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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: • • • • • • 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) • • • • 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]