module 416 also obtains the profile of the
requesting user from the profile database 116 and determines a bounded range
for
the requesting user based on the obtained profile and the group. For example,
the
bounded range includes a floor value as well as a ceiling value. The floor
value is
a lowest value that the recommendation device 112 can generate without losing
margin. The ceiling value is a highest reasonable value that the
recommendation
device 112 can request.
[0046] The range determination module 416 determines the bounded range
based on parameters obtained or included in the profile of the requesting user
including the present margin rate, the margin, the profitability score, the
risk
score, the amount of revenue, the margin percentage of revenue, the risk
relative
to revenue, etc. In various implementations, a desired margin rate included in
the
user input that is received from the requesting user may also influence the
bounded range determined by the range determination module 416. In various
implementations, the range determination module 416 may communicate
(communication not shown) with the calculator database 128 to determine the
bounded range based on operating parameters of a particular calculator
version.
[0047] A recommendation generation module 420 receives the bounded range
from the range determination module 416. In various implementations, the
recommendation generation module 420 selects a calculator version stored in
the
calculator database 128. As mentioned previously, the recommendation
generation module 420 may randomly select the calculator version or may select
the calculator version based on the parameters of the requesting user's
profile, the
user input, etc. The recommendation generation module 420 also stores
parameters of the requesting user's request in the historical recommendation
database 120 and, as previously mentioned, the historical recommendation
database 120 may be indexed according to user. For each entry for each user
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stored in the historical recommendation database 120, the user input for an
initial
recommendation request and subsequent recommendation request may be stored
along with the calculator version used by the recommendation generation
module 420, the bounded range determined by the range determination
module 416, the group of the user, and other information used to generate the
initial or subsequent recommendation.
[0048] Based on the parameters of the requesting user's profile, the bounded
range, and the group of the requesting user, the recommendation generation
module 420 generates an initial recommendation. The recommendation
generation module 420 accounts for the present margin rate, the margin, the
profitability score, the risk score, the amount of revenue, the margin
percentage of
revenue, the risk relative to revenue, etc., to generate the initial
recommendation.
[0049] When generating the initial recommendation, the recommendation
generation module 420 may also generate, using the calculator version, a
subsequent recommendation that may be transmitted to the analyst
communicating with the requesting user in the event that the requesting user
does
not accept the initial recommendation. The recommendation generation
module 420 then transmits the initial recommendation to a web portal to be
displayed to the analyst using the analyst device, which can then be
communicated to the requesting user. In various implementations, the initial
and
subsequent recommendations may be a set percentage of the bounded range. For
example, the initial recommendation may be set at the 70% point of the bounded
range. In other words, if the bounded range is defined by values named Lower
and
Upper, the 70% point can be calculated as Lower + 0.7 * (Upper ¨ Lower). As a
further example, the subsequent recommendation may be set at the 50% point of
the bounded range.
[0050] If the user input is a response to an initial or subsequent
recommendation, the recommendation device 112 determines whether the
response is an acceptance or denial of the initial or subsequent
recommendation
and stores the acceptance or generates a subsequent recommendation in response
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to the denial. When the input type determination module 404 receives the
response, the input type determination module 404 identifies the response as a
response and transmits the user input to a recommendation response module 424.
[0051] The recommendation response module 424 determines whether the user
input is an acceptance of a most recent recommendation or a denial of the most
recent recommendation. In response to receiving an acceptance, the
recommendation response module 424 updates the responding user profile with
the most recent recommendation that the responding user has accepted. The
recommendation response module 424 also stores in the historical
recommendation database 120 that the responding user has accepted the most
recent recommendation.
[0052] In response to receiving a denial, the recommendation response
module 424 retrieves an entry corresponding to the responding user from the
historical recommendation database 120 and transmits the responding user's
response as well as the history of previous recommendations provided to the
responding user to the recommendation generation module 420. The
recommendation generation module 420 then generates a subsequent
recommendation based on the stored bounded range associated with the
responding user, the most recent recommendation provided to the responding
user
that the responding user denied, and the original factors considered, such as
the
profitability score. The recommendation generation module 420 may also
determine the subsequent recommendation based on user input from the
responding user. For example, if the responding user indicated that a
competitor
offered a more preferable recommendation, the recommendation generation
module 420 may determine that, based on the competitor or based on the more
preferable recommendation offered by the competitor, a more aggressive
subsequent recommendation should be provided to the responding user due to the
user's account information, such as the present margin rate, the margin, the
profitability score, the risk score, the amount of revenue, the margin
percentage of
revenue, the risk relative to revenue, etc. Once generated, the subsequent
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recommendation is transmitted to the web portal to be displayed on the analyst
device for communication to the user.
[0053] Referring now to FIG. 5, a flowchart depicting example operation of
generating and adjusting recommendations based on user input is shown. Control
begins upon receiving user input. At 504, control determines whether a
recommendation request or a response to a recommendation has been received. If
a recommendation request was received, control continues to 508. Otherwise, if
a
response to a recommendation is received, control proceeds to 512.
[0054] At 508, control obtains request parameters for a requesting user. The
request parameters include what the requesting user is requesting, such as a
reduced margin rate as well as any peripheral information including the
desired
rate, a competitor's rate, a retention likelihood, etc. The received request
also
includes identification information of the requesting user, such as account
information, user name, etc. Control continues to 516 to set a response_number
equal to zero for the requesting user. Control continues to 520 to obtain a
first
user profile of the requesting user. As described above, the user profile for
each
user that maintains an account (for example, for each user who has a financial
account or a loan account) includes information such as the present margin
rate,
the margin, the profitability score, the risk score, the amount of revenue,
the
margin percentage of revenue, the risk relative to revenue, etc., regarding
the
requesting user's account.
[0055] At 524, control identifies a first group of the first profile from
among a
set of groups. Each user profile is categorized into a group based on account
information, demographic information, a similarity metric, a distance metric
between a desired rate and the present rate, and/or the desired retention
value. For
example, users may be grouped based on similarity among the users. Control may
then analyze previous recommendations made to users in the same group as the
requesting user to determine a likelihood of acceptance or to adjust a
recommendation provided to the requesting user to increase the likelihood of
acceptance.
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[0056] Control continues to 528 to determine a bounded range based on the
first
group and the first profile. The bounded range is a floor value to a ceiling
value.
Each value is determined based on the requesting user's account information
and
information of users within the first group that was used to categorize the
users.
Control continues to 532 to select a calculator version from a set of
calculators
based on the requesting user. The selected calculator version may be random or
may be based on account information of the requesting user. In various
implementations, each group may have a corresponding calculator version. For
example, each calculator version may have at least one parameter that varies
with
respect to other versions, allowing for varying recommendations as well as
varying bounded ranges. In various implementations, each calculator is
compared
to other calculator versions based on one or more resulting parameters, such
as
profitability, risk score, etc.
[0057] At 536, control generates, using the selected calculator version, a
first
recommendation. The first recommendation is within the bounded range and
based on the parameters of the first profile, which may include the
profitability
score and risk score, as described above. Control continues to 540 adjust the
first
recommendation based on user input of the requesting user. That is, if the
user
input indicates a desired rate or an indication that the requesting user is
likely to
leave, the first recommendation may be adjusted to be more similar to the
desired
rate or may be adjusted to be more aggressive or preferable to the user if it
appears that the user is likely to close their account.
[0058] Control continues to 544 to transmit the first recommendation to the
analyst device for display to the analyst and communication to the user. At
548,
control stores the request, the first recommendation, and calculator version
in
association with the requested user in a database for future analysis and
reference.
Storing recommendations and user responses allows for the generation of
recommendations that a similar user is more likely to accept, reducing the
number
of recommendations generated and the amount of back and forth between the user
and the analyst or the recommendation device. Then, control ends.
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Date Recue/Date Received 2020-05-11
[0059] Returning to 512, control obtains response parameters after determining
that the user input is a response to a recommendation. The response parameters
may include an acceptance or a denial as well as additional information
pertaining
to the most recent recommendation. For example, the response to the most
recent
recommendation may include, along with the denial, a name of a competitor and
an offered rate from the competitor or an indication of frustration of the
user.
These response parameters may be used to generate a subsequent
recommendation that is more likely to be accepted by the user. At 552, control
obtains the user profile of the responding user. At 556, control obtains the
most
recent recommendation parameters, including calculator version, account
information used to calculate the most recent recommendation, the bounded
range, etc.
[0060] Control continues to 560 to determine whether the response parameters
indicate acceptance of the most recent recommendation. If yes, control
proceeds
to 564. Otherwise, control continues to 568. If the most recent recommendation
is
accepted, at 564, control marks the most recent recommendation of the
responding user as accepted. Then, control proceeds to 572 to update the
responding user's profile with the accepted recommendation, including the new
margin rate or other parameter of the user profile that is being recommended.
Then, control ends.
[0061] Returning to 568, if the most recent recommendation is not accepted,
control determines if the response_number is greater than a predetermined
threshold. The predetermined threshold is a number of times that a
recommendation may be transmitted to the responding user. Therefore, if the
response_number exceeds the predetermined threshold, control has generated a
maximum number of recommendations for the responding user and will no longer
generate recommendations for the responding user. Instead, control will
proceed
to 576 to generate and transmit a response_number exceeded alert. After
receiving
the alert, the analyst can evaluate how to proceed with the responding user
instead
of continuing to generate recommendations that the user is not accepting.
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[0062] Returning to 568, if the response_number is not greater than the
predetermined threshold, control proceeds to 580 to increment the
response_number. Then, control proceeds to 584 to generate a subsequent
recommendation based on the most recent recommendation and the response
received in the user input. For example, the subsequent recommendation may be
set to equal 60% of the most recent recommendation. In various
implementations,
if the response includes information indicating that the responding user
retention
is low (for example, based on use of argumentative words) and the
profitability
score of the user account is high, control may set the subsequent
recommendation
equal to 50% of the most recent recommendation to increase the likelihood of
user acceptance.
[0063] Further, at 588, control adjusts the subsequent recommendation based on
the original request stored in association with the responding user.
Therefore,
control may adjust subsequent recommendation further according to a desired
rate
that was supplied in the original request for a recommendation from the
responding user. Similar to the previous percentage example given above,
control
may further reduce the percentage of the most recent recommendation provided
to
the user. At 592, control transmits the subsequent recommendation to the
analyst
device via the web portal. At 596, control stores the subsequent
recommendation
in association with the responding user and then control ends.
[0064] Referring now to FIG. 6, a flowchart depicts example comparison testing
of calculator versions. In various implementations, a calculator comparison
module (not shown) may be included in the example recommendation device of
FIG. 4, accessing the historical recommendation database and profile database
to
compare the results of each calculator version. For example, the resulting
profitability from each accepted recommendation may be compared across
calculator versions. A first calculator version may have an adjusted bounded
range that prevents generated recommendations from being 80% below a user
requested rate while a second calculator version and a third calculator
version
Date Recue/Date Received 2020-05-11
may prevent generated recommendations from being 60% and 40% below the
user requested rate, respectively.
[0065] During a comparison of calculator versions, a highest performing
calculator version may be selected as validated. For example, each new
calculator
version that is developed or has a variable (such as the bounded range) that
is
altered may be added to the calculator database and marked as a beta version.
The
calculator database may also store calculator versions that are marked as
validated. When a recommendation interaction occurs, a particular calculator
version is randomly selected from a set of validated calculator versions.
[0066] To test beta calculator versions prior to validation, a predetermined
number of recommendation interactions may be conducted using beta calculator
versions. During testing, a beta calculator version is randomly selected from
the
set of beta calculator versions for each of the predetermined number of
recommendation interactions. After a sufficient amount of testing is conducted
using the beta calculator versions, the predetermined number of recommendation
interactions may be compared to validate one or more of the beta calculator
versions.
[0067] As shown in FIG. 6, control begins upon expiration of a predetermined
testing period. That is, beta calculator versions are compared after enough
time
has passed for a sufficient number of recommendation interactions to have
occurred. Control continues to 604 to obtain a list of test calculator
versions. At
608, control selects a comparison metric, such as profitability, risk score,
etc. At
612, control selects a first calculator version from the list of test
calculator
versions. Control proceeds to 616 to obtain data related to the selected
comparison metric for the selected calculator version. Data related to each
comparison metric is stored after a recommendation interaction is complete,
also
storing the corresponding calculator version. For example, if the comparison
metric is profitability, control obtains profitability values for each
recommendation interaction that used the selected calculator version. Control
continues to 620 to calculate performance of the selected calculator version
based
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Date Recue/Date Received 2020-05-11
on the comparison metric. In various implementations, the calculated
performance
is an average of the comparison metric across each recommendation interaction
that used the selected calculator version. Alternatively, depending on the
selected
comparison metric, the calculated performance may be a weighted average of the
comparison metric, and the weight may be based on a size of the account.
[0068] At 624, control adds the calculated performance of the selected
calculator version to a comparison list. Control continues to 628 to determine
if
another calculator version is in the list of test calculator versions. If yes,
control
proceeds to 632 to select the next test calculator version and return to 616.
Otherwise, control continues to 636 to select a best performance metric from
the
comparison list. For example, the calculator version that results in the
highest
profitability is selected while the calculator version that results in the
lowest risk
score is selected. Control then continues to 640 to mark the best performing
calculator version as validated. Then, control ends.
CONCLUSION
[0069] The foregoing description is merely illustrative in nature and is in no
way intended to limit the disclosure, its application, or uses. The broad
teachings
of the disclosure can be implemented in a variety of forms. Therefore, while
this
disclosure includes particular examples, the true scope of the disclosure
should
not be so limited since other modifications will become apparent upon a study
of
the drawings, the specification, and the following claims. It should be
understood
that one or more steps within a method may be executed in different order (or
concurrently) without altering the principles of the present disclosure.
Further,
although each of the embodiments is described above as having certain
features,
any one or more of those features described with respect to any embodiment of
the disclosure can be implemented in and/or combined with features of any of
the
other embodiments, even if that combination is not explicitly described. In
other
words, the described embodiments are not mutually exclusive, and permutations
22
Date Recue/Date Received 2020-05-11
of one or more embodiments with one another remain within the scope of this
disclosure.
[0070] Spatial and functional relationships between elements (for example,
between modules) are described using various terms, including "connected,"
"engaged," "interfaced," and "coupled." Unless explicitly described as being
"direct," when a relationship between first and second elements is described
in the
above disclosure, that relationship encompasses a direct relationship where no
other intervening elements are present between the first and second elements,
and
also an indirect relationship where one or more intervening elements are
present
(either spatially or functionally) between the first and second elements. As
used
herein, the phrase at least one of A, B, and C should be construed to mean a
logical (A OR B OR C), using a non-exclusive logical OR, and should not be
construed to mean "at least one of A, at least one of B, and at least one of
C."
[0071] In the figures, the direction of an arrow, as indicated by the
arrowhead,
generally demonstrates the flow of information (such as data or instructions)
that
is of interest to the illustration. For example, when element A and element B
exchange a variety of information but information transmitted from element A
to
element B is relevant to the illustration, the arrow may point from element A
to
element B. This unidirectional arrow does not imply that no other information
is
transmitted from element B to element A. Further, for information sent from
element A to element B, element B may send requests for, or receipt
acknowledgements of, the information to element A. The term subset does not
necessarily require a proper subset. In other words, a first subset of a first
set may
be coextensive with (equal to) the first set.
[0072] In this application, including the definitions below, the term "module"
or
the term "controller" may be replaced with the term "circuit." The term
"module"
may refer to, be part of, or include processor hardware (shared, dedicated, or
group) that executes code and memory hardware (shared, dedicated, or group)
that stores code executed by the processor hardware.
23
Date Recue/Date Received 2020-05-11
[0073] The module may include one or more interface circuits. In some
examples, the interface circuit(s) may implement wired or wireless interfaces
that
connect to a local area network (LAN) or a wireless personal area network
(WPAN). Examples of a LAN are Institute of Electrical and Electronics
Engineers
(IEEE) Standard 802.11-2016 (also known as the WWI wireless networking
standard) and IEEE Standard 802.3-2015 (also known as the ETHERNET wired
networking standard). Examples of a WPAN are the BLUETOOTH wireless
networking standard from the Bluetooth Special Interest Group and IEEE
Standard 802.15.4.
[0074] The module may communicate with other modules using the interface
circuit(s). Although the module may be depicted in the present disclosure as
logically communicating directly with other modules, in various
implementations
the module may actually communicate via a communications system. The
communications system includes physical and/or virtual networking equipment
such as hubs, switches, routers, and gateways. In some implementations, the
communications system connects to or traverses a wide area network (WAN)
such as the Internet. For example, the communications system may include
multiple LANs connected to each other over the Internet or point-to-point
leased
lines using technologies including Multiprotocol Label Switching (MPLS) and
virtual private networks (VPNs).
[0075] In various implementations, the functionality of the module may be
distributed among multiple modules that are connected via the communications
system. For example, multiple modules may implement the same functionality
distributed by a load balancing system. In a further example, the
functionality of
the module may be split between a server (also known as remote, or cloud)
module and a client (or, user) module.
[0076] The term code, as used above, may include software, firmware, and/or
microcode, and may refer to programs, routines, functions, classes, data
structures, and/or objects. Shared processor hardware encompasses a single
microprocessor that executes some or all code from multiple modules. Group
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Date Recue/Date Received 2020-05-11
processor hardware encompasses a microprocessor that, in combination with
additional microprocessors, executes some or all code from one or more
modules.
References to multiple microprocessors encompass multiple microprocessors on
discrete dies, multiple microprocessors on a single die, multiple cores of a
single
microprocessor, multiple threads of a single microprocessor, or a combination
of
the above.
[0077] Shared memory hardware encompasses a single memory device that
stores some or all code from multiple modules. Group memory hardware
encompasses a memory device that, in combination with other memory devices,
stores some or all code from one or more modules.
[0078] The term memory hardware is a subset of the term computer-readable
medium. The term computer-readable medium, as used herein, does not
encompass transitory electrical or electromagnetic signals propagating through
a
medium (such as on a carrier wave); the term computer-readable medium is
therefore considered tangible and non-transitory. Non-limiting examples of a
non-
transitory computer-readable medium are nonvolatile memory devices (such as a
flash memory device, an erasable programmable read-only memory device, or a
mask read-only memory device), volatile memory devices (such as a static
random access memory device or a dynamic random access memory device),
magnetic storage media (such as an analog or digital magnetic tape or a hard
disk
drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).
[0079] The apparatuses and methods described in this application may be
partially or fully implemented by a special purpose computer created by
configuring a general purpose computer to execute one or more particular
functions embodied in computer programs. The functional blocks and flowchart
elements described above serve as software specifications, which can be
translated into the computer programs by the routine work of a skilled
technician
or programmer.
Date Recue/Date Received 2020-05-11
[0080] The computer programs include processor-executable instructions that
are stored on at least one non-transitory computer-readable medium. The
computer programs may also include or rely on stored data. The computer
programs may encompass a basic input/output system (BIOS) that interacts with
hardware of the special purpose computer, device drivers that interact with
particular devices of the special purpose computer, one or more operating
systems, user applications, background services, background applications, etc.
[0081] The computer programs may include: (i) descriptive text to be parsed,
such as HTML (hypertext markup language), XML (extensible markup language),
or JSON (JavaScript Object Notation), (ii) assembly code, (iii) object code
generated from source code by a compiler, (iv) source code for execution by an
interpreter, (v) source code for compilation and execution by a just-in-time
compiler, etc. As examples only, source code may be written using syntax from
languages including C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp,
Java , Fortran, Perl, Pascal, Curl, OCaml, JavaScript , HTML5 (Hypertext
Markup Language 5th revision), Ada, ASP (Active Server Pages), PHP (PHP:
Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash ,
Visual
Basic , Lua, MATLAB, SIMULINK, and Python .
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