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

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Q & A www

Before we begin...

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Your Speakers

@parikh_shachi

Technical Analyst @tatvic Loves js and data analysis

@kushan_s

Web Analyst @tatvic Loves R, Pandas, ML

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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

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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”

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http://www.practicalecommerce.com/articles/63459-Seek-Repeat-Customers-to-Drive-A Ghttp://www.practicalecommerce.com/articles/63459-Seek-Repeat-Customers-to-Drive-ACP and GTMCP company

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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 buyers after 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

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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%

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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

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Summing up

Target your

Loyalty Campaign to this segment

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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

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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 OAuth2 Authorization Server Google Analytics API

Access Token Request

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An Aside: Extracting Google Analytics Data into R

Access Token Response Access Token Request

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An Aside: Extracting Google Analytics Data into R

Access Token Response

Call API for list of profiles Access Token Request

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An Aside: Extracting Google Analytics Data into R

Access Token Response

Call API for list of profiles

Call API for query Access Token Request

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Intuition behind Supervised Learning

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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 Algorithm Labels

Supervised Learning Model

Variables

Labels are right answers from historical data

e.g. Image of Car/Bike Input Data: Contains Images of Bike and Car

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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 Algorithm Test Data Predictive Model Predicted Outcome labels Labels

Supervised Learning Model

Variables

Labels are right answers from historical data

e.g. Image of Car/Bike Input Data: Contains Images of Bike and Car

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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

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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)

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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

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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

Register Now:

www.tatvic.com/webinar March 19th _{11:00 AM PDT}

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Maximize Revenues on your Customer Loyalty Program using Predictive Analytics