predictive analytics the customer lifecycle in a btb company

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

Manutan -

Who is Overtoom & Manutan?

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


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.

Manutan -

Predictive Analytics at Overtoom

02


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


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


Segmentation vs. Prediction

Predictio

Customer Base Action

Predictio

Segmentation

Who to select ?

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)



Other traditional segmentation used in our industry is not always helpful



Activatio

n

Development


Active Customer

Inactive Customer



Customized Offers


Profit / LT Value

Churn Prevention


Customized Offers

Customized Offers

Loyalty

Reactivation

Prospectconversion

SuspectPurchase


Customized offers

Define content for each individual...

Which products to offer?

Prediction

40.000Products


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)


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



Activatio

n

Development


Active Customer

Inactive Customer



Customized Offers


Profit / LT Value

Churn Prevention


Customized Offers

Customized Offers

Loyalty

Reactivation

Prospectconversion

SuspectPurchase


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 $$$)


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


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

Manutan -

Questions?

03

predictive analytics the customer lifecycle in a btb company

Business