Predictive Analytics: the Engine for One-to-One Marketing. More information on http://www.dmupdate.be

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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