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Offer Targeting in Financial Services: Leveraging A Big Data Clustering Strategy

Presentation to Analytics, Big Data, and the Cloud Conference, Edmonton Alberta, April 24, 2012


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Cross-sell/Up-sell Offer Targeting: A Financial Services Case

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Š Deloitte & Touche LLP and affiliated entities.


Target Marketing by Banks: We all have been recipients of their offers! Traditional Targeting Methods

Type of Analysis

RFM Analysis on Customer data • Naïve deciling of customers • Target customer groups with low value (RFM= Recency, Frequency, Monetary) and high-potential

Predictive Models: Internal or Third-party Scoring of Customer Records

Marketing Research Segmentations systems

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Score customers as targets based on their geo-spatial proximity to third party segmentation systems (e.g. Mosaic, Psyte)

Customized segment systems based on surveys. Scoring non-surveyed customers is done over time.

© Deloitte & Touche LLP and affiliated entities.


Project for Canadian FI: Cross-sell/up-sell to grow wallet The Challenge to Deloitte

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Goal

Improve closing rates on offers through unsupervised data-mining (machine learning) techniques on the customer EDW.

Project Objective

Provide sub-groups of customers each with a unique product offer where a high propensity to adopt product is identified.

Deliverables

o 3.1 million prioritized cross-sell candidates identified o SAS models implemented on client platform to enable updates by client on ongoing basis.

Deloitte Analytics

Š Deloitte & Touche LLP and affiliated entities.


Project for Canadian FI: Cross-sell/up-sell to grow wallet The Approach

Four-stage modeling process: ADS Build

 Build analytical data set (ADS) including 9.3 million customers, each with 3,200 attributes.

Segmentation

  

Self-organizing Map (SOM) + Hierarchical Clustering. Training sample was 250K with 3200 variables 40 Clusters identified.

Score EDW

Classification methodology employed to classify rest of 9.3 million customers into 40 clusters.

Create Final Target Lists

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Business Rule Criteria:   

Proximity to other product holders Propensity to take up product High wallet size

© Deloitte & Touche LLP and affiliated entities.


Project for Canadian FI: Cross-sell/up-sell to grow wallet The Modelling The Self Organising Map (SOM) methodology is an unsupervised modeling tool that clusters customers based on proximity to each other Brand Loyal customer: single, rich, a mix of city and non-city dwelling - and an active member of the loyalty program

1. SOM’s reduce high dimensional and complex data to a low-dimensional grid (two dimensions is typical). 2. Learning technique (neural network) with distance measure, learning function and stopping rule (error based).

Visual and Relational

Hierarchical and Granular

3. Essentially two stage clustering –  SOM first puts similar customers in same node.  SOM nodes can be further clustered for easier interpretation and management. 7

Deloitte Analytics

Total Flight Value (12mth) S8

Proximity and Similarity

S4

S6

S2

Highest value segments cluster in bottom left

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S3

S1 S9

S5

© Deloitte & Touche LLP and affiliated entities. 7


Project for Canadian FI: Cross-sell/up-sell to grow wallet The Modelling C39

C36

C30

C22

C25

C18

C23

C34

C7 C6 C26 C4

C20

SOM nodes rolled up to 40 discrete clusters

C35

C10

C11

C38

C40 C17 C37

C24

C3

C15

C9

C13 C16 C1

C29 C2

C21

C32

C33

C12

C27

Deloitte Analytics

C5 C14

C31

8

C8

C19

C28

Š Deloitte & Touche LLP and affiliated entities.


Example Interpretation: Cross sell a Mutual Fund SOM Attributes of Interest Using Heat Maps Age (Years)

Showing oldest customers in red and youngest customers in blue Dark green highlights opportunity based on lifestage.

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Mutual Fund Indicator

Dark green areas indicate customers without a MF, are in the right life stage, and are “similar” (close to) customers who already own a MF.

Share of Wallet

Currently client does not own much of their wallet and so cross-selling opportunity is large

Pickup Mutual fund in the last 6 months

Customers without a mutual fund are close or similar to customers who have picked up a MF recently

© Deloitte & Touche LLP and affiliated entities.


Project for Canadian FI: Cross-sell/up-sell to grow wallet The Pilot Results The pilot demonstrated that for certain products/offers, our targeting approach outperformed their Business As Usual (BAU) approach:

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Results

Lift above BAU

Credit Card migration

21% higher

Migration from “pay as you go” Overdraft Protection to Monthly

14% higher

Migration to higher value chequing plans

26% higher

Pre-Authorized Payment Plan cross-sell

14% higher

Deloitte Analytics

© Deloitte & Touche LLP and affiliated entities.


Improving the Model

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Deloitte Analytics

Š Deloitte & Touche LLP and affiliated entities.


Added Value

Analytical Maturity Roadmap We strive to move clients beyond simply tracking performance‌.

Simulation & Optimization Predictive Modelling

Segmentation

Influencing Performance

Visual data exploration KPI reporting

Tracking Performance

Operational reporting Analytical Sophistication 12

Deloitte Analytics

Š Deloitte & Touche LLP and affiliated entities.


Project for Canadian FI: Cross-sell/up-sell to grow wallet The future - toward bigger data….

1. Incorporate pilot response data into EDW – Update and refresh response models looking for other triggers to inform and customize future campaigns.

2. Incorporating more sources of data into EDW at customer level – adding volume and variety! – Use experiment design techniques to test and learn – simulate and optimize – Customer satisfaction surveys – Call center transcripts – Social media and online web data at customer level

Leverage these other sources of data to improve targeting models. 13

Deloitte Analytics

© Deloitte & Touche LLP and affiliated entities.


Tom Peters - Offer Targeting in Financial Services  
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