Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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LARGE-SCALE CUSTOMER-PRODUCT RELATIONSHIP
MAPPING AND CONTACT SCHEDULING
CROSS-REFERENCE TO RELATED APPLICATION
[00011 This application claims the benefit under 35 U.S.C. 119(e) of
U.S.
Provisional Patent Application No. 62/067,328, entitled "Rule-Based
Probabilistic Algorithm
for Large-Scale Non-Linear Customer-Product Relationship" filed October 22,
2014, the
entire contents of which are incorporated herein by reference.
BACKGROUND
100021 The present disclosure relates to mapping customer-product
relationships.
[0003] Customer-product mapping is an industry-related question that many
are
trying to answer with limited success. In particular, so far existing
solutions have not been
able to effectively (a) identify the individual customers/groups who are
potential buyers and
prioritize them for reaching out, (b) identify the customer base which cannot
be influenced to
make a purchase (regardless of the amount of effort exerted by the seller),
(c) map the
customers who have been identified as potential buyers and prioritized in a
list to the exact
products that are being sought after, and (d) facilitate customer reach-out
efforts based on the
consolidation of past purchases, potential purchases, proposed products, and
store inventory.
[0004] Moreover, in many cases, the mappings that are provided by existing
solutions
are full of errors, which can translate into an uninteresting product being
presented to the
user. This in turn leads to disengagement and divergence from a customer
transaction.
100051 As a result, many industry giants are using ineffective and
inefficient vendor-
provided methods for matching products and customers. These methods are
ineffective at
least in part because of the lack of integration between statistical,
probabilistic, and data
engineering efforts. These methods are only capable of considering a limited
amount of data,
such as only the product browsing cycles, to suggest particular products.
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100061 Other limitations and obstacles have prevented an effective
solution to this
problem from being developed include the cost of large-scale training of
individuals (e.g.,
account managers) for an effective implementation, management requirements and
constraints, and process security.
SUMMARY
[00071 The innovative technology disclosed in this document addresses the
above-
noted limitations.
100081 In one innovative aspect, the technology includes a computer-
implemented
method comprising retrieving from a data store a customer product-class
mapping that maps
a customer identifier of each of a multiplicity of customers to I) a customer
tier, 2) a product
class interacted with by the customer via one or more of an online sales
channel, a retail sales
channel, and a phone sales channel, and 3) a plurality of variables
characterizing an
interaction of the customer associated with the customer identifier with the
product class via
the one or more of the online sales channel, the retail sales channel, and the
phone sales
channel; generating a predictive score for each unique combination of the
customer identifier,
the customer tier, and the product class using one or more of predetermined
online sales
channel rules, predetermined retail sales channel rules, and predetermined
phone sales
channel rules, respectively, and the plurality of variables; selecting a first
set of customers
from the multiplicity of the customers based on the predictive score and a
revenue generated
from the each unique combination; generating a schedule for contacting the
first set of
customers based on one or more of the predictive score and the revenue; and
providing the
schedule to a client device of a stakeholder for use by the stakeholder in
contacting the first
set of customers.
[00091 In another innovative aspect, the technology includes a computer
system
comprising one or more computer processors and one or more computer memories
storing
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instructions that, when executed by the one or more computer processors, cause
the computer
system to perform operations including: retrieving from a data store a
customer product-class
mapping that maps a customer identifier of each of a multiplicity of customers
to 1) a
customer tier, 2) a product class interacted with by the customer via one or
more of an online
sales channel, a retail sales channel, and a phone sales channel, and 3) a
plurality of variables
characterizing an interaction of the customer associated with the customer
identifier with the
product class via the one or more of the online sales channel, the retail
sales channel, and the
phone sales channel; generating a predictive score for each unique combination
of the
customer identifier, the customer tier, and the product class using one or
more of
predetermined online sales channel rules, predetermined retail sales channel
rules, and
predetermined phone sales channel rules, respectively, and the plurality of
variables;
= selecting a first set of customers from the multiplicity of the customers
based on the
predictive score and a revenue generated from the each unique combination;
generating a
schedule for contacting the first set of customers; and providing the schedule
to a client
device of a stakeholder for use by the stakeholder in contacting the first set
of customers.
[0010] Other aspects include corresponding systems, methods and
apparatus,
including computer program products. It should be noted that the language used
in the
present disclosure to describe these systems, methods and apparatus has been
principally
selected for readability and instructional purposes, and not to limit the
scope of the subject
matter disclosed herein.
[0011] One or more of the embodiments described can also include
the following
features: aggregating customer data associated with each customer of the
multiplicity of the
customers from one or more data stores storing data related to the online
sales channel by
retrieving, from the one or more data stores, first product identifiers
reflecting products
purchased by the customer using the online sales channel, retrieving, from the
one or more
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data stores, second product identifiers reflecting products that were added to
a virtual cart that
was subsequently abandoned without having purchased the one or more products,
the virtual
cart being associated with the online sales channel, retrieving, from one or
more data stores,
third product identifiers reflecting product pages of products that were
viewed by the
customer but were not purchased using the online sales channel, generating an
entry for the
customer product-class mapping for the customer identifier associated with the
customer
using the first product identifiers reflecting the products purchased by the
customer, the
second product identifiers reflecting the products added to the virtual cart,
and the third
product identifiers reflecting the products that were viewed by the customer
but were not
purchased, and storing the entry in an aggregated data store storing the
customer product-
class mapping; determining product classes for the products associated with
the first product
identifiers, the second product identifiers, and the third product
identifiers; retrieving
customer tier data describing the customer tier of the customer; and
categorizing the customer
by each unique product class and customer tier combination; that the plurality
of variables
characterizing the interaction of the customer regarding a particular product
includes one or
more of browsing for product price, browsing for product features, browsing
for product
reviews, comparing the product with other products, and reviewing product
details; that
generating the predictive score for the each unique combination of the
customer identifier, the
customer tier, and the product class using the predeteimined online sales
channel rules and
the plurality of variables comprises: estimating time spent by the customer on
each of the
plurality of variables associated with a particular product that the customer
has indicated
interest in using the online sales channel; allocating weights to the time
spent by the customer
on each of the plurality of the variables based on the predetermined online
sales channel
rules; and generating the predictive score for the each unique combination of
the customer
identifier, the customer tier, and the product class based on the weights
allocated to the time
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spent by the customer on each of the plurality of variables; determining, for
the customer
identifier of each customer of the multiplicity of the customers, a visit
cadence score based on
a number of visits by the customer to one or more pages of one or more
products;
determining, for the customer identifier of each customer of the multiplicity
of the customers,
a purchase cadence score based on product purchase history associated with the
customer
identifier; determining, for the customer identifier of each customer of the
multiplicity of the
customers, a revenue cadence score customer cadence score based on amount of
revenue
generated from purchases by the customer; combining, for the customer
identifier of each
customer of the multiplicity of the customers, the visit cadence score, the
purchase cadence
score, and the revenue cadence score into an overall cadence score for the
customer
identifier; selecting a second set of customers from the multiplicity of the
customers based on
overall cadence scores associated with the second set of customers; injecting
the second set of
customers into the schedule, which includes the first set of customers;
selecting a second set
of customers from the multiplicity of the customers based on one or more of a
visit cadence
score, a purchase cadence score, and a revenue cadence score; injecting the
second set of
customers into the schedule, which includes the first set of customers that
the first set of
customers have higher priority than the second set of customers and the
schedule is sorted
based on the higher priority; registering a plurality of stakeholder accounts
for a plurality of
stakeholders including the stakeholder, each stakeholder of the plurality of
stakeholders being
assigned to a group of customer identifiers associated with customers;
selecting for each
stakeholder account of the plurality, a first subset of customer identifiers
from the group of
customer identifiers assigned to that stakeholder account based on 1) a
predictive score for
each unique combination of each customer identifier, customer tier, and
product class using
one or more of the predetermined online sales channel rules, the predetermined
retail sales
channel rules, and the predetermined phone sales channel rules, respectively,
and the plurality
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of variables, and 2) a revenue generated from each unique combination, wherein
selecting
includes selecting the first set of customers from the multiplicity of the
customers based on
the predictive score and the revenue generated from the each unique
combination; selecting
for each stakeholder account of the plurality, a second subset of customers
identifiers from
the group of customer identifiers assigned to that stakeholder based on
cadence scores
associated with the customer identifiers of the second subset; generating, for
each stakeholder
account of the plurality, a schedule for contacting certain customers of the
group of
customers associated with the stakeholder account based on a combination of
the first subset
of customers and the second subset of customers associated with the
stakeholder account;
providing, to a client device of each stakeholder account of the plurality,
the schedule
generated for that stakeholder account; aggregating customer data of a
plurality of customers
from an online data store reflecting the online sales channel, a retail data
store reflecting the
retail sales channel, and a call center data store reflecting the phone sales
channel;
statistically analyzing the aggregated customer data of the plurality of
customers to determine
the multiplicity of customers from the plurality of customers whose interest
in products
offered by a business merchant fall within a certain interval; aggregating
customer data
associated with each customer of the multiplicity of the customers from one or
more data
stores storing data related to the retail sales channel and the phone sales
channel by:
determining a total number of orders associated with the customer, determining
a number of
items included in each of the orders; determining revenue generated by each of
the orders,
determining revenue generated by each item included in each of the orders,
determining any
discounts applied to each of the orders; generating an entry for the customer
product-class
mapping for the customer identifier associated with the customer using the
total number of
orders associated with the customer, the number of items included in each of
the orders, the
revenue generated by each of the orders, the revenue generated by each item
included in each
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of the orders, and any discounts applied to each of the orders, and storing
the entry in an
aggregated data store storing the customer product-class mapping.
[0012] The innovative technology also includes various methods and
apparatus,
including computer program products, as well as other aspects. The innovative
technology
described is particularly advantageous in a number of respects. For example,
it provides the
capability to list out the customers on the basis of their prominence and
potential to buy.
Having identified the individuals and groups of customers, it uses a unique a
frequency
interval for repeating the communication process. The communication process
involves
automated, algorithmically-chosen, highly-probable products that the customer
may buy
depending on the behavioral pattern shown by past buyers and product
researchers.
[0013] The technology achieves the long-time, multi-fold, pending goals
unattainable
by others in the industry. In particular, the invention identifies the
individual
customers/groups who are potential buyers and prioritizes them for reaching
out. The
invention includes an algorithm that identifies the customer base which cannot
be influenced
with any effort to make a purchase. The invention connects the exact products
that are being
sought after to the customers, who have been identified as potential buyers
and prioritized in
a list. Finally, the invention provides and consolidates the past purchases,
potential
purchases, proposed products, and store inventory to be pursued, into a multi-
dimensional
mapping to facilitate customer reach-out efforts.
10014] The technology also includes various algorithms and automated
processes for
continuous simulation and identification of the delta changes with next to
negligible manual
cumbersome effort to dig the data. It also includes a method of analyzing
product browsing
cycles and suggesting the particular products. The present invention uses a
rule-based
algorithm that looks at non-linear patterns and finds specific thresholds, and
uses heuristics to
determine the appropriate time to approach the customer with his/her preferred
products.
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100151 The above list of features and advantages is not all-inclusive and
many
additional features and advantages are within the scope of the present
disclosure. Moreover,
it should be noted that the language used in the present disclosure has been
principally
selected for readability and instructional purposes, and not to limit the
scope of the subject
matter disclosed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The present disclosure is illustrated by way of example, and not by
way of
limitation in the figures of the accompanying drawings in which like reference
numerals are
used to refer to similar elements.
[0017] Figure I is a block diagram of an example system for large-scale
customer-
product relationship mapping and contact scheduling.
[0018] Figures 2A-2C are flow diagrams of the example system.
[0019] Figure 3 is a block diagram of a method 300 for customer reach-out
based on a
generated schedule.
[0020] Figure 4 is a flowchart of an example method 400 for generating and
providing schedule.
[0021] Figure 5A is a flowchart of an example method for aggregating
customer data
associated with each of a multiplicity of the customers from one or more data
stores (e.g.,
databases, web servers, etc.) storing data related to the online sales
channel.
10022] Figure 5B is a flowchart of an example method for aggregating
customer data
associated with each of a multiplicity of the customers from one or more data
stores (e.g.,
databases, web servers, etc.) storing data related to the retail and phone
sales channels.
100231 Figure 6 is a flowchart of an example method for categorizing a
multiplicity of
customers by each unique product class and customer tier combination.
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[0024] Figure 7 is a flowchart of an example method for generating a
predictive
score.
[0025] Figure 8 is a flowchart of an example method for providing a
customized
schedule to a stakeholder.
[0026] Figure 9 is a flowchart of an example method for generating a
schedule for
contacting a high-value subset of customers associated with each stakeholder.
[0027] Figure 10 is a block diagram of an example system for large-scale
customer-
product relationship mapping and contact scheduling.
[0028] Figure 11 is a block diagram of an example computing system.
DETAILED DESCRIPTION
[0029] Figure 1 is a block diagram of an example system 100 for large-
scale
customer-product relationship mapping and contact scheduling. Figure 1
includes a scoring
engine 200, a scheduler 201, an aggregator 202, and a priority engine 203, and
various other
components such as a UI module 148, a SKU data store 150, and/or a CRM 158,
which are
coupled for communication with one another.
[0030] The aggregator 202 is executable to aggregate and processing raw
data, and
store the aggregated data for retrieval. The aggregator 202 accumulates the
data from various
information sources. In some embodiments, the information sources may include
retail
purchase data source(s), such as the retail purchase data store 102, and raw
browse data
source(s) such as raw browse data store 108. The retail purchase data store
102 stores data
reflecting purchases by customers in physical stores using rewards accounts.
In some
instances, the retail purchase data may be stored by point-of-sale systems
and/or other
systems associated with retail sales. The retail purchase data may describe
the product
identifiers of the products that were purchased, the date and time the
purchases were made,
the customer identifiers associated with the purchases, and/or any other
related data, etc. The
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raw browse data store 102 stores data reflecting customers' actions in
interacting with an
online sales channel. The online sales channel is embodied by an e-commerce
application
providing a virtual market place associated with a merchant (e.g., Staples) to
customers to
browse and purchase products. The virtual market place is, in some
embodiments, linked to
the retail and phone sales channels offered by the same merchant.
[00311 The retail data aggregator is executable to aggregate the retail
purchase data,
standardize it, and store it into the aggregated retail data store 106 for
efficient access and/or
retrieval by the schedule module 118, as discussed in further detail elsewhere
herein. The
schedule aggregator 110 and the scoring aggregator 112 may each aggregate and
standardize
raw browse data from the raw browse data store and store in a corresponding
aggregated data
store (e.g., the data stores 114 and 116, respectively), as discussed in
further detail elsewhere
herein.
[00321 By way of further example, the data collection process by the
aggregator 202
performed for the online, retail, and/or phone sales channels (e.g., online,
store-based, and
phone customers). For all channels, the aggregator 202 aggregates information
such as
customer IDs, customer tier (e.g., reward tier), products bought over certain
timeframe (e.g.,
over last 5/10/15 orders, during a certain period of time, etc.), repeat flag
(reflecting if
multiple repeat purchases have been made), etc. The timeframe of the purchases
is an
indicator of the frequency of the repetitions.
10033] Further, for the online sales channel, the aggregator 202
aggregates data
describing products purchased, products not bought but added to a virtual
cart, and products
not bought but viewed, etc. A "Product View" interaction and an "Add To Cart
(ATC)"
interaction have established definitions in the data aggregation process, and
the dates on
which a given customer viewed products and/or added products to cart are vital
signs that are
used to optimize the schedules generated herein.
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[00341 Regarding the retail and/or phone channels, the aggregator 202
aggregates
purchase data reflecting discounts available on products bought over a certain
timeframe
(e.g., last 5/10/15 orders, during a certain period of time, etc.), any
product identifiers
describing cross-sell products related to previously purchased products, any
product
identifiers reflecting supplies and accessories related to previously
purchased products, etc.
For any missing data, the aggregator 202 may aggregate product identifiers
describing related
products, cross-sell products, supplies and accessories in the same product
class, etc.
[00351 In some embodiments, the data is correlated by the aggregator 202
with the
applicable customer identifiers reflecting the customers that performed the
actions. In some
embodiments, only data meeting certain action thresholds (e.g., page views,
purchases, etc.)
is aggregated, although other variations are also possible and contemplated.
In addition, in
some embodiments, certain product criteria may be required to be met in order
for data about
that product to be aggregated to reduce the time and intensity of the
processing that must be
performed by the aggregator 202, especially considering that thousands,
hundreds of
thousands, etc., of product may be processed. For example, a SKU may need to
have a
minimum price to be aggregated, or may need to belong to a certain SKU
category.
10036] The aggregator 202 may standardize the raw data (e.g., format,
classify,
stagger, group, etc.) aggregated by it and stage it for retrieval by the
scoring engine 200
and/or the scheduler 201. By way of further example and not limitation, during
the
aggregation phase, the aggregator 202 calculates visits by a customer
(reflected by that
customer's identifier) to product pages associated with products. In some
embodiments,
absent page view data, as a heuristic the aggregator 202 may consider the
number of orders
by a customer identifier as visits and the number of product purchases
(independent and
repeat) as product views (e.g., during a certain period), and store/provide
them as such as
aggregated data for retrieval by the scoring module 132.
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[0037] The aggregator 202 is coupled for communication to the other
components of
the system 100, including the scheduler 201, the scoring engine 200, the CRM
158, etc.
[00381 The scoring engine 200 is executable to generate and update data
for the
customer product-class mapping for use by the priority engine 203 and/or
scheduler 201, as
discussed elsewhere herein. In generating data for the mapping, the scoring
engine 200 may
process the aggregated data and scores for unique combinations ofzustomer
identifiers,
customer tiers, and product classifications. The scores are generated for each
of the sales
channels based on unique rules associated with each of those channels (e.g.,
retail, online,
and phone).
[0039] The scoring engine 200 may perform data preparation on the
aggregated data
in preparation for generating the scores for each channel. In some
embodiments, the scoring
engine 200 includes a scoring data preparation module 122 that retrieves data
from the retail
purchase data store 102, the aggregated data store for scoring 116, the legacy
orders data
store 120, and the shared orders data store 124. For each channel (e.g.,
browse, retail, and
phone), the scoring data preparation module 122 may standardize the data for
processing by
the scoring module 132.
[0040] In some embodiments, using the retrieved data, the scoring data
preparation
module 122 generates a list of all the products bought by each customer with
dates (e.g., by
ordered date, shipped date, delivery date, etc.) and products that have not
been bought but
added to cart and/or viewed (e.g., by descending product views). The scoring
data
preparation module 122 may store each set of prepared data in a corresponding
data store.
For example, the scoring engine 200 may store the prepped browse data in the
browse data
store 126, the prepped retail data in the retail data store 128, and the
prepped phone data in
the phone data store 130.
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[00411 The scoring module 132 is executable to generate, for each unique
combination of customer ID, customer tier, and product class, a score for each
sales channel,
as discussed in further detail elsewhere herein, such as with respect to
Figure 4. The scoring
module 132 is coupled to the browse, retail, and phone data stores 126, 128,
and 130 to
retrieve the data prepped by the scoring data preparation module 122. The
scoring module
132 uses this data. to compute the various scores.
- [0042] In some embodiments, for the online sales channel, the scoring
engine 200
may generate the scores using the below equations. In particular, the scoring
data preparation
module 122 may prepare the data by performing brute force analytics of laying
out scatter
plots to look for outliers, trends, and biases. The scoring data preparation
module 122 may
use a residual sum of squares (RSS) method to select a certain relevant
portion of the data.
For instance, the method may select the customers that fall within a certain
closeness to mean
(e.g., mean and confidence interval, such as 2-sigma, 95% CI). In this
example, the method
selects 95% of the mined data thus deleting the bottom and top 2.5%
respectively.
[0043] Once the data has been prepped, the scoring module 132 compute the
scores
by modeling the relationship between the input variables and the output
function by
computing coefficients of an equation and identifying the relationship between
products
purchased and the browsing purpose (e.g., price, features, details,
comparisons, reviews,
number of offerings similar to the product, visits, views, etc.).
[0044] Example variables of the equation may include:
y = Number of units of a particular product ordered;
x1 = Time spent on browsing price;
X2 = Time spent on browsing for features;
x3 = Time spent on browsing for reviews;
x4= Time spent on comparing products;
xs = Time spent on product details page; and
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E = Random error.
[0045] The above question may in some cases take the form of the following
consolidated linear equation:
y =bp+ bi*xi + b2*x2+ b34`x3+ b4*x4+ bs*xs + E
[0046] In the above question, coefficients 1)0, b1, etc., may be derived
for a multiple
linear regression (MLR) model. By way of further example, the coefficients may
be derived
using "R" code and the following equations:
Zin-i(x10)
b ¨ xid * Oro) ¨ y
1 ,)
- _____________________________________________
Zin xip)2
a1(x20) x2m) * (Y(i) - Y )
b2 ¨
=1(x2 (O x2 )2
[0047] The model in some instances may begin with a single linear
regression model
that considers the response of each input variable on the total orders (y)
with calculation of
intercepts and slopes as follows:
y =1301 + bi*xi
y = b02+1:42*x2
[0048] The random error may be computed as follows:
= Er-1 (Y(i) Yp)2
[0049] Intercepts may be calculated as follows:
bo, = y ¨ [ b1 * X1 + El
b02 = y ¨ [b2 * xz +
[0050] The scoring module 132 may apply a null hypothesis of the
coefficients for
each variable (Ho: b1 = b2 = = 0), in which case if Null is rejected then
the alternate
hypothesis stands accepted. Using the above equations, the F-statistic test
would turn out to
CA 02909957 2015-10-22
be close to "1" if there is no relationship between response and predictor.
Otherwise it would
be much greater than I.
[0051] The RSS may be computed using the following equation:
RSS = ¨ y(0))2
[00521 In the above equation, y(i) = actual observation and y(0) =
calculated line (or
plane) value using the linear regression equation.
[00531 The scoring module 132 may group the customer ids into unique
product-class
customer-tier groups and then compute scores for each unique combination and
channel. The
scoring module 132 may process the data associated with each of the product-
class customer-
tier groups to generate a score. By way of example and not limitation, a
customer associated
with a customer ID may belong to a Gold customer tier and purchase a product
in a product
class #500, and the scoring module 132 generates a unique score for that
product-class,
customer tier, customer ID combination based on the variables discussed
herein. The scoring
module 132 retrieves a set of customer rules associated with the channel that
it is generating a
score for, and applies the rules to the relevant data when generating the
score.
[0054] In some embodiments, the scoring module 132 may include specialized
"R-
code" adapted to each of the product-class customer-tier groups and executed
for that
particular group's dataset, which allows for the possibility of migration of a
customer ID to
another unique combination (e.g., a Gold rewards customer becomes a Platinum
rewards
customer (and possibly begins purchasing products with higher margin of
profit)).
100551 As a further example, each product class may have a customer group
for an
account manager customer tier (e.g., Gold, Platinum, etc.). In this example,
the group
designation combines the product class ID "500" with the reward tier
designation "Platinum"
(e.g., Group name = "500-Platinum"). Based on the data accumulated, the
scoring module
132 includes an equation (e.g., linear, non-linear, etc.) that is
representative of its character.
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As discussed further herein, the system 100 may promote, recommend, etc.,
products
associated with each unique group based on the findings of the product
purchase criteria and
customer buying pattern(s) identified from the output of the scoring module
132 algorithm.
[00561 In some embodiments, when generating the scores, the scoring module
132
establishes a relationship of each variable with the response function. The
scoring module
132 may calculate an F-statistics value. If this value is very high
corresponding to the p-
value, then it will result in rejection of NULL hypothesis and the acceptance
that a
relationship exists. The strength in relationship between the variable,
(predictor) and
response function, is indicated by calculating the RSE and adjusted R2
explaining the
variances. The contribution of each predictor to the response function is
examined by t-
statistics. To determine the largest contributor, the scoring module 132 may
perform a single
linear regression. The scoring module 132 may further identify the synergy
among the
predictors. Non-linear terms along with interaction may often end up giving
the lowest p-
value. The MLR equation with lowest p-value thus gets accepted as the
likelihood function
for the product-consumer group in consideration.
[00571 The scoring engine 200 may limit the data processed to a certain
period of
time. In an example, 38 days of sales data may be substantial for developing
rule-based
product mapping model for retail customers, although it should be understood
that a different
date range (e.g., past two weeks, 30, 60, 90, 180, past year, etc.) may also
be used.
[0058] The priority engine 203 is executable to determine the priority of
the
customers based on the scores generated by the scoring engine 200. The
priority engine 203
includes a priority module 142 and a top scores data store 144. The priority
module 142 is
executable to combine the browse, retail, and phone score(s) associated with
each unique
combination into a total score. The priority module 142 may retrieve the given
scores from
the browse, retail, and phone scores databases 136, 138, and 140, and combine
them into a
CA 02 90 9957 2 015 ¨ 10-22
total score where applicable, select the total scores meeting a certain
threshold as top scores,
and store the top scores into a top scores database 144.
[0059] In some embodiments, the priority module 142 evaluates whether,
for a given =
unique product-class customer-tier combination, a score exists for more than
one channel,
and if so, combines the scores into a total score. The priority module 142 may
access
information about a product class from the SKU data store 150. In some
embodiments, if a
customer identifier has transactions in two or three of the sales channels,
and the scoring
engine 200 applies a predefined precedence to sort the transactions. For
example, the scoring
=
engine 200 may apply the following order: online interactions > cross-sell
interactions >
interactions involving supplies and accessories. In other embodiments, other
sorting
algorithms may be used. If a score exists for a single channel only, the
priority module 142
uses that score as for the total score and evaluates it as a top score based
thereon.
[0060] In some embodiments, the priority module 142 sums a given list
generated
from the online, retail, and/or phone channels and sorts select entries from
the customer
product-class mapping in a particular order (e.g., descending order). The
sorting may be
performed per the score for the product classes and revenue generated in
recent past, as
discussed elsewhere herein.
10061] Entries from an example customer-product class mapping are
provided below,
although it should be understood that the mapping may take other forms and
include
additional or other data and may be stored in single data store or distributed
across data
stores.
Group Customer Time r Tiers Units Total Total
Total Visits Product Page Cart Orders Score
(tier/class) identifier spent revenue revenue visits
views views adds
in
group
GOLD-568 5346563314 211 G10 0 0 466.03 152 1. 2 2 0 0 1
GOLD-568 5368109244 15 610 0 0 0 1 1 2 2 0 0 2
GOLD-568 5373990521 249 P65 1 27.36 27.36 12 2 6 10 1 1 1
GOLD-568 5385707244 66 610 1 20.59 23653 2 2 1 2 1 1 1
GOLD-568 5387115636 0 P65 0 0 339.13 16 1 1 1 0 0 1
GOLD-568 5395084527 482 G10 1 11.99 403.14 50 2 3 10 0 1
2_
CA 02 90 9957 2015-10-22
GOLD-568 5605719201 61 610 1 7.99 151.27 1. 1 1 2
1 1 2-
_
6OLD-568 5669183690 31 P55 0 0 471.35 10 1 1, 1
0 0 2
GOLD-568 5755845277 6 610 0 _ 0 220.56 2 _ 1 0 1
0 0 2
6OLD-568 5759266520 373 610 0 0 92.38 1 1 4 4 0 0
0
-
GOLD-569 23798028 89 P65 0 0 322.11 3 1 0 1
0 0 1 -
GOLD-569 2109251708 4 G10 _ 0 0 173.85 20 1 0 0
0 0 -4
601D-569 2379020973 145 P65 0 0, 382.73 5 2 1 4
2 0 0
, _
GOLD-569 2712237177 18 610 0 0 96.41 3 1 0 1 0 0
0
_ ,
6010-569 2720989454 . 90 PG5 0 0 416,23 10 . 1 4
4 0 0 -1
GOLD-569 2843758968 26 _ 510 0 0 431.95 1 _1 1 1 0
0 -
1
GOLD-569 2851566758 18 610 0 0 199.61 1 1 1 1 0 0
0
GOLD-569 2901112629 0 , 610 0 0 97.56 9 1 1 1 0
0 -
-1
GOLD-569 3486587946 97 PG5 - 0 0 372.33 66 , 1 4 , 4
, 0 0 -1
GOLD-569 _4025389505 73 610 . 0 _0 394.68 4 1 3 3
0 0 0
_
6010-569 4047523735 12 _ 610 0 0 199.27 14 1 0 1 0
0 , 0
GOLD-569 4075867574 49 P65 1 33.39 , 174.8 1 1 1
.2 1 1 1 _
_
5010-569 5332520229 320 610 4 119.96 379.75 4 1 4 12
2 2 1 .
- ,
6010-570 219655529 0 , P65 _ 0 0 321.05 33 1 1
1 0 0 5
GOLD-570 553340589 0 610 0 0, 0 1 1 1 1 . 0
0 5
6010-570 1998227589 439 P65 0 0 . 382.9 2 2 4 6 1
0 5
GOLD-570 2001004734 32 610 0 0_ 1338.35 97 1 1
, 1 , 0 0 _ 5
_
GOLD-570 2165236791 102 610 r 0 - 0 133.98 1 1 2 0
0 0 5
_
GOLD-570 2721101174 32 G10 0 0 0 3 1 1 1 _ 0
0
,
5 ,
6OLD-570 3455954127 160 610 1 19,19 228.4 58 2 7_ 8
1 , 1 5
-
GOLD-570 3956448819 106 P65 00 2207.13 12 1 1 1
1 0 5
6010-570 4017357569 10 ' 610 0 - 0 0 3 1 1 1 0
0 5
60L0-570 4052140748 94 510 0 0 666.59 _ 43 1 1 1
0 0 5
_
- _
GOLD-570 4089797148 0 610 0 0 83.07 5 1 1 1 0
, 0 5
6010-570 4193300987 90 610 G 0 387.12 26 1 3 3
0 0 5
-
GOLD-570 4205883582 21 510 0 o _ 229.99 5 1 1 1 0
0 . 5 _I
GOLD-570 4217988247 0 P65 0 _ 0 67.73 2 1 1 1 0
0 5
GOLD-570 5675366081 116 , P65 _ 0 0 360.48 15 , 1 2 2
. 0 0 5
GOLD-570 5677524406 288 G10 0 0 0 2 , 1 6 10 1
, 0 5
_
GOLD-571 2482099 120 P65 4 26.76 26.76 2 2 1 8 3 2
0
6010-571 4666509 88 610 1 23.99 204.07 85 2 1
2 1 1 2
=4-
6OLD-571 8584856 31 610 0 247.24 9 1 1 1 0 0
3
GOLD-571 8595969 6 PGD - 0 .. 0 - 0 81.98 23 1 1
1 0 0 3
GOLD-571 11182946 41 G10 _ 0 _0 313.46 13 1 2 2
0 0 1
GOLD-571 11958279 22 P65 0 , 0 354.23 6 , 1 2 2
0 0 1
_
6010-571 12588810 1009 P60 _ 0 0 25135 23 , 1 1
1 0 0 2
_
GOLD-571 13438296 99 PGS___ _ 1 7.09 44.66 1 1 0
2 0 1 2
_
.
6010-571 21940077 73 610 1 12.39 170.52 7 _ 2 2 3
1 1 1
GOLD-571 23732704 167 510 20 99.8 209.25 1 1 2 6 1 1
0
[00621 The scheduler 201 is executable to generate a schedule for each
registered
stakeholder (e.g., account manager). The scheduler 201 includes the schedule
module 118,
the config data store 156, the priority classes data store 154, and the
schedule data store 152.
The schedule module 118 is executable to generate a schedule including ranked
list(s) of
customer identifiers for each stakeholder. The schedule module 118 stores the
data generated
by it into the schedule data store 152 and/or provides it to another module,
such as the III
module 148.
.._
CA 02909957 2015-10-22
10063] The schedule module 118 can evaluate the cycle of purchasing by
each
customer identifier (representing a customer) to determine optimal timing for
contacting a
certain customer. A customer may be contacted via a variety of means, include
by phone, via
electronic message (e.g., email, chat, SMS, etc.), etc. In some embodiments,
the scheduler
188 can evaluate historical data associated with each customer identifier
including frequency
of the purchases associated with the customer identifier.
[00641 The schedule module 118 generates, for each stakeholder identifier,
a schedule
describing ranked list of customer identifiers based on the top scores 144.
The schedule
module 118 may retrieve data about the customer identifiers (e.g., accounts)
and the
stakeholder identifier (e.g., profile) from the CRM 158. In some embodiments,
the schedule
module 118 retrieves aggregated transactional data for browse, retail and
phone channels
from one or more information sources, such as the data store 114 and 106, for
example. The
schedule module 118 may prune the data retrieved by it to remove noise,
outliers, etc.
100651 In some embodiments, the schedule module 118 determines a cadence
for each
customer identifier/account. For instance, for each account, the schedule
module 118 may
determine visit, purchase and revenue cadence scores, and then combine the
three scores to
an overall cadence score using a sigmoidal function, as discussed elsewhere
herein.
Operations for computing cadence scores and selecting customer identifiers
based there is
discussed elsewhere herein, such as with respect to Figure 8.
[0066] The schedule module 118 may determine the schedulable accounts for
each
stakeholder identifier (e.g., account manager) based on customer rules
retrieved from the
customer rules data store 134. The customer rules may define the business
rules unique to
each customer account and the schedule module 118 may apply the rules to
generate the
schedule for a given account manager. By way of example, for each account
manager, the
schedule module 118 may generate and provide a list of accounts including one
or more of a
CA 02909957 2015-10-22
first segment of accounts determined based on the top scores generated by the
priority
module 142, and a second segment of accounts determined based on the cadence
scores. In
implementations involving both segments, the schedule module 118 may selects a
first set of
customers based on priority and a second set of customers from the
multiplicity of the
- customers based on one or more of a visit cadence score, a purchase cadence
score, and a
revenue cadence score and injects the second set of customers into the
schedule, which
includes the first set of customers. In further example, the schedule module
118 may
interweave the segments based on one or more rule and cadence strategies.
[0067] By way of further example and not limitation, the business needs
of a
merchant may drive the persuasion of specific products with potential
customers. The
scheduler 201 is configured to provide curated lists of customers to for
display on client
devices of account managers. The curated lists are generated to include
customers with the
highest propensity to satisfy the particular business need (e.g., purchase
products from certain
product classes, etc.). For instance, assume the current business need is to
sell furniture
products for next 8 weeks.
100681 The schedule module 118 may execute the following sequential
process to
identify the customers with a persuasion toward furniture. A list of classes
for furniture (for
that matter any business need) is input by and received from stakeholders. For
instance,
using a corresponding interface, a manager may input the furniture product
classes that
should be promoted. The scheduler 201 will retrieve and rank customer
identifiers associated
with 1) the product classes; and 2) the highest scores for the product
classes. The output of
this processing (e.g., customer ID, class ID, and rank score) is fed to a
scheduling algorithm
of the schedule module 118, which evaluates the purchase and browsing cadence
of the
customers, and generates the schedule (e.g., appropriate list) of customers,
which is formatted
and/or transmitted by the UI module 148 to the client device of each of the
account managers.
CA 02909957 2015-10-22
In some instances, a schedule may be transmitted upon receiving a schedule
request, may be
transmitted upon login into an account manager system portal, may be
transmitted via
electronic message according to a certain schedule (e.g., on a daily basis),
etc.
[0069] In some embodiments, the schedule (e.g., including the customer
list) provided
to a given stakeholder (e.g., account manager) may contain the customers
already considered
for the business need at the top (considered list) followed by a list of
customers (consolidated
list) being pursued for the business need based on priority (selected from
high rank, about to
purchase, intent shown through browsing, and falling-off curve). In some
instances, various
business needs are cyclically pursued and the system 100 updates data of the
mapping on a
regular basis for use by the scheduler 118.
[0070] The Ul module 148 receives and replies to content requests. The Ul
module
148 may retrieve schedule data generated by the schedule module 118, retrieve
data from the
SKU data store 150, prepare the schedule customized for a given stakeholder
based on the
retrieved data, and transmit the schedule to the client device of the
stakeholder for display.
[0071] In some embodiments, the UT module 148 is associated with a web
server,
such as the web server 1024 depicted in Figures 10 and 11. The UI module may
provide data
forming an account manager portal associated with the CRM 158. In this
embodiment,
stakeholders can use the account manager portal for customer reach-out and
follow-up with
prescriptions for products and predictions of particular business group
progressions. In some
further embodiments, a stakeholder may use the account manager portal to
access other data
associated with a given customer or customer order, such as the date of
purchase, customer
ID, order number, SKU's in the Order, revenue from the order, revenue from
individual
SKUs, coupons/discounts applied to individual SKUs, coupons/discounts applied
to the
order, discounted or free product attached to purchase of another product,
etc.
CA 02909957 2015-10-22
[00721 It should be understood that the UI module 148 may embody other
communication platforms, such as a messaging or data service (e.g., email,
SMS, chat, API,
etc.). For instance, the UI module 148 or another component of the system
(e.g., the
scheduler 201) may manager and triggering targeted electronic notifications
(e.g., email,
SMS, social media, etc.) with product recommendations to the high-value
customers in the
schedule data at an effective adaptable frequency.
44- .
[0073] In some embodiments, the data presented to a given account manager
has been
aggregated within a particular timefi-ame (e.g., the past 24 hours), or is
real-time or near-real
time (e.g., less than an hour old). In some embodiments, the schedule includes
1) products
that were previously purchased and 2) products that were not bought but added
to cart or not
bought but viewed, with hyperlinks selectable to land on the corresponding
product pages and
make virtual cart additions along with provisions to place orders. Other
variations are also
possible and contemplated.
[0074] The SKU data store 150 is an information source storing SKU pricing
(e.g.,
pricing for the products, such as the list, discount, and/or dynamic price for
each SKU), SKU
categories describe the category(ies) to which a SKU belongs, etc. The
categories may be
hierarchical in nature, and a give SKU may belong to more than one category.
[00751 The CRM 158 reflects a customer relations management information
source
storing the accounts and relationships between one or more vendors and/or
customers. The
CRM 158 may store data associated with sales, marketing, customer service, and
technical
support. The vendor may employ a plurality of individuals (e.g., account
mangers) that are
each responsible for managing certain customer accounts. Data reflecting the
relationship
between the account manager profiles and the profiles of the customer accounts
may be
stored in the CRM 158.
no
CA 02909957 2015-10-22
[00761 Figures 2A-2B are data flow diagrams of the example system 100, and
Figure
3C is a code dependency diagram of the example system 100. As shown in Figure
2A, the
aggregator 202 may retrieve raw browse clickstream data 250 describing user
interactions
with a virtual marketplace, rewards direct purchased items describing products
purchased by
customers by phone, rewards retail purchase items describing products
purchased by
customers in-store, and orris purchased items describing products process by
the order
management system associated with the virtual marketplace.
[00771 The aggregator 202 processes this data into aggregated browse
clickstream
data 258 and CRM account manager data 260, and stores this data accordingly
for use by the
browse scoring module as shown in Figure 2B. The aggregator 202 and processes
this data
outputs config data 262, CRM retail purchase items data 266, CRM oms purchased
items data
268, aggregated scheduler browse data 270, priority classes customer data 272,
and customer
rules data 274, and stores this data accordingly for use by the other
components of the system
100, as shown in Figure 2B.
[0078] Referring to Figure 2B, the browse scoring module 204 receives
aggregated
browse clickstream data 258 and CRM account manager data 260, the phone
scoring module
206 receives oms purchased items data 256, CRM account manager data 260, and
the SKU
category data 276, and the retail scoring module 208 receives retail purchased
items data 286
and SKU categories data 288. Each of these modules 204, 206, and 208 processes
their
respective retrieved data and updates the priority considered classes data
278, SKU categories
data 276, top scores browse data 280, top scores phone data 282, and/or top
scores retail data
284. The priority engine 203 receives and processes this data, and updates
config data 262,
CRM account manager data 260, CRM retail purchase items data 266, CRM oms
purchased
items data 268, aggregated scheduler browse data 270, priority classes
customer data 272,
and/or customer rules data 274. The scheduler receives and uses this data to
generate the
CA 02909957 2015-10-22
schedule 290. In some embodiments, the components 250, 252, 254, 256, 258,
260, 262,
266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, and 290 embody
tables that are
updated by the aggregator 202, scoring engine 200, priority engine 203, and
scheduler 201
based on the operations discussed herein.
[0079] As shown in Figure 2C, the scheduler 201 may include a batch module
214.
The batch module may be dependent upon, and trigger the operation of the
aggregator 202,
the browse scoring module 204, the phone scoring module 206, the retail
scoring module
208, the priority module 210, and the scheduler 212. The browse scoring module
204, the
phone scoring module 206, and the retail scoring module 208 may be dependent
upon and use
the browse rules 216, phone rules 218, and the retail rules 220 to perform
their respective
operations, as discussed in further detail elsewhere herein.
[0080] The methods 300-900 depicted in Figures 3-9, respectively, are now
described. However, it should be understood that the methods 300-900 are
provided by way
of example, and that variations and combinations of these methods, as well as
other methods,
are contemplated. For example, in some embodiments, at least a portion of one
or more of
the methods 300-900 represent various segments of one or more larger methods
and may be
concatenated or various steps of these methods may be combined to produce
other methods
which are encompassed by the present disclosure. Additionally, it should be
understood that
various operations in the methods 300-900 may be iterative, and thus repeated
as many times
as necessary generate the results described herein. Further the ordering of
the operations in
the methods 300-900 is provided by way of example and it should be understood
that various
operations may occur earlier and/or later in the method without departing from
the scope
thereof.
[0081] Figure 3 is a block diagram of a method 300 for customer reach-out
based on a
generated schedule. In block 302, the account manager logs into a CRM and in
block 204,
CA 02909957 2015-10-22
accesses a call list. Responsive thereto, the method 300 displays the call
list in block 306.
The call list reflects the schedule generated using the systems and methods
discussed herein.
The call list may include information relating to who, what, where, when, and
why a
particular customer should be called for a given product or product class.
[0082] In block 308, the method 300 determines whether to call a
particular customer
from the call list (responsive to. a selection by the account manager). If
yes, the method 300
calls the customer in block 310 and the account manager uses the call list for
talking points in
block 312. In block 314, the method 300 logs the call and the routine ends
(e.g., returns to
block 308 or another operation; terminates, etc.). If in block the
determination is negative,
the method 300 receives an account manager's request for more details on
customer in block
316. Responsive thereto, the method 300 retrieves company details containing
browse and
purchase history associated with the customer in block 318, at which point the
method 300
determines whether to call the customer in block 320. If the determination is
affirmative in
block 320, the method 300 proceeds to block 310 and the method 300 proceeds
accordingly.
If the determination in block 320 is negative, the method 300 logs notes input
by the account
manager for a given customer and then ends (e.g., returns to block 308 or
another operation,
terminates, etc.).
[0083] The method 300 may replicate the data stored in the CRM data store
324 in
the scoring and scheduling data store 326. In some embodiments, the scoring
and schedule
data store 326 reflects the data stores described in Figure 1 with respect to
the aggregator
202, the scheduler 201, and/or the scoring engine 200, etc. In addition, data
from other
external data sources 328 may be aggregated into the scoring and schedule data
store 326.
100841 Figure 4 is a flowchart of an example method 400 for generating and
providing a schedule.
CA 02909957 2015-10-22
=
[0085] In block 402, the method 400 retrieves from a data store a
customer product-
class mapping that maps a customer identifier of each of a multiplicity of
customers to 1) a
customer tier, 2) a product class interacted with by the customer via one or
more of an online
sales channel, a retail sales channel, and a phone sales channel, and 3) a
plurality of variables
characterizing an interaction of the customer associated with the customer
identifier with the
product class via the one or more of the online sales channel, the retail
sales channel, and the
phone sales channel. Examples of the variables characterizing the interaction
of the customer
regarding a particular product include browsing for product price, browsing
for product
features, browsing for product reviews, comparing the product with other
products, and/or
reviewing product details, etc.
[0086] In block 404, the method 400, generates a predictive score
for each unique
combination of the customer identifier, the customer tier, and the product
class using one or
more of predetermined online sales channel rules, predetermined retail sales
channel rules,
and predetermined phone sales channel rules, respectively, and the plurality
of variables. A
further example method for generating the predictive score is described herein
with respect to
Figure 7.
100871 In block 406, the method 400 selects a first set of
customers from the
multiplicity of the customers based on the predictive score and a revenue
generated from the
each unique combination.
[0088] In block 408, the method 400 generates a schedule for
contacting the first set
of customers regarding products that the customers of the first set have
indicated interest in.
[00891 In block 410, the method 400 provides the schedule to a
client device of a
stakeholder for use by the stakeholder in contacting the first set of
customers regarding
potential purchases.
CA 02909957 2015-10-22
[0090] As discussed elsewhere herein, such as with respect to the
aggregator 202, the
aggregator 202 may aggregate customer data of a plurality of customers from an
online data
store reflecting the online sales channel, a retail data store reflecting the
retail sales channel,
and a call center data store reflecting the phone sales channel and
statistically analyze the
aggregated customer data of the plurality of customers to determine the
multiplicity of
customers from the plurality of customers whose interest in products offered
by a business
merchant fall within a certain interval. Figure 5A is a flowchart of one such
example method
500 for aggregating customer data associated with each of a multiplicity of
the customers
from one or more data stores (e.g., databases, web servers, etc.) storing data
related to the
online sales channel.
[0091] In block 502, the method 500 retrieves, from one or more data
stores, first
product identifiers reflecting products purchased by the customer using the
online sales
channel.
[0092] In block 504, the method 500 retrieves, from the one or more data
stores,
second product identifiers reflecting products that were added to a virtual
cart that was
subsequently abandoned without having purchased the one or more products, the
virtual cart
being associated with the online sales channel.
[00931 In block 506, the method 500 retrieves, from one or more data
stores, third
product identifiers reflecting product pages of products that were viewed by
the customer but
were not purchased using the online sales channel.
[0094] In block 508, the method 500 generates an entry for the customer
product-
class mapping for the customer identifier associated with the customer using
product classes
associated with the first product identifiers reflecting the products
purchased by the customer,
the second product identifiers reflecting the products added to the virtual
cart, and the third
CA 02909957 2015-10-22
product identifiers reflecting the products that were viewed by the customer
but were not
purchased; and
[0095] In block 510, the method 500 stores the entry in an aggregated data
store
storing the customer product-class mapping.
[00961 Figure 5B is a flowchart of an example method 550 for aggregating
customer
data associated with each of a multiplicity of the customers from one or more
data stores -
(e.g., databases, web servers, etc.) storing data related to the retail and
phone sales channels.
[0097] In block 552, the method 550 determines a total number of orders
associated
with the customer, a number of items included in each of the orders, the
revenue generated by
each of the orders, the revenue generated by each item included in each of the
orders, and/or
any discounts applied to each of the orders.
[0098] In block 554, the method 550 generates an entry for the customer
product-
class mapping for the customer identifier associated with the customer using
the total number
of orders associated with the customer, the number of items included in each
of the orders,
the revenue generated by each of the orders, the revenue generated by each
item included in
each of the orders, and any discounts applied to each of the orders.
[0099] In block 556, the method 550 stores the entry in an aggregated data
store
storing the customer product-class mapping.
[00100] Figure 6 is a flowchart of an example method 600 for categorizing
the
multiplicity of customers by each unique product class and customer tier
combination.
[00101] In block 602, the method 600 determines product classes for the
products
associated with the first product identifiers, the second product identifiers,
and the third
product identifiers. In block 604, the method 600 retrieves customer tier data
describing the
customer tier of the customer. In block 606, the method categorizes the
customer by each
unique product class and customer tier combination.
CA 02909957 2015-10-22
[001021 Figure 7 is a flowchart of an example method 700 for generating a
predictive
score. In block 702, the method 700 estimates time spent by the customer on
each of the
plurality of variables associated with a particular product that the customer
has indicated
interest in using the online sales channel. In block 704, the method 700
allocates weights to
the time spent by the customer on each of the plurality of the variables based
on the
predetermined online sales channel rules. In block 706, the method 700
generates the
predictive score for the each unique combination of the customer identifier,
the customer tier,
and the product class based on the weights allocated to the time spent by the
customer on
each of the plurality of variables.
[001031 Figure 8 is a flowchart of an example method 800 for providing a
customized
schedule to a stakeholder.
[001041 In block 802, the method 800 determines, for the customer
identifier of each
customer of the multiplicity of the customers, a visit cadence score based on
a number of
visits by the customer to one or more pages of one or more products.
[001051 In block 804, the method 800 determines, for the customer
identifier of each
customer of the multiplicity of the customers, a purchase cadence score based
on product
purchase history associated with the customer identifier.
[00106] In block 806, the method 800 determines, for the customer
identifier of each
customer of the multiplicity of the customers, a revenue cadence score
customer cadence
score based on amount of revenue generated from purchases by the customer.
[00107] In block 808, the method 800 combines, for the customer identifier
of each
customer of the multiplicity of the customers, the visit cadence score, the
purchase cadence
score, and the revenue cadence score into an overall cadence score for the
customer identifier.
CA 02909957 2015-10-22
[00108] In block 810, the method 800 selects a second set of customers from
the
multiplicity of the customers based on overall cadence scores associated with
the second set
of customers.
[00109] In block 812, the method 800 injects the second set of customers
into the
schedule, which includes the first set of customers. In some embodiments, the
first set of
customers have higher priority than the second set of customers and the
schedule is sorted
based on the higher priority.
[00110] Figure 9 is a flowchart of an example method 900 for generating a
schedule
for contacting a high-value subset of customers associated with each
stakeholder.
1001111 In block 902, the method 900 registers a plurality of stakeholder
accounts for a
plurality of stakeholders including the stakeholder. Each stakeholder of the
plurality of
stakeholders is assigned to a group of customer identifiers associated with
customers.
[00112] In block 904, the method 900 selects for each stakeholder account
of the
plurality, a first subset of customer identifiers from the group of customer
identifiers assigned
to that stakeholder account based on a predictive score for each unique
combination of each
customer identifier, customer tier, and product class and a revenue generated
from each
unique combination. The predictive score is generated using one or more of the
predetermined online sales channel rules, the predetermined retail sales
channel rules, and the
predetermined phone sales channel rules, respectively, and the plurality of
variables.
[00113] In block 906, the method 900 selects for each stakeholder account
of the
plurality, a second subset of customer identifiers from the group of customer
identifiers
assigned to that stakeholder based on cadence scores associated with the
customer identifiers
of the second subset.
[00114] In block 908, the method 900 generates, for each stakeholder
account of the
plurality, a schedule for contacting certain customers of the group of
customers associated
CA 02909957 2015-10-22
with the stakeholder account based on a combination of the first subset of
customers and the
second subset of customers associated with the stakeholder account.
[001151 In block 910, the method 900 provides, to a client device of each
stakeholder
account of the plurality, the schedule generated for that stakeholder account
[001161 Figure 10 is a block diagram of an example system 1000 for large-
scale
customer-product relationship mapping and contact scheduling. The illustrated
system 1000
may include client devices 1006a....1006n (also referred to herein
individually and/or
collectively as 1006), a third-party server 1018, and an application server
1022, which are
electronically communicatively coupled via a network 1002 for interaction with
one another,
although other system configurations are possible including other devices,
systems, and
networks. For example, the system 1000 could include any number of client
devices 1006,
third-party servers 1018, application servers 1022, and other systems and
devices.
[001171 The client devices 1006a...1006n, and their components, may be
coupled to
the network 1002 via signal lines 1012a.. .1012n. The application server 1022
and its
components may be coupled to the network 1002 via signal line 1020. The third-
party server
1018 and its components may be coupled to the network 1002 via signal line
1016. The users
1014a...1014n may access one or more of the devices of the system 1000.
1001181 The netWork 1002 may include any number of networks and/or network
types.
For example, the network 1002 may include, but is not limited to, one or more
local area
networks (LANs), wide area networks (WANs) (e.g., the Internet), virtual
private networks
(VPNs), wireless wide area network (WWANs), WiMAX networks, personal area
networks
(PANs) (e.g., Bluetooth communication networks), various combinations
thereof, etc.
These private and/or public networks may have any number of configurations
and/or
topologies, and data may be transmitted via the networks using a variety of
different
communication protocols including, for example, various Internet layer,
transport layer, or
CA 02909957 2015-10-22
application layer protocols. For example, data may be transmitted via the
networks using
TCP/IP, UDP, TCP, HTTP, HTTPS, DASH, RTSP, RTP, RTCP, VOIP, FTP, WS, WAP,
SMS, MMS, XMS, 11\4AP, SMTP, POP, WebDAV, or other known protocols.
[001191 The client device 1006 includes one or more computing devices
having data
processing and communication capabilities. The client device 1006 may couple
to and
communicate with other client devices 1006 and the other entities of the
system 1000 via the
network 1002 using a wireless and/or wired connection. Examples of client
devices 1006
may include, but are not limited to, mobile phones, tablets, laptops,
desktops, nctbooks,
server appliances, servers, virtual machines, TVs, etc. The system 1000 may
include any
number of client devices 1006, including client devices of the same or
different type.
1001201 A plurality of client devices 1006a... 1006n are depicted in Figure
10 to
indicate that the application server 1022 and its components may aggregate
information about
and provide product suggestions to a multiplicity of users 1014a...1014n on a
multiplicity of
client devices 1006a... 1006n. In some embodiments, a single user may use more
than one
client device 1006, which the application server 1022 may track and aggregate
interaction
data associated with the user through a variety of different channels
including online, retail,
and phone sales channels, as discussed elsewhere herein.
[00121] The application server 1022 may include a web server 1024 and an
enterprise
application 1026. hi some configurations, the enterprise application 1026 may
be distributed
over the network 1002 on disparate devices in disparate locations or may
reside on the same
locations, in which case the client device 1006a and/or the application server
1022 may each
include an instance of the enterprise application 1026 and/or portions
thereof. The client
devices 1006 may also store and/or operate other software such as a client
application 1008,
an operating system, other applications, etc., that are configured to interact
with the
application server 1022 via the network 1002.
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[00122] As shown, the enterprise application 1026 may include a scoring
engine 200,
an aggregator 202, a scheduler 201, and a priority engine 203, which are
discussed in further
detail elsewhere herein.
[00123] The application server 1022 and the third-party server 1018 have
data
processing, storing, and communication capabilities, as discussed elsewhere
herein. For
example, the servers 1022 and/or 1018 may include one or more hardware
servers, server
arrays, storage devices and/or systems, etc. In some embodiments, the servers
1022 and/or
1018 may include one or more virtual servers, which operate in a host server
environment.
[00124] Third-party server 1018 can host services such as a third-party
application (not
shown), which may be individual and/or incorporated into the services provided
by the
application server 1022.
[00125] It should be understood that the system 1000 illustrated in Figure
10 is
representative of an example system and that a variety of different system
environments and
configurations are contemplated and are within the scope of the present
disclosure. For
instance, various acts and/or finictionality may be moved from a server to a
client, or vice
versa, data may be consolidated into a single data store or further segmented
into additional
data stores, and some embodiments may include additional or fewer computing
devices,
services, and/or networks, and may implement various functionality client or
server-side.
Further, various entities of the system may be integrated into a single
computing device or
system or divided into additional computing devices or systems, etc.
[00126] Figure 11 is a block diagram of an example computing system 1100,
which
may represent the computer architecture of a client device 1006, a third-party
server 1018,
and/or an application server 1022, as depicted in Figure 10, depending on the
implementation.
CA 02909957 2015-10-22
[001271 As depicted in Figure 11, the computing system 1100 may include an
enterprise application 1026, a web server 1024, and a client application 1008,
depending on
the configuration. For instance, a client device 1006 may include one or more
of the client
application 1008 (which could incorporate various aspects of the enterprise
application 1026
in some embodiments); and the application server 1022 may include the web
server 120, the
enterprise application 1026, and/or components thereof, although other
configurations are
also possible and contemplated.
[001281 The enterprise application 1026 includes computer logic executable
by the
processor 1104 to provide large-scale customer-product relationship mapping
and contact
scheduling, as discussed elsewhere herein. The enterprise application 1026 is
coupled to the
data stores 1108 to store, retrieve, and/or manipulate data stored therein and
may be coupled
to the web server 1024, the client application 1008, and/or other components
of the system
100 to exchange information therewith. In the depicted embodiment, the
enterprise
application 1026 includes the scoring engine 200, the scheduler 201, the
aggregator 202, and
the priority engine 203.
[001291 The web server 1024 includes computer logic executable by the
processor
1104 to methods content requests. The web server 1024 may include an HTTP
server, a
REST (representational state transfer) service, or other suitable server type.
The web server
1024 may receive content requests (e.g., product search requests, HTTP
requests) from client
devices 1006, cooperate with the enterprise application 1026 to determine the
content,
retrieve and incorporate data from the data stores 1108, format the content,
and provide the
content to the client devices. In some embodiments, the web server 1024
includes the Ul
prep module 146 and the UI module 148, although other configurations are also
possible and
contemplated.
A
CA 02909957 2015-10-22
[001301 In some instances, the web server 1024 may format the content using
a web
language and provide the content to a corresponding client application 1008
for processing
and/or rendering to the user for display. The web server 1024 may be coupled
to the data
stores 1108 to store retrieve, and/or manipulate data stored therein and may
be coupled to the
scheduler 201 to facilitate its operations. For example, the web server 1024
may allow a user
on a client device 1006 to communicate with scheduler 201, the CRNI 158,
and/or the other
components of the system 100.
[00131] The client application 1008 includes computer logic executable by
the
processor 1104 on a client device 1006 to provide for user interaction,
receive user-input,
present information to the user via a display, and send data to and receive
data from the other
entities of the system 1000 via the network 1002. In some embodiments, the
client
application 1008 may generate and present user interfaces based at least in
part on
information received from the enterprise application 1026 and/or the web
server 1024 via the
network 1002. For .example, a customer/user 1014 may use the client
application 1008 to
receive the product pages provided by the application server 1022, view
various products
available online, add products to a virtual cart, purchase products, receive
discounts on
products, etc. In some embodiments, the client application 1008 includes a web
browser
and/or code operable therein, a customized client-side application (e.g., a
dedicated mobile
app), a combination of both, etc.
[001321 As depicted, the computing system 1100 may include a processor
1104, a
memory 1106, a communication unit 1102, an output device 1106, an input device
1104, and
a data stores 1108, which may be communicatively coupled by a communication
bus 1110.
The computing system 1100 depicted in Figure 11 is provided by way of example
and it
should be understood that it may take other forms and include additional or
fewer
components without departing from the scope of the present disclosure. For
instance, various
CA 02909957 2015-10-22
components of the computing devices may be coupled for communication using a
variety of
communication protocols and/or technologies including, for instance,
communication buses,
software communication mechanisms, computer networks, etc. While not shown,
the
computing system 1100 may include various operating systems, sensors,
additional
processors, and other physical configurations. The processor 1104, memory
1106,
communication unit 1102, etc., are representative of one or more of these
components.
[00133] The processor 1104 may execute software instructions by performing
various
input, logical, and/or mathematical operations. The processor 1104 may have
various
computing architectures to method data signals (e.g., CISC, RISC, etc.). The
processor 1104
may be physical and/or virtual, and may include a single core or plurality of
processing units
and/or cores. In some embodiments, the processor 1104 may be coupled to the
memory 1106
via the bus 1110 to access data and instructions therefrom and store data
therein. The bus
1110 may couple the processor 1104 to the other components of the computing
system 1100
including, for example, the memory 1106, the communication unit 1102, the
input device
1104, the output device 1106, and the data stores 1108.
100134] The memory 1106 may store and provide access to data to the other
components of the computing system 1100. The memory 1106 may be included in a
single
computing device or a plurality of computing devices. In some embodiments, the
memory
1106 may store instructions and/or data that may be executed by the processor
1104. For
example, the memory 1106 may store one or more of the enteiprise application
1026, the web
server 1024, the client application 1008, and their respective components,
depending on the
configuration. The memory 1106 is also capable of storing other instructions
and data,
including, for example, an operating system, hardware drivers, other software
applications,
databases, etc. The memory 1106 may be coupled to the bus 1110 for
communication with
the processor 1104 and the other components of computing system 1100.
CA 02909957 2015-10-22
[00135] The memory 1106 may include a non-transitory computer-usable (e.g.,
readable, writeable, etc.) medium, which can be any non-transitory apparatus
or device that
can contain, store, communicate, propagate or transport instructions, data,
computer
programs, software, code, routines, etc., for processing by or in connection
with the processor
1104. In some embodimentthe memory 1106 may include one or more of volatile
memory
and non-volatile memory (e.g., RAM, ROM, hard disk, optical disk, etc.). It
should be
understood that the memory 1106 may be a single device or may include multiple
types of
devices and configurations.
[00136] The bus 1110 can include a communication bus for transferring data
between
components of a computing device or between computing devices, a network bus
system
including the network 1002 or portions thereof, a processor mesh, a
combination thereof, etc.
In some embodiments, the enterprise application 1026, web server 1024, client
application
1008, and various other components operating on the computing system/device
1000
(operating systems, device drivers, etc.) may cooperate and communicate via a
communication mechanism included in or implemented in association with the bus
1110.
The software communication mechanism can include and/or facilitate, for
example, inter-
method communication, local function or procedure calls, remote procedure
calls, an object
broker (e.g., CORBA), direct socket communication (e.g., TCP/IP sockets) among
software
modules, UDP broadcasts and receipts, HTTP connections, etc. Further, any or
all of the
communication could be secure (e.g., SSH, HTTPS, etc.).
[001371 The communication unit 1102 may include one or more interface
devices (I/F)
for wired and wireless connectivity among the components of the system 1000.
For instance,
the communication unit 1102 may include, but is not limited to, various types
known
connectivity and interface options. The communication unit 1102 may be coupled
to the
other components of the computing system 1100 via the bus 1110. The
communication unit
CA 02909957 2015-10-22
1102 can provide other connections to the network 1002 and to other entities
of the system
1000 using various standard communication protocols.
[001381 The input device 1104 may include any device for inputting
information into
the computing system 1100. In some embodiments, the input device 1104 may
include one
or more peripheral devices. For example, the input device 1104 may include a
keyboard, a
pointing device, microphone, an image/video capture device (e.g., camera), a
touch-screen
display integrated with the output device 1106, etc. The output device 1106
may be any
device capable of outputting information from the computing system 1100. The
output
device 1106 may include one or more of a display (LCD, OLED, etc..), a
printer, a 3D printer,
a haptic device, audio reproduction device, touch-screen display, a remote
computing device,
etc. In some embodiments, the output device is a display which may display
electronic
images and data output by a processor of the computing system 1100 for
presentation to a
user 106, such as the processor 1104 or another dedicated processor.
[00139] The data stores 1108 are information sources for storing and
providing access
to data. In some embodiments, the data stores 1108 may store data associated
with a database
management system (DBMS) operable on the computing system 1100. For example,
the
DBMS could include a structured query language (SQL) DBMS, a NoSQL DMBS,
various
combinations thereof, etc. In some instances, the DBMS may store data in multi-
dimensional
tables comprised of rows and columns, and manipulate, e.g., insert, query,
update and/or
delete, rows of data using programmatic operations.
[00140] The data stored by the data stores 1108 may organized and queried
using
various criteria including any type of data stored by them, such as a customer
identifier,
employee identifier, account identifier, IP address, rewards account number,
product
classification, product identifier, etc_ The data stores 1108 may include data
tables,
databases, or other organized collections of data. Examples of the types of
data stored by the
CA 02909957 2015-10-22
data stores 1108 may include, but are not limited to the data described with
respect to Figures
1-2C, for example.
[00141.1 The data stores 1108 may be included in the computing system 1100
or in
another computing system and/or storage system distinct from but coupled to or
accessible by
the computing system 1100. The data stores 1108 can include one or more non-
transitory
computer-readable mediums for storing the data. In some embodiments, the data
stores 1108
may be incorporated with the memory 1106 or may be distinct therefrom.
[001421 The components 1008, 1024, 1026, 200, 201, 202, and/or 203 may be
communicatively coupled by the bus 1110 and/or the processor 1104 to one
another and/or
the other components 1102, 1106, 1108, 1114, and 1116 of the computing system
1100. In
some embodiments, the components 1008, 1024, 1026, 200, 201, 202, and/or 203
may
include computer logic (e.g., software logic, hardware logic, etc.) executable
by the processor
1104 to provide their acts and/or functionality. In any of the foregoing
embodiments, these
components 1008, 1024, 1026, 200, 201, 202, and/or 203 may be adapted for
cooperation and
communication with the processor 1104 and the other components of the
computing system
1100.
[001431 In the above description, for purposes of explanation, numerous
specific
details are set forth in order to provide a thorough understanding of the
present disclosure.
However, it should be understood that the technology described herein can be
practiced
without these specific details. Further, various systems, devices, and
structures are shown in
block diagram form in order to avoid obscuring the description. For instance,
various
embodiments are described as having particular hardware, software, and user
interfaces.
However, the present disclosure applies to any type of computing device that
can receive data
and commands, and to any peripheral devices providing services.
CA 02909957 2015-10-22
1001441 In some instances, various embodiments may be presented herein in
terms of
algorithms and symbolic representations of operations on data bits within a
computer
memory. An algorithm is here, and generally, conceived to be a self-consistent
set of
operations leading to a desired result. The operations are those requiring
physical
manipulations of physical quantities. Usually, though not necessarily, these
quantities take
the form of electrical or magnetic signals capable of being stored,
transferred, combined,
compared, and otherwise manipulated. It has proven convenient at times,
principally for
reasons of common usage, to refer to these signals as bits, values, elements,
symbols,
characters, terms, numbers, or the like.
[001451 To ease description, some elements of the system 100 and/or the
methods are
referred to using the labels first, second, third, etc. These labels are
intended to help to
distinguish the elements but do not necessarily imply any particular order or
ranking unless
indicated otherwise.
1001461 It should be borne in mind, however, that all of these and similar
terms are to
be associated with the appropriate physical quantities and are merely
convenient labels
applied to these quantities. Unless specifically stated otherwise as apparent
from the
following discussion, it is appreciated that throughout this disclosure,
discussions utilizing
terms including "processing," "computing," "calculating," "determining,"
"displaying," or
the like, refer to the action and processes of a computer system, or similar
electronic
computing device, that manipulates and transforms data represented as physical
(electronic)
quantities within the computer system's registers and memories into other data
similarly
represented as physical quantities within the computer system memories or
registers or other
such information storage, transmission or display devices.
[001471 Various embodiments described herein may relate to an apparatus for
performing the operations herein. This apparatus may be specially constructed
for the
A CI
CA 02909957 2015-10-22
required purposes, or it may comprise a general-purpose computer selectively
activated or
reconfigured by a computer program stored in the computer. Such a computer
program may
be stored in a computer readable storage medium, including, but is not limited
to, any type of
disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-
only
memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or
optical cards, flash memories including USB keys with non-volatile memory or
any type of
media suitable for storing electronic instructions, each coupled to a computer
system bus.
[00148] The technology described herein can take the form of an entirely
hardware
embodiment, an entirely software embodiment, or embodiments containing both
hardware
and software elements. For instance, the technology may be implemented in
software, which
includes but is not limited to firmware, resident software, microcode, etc.
Furthermore, the
technology can take the form of a computer program product accessible from a
computer-
usable or computer-readable medium providing program code for use by or in
connection
with a computer or any instruction execution system. For the purposes of this
description, a
computer-usable or computer readable medium can be any non-transitory storage
apparatus
that can contain, store, communicate, propagate, or transport the program for
use by or in
connection with the instruction execution system, apparatus, or device.
[00149] A data processing system suitable for storing and/or executing
program code
may include at least one processor coupled directly or indirectly to memory
elements through
a system bus. The memory elements can include local memory employed during
actual
execution of the program code, bulk storage, and cache memories that provide
temporary
storage of at least some program code in order to reduce the number of times
code must be
retrieved from bulk storage during execution. Input or I/0 devices (including
but not limited
to keyboards, displays, pointing devices, etc.) can be coupled to the system
either directly or
through intervening I/O controllers.
hi
CA 02909957 2015-10-22
[00150] Network adapters may also be coupled to the system to enable the
data
processing system to become coupled to other data processing systems, storage
devices,
remote printers, etc., through intervening private and/or public networks.
Wireless (e.g., Wi-
FiTm) transceivers, Ethernet adapters, and Modems, are just a few examples of
network
adapters. The private and public networks may have any number of
configurations and/or
topologies. Data may be transmitted between these devices via the networks
using a variety
of different communication protocols including, for example, various Internet
layer, transport
layer, or application layer protocols. For example, data may be transmitted
via the networks
using transmission control protocol / Internet protocol (TCP/IP), user
datagram protocol
(UDP), transmission control protocol (TCP), hypertext transfer protocol
(HTTP), secure
hypertext transfer protocol (HTTPS), dynamic adaptive streaming over HTTP
(DASH), real-
time streaming protocol (RTSP), real-time transport protocol (RTP) and the
real-time
transport control protocol (RTCP), voice over Internet protocol (VOIP), file
transfer protocol
(FTP), WebSocket (WS), wireless access protocol (WAP), various messaging
protocols
(SMS, MMS, XMS, IMAP, SMTP, POP, WebDAV, etc.), or other known protocols.
1001511 Finally, the structure, algorithms, and/or interfaces presented
herein are not
inherently related to any particular computer or other apparatus. Various
general-purpose
systems may be used with programs in accordance with the teachings herein, or
it may prove
convenient to construct more specialized apparatus to perform the required
method blocks.
The required structure for a variety of these systems will appear from the
description above.
In addition, the specification is not described with reference to any
particular programming
language. It will be appreciated that a variety of programming languages may
be used to
implement the teachings of the specification as described herein.
[001521 The foregoing description has been presented for the purposes of
illustration
and description. It is not intended to be exhaustive or to limit the
specification to the precise
CA 02909957 2015-10-22
form disclosed. Many modifications and variations are possible in light of the
above
teaching. As will be understood by those familiar with the art, the
specification may be
embodied in other specific forms without departing from the spirit or
essential characteristics
thereof. Likewise, the particular naming and division of the modules,
routines, features,
attributes, methodologies and other aspects are not mandatory or significant,
and the
mechanisms that implement the specification or its features may have different
names,
divisions and/or formats.
Furthermore, the modules, routines, features, attributes, methodologies and
other aspects of
the disclosure can be implemented as software, hardware, firmware, or any
combination of -
the foregoing. Also, wherever a component, an example of which is a module, of
the
specification is implemented as software, the component can be implemented as
a standalone
program, as part of a larger program, as a plurality of separate programs, as
a statically or
dynamically linked library, as a kernel loadable module, as a device driver,
and/or in every
and any other way known now or in the future. Additionally, the disclosure is
in no way
limited to implementation in any specific programming language, or for any
specific
operating system or environment.
