predictive analytics: the engine for one-to-one marketing

Ghislaine Duymelings █ Jo De Lange █ Geert VerstraetenFebruary 18, 2011

Predictive Analytics: the Engine for One-to-One Marketing

Predictive Analytics - February 18, 2011 █ 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



█ Marketing Channels

Yearly Catalogue:

Office Supplies

Yearly Catalogue: Warehouse

Supplies

Monthly Leaflet:Promotional

Brochure



█ Marketing Channels

Company Website www.overtoom.be

Email promotions


█ Challenges


Reaching the rightCustomer

By offering the rightProduct(s)

Through the most appropriate Marketing Channel


Python Predictions

█ Core business: Predictive AnalyticsD

ata

Using all available customer

information… An

alyt

ics

…we predict

future customer behavior…

Ma

rket

ing

…in order to manage

one-to-one

relationships.


Python Predictions

█ Core business: Predictive Analytics█ Based in Brussels█ Since 2006█ Team█ Customers:


Predictive AnalyticsBenefits

Efficient resource deploymt

Respect for the

customer

Marketing Accountability

Marketing Relevance


One-to-one marketingWhat it could be…

Minority Report (2002)


One-to-one marketing The near future?

New York, November, 2010

Japan, September, 2010


One-to-one marketing Well known example: Amazon



█ How it all started…

One-to-one marketingat Overtoom


Reaching the right

Customer


█ Segmentation Predictive Analytics

█ Increase targeting efficiency of current marketing actions to existing clientsYearly cataloguesMonthly leaflets

█ Increase response and turnover

Reaching the right customer


█ Segmentation is exploratory█ Prediction is discriminatory

Prediction

Segmentation

Prediction




█ Turnover during field testShort term

Reduction target size: -10%Turnover: +28%

Long term Reduction target size: -10%Turnover : +10% (average)


█ The plot thickens…


Personalized Targeting




Customized OffersMotivation: the paradox of choice

6 jams

24 jams

40% stops

60% stops

30% purchased

3% purchased

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

Source


Customized OffersThe 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


Customized OffersMotivation: Overtoom facts

A B C D E F G H I J K L M N O P Q R S T0%

1%

2%

3%

4%

5%

6%

Percentage of Purchases

Product Categories

All categories are purchased to a certain degree

Most customers purchase in a limited number

of categories

1 2 3 4 5 6 7 8 9 100

5000

10000

15000

20000

25000

30000

35000

40000Number of Customers

Number of Different Categories Purchased


Customized OffersSolutions

Market BasketAnalysis

ResponseModeling

SimilarityModeling


Product

Model

Customer X

Best offer

A

A

A

C

B

B

B

C

C

C

Þ Company ‘O’ has 3 products

Þ 3 propensity-to-buy models are built

Þ Customer X is scored on each of these models

Þ The product with the highest probability-to-buy/expected return is offered to the customer

█ Method

Customized OffersResponse Models


Customized OffersInitial format (April 2009)


Customized OffersExtended Format


Customer X

Customers

Products

Best offer

1

A

C

2

X

B

3

C

Þ We compare any customer with all other customers

Þ Company ‘O’ has 3 customers

Þ Company ‘O’ has 3 products

Þ Based on the purchases of the most similar customers, we offer the best possible suggestion to each customer

█ Method

Þ Customers have bought products

Customized OffersSimilarity Model


Customer X

Customers

Products

Best offer

1

A

C

2

X

B

3

C

█ Method

Customized OffersSimilarity Model

Advantages

█ Client-based vs product-based█ 1 model, simple data structure█ Inclusion of all products, categories█ Development time█ Comparison with existing models

possible Performance Variety


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 indexIndicator of the global variety of the offers across all

customers


1 2 3 4 5 6 7 8 9 10 11 12 13 140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Success Rate

Customized Offers Benchmark LogReg Benchmark Most Popular Product

1 2 3 4 5 6 7 8 9 10 11 12 13 140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Success Rate


1 2 3 4 5 6 7 8 9 10 11 12 13 140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Success Rate


+14.6% +8.6%

Results - development

█ Summary

Similarity Modeling Response Modeling Most Popular Product


1 2 3 4 5 6 7 8 9 10 11 12 13 140%

1%

2%

3%

4%

5%

6%

7%

Conversion Rate

Customized Offers

Folder

Baseline

Number of Recommendations

1 2 3 4 5 6 7 8 9 10 11 12 13 140%

1%

2%

3%

4%

5%

6%

7%

Conversion Rate

Customized Offers

Folder

Baseline


█ Conversion rate based on rank of the offer: Extended format (14 customized offers)

Results - infield

300 %more

relevant

1 2 3 4 5 6 7 8 9 10 11 12 13 140%

1%

2%

3%

4%

5%

6%

7%

Conversion Rate

Customized Offers

Folder

Baseline



█ Stakeholders

Implementation

Purchasing

Inventory Management

Marketing Management

Digital PrintingPartner

Communication Partner

AnalyticalPartners

General Management


█ The future…

Through the most appropriate

Marketing Channel

Personalized Targeting




Analytics & the Customer Lifecycle

Activatio

n

Development

Prospect New Customer

Active Customer

Inactive Customer

Suspect Customer

At Risk

RetentionAcquisition

SuspectPurchase

ProspectConversion

Customized Offers

Segmenting & Targeting

Profit / Long Term Value

Loyalty

Churn Prevention

ReactivationSegmenting & Targeting

Customized Offers

Customized Offers


█ SAS Success Story

█ Visit our websites:

█ Contact information:

www.overtoom.bewww.pythonpredictions.com

[email protected] [email protected]@pythonpredictions.com