Dynamic customer segment analysis and behavior prediction using data mining

1. National Tsing Hua University Department of Industrial Engineering and Engineering Management Dynamic customer segment analysis and behavior prediction using data mining Group 1: Margaret Dlamini Saumen Bhaumick Daniel Chen Ricky Huang July Panoso

2. Abstract • CRM is mainly to Understand customer well • By • Studying the difference between the Customers through customer segmentation. • Track customers shift from segment to segment • Discover customer segment knowledge • Predict Customer segments behavior pattern

3. CRM We believe keeping and managing the customer is most important: • Attractive Personalized services to satisfy Customer needs • CRM- Closer and deeper relationships with customers

4. Understanding Customers. • Analyzing Customers Information. • Differentiate Customers through Segmentation • Increase Customer loyalty through Customized products • Predict Customers Purchase behavior

5. Contact and Serve CustomersThrough Channels To understand customers its essential to integrate the data collected thru. • Web browsing • Purchase behavior • Complaints • Demographics

6. THE DATA The Customer segments and related knowledge discovered from multiple data sources change as Customer base changes • Thus valid for a particular period • Most existing predictions methods fundamentally are based on numerical and historical data patterns (using simple regression or neural network techniques)

7. FLUCTUATIONS This can be quite fluctuating caused due to • Promotions • New product launching • Customer support policies

8. Customer Segment • This study tracks the customer shift among customer segments • Monitor changes overtime • To discover customer segment knowledge • Predict Customer’s segment behavior pattern

9. Prediction on Customers behavior By studying the segment shift each customer might shift • Build a career path of each customer • By aggregating each customers career path, derive the Dominant career paths (majority of customers follow)

10. Process to Segment Customers • Choose a basis of segmentation, with appropriate variables (demographic or behavioral) • Use a multivariate analysis to group together or split customers. • Evaluate and validate the outputs. • Analyze the results in economic terms

11. Segmentation Design schemes • Measure used for segmentation • Number of resulting segments • View about the change overtime • Segmentation techniques used • Number of the customers selected

12. Segmentation Measures The segmentation variables consists of one or a combination of the following • Demographic • Geographic • Psychographic or Behavioral The behavioral purchase pattern can be RFM (Recency, Frequency and Monetary) FRAT (Frequency, Recency, Amount & Type)

13. Number of Resulting Segments • Minimize combined direct and opportunity cost of the Segmentation as critera for optimum number of segments • Allow the derivation of equal sized segments • Judgmental decisions are on the basis of number of segments

14. View about change overtime • Through occasion based design that assumes that people vary in their needs across occasions of product purchase. • Other way is to consider time-segmented customers through repeated measurements of the same customer at different point in times

15. Segmentation Techniques Statistical Methods • K -mean algorithm • Discriminant Analysis • Logistic Regression Machine learning Techniques • Neural Networks (Normally its considered that neural network are more accurate compared to statistical methods)

16. Number of Customers • The Customer segmentation can incorporate all the customers or can be limited to sample of them. • If the segmentation is based on sample, its important to predict how many customer falls in that group(Via inferential statistics)

17. Profitability • Predict changes in the segment to derive static characteristic of the segment • Changes in the segment closely relates to increase or decrease in profitability obtained from the segment

18. Research Overview • This study focus on behavioral variables include customer’s product usage. • Recency, Frequency, Monetary (RFM) analysis. • Self-Organizing Map (SOM) : uses neural clustering method to divided the retailer’s customer into numerous groups.

19. Cont. • This paper collect data from July 2001 to September 2002. • Segment customers five times during fifteen months • One quarter is a time window to create new segmentation.

20. Cont. • Individual career path: present a single customer’s history of shifts. • Dominant career path: a descriptive pattern, which explains common histories most customer might follow. • One leading to a loyal segment and the other leading to a vulnerable segment. • This study also provide a analytical method for predicting time-variant segment movement a customer might show.

21. SEGMENTING CUSTOMERS • We should be use a clustering analysis of product usage or purchase. • Purchase transactions have four features: • Customer number or customer ID • Recency value • Frequency value • Monetary value

22. Data preparation for the segmentation We have 3 situations: • Newcomers (don’t have any purchase before period t) • Old customers (but made purchase during period t) • Old customers (but don’t make purchase during period t) How can we calculate RFM?

23. Newcomers (do not have any purchase history before period t) rt = measures how long they made purchase ft = measures how frequently they make purchase mt = measures how much money they spend

24. Old customers (but made purchase during period t) Rt-1 - rt = Recency value for period t Ft-1 - ft = Frequency value for period t Mt-1 - mt = Monetary value for period t Note. Rt-1,Ft-1 ,Mt-1, stand for cumulative to period t-1

25. Old customers (but don’t make purchase during period t) Rt-1 + 3 months = Recency value for period t Ft-1 + 3 months= Frequency value for period t Mt-1 + 3 months = Monetary value for period t

26. Self-organization of customers The SOM does unsupervised clustering • Records within a group or cluster tend to be similar to each other • Records in different groups are dissimilar The SOM will end up with a few output units: • Strong units • Weak units The strong Units represent probable cluster centers

27. Segmentation results 2 techniques to speed up the SOM: • It is to vary the size of the neighborhoods: From large to small • The other is to have the winning neuron use a larger learning rate than that of the neighboring neurons

28. Summary of customer statistics per quarter

29. Loyal Vulnerable Newcomer Summary of customer segment characteristics for the third quarter of 2001

30. Result of the successive five-time segmentation

31. Discovering individual career path and dominant career path • Five-time segmentation makes it possible to combine segment shift histories into a career path. • Natural life cycle • Migration • External factors

32. Changes in segments Over successive quarters there are changes in the number of customers in a segment indicating certain strategies that management should review for the CRM

33. Segment shifts of customers from Q3 2001 to Q4 2001

34. Dominant career paths of length 3, which lead to segment R↓F↑M↑ Path No.of customersProbability (%)

35. Dominant Career Paths of length 5, which lead to segment R↑F↓M↓ Path No.of customersProbability (%)

36. Predicting Career Paths • Prediction of customer’s segment shifts can be classified as a classification task from the data mining perspective. • This case study use a decision tree induction technique and choose C5.0 to predict the time-variant career paths.

37. Decision Tree Induction Technique • The C5.0 algorithm has a special method form improving its accuracy rate called boosting. • Boosting working by building mutiple models in a seqience. • The next tree is used to modify and improve the previous one.

38. Data Preparation for the Prediction • The case generate 6 models for categorical predictions. • Choose the best model with the highest accuracy.

39. Training six prediction models

40. Summary of the Prediction Accuracy of C5.0 Models

41. Prediction Accuracy Statistics for Best Model, PMe

42. Prediction Accuracy Statistics for Best Model, PMe Newcomer Segment

43. Prediction Accuracy Statistics for Best Model, PMe Total Predict Accuracy (36286+ 33133) / 97856 *100% = 71% R↓F↑M↑ Predict Accuracy (36286) / 50478 *100% = 72% R↑F↓M↓ Predict Accuracy (33133) / 47378 *100% = 70%

44. Performance evaluation of PMe Model • Because the training set contains only a few cases about the newcomer segments(7.8%), the model PMe could hardly learn the pattern about them. • Accuracy predictions for rare categories will earn a higher performance evaluation.

45. Conclusion • This paper have proposed segment-based knowledge discovery method used for derivation of the descriptive pattern: predict the path customer will shift. • Try to resolve the fundamental problems : changing characteristics of customer in segment and change in its composition.

46. Cont. • Further research • Extend the prediction accuracy • Using neural network • Building a separate classifier for different segments and combining result from multiple classifier.