Customer Loyalty Optimization

Transcription

1 Organizational Intelligence Forum June Boston, MA Customer Loyalty Optimization Executing customer loyalty and relationship management with a sound insights and data strategy JPK Group June 16, 2:15pm John Giaquinto Hannaford Supermarkets View presentation online at:

2 supermarkets Supermarkets have been collecting customer data and leveraging it for over a decade. It s transformed the industry. The scale of data and the frequency of repeat purchases make it a great proving ground for using customer data. The lessons learned (or ignored) in supermarkets may give insight into the potential and the pitfalls of introducing customer data.

3 1. supermarkets: big data, big opportunity 2. data limits (inherent) 3. data limits (marketing focus)

4 supermarkets: big data, big opportunity Businesses are now generating customer data. Supermarkets have been generating A LOT OF DATA.

5 supermarkets: big data, big opportunity 1 million customers X 50+ item transactions X shops per year and that s just TOP CUSTOMERS.

6 supermarkets: big data, big opportunity Now imagine those customers defined across multi- dimensional segmentation schemes, also defined by address and by channel preferences & rules, choosing among 40,000 skus each with their own categories and attributes.

7 supermarkets: big data, big opportunity How did we even do it BEFORE customer DATA?

8 top shopper churn Supermarket $4 Billion annual retail sales Fewer than 20% of customers are fully engaged These Primary customers shop broadly and frequently Top shoppers appear to be a stable metric. But personal data will show that UP TO ONE THIRD migrate from the group annually; churn may be $500mm opportunity. 40% Of sales or more come from Primary shoppers annually It s always EASIER and cheaper to RETAIN a customer than it is to win them back

9 customer focus v. product focus There s a difference between choosing the right customers to buy products, and choosing the right products for customers to buy. YOU MUST START WITH THE CUSTOMER if you want to create loyalty.

10 measurement: return versus loyalty Are you looking for campaign ROI? Or do you want to increase loyalty over time? You need to MEASURE what you want to IMPROVE. Retention and loyalty work over longer time scales. Measurement methodology needs to match the objective.

11 quickstart with customer data Early campaigns can target early adopters of the loyalty program and can deliver content and offers which will increase frequency and basket size of these customers. However results of these programs may not be reliable predictors of what works with a mature program. Effective customer-focused retailing requires data from a sufficient number of customers. 70% household penetration may be an important threshold. Before customer programs are created it s best to have a TEST and LEARN plan. The objective is accumulation of knowledge.

12 customer focus v. product focus When is it better to drop a product with higher sales instead of one with lower sales? Not all products have the same loyalty from purchasers. With customer data you can distinguish between REPLACEABLE items and customer FAVORITE items.

13 customer focus v. product focus $$$$ $$$ $$ $ When is it better to drop a product with higher sales instead of one with lower sales? Not all products have the same loyalty from purchasers. With customer data you can distinguish between REPLACEABLE items and customer FAVORITE items.

14 supermarkets: big data, big opportunity With all this DATA, why do we still seem DISCONNECTED?

15 supermarkets: big data, big opportunity The PROBLEM with data (especially A LOT OF DATA)

16 data limits DATA are RANDOM and MISCELLANEOUS. INFORMATION is ORDERLY and CUMMALATIVE.

17 data limits Take a piece of PAPER that s thick. Now fold it 50 TIMES.

18 data limits It would reach the SURFACE of the SUN.

19 data limits inches feet miles inches feet miles E E E E

20 data limits What does your MEMORY look like?

21 data limits 1. Our brains aren t wired for big data 2. We bring biased perspectives, even from our direct observations

22 data limits ARE WE DIRECTLY OBSERVING PHENOMENA, OR MERELY SHADOWS?

23 data limits ARE WE DIRECTLY OBSERVING PHENOMENA, OR MERELY SHADOWS?

24 data limits

25 data limits NEW CUSTOMERS EXISTING CUSTOMERS

26 data limits undifferentiated targeted segmented

27 data limits One to one

28 data limits Treatment selection (propensity model) Channel rules? Campaign mgt rules

29 data limits universe targeted opened clicked printed redeemed 26,000,000 2% 2% 3% 4% 15% 520,000 10,

30 data limits