Customer Segmentation

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Customer Segmentation Katarína Frühwaldová

Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

Customer Intelligence Data for modeling process Profile & Segment Customers Tools Demo

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Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

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Customer Intelligence Data for modeling process Profile & Segment Customers Tools Demo

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Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

Customer Intelligence

Customer Intelligence is the concept of gathering, analyzing and exploiting information of the company's customers with the aim to create and maintain a mutually beneficial relationship between the Organization and its Customers where the Organization attempts to maximize the Life Time Value of the relationship.

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Marketing Analytics

MARKETING MARKET RESEARCH

MARKETING ANALYTICS

RESEARCH

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The task of Marketing Analytics

Marketing Analytics turns data into knowledge (profits)

Marketing Database

KNOWLEDGE DATA

Operational CRM

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Marketing Analytics helps to answer... More efficient customer retention

Better Cross-/ Up-Selling

Recovery of potentially valuable customer relationships

More efficient acquisition of new customers

Profit

Faster termination of potentially less valuable customer relationships

Acquisition

Intensification

Retention

time

Termination/Recovery 7

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Types of Analyses used in Marketing Analytics Fraud Probability Probability of Default

Cross-sell Probability

Segmentation

Product Association Expected Value of Response

Channel Preference

Up-sell Probability

Churn Probability

Activation Probability 8

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Customer Intelligence Definition Data for modeling process Profile & Segment Customers Tools Demo

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Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

A Single Customer View…

Simple Data  Customers

Derived / Additional Data  Contact/Response History

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Households Products Transactions Relationship Managers

Scores Events Surveys/Questionnaires Demographics Profitability Measures Segments

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Customer Data You Should Collect - Banking

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Customer demographics (retail/corporate client) Product information (number and types of products, loyalty) Transactional behavior



Balances

» Number, amount and types of transactions per defined time period » Average, opening and closing balances per defined time period.



Utilization of distribution channels

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Complaints Risk profile

» Channel usage number (ATM, online, branch) per given time period.

» Delinquency/claim (number and amount per defined period)

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Household relations Contact History Competitive information 11

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Customer Data You Should Collect Insurance  Customer Demographics  Relationship with the insurance company (tenure etc)  Product Portfolio (Policy and Riders Information)  Coverage Information  Claims Information  Premium and Payment Information  Agent Information  Complaints  Contact History  Competitive Information 12

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Customer Data You Should Collect - Telco

 Customer Demographics  Relationship with the insurance company (tenure etc)  Product Portfolio (number and types of products – prepaid/postpaid)  Messages Information  Airtime Information  Payment Information  Contact History  Competitive Information 13

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Customer Intelligence Definition Data for modeling proces Profile & Segment Customers Tools Demo 14

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Terminology

Segmentation is in essence the process by which items or subjects are categorized or classified into groups with similar characteristics. That characteristic could be one or more attributes. Randal S. Collica CRM Segmentation and Clustering

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Clustering Example Partition data into groups with similar characteristics.

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Behavioral and Marketing Segments

 Marketing segments - groups of customers who are similar to each other with respect to some socio-economic-demographic factors like: age, sex, family status, occupation, living area etc.  Behavioral segments - groups of customers who behave in a similar manner in relation with the business.

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Directed versus Undirected Segmentation

 Supervised (directed) segmentation – business analyst defines one or more target variables that should drive the segmentation  Unsupervised (undirected) segmentation – analytical algorithm uncovers hidden patterns that may be significant and useful for the given purpose.

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Segmentation Process

1. Segmentation Objective Definition 2. Data Preparation/Model Development/Scoring 3. Segment Profiling 4. Identification of Segment Strategies 5. Implementation of Segment Strategies 6. Measurement and Evaluation

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Segmentation Objectives

 Improving profitability through more effective marketing  Serving better to high priority customers  Changing the customer mix to provide a greater proportion of high-profit or high-profit-potential customers in the customer base  Provide global vision of company’s customers  Identify valuable customers  Identify cross-sell opportunities

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Data Preparation – Segmentation Variables

 Active variables - variables, which are expected to have an influence on clustering.  Descriptive variables - further profiling of the segments that are determined by active variables. They are used for identification of the main characteristics of the clusters.

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Number of Segments

 Trial and Error Process  Good Cluster Definition (for CRM purposes) – clusters whose members are very similar to each other while at the same time the clusters themselves are well separated (by criteria selected for clustering)  Business Purpose – 5 to 12 segments

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Segment Profiling

 Explanation of the assigned customer segments  The result - characteristics of a typical customer within each segment  Utilization in assigning the correct marketing segment for each behavioral segment  The typical explanative variables used in profiling:  Age, Marital status, Occupation, Education level, Annual income, Postal code (or information derived from that, such as city, town, or village), Activity level of customer, Life cycle of customer, Customers market segment, Residence status code

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Segment Profiling – Example

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Segment Scoring

Process of assigning the segment identification variable for each customer on the basis of some pre-specified segment structure.

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Segmentation Reports

 Customer Segment Size Report  Segment Movement Report  Migration  Distribution of Segments over Time

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Segment Strategies Define Segment Strategy and Action Plans for each Segment

WHO? WHAT? WHY? HOW MUCH? WHEN? HOW?

Segment description. Differentiation comparing to other segments. What products, bundles? Segment’s needs based message. Pricing rules. Time for interactions. Preferred distribution channels.

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Customer Intelligence Definition Data for modeling proces Profile & Segment Customers Tools Demo 28

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SAS Enterprise Guide

 Reporting, graphical and analytical tasks  Data management - preparation and cleaning  Statistical Analysis  OLAP access, visualization and manipulation  Result distribution and sharing  Administration and security  Scripting and automation 29

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SAS Enterprise Miner: Answers Questions Such As  Which customers are likely to respond to an offer?  Which transactions are likely to be fraudulent?  Which factors contribute to equipment failure?  Which prospects will be the best customers?  Which customers are likely to churn in short-term?  Which portfolios are likely to be the most risky?  Which claims are likely to be paid at less than 100%?

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Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

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Customer Intelligence Definition Data for modeling proces Profile & Segment Customers Tools Demo 33

Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

SEMMA Metodológia  SAMPLE – vzorky dát, vytvorením jednej alebo viacerých tabuliek

• Append • Data Partition • Filter • Input Data • Merge • Sample • Time Series

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SEMMA Metodológia  EXPLORE – preskúmanie dát, hľadanie vzťahov, vzorov, neočakávaných trendov...

• Association • Cluster • DMDB • Graph Explore • Market Basket • MultiPlot • Path Analysis • SOM/Kohonen • StatExplore • Text Miner (optional) • Variable Clustering • Variable Selection

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SEMMA Metodológia  MODIFY – úprava údajov vytváraním nových premenných, transformovaním, nahrádzaním hodnôt, rekódovaním...

• Drop • Impute • Interactive Binning • Principal Components • Replacement • Rules Builder • Transform Variables

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SEMMA Metodológia

 MODEL – vytváranie modelu použitím analytických metód

• AutoNeural • Decision Tree • Dmine Regression • DMNeural • Ensemble • Gradient Boosting • MBR • Model Import • Neural Network • Partial Least Squares • Regression • Rule Induction • SVM • TwoStage

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SEMMA Metodológia  ASSESS – výber najlepšieho modelu, aplikácia modelu na nové dáta

• Cutoff • Decisions • Model Comparison • Segment Profile • Score

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Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

Company Confidential - For Internal Use Only Copyright © 2010, SAS Institute Inc. All rights reserved.

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