Welcome
Manutan -2
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 2013
Predictive analytics & the customer lifecycle in a BTB company29 April 2013
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Who is Overtoom & Manutan?
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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
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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.
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Predictive Analytics at Overtoom
02
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Analytics & the customer lifecycle
Activatio
n
Development
Prospect New Customer
Active Customer
Inactive Customer
Suspect CustomerAt Risk
RetentionAcquisition
Customized Offers
Segmenting & Targeting
Profit / LT Value
Churn Prevention
ReactivationSegmenting & Targeting
Customized Offers
Customized Offers
Loyalty
Reactivation
Prospectconversion
SuspectPurchase
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Segmenting & targeting
Customer Information
Orders
Firmo-graphics
Complaints
ProductsSurveys
ActionHistory
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
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Segmentation vs. Prediction
Predictio
Customer Base Action
Predictio
Segmentation
Who to select ?
Prediction
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Segmentation vs. Prediction
Field test 2008
PredictionSegmentation
Customer Ranking
Cumulative Turnover
bestcustomers
worstcustomer
s
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)
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Segmentation vs. Prediction
Other traditional segmentation used in our industry is not always helpful
Manutan - Safeshops Manutan Belgium 201313
Analytics & the customer lifecycle
Activatio
n
Development
Prospect New Customer
Active Customer
Inactive Customer
Suspect CustomerAt Risk
RetentionAcquisition
Customized Offers
Segmenting & Targeting
Profit / LT Value
Churn Prevention
ReactivationSegmenting & Targeting
Customized Offers
Customized Offers
Loyalty
Reactivation
Prospectconversion
SuspectPurchase
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Customized offers
Define content for each individual...
Which products to offer?
Prediction
40.000Products
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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)
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Customized offers
Customized Offers are 300% more relevant
Average response after 1 monthOffer 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
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Analytics & the customer lifecycle
Activatio
n
Development
Prospect New Customer
Active Customer
Inactive Customer
Suspect CustomerAt Risk
RetentionAcquisition
Customized Offers
Segmenting & Targeting
Profit / LT Value
Churn Prevention
ReactivationSegmenting & Targeting
Customized Offers
Customized Offers
Loyalty
Reactivation
Prospectconversion
SuspectPurchase
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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 $$$)
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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
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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
First
purchase Last
purchase
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Questions?
03
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