CRM and Information Visualization

1. CRM and Information Visualization Gürdal Ertek, Ph.D. Tuğçe Gizem Martağan

2. Customer Relationship Management (CRM)

3. What is CRM? “The approach of identifying, establishing, maintaining, and enhancing lasting relationships with customers.” “The formation of bonds between a company and its customers.”

4. Strategies in CRMfor Mass Customization • Prospecting (of first-time consumers) • Loyalty • Cross-selling / Up-selling • Win back or Save

7. The Marketing PerspectiveCAMPAIGN MANAGEMENTRECENCY FREQUENCY MONETARY VALUE METHODCUSTOMER VALUE METRICS

8. Campaign Management: The Marketing Perspective • Developing effective campaigns • Effectively predicting the future • Retaining existing customers • Acquiringnew customers

9. Campaign Management: The Cap Gemini Model KNOW Understand market and consumers’ needs and preferences Exploit customer intelligence, Perform segmentation TARGET ( Offer is developed ) Define market strategies Use channel integration SERVICE Retain customers by: Loyalty programs Communication Service forces SELL Acquire customers Use sales force effectively Develop marketing programs

10. Campaign Management:The Marketing Perspective The marketing manager... • Defines objectives • Identifies customers • Defines communication strategies • Designs/improves products/offers/services/promotions • Tests the impacts of her decisions • Revises her decisions for maximum effectiveness

11. Campaign ManagementStep 1: Define Objectives Targeting Existing Customers Retention Strategy Creating Loyalty? Increasing the satisfaction level? Cross-selling or Up-selling? Targeting New Customers Acquisition Strategy Target customers that show characterstics similar to existing groups of customers

12. Campaign ManagementStep 2: Identify Customers Perform SEGMENTATION • Define the right customers • Use information of past transactions as key for making predicting future ones • Define the segments and their characteristics • Develop customized marketing strategies for the different segments

13. Campaign ManagementStep 3: Communication Strategies • Which message should be transmitted? • Which channel should be used?

14. Campaign ManagementStep 4: Design the Products, Offers, Services and Promotions • Analyze the price, time period, risks, marketing costs • Define the product / offer / service / promotion and its general structure • Identify effective use of sales and communication channels

15. Campaign ManagementStep 5: Test the Impacts • Impacts of the decisions have to be tested and and assessed on a sample

16. Campaign ManagementStep 6: Revise the Decisions • Make revisions to the targeted offer / service / promotions • Finally apply the decisions to the whole segment or population

19. RFM Method(Recency, Frequency, Monetary Value ) • Recency • When was the last customer interaction? • Frequency • How frequent was the customer in its interactions with the business? • Monetary value of the interactions

20. RFM Method(Recency, Frequency, Monetary Value ) Marketing Problem: A firm has sent e-mail to 30,000 of its existing customers, announcing a promotion of $100. 458 of them responded (1.52% of the customers) Is there any relation between the responding customers and their historical purchasing behaviours?

21. RFM Method:Recency Coding • 30,000 customers are sorted in descending order with respect to their most recent purchases • Sorted data is divided into 5 equal groups, each of them containing 6,000 people • Recency codes are assigned: Top group has code 5, bottom group has code 1

22. RFM Method:Recency Coding Recency Results • According to analysis based on customer recency, the group having the highest recency group has also the highest response rate • Remark: (3.10% + 2.00% + 1.50% + 0.62% + 0.38) / 5= 1,52% which is the response rate • Strict Rule: Ones who have purchased recently are much more willing to buy new products than others purchasing in the past

23. RFM Method:Frequency Coding • Sort the 30,000 customers with respect to frequency metrics. • Frequency metrics: Average number of purchases made by customer in a time period t • Sort customers in descending order with respect to their purchase frequency. • Assign them to 5 groups, top %20 in the first frequency group. • Assign frequency codes such that the top group has code 5 and the bottom group has code 1.

24. RFM Method:Frequency Coding Frequency Results • It is observed that highest response rate is from the customers having highest frequency • Frequent people respond better than less frequent ones but differences between groups are less than the ones in the recency • The lowest frequency group always contains new customers • That is why it is named RFM

25. RFM Method:Monetary Value Coding • The same process as recency and frequency coding • Sorting is done with respect to monetary value metric • Monetary value metric is the average amount purchased in a time period t • At the end of the monetary value coding, assign monetary value codes M = 1,...,5 to groups according to their groups.

26. RFM Method:Monetary Value Coding Frequency Results • It is observed that highest response rate is from the customers having highest monetary value • Unlike the recency case, there are not big differences between groups

27. RFM Method:Putting the Codes Together • At the end of the monetary coding firm obtain R F M metrics for customers. Each customer belongs to one of 125 possible combinations of the RFM values: Database R 1 2 3 4 5 F 21 22 23 24 25 M 231 232 233 234 235

28. RFM Method:STEPS • Create 3 digits RFM codes cells • All cells having the same number of customers in them • RFM values are used to define group of customers that marketing campaign should target or should avoid • Used for identifying customers having high probability to respond to campaigns: 555’s response rate > 552’s > 543’s >541.... • Increase the response rate • Increase profitability

29. Customer Value Metrics • Critical measures used to define customer worth in knowledge-driven and customer-focused marketing

30. Customer Value Metrics:Size of Wallet • Size of wallet = • Assumption: Firms prefer customers with large size of wallet in order to retain large revenues and profits Sales to focal customer by firm j

31. Customer Value Metrics:Individual Share of Wallet (SW) • A proportion expressed in terms of percentage, calculated among buyers • Measured at individual level • A measure of loyalty • Can be used in future predictions • Different from the “market share”, which also considers customers with no purchase • Individual share of wallet % = Sales to focal customer by firm j

32. Share of wallet and size of wallet should be analyzed together because... Customer Value Metrics

33. Shows expected share of wallet from multiple brands Depicts consumer’s willingness to buy over time Transition probability from B to A, than from A to C: 10%*20% = 2% Customer Value Metrics:Transition Matrix

34. The Engineering PerspectiveDATA MINING

35. Data Mining • Collection, storage, and analysis of –typically huge amounts of- data • Data readily resides in the company’s data warehouse • Data cleaning is almost inevitable

36. Data Mining Goals of Data Mining • Developing deeper understanding of the data • Discovering hidden patterns • Coming up with actionable insights • Identifying relations between variables, inputs and outputs • Predicting future patterns

37. Data Mining:Steps • Data selection • Data cleaning • Sampling • Dimensionality reduction • Data mining methods

38. Data Mining:Methods • Exploratory Data Analysis • Segmentation • Cluster Analysis • Decision Trees • Market Basket Analysis • Association rules • Information Visualization • Prediction • Regression • Neural Network • Time Series Analysis

39. Information Visualization Data mining algorithms... • Can only detect certain types of patterns and insights • Are too complex for end users to understand

40. Information Visualization • A field of Computer Science which has evolved since the 1990s. • Before 1990s: Graphical methods for data analysis to pave the way for statistical methods • After 1990s: • Computer hardware has advanced with respect to memory, computational power, graphics calculations • Software has advanced with respect to user interfaces • Data collection systems have advanced (barcodes, RFID, ERP)

41. Information Visualization • The analyst does not have to understand complex algorithms. • Almost no training required. • There are no limits to the types of insights that can be discovered.

42. Case StudiesAnalysis of Supermarket Sales Data

43. The Data

44. Frequent Itemsets

45. Frequent Itemsets

46. Association Rules

47. Case StudiesAnalysis of Spare Parts Sales Data

48. The Data Assumption: Each customer gives at most one order each day.

49. Determining Top Products:Pivot Table for Determining REVENUE_SUM

50. Determining Top Products:Pivot Table for Determining COUNT (Frequency)