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Big Data and Loyalty Disruption

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Big Data and Loyalty Disruption

  1. 1. Big Data Loyalty disruption Stephane Baizeau Senior Vice President Digital Catalina International XVI edizione del Convegno Osservatorio Fedeltà Università di Parma, 21 Ottobre 2016
  2. 2. ©2016 Catalina Confidential 2
  3. 3. Volume of data is doubling every 2 years ©2016 Catalina Confidential 3
  4. 4. We help retailers and brands to acquire, retain and maximize loyalty around the world ©2016 Catalina Confidential 4 250 Retailers 50 000 Stores 700 Brands
  5. 5. Source: which.co.uk Loyalty scheme are mainly transactional Loyalty Card Points per £1 in the supermarket What's a point worth in store? What do I save per £100 spent? How many points will I get from using the card on other services? Tesco Clubcard 1 1p £1 It depends, but 0.5 for Tesco fuel and 0.25 for credit card spend Sainsbury's Nectar 1 0.5p 50p It depends, but 1 per litre of fuel at Sainsbury's Morrisons Match& More 5 0.1 50p 10 for a litre of fuel at Morrisons petrol stations My Waitrose No points but rewards instead Rewards include free tea and coffee and money off when you buy newspapers n/a n/a ©2016 Catalina Confidential 5
  6. 6. Targeting is a major way to stimulate loyalty scheme Identify an eligible audience selected by behavior, for a specific media campaign based on a business need/objective. Jill Premium Shopper during the last year Lives close to the store Retailer Request: Please target shoppers who have decreased frequency in the last 3 months Selection Criteria: • Currently active across store • Total Audience in Target: 0.5MM Available Shoppers Selected Shoppers Targeting Request Shopper Selection Offer Delivery • Eligible shoppers receive the targeted offer • Distribution reporting and Control vs. control group analysis, showing incremental sales generated ©2016 Catalina Confidential 6
  7. 7. Digital is opening new opportunities ©2016 Catalina Confidential 7
  8. 8. Introduction of real-time personalization’s model Needs for flexible, continually adaptive models to predict consumer needs and match them with personalized offer contents ©2016 Catalina Confidential 8
  9. 9. Case1: Driving consumer to store ©2016 Catalina Confidential 9
  10. 10. Case 2: Engage consumer with the brand ©2016 Catalina Confidential 10
  11. 11. Case 3: Optimise ad’s efficiency ©2016 Catalina Confidential 11
  12. 12. BuyerVision Case 3: Optimise ad’s efficiency ©2016 Catalina Confidential 12
  13. 13. More data New type of data In real-time

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