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What problem does the business want to solve?
The client is a leading American retail company, primarily involved in the sale of office supplies and related products. It aims to
-
drive traffic to online and retail stores
-
increase customer acquisition
-
achieve incremental results
What are the business goals?
-
Predict whether a customer really need a coupon to motive the purchase
-
Find the probability of purchase for the persuadable customer
Model Solution
-
Data Universe
-
Prior campaign results of 13-month consumers information
-
17M campaign circulations in total, 6M unique customers
-
15 M Treatment Group
-
2 M Control Group
-
Measurement Window & Prediction Window
Campaign start to end date (40 days around)
-
Dependent Variable
Whether an account purchase using coupon redemption during measurement window (Comparing with the holdout with purchase)
-
Data Selection
-
Data Type
-
Reward/non-rewards customers
-
Payment types
-
Coupon need
-
Payment types
-
Product category
-
High valuable customer
-
Recency
-
Variable Selection
The top 12 drivers are shown below in order of driver importance
-
non-rewards purchase amount
-
payment method: using cash
-
coupon related variable: redemption rate
-
coupon related variable: retail sales
-
coupon related variable: weeks of use coupon
-
coupon related variable: using coupon vs total sales ratio
-
coupon related variable: redemption amount
-
payment method: combing with staples coupon
-
Facilities & Supplier average margin
-
reward customer margin
-
all net margin historically
-
most recently net margin
What’s our approach?
-
Based on the variables we can derive even more
-
How did we use SSB to generate derived variables?
-
Examples of the power of the new variables?
-
original and the new and compare them
-
the purpose is to showcase why FocusKPI is unique
-
We can automate the estimation and result in the higher lift and more power models
-
Bring in the tableau reporting linked to the model results
-
put in some screenshots of SSB
Exploratory Data Analytics
-
Select appropriate prior campaign data
-
Identify key drivers to split the universe into two groups
-
Validate model
-
Rank and profile customers by model scores
-
Select top decile (retail sales) for the campaign
Direct Mail (DM) Campaign Objective
Reactivate lapsed audience with a coupon offer available to redeem in-store to drive incremental results
Direct Mail (DM) Campaign Operation
Postcard coupon was sent to a total of 530K Treatment Group (320K Business and 210K Business-like Customer), with a Campaign cost of $630K and measurement time of 40 days
What are the insights?
As a result, after 40 days of measurement, both groups performed significantly better. The campaign drove significant incremental response and net sales.
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WHAT PROBLEM DOES THE BUSINESS WANT TO SOLVE?
The client is a leading American retail company, primarily involved in the sale of office supplies and related products. It aims to
-
drive traffic to online and retail stores
-
increase customer acquisition
-
achieve incremental results
WHAT ARE THE BUSINESS GOALS?
-
Predict whether a customer really need a coupon to motive the purchase
-
Find the probability of purchase for the persuadable customer
MODEL SOLUTION
-
Data Universe
-
Prior campaign results of 13-month consumers information
-
17M campaign circulations in total, 6M unique customers
-
15 M Treatment Group
-
2 M Control Group
-
-
-
Measurement Window & Prediction Window
Campaign start to end date (40 days around)
-
Dependent Variable
Whether an account purchase using coupon redemption during measurement window (Comparing with the holdout with purchase)
-
Data Selection
-
Data Type
-
Reward/non-rewards customers
-
Payment types
-
Coupon need
-
Payment types
-
Product category
-
High valuable customer
-
Recency
-
-
Variable Selection
-
The top 12 drivers are shown below in order of driver importance
-
-
non-rewards purchase amount
-
payment method: using cash
-
coupon related variable: redemption rate
-
coupon related variable: retail sales
-
coupon related variable: weeks of use coupon
-
coupon related variable: using coupon vs total sales ratio
-
coupon related variable: redemption amount
-
payment method: combing with staples coupon
-
Facilities & Supplier average margin
-
reward customer margin
-
all net margin historically
-
most recently net margin
-
WHAT’S OUR APPROACH?
-
Based on the variables we can derive even more
-
How did we use SSB to generate derived variables?
-
Examples of the power of the new variables?
-
-
original and the new and compare them
-
the purpose is to showcase why FocusKPI is unique
-
-
We can automate the estimation and result in the higher lift and more power models
-
Bring in the tableau reporting linked to the model results
-
-
put in some screenshots of SSB
-
EXPLORATORY DATA ANALYTICS
-
Select appropriate prior campaign data
-
Identify key drivers to split the universe into two groups
-
Validate model
-
Rank and profile customers by model scores
-
Select top decile (retail sales) for the campaign
DIRECT MAIL (DM) CAMPAIGN OBJECTIVE
Reactivate lapsed audience with a coupon offer available to redeem in-store to drive incremental results
DIRECT MAIL (DM) CAMPAIGN OPERATION
Postcard coupon was sent to a total of 530K Treatment Group (320K Business and 210K Business-like Customer), with a Campaign cost of $630K and measurement time of 40 days
WHAT ARE THE INSIGHTS?
As a result, after 40 days of measurement, both groups performed significantly better. The campaign drove significant incremental response and net sales.