Download Between myth and reality Customer Segmentation

Between myth and reality
Customer Segmentation
Iuliana Maria
Project coordinator, CFE
Today’s Agenda
I.
Customer segmentation vs. Market segmentation
II.
Tips and … traps in customer profiling
III. Building the models - Oracle Data Mining
About customer segmentation
Customer segmentation aims at optimizing the effort, in other words, at
reducing unproductive segments while focusing on profitable ones.
Segmentation helps to:
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Prioritize new product development efforts
Develop customized marketing programs
Choose specific product features
Establish appropriate service options
Design an optimal distribution strategy
Determine appropriate product pricing
How about Data Mining?
Data mining automatically sifts through data to find hidden patterns,
discover new insights and make predictions
Data Mining helps to:
Predict customer behavior (Classification)
Predict or estimate a value (Regression)
Segment a population (Clustering)
Identify factors more associated with a business
problem (Attribute Importance)
• Find profiles of targeted people or items (Decision Trees)
• Determine important relationships and “market baskets” within the
population (Associations)
• Find fraudulent or “rare events” (Anomaly Detection)
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Segmentation in customer-centric
company
1. Customer segmentation is NOT market segmentation!
Product-centric companies segment markets (macro-segmentation). They do not
know (and don’t care to know) their customers individually.
Customer-centric companies segment their known customers. Each individual is
understood to be different from others and belongs to a specific segment.
2. You segment ONLY by Value and Needs. Nothing else!
Demographics, attitudes, behaviors, lifestyles, preferences – are all valid
segmentation dimensions, but we use them ONLY as indicators of Needs.
Needs and Value are fundamentally related by a cause-effect relationship at the very
core of economics.
3. DO something with the knowledge!
Today’s Agenda
I.
Customer segmentation vs. Market segmentation
II.
Tips and … traps in customer profiling
III. Building the models - Oracle Data Mining
Descriptive and predictive models
Business Goal
Priority
Get customers
Acquisition
Keep
customers
Retention
Grow
customers
Development
Retain
customer
Re-target needs
Ingenio models:
Acquisition
Up-sell
Cross-sell
Loyalty
Attrition
Typical development workflow
Behavioral data
Background knowledge (e.g. socio demographics, income)
Latent variable analysis (e.g.
competition offers)
Profiling models
Prediction of behavioral pattern
Customer feed-back
analysis
Marketing campaign
Segmentation of target population
Propensity to buy
models
Segment - specific targeting
Typical development workflow
1. Sampling
•
Propensity to buy models are particularly vulnerable to data sampling, due
to the lack of confirmed training data. E.g. a bank knows with certainty the
products the client owns/does not own in the bank, but has little/no
information about products the client has in other banks. Thus, a 2 –step
methodology is commonly used, allowing to identify profiles first, verify
predicted data by a marketing campaign on a sample of selected
customers, and refine the first scorecard into an accurate Propensity to buy
model.
2. Just Identified Model (Profiling model)
• Firstly, a Profiling model is sketched based on behavior data, and, where
possible, on socio-demographical and income variables
Typical development workflow
3. Preliminary analysis of the model
• Correlation between variables are identified at this stage, as well as
independent and dependent variables
4. Identifying non-causal paths
• At this stage, variables with low predictability power are eliminated, along
with characteristics or records biased by latent variables (e.g. competition
offers). Where consistently collected, latent variables are integrated into the
model.
5. Over identified Model
• Eliminating the non-causal paths from the Just Identified Model, the basic
Profiling model is reweighted and calibrated.
6. Further refinement of the model
• At this stage, the model is being fitted against multiple behavior patterns.
Causality links are reassessed after receiving real customer feed-back e.g.
targeted populations prove to be users of the product at other banks.
Typical development workflow
7. Building the Propensity to Buy model - the revised coefficients
•
The scores will be reweighted after a detailed reconstruction of training data. In the
example above, initially „bad‖ customers will be included into „good‖ customers at
dataset selection for a propensity to buy model. For further segmentation, the
recommended approach is to define 2 separate models, in order to identify the
underlying factors behind buying decision with competitors.
Typical development workflow
8. Testing the over identified model – the Challengers
•
When the model is validated on test datasets, it does not necessarily mean
that the phenomenon is completely understood. Latent variables must
always be searched and reevaluated, e.g. employment rate, drop in retail
lending market, special offers etc. These can change the working
hypothesis, leading to changes in training datasets and characteristics. New
models are thus build to „challenge‖ the existing model, following the
Champion – Challenger methodology. The „challengers‖ are monitored
throughout established periods of time on the same data as the „champion‖,
and if conclusive superior prediction performance is demonstrated, one
Challenger will replace the Champion.
Segmentation Models (examples)
Two quick examples of crossselling products are deposits and
internet banking.
The right panel illustrates with
medium-detail the attributes
which shall be analyzed in order
to profile the ―client willing to
purchase a deposit‖.
Model type: cross-sell
Example subject: deposit
Analyzed attributes (detailed):
• Balance of RON-credits
• Balance of current accounts
• Consumer-credits
• Credits for personal needs
• Credits for personal needs related to mortgage
• Mortgage credits
• Car-leasing credits
• Credit Cards
• Insurance
• Gross-income
• History of client-bank relation
• Debit/credit transactions related to current
accounts
• Internet/ATM/POS/Cash transactions
Segmentation Models (simplified)
Model type: loyalty
Subject: identify the most effective cost-result product to satisfy the client
Analyzed offer types:
• Discounts
• Enhanced credit limits
• Other products/services relevant to the customer
Model type: attrition
Subject:
a) profile client with a pattern for ―exit‖
b) identify source of unhappiness to develop an offer which revive his
interest
Analyzed attributes:
• Correlation between the product-cancelation and competitors’ offers
• Correlation between the product-cancelation and economic situation
• Correlation between the quality of relation bank-client and productcancelation
Segmentation Models (simplified)
Model type: up-sell
Subject: raising the credit limit of a credit-card
Analyzed attributes (simplified):
• Products type which belong to client
• Trend in income
• Spending behavior (value, transaction type, place)
Model type: profiling as part of an up-selling strategy
Subject: top-affluent
Analyzed attributes:
• Expense behavior in restaurants (with a focus on luxury
segment)
• Purchase behavior regarding luxury market
• Air travels (non low-cost)
• ATM/POS transaction
• Retail transaction
Today’s Agenda
I.
Customer segmentation vs. Market segmentation
II.
Tips and … traps in customer profiling
III. Building the models- Oracle Data Mining
Oracle Data Mining 11g
• Oracle environment:
– Eliminates data movement
– Eliminates data duplication
Oracle 11g
Data Warehousing
ETL
• Oracle Database #1
• Oracle Relational Database #1 in Revenue
• Now, analytical database platform
– 12 cutting edge machine learning algorithms and
50+ statistical functions
– Anomaly detection
– Association rules (Market Basket analysis)
– Attribute importance
– Classification & regression
– Clustering
– Feature extraction (NMF)
– Structured & unstructured data (text mining)
OLAP
Statistics
Data Mining
In-Database Data Mining
Traditional Analytics
Oracle Data Mining
Results
Data Import
Data Mining
Model ―Scoring‖
Data Preparation
and
Transformation
Savings
Data Mining
Model Building
Model ―Scoring‖
Data remains in the Database
Embedded data preparation
Data Prep &
Transformation
Model ―Scoring‖
Embedded Data Prep
Data Extraction
Model Building
Data Preparation
Hours, Days or Weeks
Source
Data
SAS
Work
Area
SAS
Process
ing
Process
Output
SAS
SAS
SAS
• Faster time for
―Data‖ to ―Insights‖
• Lower TCO—Eliminates
• Data Movement
• Data Duplication
• Maintains Security
Target
Secs, Mins or Hours
Cutting edge machine learning
algorithms inside the SQL kernel of
Database
SQL—Most powerful language for data
preparation and transformation
Data remains in the Database
Oracle Data Mining Algorithms
Problem
Algorithm
Classification
Logistic Regression (GLM)
Decision Trees
Naïve Bayes
Support Vector Machine
Classical statistical technique
Popular / Rules / transparency
Embedded app
Wide / narrow data / text
Regression
Multiple Regression (GLM)
Support Vector Machine
One Class SVM
Classical statistical technique
Wide / narrow data / text
Data pre-processing
Minimum Description
Length (MDL)
Attribute reduction
Identify useful data
Reduce data noise
Market basket analysis
Link analysis
Anomaly
Detection
Attribute
Importance
Association
Rules
Clustering
Feature
Extraction
A1 A2 A3 A4 A5 A6 A7
Apriori
Hierarchical K-Means
Product grouping
Text mining
Hierarchical O-Cluster
Gene and protein analysis
Text analysis
Feature reduction
NMF
F1 F2 F3 F4
Applicability
Model development workflow
Model development workflow
Model development workflow
Model development workflow
Model development workflow
Model development workflow
Model development workflow
Thank you!
Iuliana Maria
Project coordinator, CFE
0740 10 2002
[email protected]
Ingenio Software
3 Gh. Sontu, Bucharest
Tel: +4021/407-8100;
Fax: +4021/260-0890
E-mail: [email protected]