Market Segmentation: Heterogeneity of consumers with respect to shopping and spending behavior.

2. Market Segmentation: Heterogeneity of consumers with respect to shopping and spending behavior. Consumers are different from each other And they respond differently to: Stores that are different from each other

3. Market Segmentation: Why is it Important? Merchandising Supermarket design Sales Forecasting Advertising

4. Why is Market Segmentation a Growing Concern? Wider variety of supermarket formats and alternatives Immigration (legal and illegal) Growing separation of classes Analysts are getting smarter!

5. Gravity Model Assumptions 1. Equal consumers

6. Gravity Model Assumptions 2. Equal stores

7. Huff Model Uses Flat Vector Space Pij = F(distanceij, sizej)

8. What Really Happens: n-Dimensional Vector Space Pij = F(distanceij, sizej,lifestyle)

9. What Really Happens: n-Dimensional Vector Space Pij = F(distanceij, sizej,lifestyle,format)

10. What Really Happens: n-Dimensional Vector Space Examples Consumer-1: Upper income, older, no children Store-1: WM Supercenter Consumer-2: Lower middle income, older, young children Store-2: Wegman’s Consumer-3: Low income, young, Hispanic, several children Store-3: H.E.B. Flat-vector Gravity will not forecast well (naturally)

11. How Do We Deal With Market Segmentation Now?  Manual powers (images) Guesswork?  Manual curves Guesswork? Mass Guesswork?  Directional curves  Store Loyalty Variable Kind of guesswork?  Other creative approaches ??????? These are clearly imperfect solutions to market segmentation

12. How Do We Deal With Market Segmentation Now? Let’s Design a Better Solution

13. How Are Consumers Segmented? Basic Demographics  Age  Income  Family size and makeup  Group quarters  Race Ethnic Characteristics  Hispanic  Jewish  Much more …

14. How Are Consumers Segmented? Neighborhood Habits  Commuting  Home ownership / renting  Traffic patterns Shopping preferences  Focus on price  Focus on quality  Need for one-stop shopping  Special food needs  Dislike of “big box” stores Attitude Scales

15. How Many Consumer Profiles? Age groups (3) Income levels (4) Family Size / Children (3) Group Quarters (2) Race (4) Ethnic (3) Total: 864 Profiles! The need for thoughtful discrimination is obvious

16. Store Profiles Store size Specialty foods Variety / selection Extended merchandise Price position Memberships Quality Housekeeping Service Parking/accessibility Departments Public image Although these are all important store traits, we know that the number of useful store types is limited

17. Useful Store Profiles Neighborhood “Mom and Pop” Conventional small store Conventional large High-end up-scale Large discount with extended GM Limited assortment box Supercenter Club Ethnic

18. Making Consumer Profiles What is a “Useful” Profile?  Has significant population  Represented in your trade areas  Group shows discriminating shopping pattern  Counts can be obtained, updated  Provides some resolution to MS deviation

19. Making Consumer Profiles: Methods “Blind” Profile Creation by Data Vendors  Has significant population  Represented in your trade areas  Group shows discriminating shopping pattern  Counts can be obtained, updated  Provides some resolution to MS deviation Three essential elements are missing

20. Making Consumer Profiles Filling in the Missing Elements of Discrimination 1. Start with existing profiles 2. Create a table of MS deviations (percentage)  Do not use MSactual of zero  Use sectors close to store only  Do not use sales figures 3. Use correlation to select profiles 4. Use regression to predict deviations

21. Discrimination Example

22. Discrimination Example

23. Discrimination Example

24. Discrimination Example

25. Discrimination Example Discard unused profiles, then use regression to create predictive formula for MS deviations.

26. Discrimination Example We have now linked one store to useful lifestyle data  It may be useful for other stores of the same chain  Similar stores of different chain?  Research process must be continuous

27. Getting Data on Competitive Stores Intuition? Telephone Studies, esp. before and after opening Customer loyalty changes: who stayed, who left? Sharing information?

28. Gravity Implementation of Results Wrong Way: Manipulation of images Right Way: n-Dimensional Vector Spaces

29. Gravity Implementation: The “Rules”  Must be transparent to analysts  Must be unique and alterable for each company If you can’t edit and modify the links between stores and lifestyles, implementation is useless.  Must retain standard balancing/projecting procedures  Must allow variable vector depth  Cannot significantly increase time or cost  Must be transportable with projects!

30. Gravity Implementation: The “Rules” Must be transportable with projects! Examples Other offices in your company Use of consultants Remote presentations What happens if your database changes?  Regular updates  Change of vendors or products

31. Summary Segmented gravity programs are becoming more necessary for supermarkets, but incorrect implementation could be disastrous! Significant risks:  Inaccuracy!  Non-portability!  Higher costs / slower projects!  Over-complication!  Slowly, carefully, thoughtfully