Predictive analytics the customer lifecycle in a btb company
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Semelhante a Predictive analytics the customer lifecycle in a btb company
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Predictive analytics the customer lifecycle in a btb company
- 1. Welcome
- 2. Manutan - Program 16.20 – Welcome by Marc Perin, Safeshops 16.30 – Introduction Overtoom - Manutan 16.35 – Predictive modeling & the customer lifecycle 17.15 – Questions 17.30 – Networking Safeshops Manutan Belgium 20132
- 3. Predictive analytics & the customer lifecycle in a BTB company 29 April 2013
- 4. Manutan - Who is Overtoom & Manutan?
- 5. Manutan - Safeshops Manutan Belgium 20135 Overtoom Who are Overtoom & Manutan? Manutan 1947 Founded by Rein Sjenitzer 1974 1995 1997 2014 1947 Founded by Rein Sjenitzer 1974 Start Belgian Branch 1995 Acquisition by Manutan 1997 Launch of the Overtoom web shop 2014 Manutan-Overtoom 1 Belgian Brand 1966 Founded by André Guichard 1973 Start European expansion / Belgian Branch 1985 Introduction of Manutan SA on Euronext Market 2011 Move to the Overtoom-Building in Ternat 2014 Manutan-Overtoom 1 Belgian Brand
- 6. Manutan - Safeshops Manutan Belgium 20136 Who is Overtoom & Manutan? One big European Family Our presence in 19 European countries through 23 affiliates reflects our desire to spread our company vision throughout the world. We are keen to share our principles with the widest possible audience across all borders, wherever and whatever they may be.
- 7. Manutan - Predictive Analytics at Overtoom 02
- 8. Manutan - Safeshops Manutan Belgium 20138 Analytics & the customer lifecycle Prospect New Customer Active Customer Inactive Customer Suspect Customer At Risk Acquisition Customized Offers Segmenting & Targeting Profit / LT Value Churn Prevention ReactivationSegmenting & Targeting Customized Offers Customized Offers Loyalty Reactivation Prospect conversion Suspect Purchase
- 9. Manutan - Safeshops Manutan Belgium 20139 Segmenting & targeting Customer Information Orders Firmo- graphics Complaints Products Surveys Action History ID Nace Code Company Size Product Possession Purchase Variety 001 2651 1 0 2 002 4020 5 4 3 003 4544 2 2 1 004 7412 1 1 7
- 10. Manutan - Safeshops Manutan Belgium 201310 Segmentation vs. Prediction Predictio Customer Base Action Predictio Segmentation Who to select ? Prediction
- 11. Manutan - Safeshops Manutan Belgium 201311 Segmentation vs. Prediction Cumulative Turnover Customer Ranking Field test 2008 Prediction Segmentation best customers worst customers The predictive model shows an increase in turnover of more than 10% compared to the traditional segmentation (RFM) scheme Forrester Research: There is a causal effect of personalization on loyalty, customer satisfaction and customer retention (Feb 17,2012; Whitepaper: Use customer analytics to get personal)
- 12. Manutan - Safeshops Manutan Belgium 201312 Segmentation vs. Prediction Other traditional segmentation used in our industry is not always helpful
- 13. Manutan - Safeshops Manutan Belgium 201313 Analytics & the customer lifecycle Prospect New Customer Active Customer Inactive Customer Suspect Customer At Risk Acquisition Customized Offers Segmenting & Targeting Profit / LT Value Churn Prevention ReactivationSegmenting & Targeting Customized Offers Customized Offers Loyalty Reactivation Prospect conversion Suspect Purchase
- 14. Manutan - Safeshops Manutan Belgium 201314 Customized offers Define content for each individual... Which products to offer? Prediction 40.000 Products
- 15. Manutan - Safeshops Manutan Belgium 201315 Customized offers Personalization is a win-win initiative: Customer benefits from better recognition and gets relevant offers & experiences Organizations benefit from increased customer retention & realize significant returns Forrester Research, Feb 17,2012; Whitepaper: Use customer analytics to get personal)
- 16. Manutan - Safeshops Manutan Belgium 201316 Customized offers Customized Offers are 300% more relevant Average response after 1 month Offer 1: most relevant product based on predictions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Vitrine Folder Baseline Conversion Offer ranking Response most relevant product is 300% higher than average response
- 17. Manutan - Safeshops Manutan Belgium 201317 Analytics & the customer lifecycle Prospect New Customer Active Customer Inactive Customer Suspect Customer At Risk Acquisition Customized Offers Segmenting & Targeting Profit / LT Value Churn Prevention ReactivationSegmenting & Targeting Customized Offers Customized Offers Loyalty Reactivation Prospect conversion Suspect Purchase
- 18. Manutan - Safeshops Manutan Belgium 201318 Other Examples: Net impact modeling Response modeling: Traditional Approach Limitations: Neglects impact of campaigns React if not treated by marketing campaign? YES NO React if treated by marketing campaign? YES NO ActionNo Action Lost- Net impact modeling Test : # of customers have not received a catalog in 2012, to find out which customer groups are not influenced in their purchase by a catalog (i.e. what is the actual impact of our catalog on customer’s purchasing behavior) Outcome: sending out considerably less catalogs in 2013 (saving $$$)
- 19. Manutan - Safeshops Manutan Belgium 201319 Other Examples: Mailing plan on economically justified costs Calculation of customer lifetime value Outcome: know how much we can spend marketing on each customer Expected profit/Contact = Probability * (TO * Margin) – Action cost Top Scores Medium Scores Low Scores Bad Scores
- 20. Manutan - Safeshops Manutan Belgium 201320 Suspect purchase • New suspects we purchase from Graydon (based on Twins) • Outcome: reduction in cost with Graydon + reduction in mailing cost Churn = customers at risk • Look at maximum inter-purchase time • Outcome: clean d-base Set up of customer lifecycle action plan • E.g. new customer, different automated actions we take: call after week 1, letter & email after x months if not repurchased; with personalized offers for new customers. • Outcome: better customer knowledge + increased customer duration Other examples