Coupon RedemptionPower to predict valued shoppers

Group 3

Akshay KherAdwait Ghule

Priya WaghMidhu C BabyKuldeep Ahir

Agenda• Introduction•Project Motivation•The Challenge•Data Description•Pre - processing Techniques•BI Techniques• Interpretations•Model Comparison•Business Value

Introduction

•Challenge for manufacturers and retailers to acquire and retain customers

•Spend huge amounts in promotions to draw more customers

•Understanding buying behavior of customers of utmost priority

•Coupon promotions – major vehicle used for marketing

Project Motivation

•Marketer’s dilemma: “Who should be offered discount in order to maximize sales and how much?”

•Challenge: To identify loyal buyers amongst the shoppers who redeem coupons

•Use of pre-offer shopping history of shoppers to predict which of them are most likely to repeat purchase after being exposed to a coupon offer

Data•First hand data obtained from the client •350 million rows of transactional data for over

300,000 shoppers•Only one offer per customer

The Challenge

•No ready predictor variables available•To mine this huge data dump and identify variables

that could be used as predictors in the model•Create variables using existing research and our

intuition•Existing techniques : Descriptive – RFM•Pareto Principle•Offer Attractiveness

Variable Creation•No. of transactions by the customer for the product

on offer•Amount spent by the customer for the product on

offer•No. of items bought by the customer for the product

on offer•No. of days since last purchase for the product on

offer •Similar variables for category, brand and company•Offer value for the product on offer

Variable Creation

•No. of pack-sizes for the product on offer•No. of competitors for the product on offer•No. of times the product was bought in the last 30 days•No. of transactions in the store in which the offer is

made•Total units sold of the brand on offer•Average price of the product on offer•Market share of the product vis-à-vis the category •No. of return transactions for the product on offer

Variable Creation

•No. of transactions on discount – discount proneness•Day of the week•Store size•Tenure of the product on offer

SAS Model Diagram

Data Pre - processing

•Used StatExplore node to understand the data statistics

•Used Data Partition node to separate out data in training and validation in order to build our model

•Used Impute node to compute the missing values

Type Percentage of data No of ObservationsTotal data 100 % 160057Train 75 % 120042Validate 25 % 40015

Data Pre - processing•Reduced skewness of input variables by using

Transform Variables node and log as transforming method

Correlation check

Variable Correlated With Correlation Coefficient

No. of transactions for the product on

offer

No. of transactions for the company on

offer

0.93

Amount spent on the category on

offer

Amount spent on the company on

offer

0.87

No. of items bought for the product on

offer

No. of items bought for the company on

offer

0.83

Amount spent in the store on offer

No. of items bought in the store on offer

0.80

Variable Selection•Using default setting for Variable Selection, 37

variables are selected•Target variable ‘repeater’ is a binary variable with

value either t or f (true or false)

BI Techniques

Logistic Regression

Decision Trees

Neural Networks

Logistic Regression Model

Statistical Label Train ValidationMisclassification Rate 0.254353 0.252805Average Square Error 0.177404 0.177001

Decision TreeStatistical Label Train ValidationMisclassification Rate 0.250679 0.249182Average Square Error 0.176605 0.176425

Decision Tree

Neural NetworkStatistical Label Train Validation

Misclassification Rate 0.250421 0.248407

Average Square Error 0.174872 0.174185

Model Comparison•ROC Chart indicates that Neural Network Curve shows

better accuracy

Model Comparison

FindingsVariable Odds

RatioImpact

Total Amount spent by the customer on the brand on offer

2.789 178.93%

No. of items bought by the customer for the category on offer

2.453 145.30%

Average price of the product on offer 1.512 51.20%

No. of days since last transaction for the brand on offer

0.731 -26.86%

Total amount spent by the customer in the store on offer

1.270 27.03%

No. of sizes the product on offer is available in 1.334 33.41%

Total Amount spent for the product on offer 1.000 0.01%

No. of competitors for the product on offer 1.004 0.37%

Implications• Past purchases made in the brand and category for which the

coupon was offered are the strongest predictors of repeat purchase

• More the price of product, more the probability of customer to repeat the purchase

• As the number of days since the brand or category for which the coupon was offered increases, the probability or repeat purchase decreases

• Besides, the store in which the coupon is offered, Product size assortment and Success ratio of a product also plays a small but important role in predicting repeat purchase.

Business Implications

• Loyalty plays a huge part in estimating repeat purchase

• Product pricing and promotion location also plays a significant role

• Better understanding of the customer behavior

• Marketing strategy planning

• Managing the budget effectively