The document discusses the role of predictive analytics in enhancing one-to-one marketing strategies for Overtoom International, a market leader in business-to-business distance selling in Belgium. It highlights the importance of reaching the right customer with the appropriate product through various marketing channels while improving targeting efficiency, increasing turnover, and managing customer relationships. Additionally, it emphasizes the paradox of choice and the need for customized offers based on customer segmentation and behavior analysis to optimize marketing outcomes.

1. Predictive Analytics: the Engine
for One-to-One Marketing
February 18, 2011 Ghislaine Duymelings █ Jo De Lange █ Geert Verstraeten

2. Overtoom International
█ Business-to-Business distance selling
company (Market leader)
• Penetration rate in Belgium:
7% ( 850.000 companies)
• Database customers:
85.000 companies / 240.000 contacts
• Database products :
40.000 references
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3. Overtoom International
█ Marketing Channels
Yearly Catalogue: Yearly Catalogue: Monthly Leaflet:
Office Supplies Warehouse Supplies Promotional Brochure
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4. Overtoom International
█ Marketing Channels
Company Website www.overtoom.be Email promotions
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5. Overtoom International
█ Challenges
By offering the right
Product(s)
Reaching the right
Customer
Through the most appropriate
Marketing Channel
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6. Python Predictions
█ Core business: Predictive Analytics
…in order to
Using all …we predict manage
available future customer one-to-one
customer behavior… relationships.
information…
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7. Python Predictions
█ Core business: Predictive Analytics
█ Based in Brussels
█ Since 2006
█ Team
█ Customers:
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8. Predictive Analytics
Benefits
Respect Efficient
for the resource
customer deploymt
Marketing Marketing
Relevance Accountability
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9. One-to-one marketing
What it could be…
Minority Report (2002)
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10. One-to-one marketing
The near future?
New York, November, 2010
Japan, September, 2010
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11. One-to-one marketing
Well known example: Amazon
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12. One-to-one marketing
at Overtoom
█ How it all started…
By offering the right
Product(s)
Reaching the right
Customer
Through the most appropriate
Marketing Channel
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13. Reaching the right customer
█ Increase targeting efficiency of
current marketing actions to
existing clients
Yearly catalogues
Monthly leaflets
█ Increase response and turnover
█ Segmentation Predictive Analytics
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14. Reaching the right customer
█ Segmentation is exploratory
█ Prediction is discriminatory
Segmentation Prediction
Prediction
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15. Reaching the right customer
█ Turnover during field test
Short term
Reduction target size: -10%
Turnover: +28%
Long term
Reduction target size: -10%
Turnover : +10% (average)
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16. Personalized Targeting
█ The plot thickens…
By offering the right
Product(s)
Reaching the right
Customer
Through the most appropriate
Marketing Channel
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17. Customized Offers
Motivation: the paradox of choice
40% stops 30% purchased
6 jams
60% stops 3% purchased
24 jams
Source
S. Iyengar & M. Lepper, When Choice is Demotivating: Can One Desire Too Much of a Good Thing?
Journal of Personality and Social Psychology, 2000, Vol. 79, No. 6, 995-1006
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18. Customized Offers
The Paradox of Choice (Barry Schwartz)
█ Too much choice and too much information
• is paralyzing
• leads to bad decisions
• leads to dissatisfaction with good
decisions
█ Modern technology has helped create this
problem, but it can also help create the
solution, by tailoring options and
information in ways that are relevant to
individual consumers
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19. Customized Offers
Motivation: Overtoom facts
Percentage of Purchases Number of Customers
6% 40000
5% 35000
30000
4%
25000
3% 20000
2% 15000
10000
1%
5000
0% 0
A B CD E F GH I J K L MNOPQR S T 1 2 3 4 5 6 7 8 9 10
Product Categories Number of Different
Categories Purchased
Most customers purchase
All categories are purchased to in a limited number
a certain degree of categories
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20. Customized Offers
Solutions
Market Basket Response Similarity
Analysis Modeling Modeling
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21. Customized Offers
Response Models
█ Method
Product A B C Company ‘O’ has 3 products
Model A B C 3 propensity-to-buy models are built
Customer X A B C Customer X is scored on each of these
models
Best offer C The product with the highest
probability-to-buy/expected return
is offered to the customer
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22. Customized Offers
Initial format (April 2009)
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23. Customized Offers
Extended Format
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24. Customized Offers
Similarity Model
█ Method
Customer X X We compare any customer with all
other customers
Customers 1 2 3 Company ‘O’ has 3 customers
Customers have bought products
Products A B C Company ‘O’ has 3 products
Best offer C Based on the purchases of the most
similar customers, we offer the best
possible suggestion to each customer
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25. Customized Offers
Similarity Model
█ Method
Advantages
Customer X X
█ Client-based vs product-based
█ 1 model, simple data structure
Customers 1 2 3
█ Inclusion of all products, categories
█ Development time
Products A B C █ Comparison with existing models
possible
Best offer C  Performance
 Variety
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26. Results
█ Evaluation:
 Conversion rate
Percentage of buyers who purchased the specific offer
 Success Rate
Percentage of buyers who purchased at least 1 of the offers
 Variety index
Indicator of the global variety of the offers across all customers
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27. Results - development
█ Summary
Success Rate
0.7
+14.6% +8.6%
0.6
0.5
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Customized Offers Benchmark LogReg Benchmark
Similarity Modeling Response Modeling Most Popular
Most Popular Product Product
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28. Results - infield
█ Conversion rate based on rank of the offer:
 Extended format (14 customized offers)
Conversion Rate
7%
Customized Offers
300 % 6%
5%
Folder
more
Baseline
4%
relevant 3%
2%
1%
0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of Recommendations
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29. Implementation
█ Stakeholders
General Management Inventory Management
Marketing Communication
Management Partner
Purchasing Digital Printing
Partner
Analytical
Partners
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30. Personalized Targeting
█ The future…
By offering the right
Product(s)
Reaching the right
Customer
Through the most appropriate
Marketing Channel
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31. Analytics & the Customer Lifecycle
Acquisition
Suspect Prospect New Active Customer Inactive
Customer Customer At Risk Customer
Suspect Prospect Segmenting Segmenting Churn Reactivation
Purchase Conversion & Targeting & Targeting Prevention
Customized Customized Customized
Offers Offers Offers
Profit / Long
Term Value
Loyalty
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32. █ SAS Success Story
█ Visit our websites:
www.overtoom.be
www.pythonpredictions.com
█ Contact information:
ghislaine.duymelings@overtoom.be
jo.delange@overtoom.be
geert.verstraeten@pythonpredictions.com
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