Role of Segmentation

in Loyalty Marketing

Prof. Francisco N. de los Reyes

School of Statistics

University of the Philippines, Diliman

Marketing Maturity = Effectiveness & ROI

List Pull

Maturity of Direct Marketing

Mar

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Effe

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OI

Courtesy of SAS

SCV – Single Customer View

List Pull

SCV

Maturity of Direct Marketing

Mar

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Effe

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OI “How many customers do I have?”

Courtesy of SAS

Segmentation

List Pull

SCV

Segment

Maturity of Direct Marketing

Mar

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Courtesy of SAS

“Who are my customers?”

Analytics

List Pull

SCV

Segment

Analytics

Maturity of Direct Marketing

Mar

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Effe

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Courtesy of SAS

“How can I maximize my relationships?”

Event Detection

List Pull

SCV

Segment

Analytics

Event Detection

Maturity of Direct Marketing

Mar

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Effe

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Courtesy of SAS

“Who might leave me?”

Campaign Management

List Pull

SCV

Segment

Analytics

Event Detection

Campaign Mgmt

Maturity of Direct Marketing

Mar

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Effe

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: R

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Courtesy of SAS

“How effective are my campaigns?”

Inbound Right-Time Marketing

List Pull

SCV

Segment

Analytics

Event Detection

Campaign Mgmt

Real Time

Maturity of Direct Marketing

Mar

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Effe

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Courtesy of SAS

Optimization

List Pull

SCV

Segment

Analytics

Event Detection

Campaign Mgmt

Real Time

Optimize

Maturity of Direct Marketing

Mar

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Effe

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Courtesy of SAS

Levels of Segmentation

Info

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Courtesy of SAS

No Segmentation

Levels of Segmentation

Info

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Products Owned

No Segmentation

Levels of Segmentation

Info

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Channel Utilization

Products Owned

No Segmentation

Levels of Segmentation

Info

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Demographics

Channel Utilization

Products Owned

No Segmentation

Levels of Segmentation

Info

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Transaction Information

Demographics

Channel Utilization

Products Owned

No Segmentation

Levels of Segmentation

Info

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Courtesy of SAS

Psycho-graphics

Transaction Information

Demographics

Channel Utilization

Products Owned

No Segmentation

Levels of Segmentation

Info

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Psycho-graphics

Transaction Information

Demographics

Channel Utilization

Products Owned

No Segmentation

Segment of One

Customer Segmentation

Which customer segment contributes most to our bottom line?

Key Business Questions

Customer Segmentation

Which segments should we grow?

Key Business Questions

Customer Segmentation

Which segments should be retained or closely monitored?

Key Business Questions

Customer Segmentation

What are the profiles of customers in each segment?

Key Business Questions

Customer Segmentation

What products are saleable in each segment?

Key Business Questions

Customer Segmentation

• Identifies strategic business focus and direction

• Analysis of customer behavior to gain insight

into customer needs and preferences

Key Benefits & Capabilities

What makes a segment?

Measurable identifying elements that distinguish from others

Segments desirably have these characteristics:

What makes a segment?

Defined contact points or channels through which communication is possible

Segments desirably have these characteristics:

What makes a segment?

Quantifiable size

so that cost computations may be done for targeting them

Segments desirably have these characteristics:

What makes a segment?

Have generally unique stated or implied needs

regarding the product or service

Segments desirably have these characteristics:

What makes a segment?

Stability and robustness to random shocks

(applies to some applications)

Segments desirably have these characteristics:

What is Segmentation?

“a process of creating groups of customers whohave SIMILAR behavior and characteristics”

Segmentation Types

Unsupervised data-driven segmentation; segments determined after data gathering and processing using statistical analyses

Supervised segmentation based on pre-defined factors

Supervised Segmentation

• Usually uses less variables with pre-defined “cuts”.

• Ad-hoc, user-driven

• Other variables are used as mere profilers and not active segmenters

• Applicable when user has a distinct focus and variables of interest are readily available.

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Some Prototype Segmentations

Customer Value versus Tenure

Customer Value versus Transaction Type & Frequency

Customer Value versus Risk

Profit Margin or Profit Rate against Tenure, Transaction Frequency or Risk

Purchase Behavior

Other possible information:

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Variety of Products Availed Life Stage Family Life Cycle The Remittance Market

Segmentation Variables

• Measures the amount of business brought in by the customer

• Also measures the capacity of a customer for cross-sell/upsell

• There is difficulty in measuring “high”, “medium” and “low” value.

• There are varying indicators of value• ADB (CA/SA) , Investments

• Loan amount/ Outstanding Balance

• Total purchase per transaction

Customer Value

Segmentation Variables

• Measures the loyalty of customer with respect to time

• Usually a “net time value”, i.e. lulls between product availment are not counted

• Skewness in data is an issue

Tenure

Segmentation Variables

• Identifies the “sleepers” from “transactors”

• Number of Transactions per Month is a usual metric.

• Time-between-transactions is a good substitute segmentation variable

Transaction Frequency

Segmentation Variables

• Tag customers given certain warning signals

• common indicators are:• Low ADB

• Defaults

• Lapses and claims

Risk Indicators

Segmentation Variables

• Metric for each customer’s contribution to total profit

• Used to level the number of products with the value of products availed

Profitability

Segmentation Variables

Common in Market Research but also evident in transactional information

• Utility/benefit from product

• Usage rate

• Loyalty vis-à-vis switching, hopping, ambivalence

• Propensity/Proclivity to buy/avail/take-up

• Temporal stimuli (payday, holidays, special events)

Behavior

Segmentation Variables

Some segmentation variables are also profiling variables

• Age, number of dependents, marital status

• Ownerships (home, car, business, etc.)

• Employment (nature of business, position, job tenure)

• Geographic information

• Delinquencies/ Fraud history, if any

• Channels

Profiling Variables

Cases in Point

Company A

• Launched a loyalty card

• Has big data on transactions

• Known as an innovator

• Challenge is to avert the impact of patent expiry and generic erosion

Company B

• Has different/diverse businesses in different industries

• Has product ownership, transactional data

• Challenge is to maximize customer relationship through cross-sell and upsell

Step 1: List Pull

• Involves definition of target population• By featured product/s

• By time period of observation and analysis

• By geographic coverage

• Brainstorm on Key Metrics and required raw data• Demographics

• Transactional behavior

• Profitability Drivers

List of Customers

List of Customers

Step 2: Single Customer View

• Consolidation of customer level information throughout the entire collection of data to be used for analytics

• Through the SCV, the analyst can tract a specific customer’s profile, behavior & profit contribution.

• The SCV is the recipient of scores

derived from analytics exercises.

Step 2: Single Customer View

SCV lends itself to queries

Statistical Matching

Removed inactive accounts

Removed cancelled accounts

Corporate Retail

Step 3: Segmentation

• Identify and understand best and worst performing customers

• Input for programs that focus on the following:• Increasing profitability

• Motivating positive behavioral changes:• Activate sleepers

• Increase usage of active customers

• Leads to best targets for cross-selling and up-selling

• Protect our most valued customers• It’s more expensive to acquire a new customer than retain a good one.

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Use of card for entertainment (bars, resto)Use of card for gym, fitness centers. Highest internet usage

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Increased purchases at apparel stores and accessory storesHigh balances

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Use of card for travel & airfareHighest international usageHighest internet usage

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Daily needsUse of the card mainly for supermarkets and gas.

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Lowest purchase frequencyInfrequent but high value transactionsMain spend is electronic / appliance

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

>=50% spend on InstallmentLow retail spendRevolver

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Use of card for heath purposes and DIY shopsLowest internet usageInfrequent but high value purchases

Sample Segmentation

Source: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos

Diverse Card Usage.Purchase at different merchantsModerate balance amountHigh purchase frequency

Sample Segmentation

One segmentation led to another segmentation that targets loyalty.

Patient Segmentation

Doctor Segments (Example)

High Growth Potential

Highest % Highly-compliant low dosage usersAlso some highly-compliant high dosage users.

Lowest % Low-value patients

Profile Not recruiting actively. Most are interns.

Step 4: Analytics

• Wide array of statistical analysis aimed at understanding the customer base and the derived segments.

• Typical techniques are product association (market basket analysis), portfolio analysis (reports).

Companies A and B reached up to here.

Step 5: Event Detection

• Attempt to answer the question, “Who among my customers are likely to leave me?”

• This is usually addressed by Churn Modeling.

Example: ActualChurned Stayed Total

Model

Says

“Churn” 3,151 1,335 4,486

“Stay” 529 2,985 3,514

Total 3,680 4,320 8,000

Using logistic regression analysis, the model was able to capture

87% of the true state of nature (true churners and true stayers).

Further drill-down is done within the four outcome states.

Step 6: Campaign Management

Action: Prioritization & Retention

Step 6: Campaign Management

Action: Cross/Up Selling & Retention

Step 6: Campaign Management

Action: Brand Awareness

There are solutions which optimize Customer Management Process that reflects the voice of the customer, promotes retention and relationship building, supports business goals, leverages events / triggers, and is cross-channel and cross Business Unit.

Step 7: Inbound Right-Time Marketing

• “Right message at the right place and at the right time”

• Objective is to make heralds out of the customers

Step 8 : Optimization

• Cutting edge innovation

• Tailor-fit customer relationship

• Affinity and pride is established

• Must beware of oversolicitation.

Please Remember

• The goal of the segmentation analysis is to create manageable and meaningful customer groups among customers.

Please Remember

• Segmentation is instrumental in increasing shareholder value by identifying:• Most high-value segment(s)

• Segments with high potential for cross selling and/or up-selling

• By focusing communications on a targeted segment, a causal effect would be a reduction in campaign costs

Please Remember

• Segment definition • Supports retention, service prioritization and cross selling / up-selling efforts

• Serves as input in developing new products

• Segmentation is both a science and an art!

Thank you for your attention!

Prof. Francisco N de los Reyes

School of Statistics

University of the Philippines, Diliman