1. Segmentation plays a key role in loyalty marketing by dividing customers into groups based on common attributes and behaviors. This allows companies to better understand their customers and maximize relationships. 2. There are various levels of segmentation from basic demographics and purchases to more advanced psychographics and transaction data. Companies can use both supervised and unsupervised segmentation. 3. Effective segmentation identifies strategic business focuses, provides insights into customer needs, and helps companies focus communications and campaigns. It is a process that aims to create meaningful customer groups.

1. Role of Segmentation in Loyalty Marketing
Prof. Francisco N. de los Reyes
School of Statistics
University of the Philippines, Diliman

2. Marketing Maturity = Effectiveness & ROIList PullMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS

3. SCV –Single Customer ViewList PullSCVMaturity of Direct Marketing Marketing Effectiveness: ROI “How many customers do I have?” Courtesy of SAS

4. SegmentationList PullSCVSegmentMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS“Who are my customers?”

5. AnalyticsList PullSCVSegmentAnalyticsMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS“How can I maximize my relationships?”

6. Event DetectionList PullSCVSegmentAnalyticsEvent DetectionMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS“Who might leave me?”

7. Campaign ManagementList PullSCVSegmentAnalyticsEvent DetectionCampaign MgmtMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS“How effective are my campaigns?”

8. Inbound Right-Time MarketingList PullSCVSegmentAnalyticsEvent DetectionCampaign MgmtReal TimeMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS

9. OptimizationList PullSCVSegmentAnalyticsEvent DetectionCampaign MgmtReal TimeOptimizeMaturity of Direct Marketing Marketing Effectiveness: ROI Courtesy of SAS

10. Levels of Segmentation Information Required Courtesy of SASNo Segmentation

11. Levels of Segmentation Information Required Courtesy of SASProducts OwnedNo Segmentation

12. Levels of Segmentation Information Required Courtesy of SASChannel UtilizationProducts OwnedNo Segmentation

13. Levels of Segmentation Information Required Courtesy of SASDemographicsChannel UtilizationProducts OwnedNo Segmentation

14. Levels of Segmentation Information Required Courtesy of SASTransaction InformationDemographicsChannel UtilizationProducts OwnedNo Segmentation

15. Levels of Segmentation Information Required Courtesy of SASPsycho- graphicsTransaction InformationDemographicsChannel UtilizationProducts OwnedNo Segmentation

16. Levels of Segmentation Information Required Courtesy of SAS1Psycho- graphicsTransaction InformationDemographicsChannel UtilizationProducts OwnedNo SegmentationSegment of One

17. Customer SegmentationWhich customer segment contributes most to our bottom line? Key Business Questions

18. Customer SegmentationWhich segments should we grow? Key Business Questions

19. Customer SegmentationWhich segments should be retained or closely monitored? Key Business Questions

20. Customer SegmentationWhat are the profiles of customers in each segment? Key Business Questions

21. Customer SegmentationWhat products are saleable in each segment? Key Business Questions

22. Customer Segmentation•Identifies strategic business focus and direction•Analysis of customer behavior to gain insight into customer needs and preferencesKey Benefits & Capabilities

23. What makes a segment? Measurable identifying elements that distinguish from othersSegments desirably have these characteristics:

24. What makes a segment? Defined contact points or channels through which communication is possibleSegments desirably have these characteristics:

25. What makes a segment? Quantifiable size so that cost computations may be done for targeting themSegments desirably have these characteristics:

26. What makes a segment? Have generally unique stated or implied needsregarding the product or serviceSegments desirably have these characteristics:

27. What makes a segment? Stability and robustness to random shocks(applies to some applications) Segments desirably have these characteristics:

28. What is Segmentation? “a process of creating groups of customers whohave SIMILAR behavior and characteristics”

29. Segmentation TypesUnsupervised data-driven segmentation; segments determined after data gathering and processing using statistical analysesSupervised segmentation based on pre-defined factors

30. Supervised Segmentation
•Usually uses less variables with pre-defined “cuts”.
•Ad-hoc, user-driven
•Other variables are used as mere profilers and not active segmenters
•Applicable when user has a distinct focus and variables of interest are readily available.
30

31. Some Prototype Segmentations
Customer Value versus Tenure
Customer Value versus Transaction Type & Frequency
Customer Value versus Risk
Profit Margin or Profit Rate against Tenure, Transaction Frequency or Risk
Purchase Behavior
Other possible information:
31
Variety of Products Availed
Life Stage
Family Life Cycle
The Remittance Market

32. Segmentation Variables
•Measures the amount of business brought in by the customer
•Also measures the capacity of a customer for cross-sell/upsell
•There is difficulty in measuring “high”, “medium” and “low” value.
•There are varying indicators of value
•ADB (CA/SA) , Investments
•Loan amount/ Outstanding Balance
•Total purchase per transactionCustomer Value

33. Segmentation Variables
•Measures the loyalty of customer with respect to time
•Usually a “net time value”, i.e. lulls between product availment are not counted
•Skewness in data is an issueTenure

34. Segmentation Variables
•Identifies the “sleepers” from “transactors”
•Number of Transactions per Month is a usual metric.
•Time-between-transactionsis a good substitute segmentation variableTransaction Frequency

35. Segmentation Variables
•Tag customers given certain warning signals
•common indicators are:
•Low ADB
•Defaults
•Lapses and claimsRisk Indicators

36. Segmentation Variables
•Metric for each customer’s contribution to total profit
•Used to level the number of products with the value of products availedProfitability

37. Segmentation Variables
Common in Market Research but also evident in transactional information
•Utility/benefit from product
•Usage rate
•Loyalty vis-à-vis switching, hopping, ambivalence
•Propensity/Proclivity to buy/avail/take-up
•Temporal stimuli (payday, holidays, special events) Behavior

38. Segmentation Variables
Some segmentation variables are also profiling variables
•Age, number of dependents, marital status
•Ownerships (home, car, business, etc.)
•Employment (nature of business, position, job tenure)
•Geographic information
•Delinquencies/ Fraud history, if any
•ChannelsProfiling Variables

39. Cases in Point

40. Company A
•Launched a loyalty card
•Has big data on transactions
•Known as an innovator
•Challenge is to avert the impact of patent expiry and generic erosion

41. Company B
•Has different/diverse businesses in different industries
•Has product ownership, transactional data
•Challenge is to maximize customer relationship through cross-sell and upsell

42. Step 1: List Pull
•Involves definition of target population
•By featured product/s
•By time period of observation and analysis
•By geographic coverage
•Brainstorm on Key Metrics and required raw data
•Demographics
•Transactional behavior
•Profitability Drivers
List of Customers

43. List of CustomersStep 2: Single Customer View
•Consolidation of customer level information throughout the entire collection of data to be used for analytics
•Through the SCV, the analyst can tract a specific customer’s profile, behavior & profit contribution.
•The SCV is the recipient of scores
derived from analytics exercises.

44. Step 2: Single Customer ViewSCV lends itself to queriesStatistical MatchingRemoved inactive accountsRemoved cancelled accountsCorporateRetail

45. Step 3: Segmentation
•Identify and understand best and worst performing customers
•Input for programs that focus on the following:
•Increasing profitability
•Motivating positive behavioral changes:
•Activate sleepers
•Increase usage of active customers
•Leads to best targets for cross-selling and up-selling
•Protect our most valued customers
•It’s more expensive to acquire a new customer than retain a good one.

46. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Use of card for entertainment (bars, resto)
Use of card for gym, fitness centers.
Highest internet usage

47. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Increased purchases at apparel stores and accessory stores
High balances

48. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Use of card for travel & airfare
Highest international usage
Highest internet usage

49. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Daily needs
Use of the card mainly for supermarkets and gas.

50. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Lowest purchase frequency
Infrequent but high value transactions
Main spend is electronic / appliance

51. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
>=50% spend on Installment
Low retail spend
Revolver

52. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Use of card for heath purposes and DIY shops
Lowest internet usage
Infrequent but high value purchases

53. Sample SegmentationSource: Data Mining Techniques in CRM: Inside Customer Segmentation, K Tsiptsis & A Korianopoulos
Diverse Card Usage.
Purchase at different merchants
Moderate balance amount
High purchase frequency

54. Sample SegmentationOne segmentation led to another segmentation that targets loyalty. Patient SegmentationDoctor Segments (Example) High Growth Potential
Highest %Highly-compliant low dosage users
Also some highly-compliant high dosage users.
Lowest %Low-value patients
ProfileNot recruiting actively.
Most are interns.

55. Step 4: Analytics
•Wide array of statistical analysis aimed at understanding the customer base and the derived segments.
•Typical techniques are product association (market basket analysis), portfolio analysis (reports).

56. Companies A and B reached up to here.

57. Step 5: Event Detection
•Attempt to answer the question, “Who among my customers are likely to leave me?”
•This is usually addressed by Churn Modeling.
Example:
Actual
Churned
Stayed
Total
Model
Says
“Churn”
3,151
1,335
4,486
“Stay”
529
2,985
3,514
Total
3,680
4,320
8,000
Using logistic regression analysis, themodelwas able to capture 87% of the true state of nature (true churners and true stayers). Further drill-down is done within the four outcome states.

58. Step 6: Campaign ManagementAction: Prioritization & Retention

59. Step 6: Campaign ManagementAction: Cross/Up Selling & Retention

60. Step 6: Campaign ManagementAction: Brand Awareness

61. There are solutions which optimize Customer Management Process that reflects the voice of the customer, promotes retention and relationship building, supports business goals, leverages events / triggers, and is cross- channel and cross Business Unit.

62. Step 7: Inbound Right-Time Marketing
•“Right message at the right place and at the right time”
•Objective is to make heralds out of the customers

63. Step 8 : Optimization
•Cutting edge innovation
•Tailor-fit customer relationship
•Affinity and pride is established
•Must beware of oversolicitation.

64. Please Remember
•The goal of the segmentation analysis is to create manageable and meaningful customer groups among customers.

65. Please Remember
•Segmentation is instrumental in increasing shareholder value by identifying:
•Most high-value segment(s)
•Segments with high potential for cross selling and/or up-selling
•By focusing communications on a targeted segment, a causal effect would be a reduction in campaign costs

66. Please Remember
•Segment definition
•Supports retention, service prioritization and cross selling / up-selling efforts
•Serves as input in developing new products
•Segmentation is both a science and an art!

67. Thank you for your attention! Prof. Francisco N de los ReyesSchool of StatisticsUniversity of the Philippines, Diliman