Product Catalogue Segmentation Business Presentation

1. Product CatalogueSegmentationBusiness Presentation www.InstantBI.com 1/1/2012

2. Agenda • Introduction to Segmentation • The process of segmentation • The relationship framework • Customer Intelligence • The Marketing Message • Example Project Using BI4Retail Segmentation • Other Examples of Retail Segmentation • Summary

3. Introductions

4. Introduction to Segmentation

5. Introductionto Segmentation • What is Segmentation? • Segmentation is the act of dividing a set of objects into groups that share common characteristics. In the world of marketing and sales, segmentation is applied to customers, usually in order to better service or understand those customers. • The marketing process in most companies pays little attention to customer segmentation • Typically very little effort is madeto ongoingly understand customers • In most cases effort that is made is poorly directed and not able tobe performed easily continuously • The result is most companies still competebased on product not relationship with the customer

6. Segmentation Market segmentation is: ‘The process of identifying customers who comprise a homogenous group of consumers for a specific range of goods and services’ Most companies do market segmentation pretty well Customer segmentation is: ‘The differentiation of customers within a defined market. A segment consists of a group of consumers that react in a similar way to a given set of marketing stimuli.’ Most companies are abysmal at customer segmentation

7. Internet/VirtualBank RelationshipManagement Telemarketing DirectMarketing FacetoFace Advocate Supporter Premium Premium Better Off Better Off Loyalty Segmentation Customer Middle Market Middle Market Financially Passive Financially Passive Prospect Inactives Inactives Marketing&Proposition Segmentation Suspect Single Adults Childless Couples Young Families Established Families Empty Nesters Mature Adults Time LifestageSegmentation Time The Process of Segmentation Channel Migration Loyalty Migration

8. The Relationship Framework High Fragile Brittle Secure Bonded Detached Developing Intimate Value of Relationship Valued Potential Loyal Explorer Engaged Nascent Occasional Regular Habitual Low Low Strength of Relationship High

9. Usage CLTV Demographics Channel Psychographics Customer Profitability Benefits Loyalty Product CustomerIntelligence • How much do we know about our customers? • How well do we maintain a memory of customer behavior? • How frequently do we update our view of customers? • Do we involve our customers in dialog? • Are we capable of a systematic response to customer profiles?

10. PersistentMarketingProcesses Prospecting Customer Acquisition Profiling Segmentation Promotion Campaign Management Promotion Prospect Profile

11. RefinedMarketingProcesses Dialog Information Acquisition Analysis Intelligence Acquisition Feedback Customer Acquisition Feedback Dialog Analysis

12. Surveillance-based techniques of customer analysis such as data warehousing, data mining, pattern discovery, click-stream analysis and device monitoring belong to the 1st Wave Dialog-based techniques are enabled by the Internet and actively involve customers in the development and deployment of value propositions Benefits of Dialog Ability to obtain demographic and psychographic information Entices the use of the Web channel by Customers Personalizes the Web Experience for the consumer Enables the Organization to gauge sensitivities to products, brand, packages, pricing etc Empowers quality customer dialog Increases customer loyalty Resolves customer permissions and privacy issues FromSurveillancetoDialog

13. AdvertisingEffectivenessisdeterminedbyMessageQuality • Message Quality is the measure of effectiveness of a marketing message. • The 7 key determinants of Message Quality are: • The relevance of the message to the needs of the customer • The relevance of the message to the profile of the customer • The consistency of the message in the context of other messages transmitted • The conformance of the message to the permissions registered by the customer • The timeliness of the message transmitted to the customer • The appropriateness of the channel used to transmit the message • The degree to which the customer can easily respond to the message

14. BI4 Retail ExampleThe Client and the Project

15. Product Catalogue Segmentation • EUR800M retailer • 800 stores • 3M loyalty card holders • Pilot for one major city (110,000 card holders) • 25,000 SKUs • Business Problem • How do we up sell and cross sell products? • Issues in performing work • Little experience in the company in doing this work • There was almost no demographic data for card members • Limited to Age/Gender and even that was not reliable

16. TasksUndertaken • All tasks done on a ‘pilot’ consulting fee basis • Gather 3 years of transaction history for pilot • Load into BI4Retail • Mark the product catalog for segmentation analysis • More on next slide • Generate Recency, Frequency, Monetary Value Scores • Analyse using excel (Reports still under development) • Propose marketing campaigns to up sell and cross sell • Campaigns not yet run

17. Mark the Product Catalog for Seg Analysis • Products purchased tell us something about the buyer • I own a Nokia E61….that tells vodafone something about me • I use it in certain ways….also tells vodafone something about me… • If vodafone wanted to listen… • The products your clients buy reveal things about them • This information can be as powerful as collecting data about them • Marking the product catalog is the process of • Deciding what segments you want to create • Giving ‘points’ to each product purchase to indicate segment affinity • Points range from 0 (no affinity) to 10 (high affinity) • Decide how many points per segment indicate ‘segment membership’ • Example on next page shows • 47 segments created (max 64) • Shows cardholders who made 1, 10, 20, 40 purchases in a segment • Highlights segments large enough to work with

18. Product CatalogSegmentation

19. AgeBandAnalysis • We performed all the normal Age Band Analysis • We banded by 5 years as well as age • See example slides… • Note. You cannot read the numbers, but you can see the shape of the data….and that age is closely related to product segment

22. GenderAnalysis • We performed all the normal Gender Analysis • The highlighted cells were the ‘unusual’ cells

23. CrossSegmentAnalysis • Most importantly, we performed cross segment analysis • Finding segments with higher than usual affinity to each other • Members purchase in co-related segments • Why is this important? To sell more product we want to: • Find affinity between purchases by members • Find out ‘what do these members look like’? • Find other members who ‘look like’ these members but do not have the cross segment purchase behavior • Offer voucher to buy into the segment with affinity • Usually offered as a discount and offered 2-3 times 1 month apart • See if the purchase across segment behavior persists after vouchers

24. CrossSegmentAnalysis - Example • In green are segments with higher than 10% co-relation • In gold are segments with high co-relation and high numbers of members in both intersection segments. • These ‘gold cells’ represent the biggest revenue/profit opportunity • We did this for all 47 segments against 12 largest to find opportunity

25. Comparison of Segment Populations after Revision of Rules

26. Comparison of Segment Populations after Revision of Rules

27. Segments to Target forCampaigns • Considered variations on membership by num products purchased • Considered segments regarded as: • Core / Non-Core • Primary / Secondary • Considered Recency / Frequency and Monetary value of members purchases in related and target segment • From this information, proposed the campaigns to run • Multi-touch via voucher to be used at cash register • Voucher for discount on purchase in target segment • 3 vouchers spread apart based on the time it takes to use product the voucher is for. (New voucher turns up around time product is used up.) • Determine if cross segment purchase behavior works

28. WhyUseVouchers? • There are many differing theories regarding member loyalty/rewards • Giving points on any usage has become a ‘discount’. • There are real questions as to whether it really motivates behaviour • Giving vouchers does a number of things: • It restricts the use of the ‘offer’ to a specific item that you want the member to try out. It is not a ‘general discount’. • If it is an item they are likely to be buying elsewhere it gives the opportunity to switch spend across to you • It encourages the cross segment purchase • It tells the customer ‘we care’

29. BI4 Retail ExampleSome More Examples

30. Normal Mothers Child Brides Early Mothers Women At Their Peak She’s Leaving Home Young & Carefree Example of Segmentation: Female Lifestage Late Mothers Kids Old Ladies Empty Nesters No Kids 65 or older 16 to 21 22 to 29 30 to 44 45 to 64

31. Example of Segmentation: Customer frequency Frequency (Transactions / Month) Ultra Frequents Very Frequents Frequents Majority Occasional Proprietary Large Basket Buyers Occasional Own Brand Deal Seekers Average Transaction Value

32. Recency Frequency Value Demographics Lifestyle Customer Product Shopping Repertoire Segmentation Mode Attitude Interests Needs Lifestage Example Segmentation: Customer understanding

33. Recency Frequency Value Demogrphcs Retain Lifestyle Customer Product Shopping Grow Repertoire Segmentation Mode Using different channels Attitude Interests Mass marketing. Unresponsive, little value growth Needs No Focus Lifestage Differentiating the offer Different strategies for Different Segments

34. Summary • Introduction to Segmentation • The process of segmentation • The relationship framework • Customer Intelligence • The Marketing Message • Example Project Using BI4Retail Segmentation • Other Examples of Retail Segmentation • Summary

35. Thank You for Your Time