How to Maximize Revenues on Your Customer Loyalty Program using Predictive Analytics?

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A GACP and GTMCP company

Maximize Revenues on your Customer Loyalty Program using Predictive Analytics

27th Feb ‘14 Free Webinar by

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Agenda

• Background and Economics of Customer Loyalty

• Defining the Business Question

• A Primer on Predictive Analytics

• Defining the data sources

• Logistic Regression

• Model Accuracy

• Improving the Model

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Customer Retention – Why should you Care?

• Customer Acquisition Costs are on the rise

• Repeat Customers– Create higher value (both in AOV & Revenue)

– Evangelize your brand

– Have Lower Service Costs

“Retailers can achieve tremendous revenue gains by shifting their marketing budgets to better target these customer segments”

Attributed from (http://www.practicalecommerce.com/articles/63459-Seek-Repeat-Customers-to-Drive-Ecommerce-Profits)

http://www.practicalecommerce.com/articles/63459-Seek-Repeat-Customers-to-Drive-Ecommerce-Profits

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Real Life Example

Sample Size: 5000 Consumers

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Contribution to Revenue

750 (repeat) customers drive 40% of the total

Revenue

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Contribution to Revenue

If 5% of these customers become repeat buyersafter Discount Targeting, what are the implications for revenue?

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Conventional Approach to Customer Loyalty• Send Discount Coupons to all Customers either via email or

some other medium

• Problems

– Non Targeted Campaign hence suffers from Low Conversion Rate

– Sending Discount Coupons to all customers erodes your sales margin

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Revenue Leakage: What If Analysis

Size of Email List 100,000

Click Through Rate of Email List 5%

Visits 5000

Conversion Rate 2.5%

Transactions 125

Average Order Value $250

Discount Provided 20%

Discount $50

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Revenue Leakage: What If Analysis

Size of Email List 100,000

Click Through Rate of Email List 5%

Visits 5000

Conversion Rate 2.5%

Transactions 125

Average Order Value $250

Discount Provided 20%

Discount $50

Persuadables (Customers Who bought after discount was provided)

75

Sure Things (Customers who would have bought anyway)

50

Loss in Revenue $2,500

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Summing up

Target your Loyalty Campaign to this segment

Image Courtesy : Dr. Eric Siegel (http://www.predictiveanalyticsworld.com/lower-costs-with-predictive-analytics.php)

http://www.predictiveanalyticsworld.com/lower-costs-with-predictive-analytics.php

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Business Question for Predictive Analytics

• Predicting Customers who would make a repeat purchase within 2 months of their initial purchase

• Outcome/Response Variable: Whether the customer would make a repeat purchase within 60 days

• Using Data of Past Customers who have made purchases on the site

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Until Now

• Repeat Customers are valuable and we need more of them

• Sending out discount coupons to all customers w/out segmentation leads to a loss in your Revenue

• Use a Predictive Model to find out those customers who would not make a return purchase without a discount coupon

• Target your Discount Coupons only to these customers

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Data Sources and Features

Google Analytics Data

Transaction Date

Product Category

Item Quantity

Shipping Cost Incurred

Medium

CRM Data

Is Newsletter Subscriber?

Discount Coupon Redeemed?

Account Creation Date

Customer ID

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An Aside: Extracting Google Analytics Data into R

User performing data extraction

Google OAuth2Authorization

Server

Google Analytics

API

Access Token Request

Image adapted from: Google Analytics Core Reporting API Dev Guide

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Server

Google Analytics

API

Access Token Response



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Server

Google Analytics

API


Call API for list of

profiles



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Server

Google Analytics API


Call API for list of profiles

Call API for query



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Intuition behind Supervised Learning

Example courtesy : Trevor Hastie, Rob Tibschirani (Statistical Learning, StanfordOnline)

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Supervised Learning

Generates a function that maps inputs (labeled data) to desired outputs (e.g. Image Classification)

Training Data

Machine Learning Algorith

mLabels

Supervised Learning ModelVariables

Labels are right answersfrom historical data

e.g. Image of Car/Bike

Input Data: ContainsImages of Bike and Car

Image Courtesy: Olivier Grisel https://speakerdeck.com/ogrisel/machine-learning-in-python-with-scikit-learn

https://speakerdeck.com/ogrisel/machine-learning-in-python-with-scikit-learn

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Supervised Learning

Generates a function that maps inputs (labeled data) to desired outputs (e.g. Image Classification)

Training Data

Machine Learning Algorith

m

Test Data

Predictive Model

Predicted Outcome

labels

Labels

Supervised Learning ModelVariables

Labels are right answersfrom historical data

e.g. Image of Car/Bike

Input Data: ContainsImages of Bike and Car

Variables

Image Courtesy: Olivier Grisel https://speakerdeck.com/ogrisel/machine-learning-in-python-with-scikit-learn

https://speakerdeck.com/ogrisel/machine-learning-in-python-with-scikit-learn

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Logistic Regression Model

• Algorithm used to predict categorical labels

• In our problem Categorical Labels are

– 0 : Did not carry out repeat purchase

– 1 : Carried out Repeat Purchase within 60 days

• Using the algorithm we predict the probability of a Customer ID belonging to either class

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Checking Model Accuracy

• Split Data Randomly into Train and Test

• Fit glm model on Train Data

• Predict labels for unseen Test Data

20 % Test Data

80% Train Data

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Model Accuracy

Confusion Matrix

Predicted Labels

(Predicted by running Model

on Test Set)

Actual Labels (From Test Set)

Not a Repeat Purchaser Repeat Purchaser

Not a Repeat Purchaser 5271 4

Repeat Purchaser 1209 1

Labels • 0 : Customer didn’t make a repeat purchase in 60 days• 1 : Customer made a repeat purchase in 60 days.

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Model Accuracy

Confusion Matrix

Predicted Labels

(Predicted by running Model

on Test Set)

Actual Labels (From Test Set)

Not a Repeat Purchaser Repeat Purchaser

Not a Repeat Purchaser 5271 4

Repeat Purchaser 1209 1

Accuracy = (Number of Correctly Predicted Labels) / Total Number of Labels= (5271 + 1) / (5271 + 4 + 1209 + 1)~ 81.3 %

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Improving Model Accuracy

• Adding New Features to the model

– Difference b/w Account Creation Date and Transaction Date

– Checking for Transactions occurring during Weekend (based on Date)

– Adding Days To Transaction, Location, Device Type as Features from Google Analytics

• Trying out additional models

– Random Forests

– Gradient Boosting

– Support Vector Machines

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Watch Full Webinar VideoWatch full Webinar Video - http://bit.ly/1M1oCNS

Webinar Video

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More Resources

• Q & A from our webinar How to perform Predictive Analytics on your Web Analytics Tool Data - http://bit.ly/1K7vNsq

• Predictive Analysis on Web Analytics tool data - http://bit.ly/1E97fLZ• Understanding the value of Predictive Analytics on Web Data -

http://bit.ly/1wJg2fU• Product Revenue Prediction with R - http://bit.ly/1wJgeLZ• Logistic Regression with R - http://bit.ly/1M1rkmM• Improving Bounce Rate Prediction Model for Google Analytics Data -

http://bit.ly/18gfWWP• How to extract Google Analytics data in R using RGoogleAnalytics -

http://bit.ly/1B18h9b

http://bit.ly/1K7vNsq

http://bit.ly/1E97fLZ

http://bit.ly/1wJg2fU

http://bit.ly/1wJgeLZ

http://bit.ly/1M1rkmM

http://bit.ly/18gfWWP

http://bit.ly/1B18h9b

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Next Webinar

How to Perform Churn Analysis for your Mobile Application

Key Takeaways

• Predict the Segment of Mobile App Users who would uninstall your app

• Remain Inactive and Churn over a period of Time

Watch Now: http://bit.ly/1wIYjFn

March 19th 11:00 AM PDT

http://bit.ly/1wIYjFn

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How to Maximize Revenues on Your Customer Loyalty Program using Predictive Analytics?

Data & Analytics

Transcript of How to Maximize Revenues on Your Customer Loyalty Program using Predictive Analytics?