Customer Segmentation Katarína Frühwaldová
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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.
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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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A Single Customer View…
Simple Data Customers
Derived / Additional Data Contact/Response History
Households Products Transactions Relationship Managers
Scores Events Surveys/Questionnaires Demographics Profitability Measures Segments
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Customer Data You Should Collect - Banking
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
Complaints Risk profile
» Channel usage number (ATM, online, branch) per given time period.
» Delinquency/claim (number and amount per defined period)
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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Customer Intelligence Definition Data for modeling proces Profile & Segment Customers Tools Demo 33
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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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