PROPENSITY MODELING PROCESS FOR CUSTOMER TARGETING
In one aspect, an example methodology implementing the disclosed techniques includes receiving a historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers and identifying a plurality of first features, the plurality of first features derived from the historical customer dataset. The method also includes generating a first training dataset from the plurality of first features, training a first machine learning (ML) model using the first training dataset, and determining, using the first ML model, a plurality of second features. The method further includes generating a second training dataset from the plurality of second features and training a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.
1 . A computer implemented method for customer targeting for a marketing campaign, the method comprising:
receiving a historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers;
identifying a plurality of first features, the plurality of first features derived from the historical customer dataset;
generating a first training dataset from the plurality of first features;
training a first machine learning (ML) model using the first training dataset;
determining, using the first ML model, a plurality of second features;
generating a second training dataset from the plurality of second features; and
training a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.
2 . The method of claim 1 , wherein identifying the plurality of first features comprises:
performing a first dimensionality reduction on the historical customer dataset;
clustering the first dimensionally reduced historical customer dataset into a plurality of first level (L1) clusters;
for each L1 cluster of the plurality of L1 clusters:
performing a second dimensionality reduction on data points in an L1 cluster; and
clustering the second dimensionally reduced data points in the L1 cluster into a plurality of second level (L2) clusters; and
sampling from the L2 clusters to achieve a uniform distribution of the L2 clusters.
3 . The method of claim 2 , wherein sampling from the L2 clusters to achieve a uniform distribution of the L2 clusters results in a reduction of inherent bias in the historical customer dataset.
4 . The method of claim 2 , wherein clustering the first dimensionally reduced historical customer dataset into the plurality of L1 clusters is via one of k-means clustering or k-medoids clustering.
5 . The method of claim 2 , wherein a number of L1 clusters in the plurality of L1 clusters is determined via one of an elbow method or gap statistics.
6 . The method of claim 2 , wherein the L2 clusters of the plurality of L2 clusters is more granular than the L1 clusters of the plurality of L1 clusters.
7 . The method of claim 1 , wherein the plurality of second features is more relevant to the propensity predictions than the plurality of first features.
8 . The method of claim 1 , wherein the propensity predictions include likelihood to make a purchase.
9 . The method of claim 1 , wherein the propensity predictions include likelihood to make a return.
10 . The method of claim 1 , wherein the propensity predictions include likelihood to require assistance.
11 . A system comprising:
one or more non-transitory machine-readable mediums configured to store instructions; and
one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to:
receive a historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers;
identify a plurality of first features, the plurality of first features derived from the historical customer dataset;
generate a first training dataset from the plurality of first features;
train a first machine learning (ML) model using the first training dataset;
determine, using the first ML model, a plurality of second features;
generate a second training dataset from the plurality of second features; and
train a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.
12 . The system of claim 11 , wherein to identify the plurality of first features comprises:
perform a first dimensionality reduction on the historical customer dataset;
cluster the first dimensionally reduced historical customer dataset into a plurality of first level (L1) clusters;
for each L1 cluster of the plurality of L1 clusters:
perform a second dimensionality reduction on data points in an L1 cluster; and
cluster the second dimensionally reduced data points in the L1 cluster into a plurality of second level (L2) clusters; and
sample from the L2 clusters to achieve a uniform distribution of the L2 clusters.
13 . The system of claim 12 , wherein to sample from the L2 clusters to achieve a uniform distribution of the L2 clusters results in a reduction of inherent bias in the historical customer dataset.
14 . The system of claim 12 , wherein to cluster the first dimensionally reduced historical customer dataset into the plurality of L1 clusters is via one of k-means clustering or k-medoids clustering.
15 . The system of claim 12 , wherein a number of L1 clusters in the plurality of L1 clusters is determined via one of an elbow method or gap statistics.
16 . The system of claim 12 , wherein the L2 clusters of the plurality of L2 clusters is more granular than the L1 clusters of the plurality of L1 clusters.
17 . A computer program product including one or more non-transitory machine-readable mediums encoding instructions that when executed by one or more processors cause a process to be carried out for customer targeting for a marketing campaign, the process comprising:
receiving historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers;
identifying a plurality of first features, the plurality of first features derived from the historical customer dataset;
generating a first training dataset from the plurality of first features;
training a first machine learning (ML) model using the first training dataset;
determining, using the first ML model, a plurality of second features;
generating a second training dataset from the plurality of second features; and
training a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.
18 . The computer program product of claim 17 , wherein identifying the plurality of first features comprises:
performing a first dimensionality reduction on the historical customer dataset;
clustering the first dimensionally reduced historical customer dataset into a plurality of first level (L1) clusters; and
for each L1 cluster of the plurality of L1 clusters:
performing a second dimensionality reduction on data points in an L1 cluster; and
clustering the second dimensionally reduced data points in the L1 cluster into a plurality of second level (L2) clusters.
19 . The computer program product of claim 18 , wherein sampling from the L2 clusters to achieve a uniform distribution of the L2 clusters results in a reduction of inherent bias in the historical customer dataset.
20 . The computer program product of claim 18 , wherein the L2 clusters of the plurality of L2 clusters is more granular than the L1 clusters of the plurality of L1 clusters.