Sku analytics loyalty nz sunz 2012


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Sku analytics loyalty nz sunz 2012

  1. 1. SKU Analytics and the Triumph Over Stone Age Segmentation Methods Vince Morder, Loyalty NZ Milo Davies, SAS 2012 SUNZ Conference, Te Papa, Wellington
  2. 2. SAS and Loyalty - A Great Partnership Rich data + Loyalty’s Techniques + SAS tools =
  3. 3. Loyalty Data Address(Historical) Gender Email Mobile Income Demographics Age Customer Final Prepare Lifestyle Survey Other Sources Census Motor KCUBE Vehicle LINZ/QV
  4. 4. Example: RFM Segmentation• RFM segmentation is a behavioural based segmentation built on:  Frequency: How many visits have they taken?  Monetary Value: How much does a customer spend each visit?  Recency: When was last transaction customer did with you?• A segmentation is built across all customers for a particular retail partner over some determined observation period. RFM Segmentation Low Monetary Value High Monetary Value Low Frequency Low Value Medium Value Infrequent Infrequents 26.2% customers, 3.1% spend 1 visit only 30.6% customers Customers High Value Infrequent 0.6% spend 7.6% customers 27% of Medium Value 27.6% spend all spend 11.6% of base (Not part of this 7.4% of spend RFM analysis) Low Value Frequents 17.5% customers High Frequency 8.7% spend High Value Frequents 6.5% customers, 52.6% spend• To add further depth and insight, we can profile the demographics of each segment• We can also track movement over time.
  5. 5. Example: Tracking RFM over time
  6. 6. Example: Profiling the RFM segments HVF’s are mostly females. HVF’s are most predominant in the 40-60 age range, All others are greater proportion males. HVI are older (retired age), spends lots, but less frequently There is a strong skew in highest income areas, lowest deprivation deciles towards higher rfm segments
  7. 7. Along Came SKU…. (S)tock (K)eeping (U)nit Literally, billions of records at the basket level
  8. 8. Loyalty Data(Current) Address Email Gender Mobile Income Demographics Age Time & Final Prepare Location Outlet Customer Lifestyle Behaviour SurveyBasket (SKU)Value Other Sources Census Items Points collected KCUBE Motor Vehicle LINZ/QV
  9. 9. New methodologies using SKU data• SKU data enables us to get an even better view of shoppers in the retail market.• If used correctly, it can help us to understand the motivations behind buying decisions.• If we can improve our understanding of our customers’ motivations we can become a lot more sophisticated in our decision making and our ability to keep customers engaged and loyal to retailers. View of the customer Profiles using using traditional data SKU data
  10. 10. Let’s take a look at some examples….
  11. 11. Milo’s Supermarket Receipts ORGANIC GLUTEN FREE FRESH HIPPIE !!
  12. 12. Milo’s Supermarket Receipts GLUTEN FREE READY MADE FANCY BEER NAPPIES
  13. 13. Milo’s Electronic/Whiteware Purchase History
  14. 14. Example: Milo • Focused on healthy/diet eating • Except for that beer! • Happy to buy premium products • High end, yet stylish, hardware • Vacations involve going overseas
  15. 15. Example: Vince • Buys big pack items • Buying for a family/kids • Prefers convenient, easy cook meals • Low end electronics • Vacation locally
  16. 16. What conclusions could we draw• We have just looked at two different customers with two very different sets of products purchased with our partners.• What is likely to be relevant and engaging to Milo is unlikely to be relevant or engaging for Vince• The SKU data has the potential to help us identify these different customers so we can be relevant and engaging to both these customers. But how to make sense of all these products and all these customers?• Before we can understand our customers we must first understand the types of products they buy (rather than the product themselves) and be able to answer questions like: Is it is it healthy Is it for a is it functional vs. product? expensive? Etc... family? showy, or both?
  17. 17. Enter the ideal dimensions • Points in the direction of a perfect representation of something we imagine. • LNZ is in the process of classifying our partners retail products against our ideal dimensions. Quick Budget Healthy Gourmet Scratch High Price Kids Fresh Alcohol Showy Organic• E.g. Tuatara would have a high association with alcohol as well, but also, load quite highly on the ‘Showy’ dimension as well. Low loadings for Tuatara on the scratch dimension• The double oven could load high on gourmet, scratch, showy, and high price.
  18. 18. The Secret Sauce• There are tens of thousands of products across our partners and it would be impossible to manually try and classify all of them.• To make it more difficult what I think is ‘healthy’ - you might disagree! • E.g., This pulse monitoring watch could be for a health nut or someone who just suffered from a heart-attack. • Instead we rely on an algorithm that sorts through characteristics of products to statistically determine how much they load on to our designated ideal dimensions. • We then trawl and loop through the entire retailers’ transactional database to ‘score’ all the products customers are purchasing.
  19. 19. Milo’s Shopping Profile• Once we have scored all products we bring it all together and create a shopping profile for Milo • Looks like we don’t need to worry too much about giving specials to Milo!
  20. 20. Vince’s Shopping Profile • Once we have scored all products we bring it all together and create a shopping profile for Vince• Vince might need to get his cholesterol checked!
  21. 21. How this helps our partners• We can apply cluster analysis to group together customers who share similar motivations. • By understanding our customers’ primary motivations we can apply it across our business by:  Increasing the relevance of marketing activity through the clustered segments or leveraging one of the attributes.  Improving the targeting, personalisation and relevance of our communications.  Get greater insight into the profile of shoppers visiting different stores. Can assist in areas from ranging to more relevant ATL offers.
  22. 22. Example: Applying to campaigns• A DM was sent to 10,000 existing retailer customers to promote a high end product X. The campaign targeted two audiences:  Customers who purchased product X.  Customers who purchased other similar speciality products.• The campaign generated an average response rate of 6.9%• Based on new dimensional profiling techniques, product X has a high gourmet attribute score. What happens when we overlay our “gourmet” attribute? • We allocated each customer a HML segment based on their gourmet attribute score. • Heavy gourmet customers responded at nearly double the rate of the next closest segment.
  23. 23. Example: Enhanced Communication• One of our retail partner’s magazine is an upmarket communication originally planned to target the most valuable customers based on their RFM segment.• Question: Because you spend a lot at the retailer, does that mean you will have an interest in a their magazine?• Not necessarily – you may spend a lot at retailer but heavily focused on value/everyday items because you’re shopping for a large family.• To increase the relevance of the magazine, we can overlay customers’ behaviour dimensions in combination with the RFM to give a much more optimised target group. Value Relevant & + Behaviour = (RFM) Optimised
  24. 24. What’s next?LNZ is currently working with it’s partners to implement and begin leveraging these behavioural dimensions.Plans are in place for our analytics to extend to Social network data Mobile applicationsThe vision for the LNZ Customer Intelligence Team is to be the undisputed Customer Loyalty experts