1. Your Best Next Business Solution
GSTAT Next Best Offer –
Optimal Personalized Promotions Recommendations
August, 2012

2. Agenda
 Company Profile
 The benefits of personalized promotions
 Business cases
 Introduction to GSTAT Next Nest Offer
 Demo
 How to start?
 Q&A

3. GSTAT Profile
 A leader in development and implementation of
advanced analytical and Data Mining solutions
 More than 60 customers worldwide
 Focused on 2 main areas:
 Analytical CRM and Targeted Marketing
 Credit Risk, Basel II and Solvency II
 More than 170 experts :
 Statisticians
 Business consultants
 System Analysts and data modelling experts
 Software engineers

4. GSTAT Profile
Professional Software
Services Development
ACRM and Subsidiary –
Targeted Marketing GSTAT Software
CoE (100+ Development
consultants)
Credit Risk and
Basel II CoE (80+
consultants)

5. Selected Customers

6. Introducing
GSTAT Next Best Offer

7. Loyalty Program Management
 Goals :
 Increasing Customers’ basket
 Retaining customers through unique offers Call Center
Loyalty
 Contact Channels : Direct Mail, Program
DWH
SMS, POS, email members’ eMail, mobile
data analysis
 The name of the game : segmentation
and personalization Direct Mail
 The challenge : giving the Marketing
tools for recommendations on the right personalized offer that will
increase customers’ revenues

8. 1-to-1 Communication with the Customers
Personalized
promotions
Communication using
generic promotions
No 1-to-1 communication

9. Personalized Promotions
 Personalized promotions based on data mining and statistical analysis of
customers’ purchase history, compared to fix generic promotions :
1-to 1 targeting based
 Increase the average basket by 2%-5% on statistical propensity
modeling, per item
 Increase redeem rates by 3-4 time
1-to-1 targeting based
on statistical basket
 Lead to higher customers satisfaction analysis methods
1-to-1 targeting based
on business rules
Segmental targeting

10. The Challenges of Executing Personalized Promotions
How to develop and deploy hundreds/thousands of
propensity models in a few hours?
How to take into consideration optimal promotions
allocation under constraints :
Manufactures conditions
Maximum/minimum per promotion constraint
Inventory constraint
Cross/up-sell coupons mix constraint
Categories mix constraint
Budget constraint
…

11. Personalized Promotions Business Case - Shufersal
 Over 1,400,000 Loyalty club members responsible for around 75%
of sales at the chain
 Sales generated through over 200 Points-of-Sale across Israel, web
site and call center
 Yearly revenues (2011) of over 2B Euro
 Shufersal is running a Teradata DWH, Unica campaign management
and formally used SAS Enterprise Miner for statistical analysis

12. Personalized Promotions Business Case - Goals
Challenges Goals
Sending all loyalty program members same Move from fixed coupons to
discount coupons led to very low personalized coupons based on
redemption rate customers purchase behaviour
analysis
Only statisticians can run DM models Enable marketers with no statistical
know-how to run DM models

13. Personalized Promotions Business Case – The Solution
GSTAT Implementation
 Shufersal implemented GSTAT Next Best
Offer as an automated personalized coupons
solution
 Implementation project took 4 months, pilot
results in 2 month
 The solutions matches each customers the
right 10 coupons based on optimization
algorithms, out of a pool of ~200
coupons, changing each month
 GSTAT recommendations are sent to print
house and delivered to customers’ address

14. •
The chain manages as
a bridge between
Personalized Promotions Business Case – The Process
manufactures (who
sponsor the discounts)
and customers
Loyalty program • Recommendation
Category combine manufactures
Manager manager:
Campaigns •Project manager requirements and
/ Buyers
Manager •Designer customers’ preferences
(trade/ •Legal consulting Chain’s Loyalty
Coupon marketing) Program
Employees Members
Creative
Coupon
Print
Coupons &direct
Tests mail, e
Pool
Coupon
mails
Analytics
Coupon 400
coupons
GSTAT
NBO
1 Day - Days 1-2 Days

15. Personalized Promotions Business Case - Results
Main Business Benefits
 Total redeem percentage moves from 1%
before to around 4%-6%
 Around 15% of customers redeem at least
one coupon every month
 Redeem percent of personalized coupons is
300% higher then redeem percent among
customers who get fixed coupons
 Customers getting personalized promotions
expend their monthly spend by average of
2% compared to customers getting fix
coupons (several millions $ increased sales,
each month)

16. An Example Personalized Promotions ROI
Segment of Customers
Non Customers 1,000,000
Bronze 1,000,000
Silver 500,000
Gold 250,000

17. An Example Personalized Promotions ROI
Segment Gold Silver Bronze
# Customers 250,000 500,000 1,000,000
Average Quarterly Basket (EUR) 500 200 30
Increase in revenues due to 5 2 0.3
personalized promotions – 1%
(EUR)
Total incremental revenues 1,250,000 1,000,000 300,000
(EUR)
Variable cost of personalized 125,000 250,000 500,000
print – 0.5 EUR per customer
(EUR)
Quarterly Incremental 1,675,000
Revenues (EUR)

18. Introducing GSTAT NBO

19. GSTAT – Automatic Data Mining Solutions
GSTAT Suite for Finance
•GSTAT NBO – a software
• Next Best Offer
solution for planning and
• Customers Retention Optimization
• Customers Segmentation Analyzer optimal allocation of
• Credit Risk Analyzer personalized
recommendations
•Based on automatic data
GSTAT Suite for Retail mining models which
• Next Best Offer (Personalized Promotions)
analyze the basket purchase
• Customers Retention Optimization history of each customer
and recommends on the
right offers for each
customer
•Operated by marketing
GSTAT Suite for Telecom analysts – now need for
• Next Best Offer statistical know-how
• Rate Plan Optimization
• Customers Retention Optimization
• Customers Segmentation Analyzer

20. What is GSTAT NBO?
 GSTAT NBO IS not a data mining tool
 GSTAT NBO is a software solution which
automatically performs processes executed by ETL
GSTAT Next Best Offer is the
and statisticians, for resolving personalized promotions
answer for companies looking allocation business challenges
for an end-to-end business  GSTAT NBO provides recommendations supporting
automatic decision making
solution for personalized
 Performs automatically all processes of data mining
promotions and optimization models development and deployment
optimization, based on  Saves resources of statisticians and integration
experts or increasing productivity
advanced data mining and
 Shortens time for development and deployment of
optimization processes
personalized promotions optimization projects from
months to hours
 No need in any statistical know-how – all work is
done by marketer using friendly GUI

21. GSTAT Differentiators Compare to Classic DM Projects
Classic Data
GSTAT NBO
Mining Projects
Months of
hours
development
•Increase customers’
Weeks of basket and revenues
Automatic by up to 5% a month
deployment
Constant Self learning •Increase analytical
models models team productivity by
100 times
Need for Does not require
Statisticians Statisticians
•Shortening time-to
market of providing
Complicated friendly personalized
recommendations
from months to hours
Room for Packaged
mistakes Best Practice

22. GSTAT NBO – Architecture
Recommendation
Inputs Engine Outputs
1. Product
1. Identifying
Catalogue
customers with high
2. Analytical Panel
propensity to
3. RFM Table
purchase an item for
1.Developing and running DM models for the first time
propensity of each offer customer-
2. Identifying
product combination
2.Optimal Allocation under constraints customers with high
propensity to re-
purchase an item
22

23. GSTAT NBO – Retailers Functionality
 Coupons data input to the system –
 Manually
 Fast load mechanism for importing data on thousands of products
 Conditions –
 Overall (“exclude all black-list customers”,…)
 Per each promotion (“Score all the male customers who have bought Carlsberg beer in the
last 3 months, for an Amstel beer coupon of buy 4 get 1 for free”,…)
 Constraints for optimal allocation –
 Minimum/Maximum for each coupon
 Number of coupons from each category (“not more than 2 coupons from non-food category”,
not more than 1 coupon from coupons with a discount higher than 2 Euros”,…)
 Mix of cross-sell/Up-sell coupons (“for high churn risk customers at least 5 up-sell
coupons”,…)
 Optimal allocation process on chain level or store level (for avoiding out-of-stock cases)
 …

24. • The system runs a variable • The system builds periodic
GSTAT Automatic• DM Engine
• The system using GSTAT
selection process calculates propensity
scores for each customer per
proprietary algorithms based on
The system uses Regressionfor re-
scoring processes methods for
estimating customers’models or to buy
building the propensity
product
chi square statistics for multi- updating the scores and
the product
• The system runs Optimal
dimension reduction and • running allocation every
The system runs validation processes
prevention of over-fitting re-
allocation process for Data selected period
and present Lift and Captured Response
prioritizing customers-products extraction, data (day/week/months,…)
charts as well as the main explaining
based on different constraints management
parameters
and Sampling
Implementing
Variable
periodic scoring
Selection
process
• The system samples customers
who have/haven’t bought the
Scoring and Modeling and
product in the last months
Optimization Validation
• The system prepares the data for
modeling, including target and
explanatory parameters

25. Example – GSTAT Next Best Offer Architecture
GSTAT
DWH
Server
DWH

26. Unique Advantaged of GSTAT NBO
•All promotions recommendations are based on a software solution which runs automatically instead of professional
services
•The chain controls parameters, conditions and constraints and can review the results ongoing
•Using Logistic Regression for modeling provide better results as compare to other methods, leading to more accurate
Software recommendations and higher response rates
• A special GUI designated for Marketers in Retail , enables them to easily run the most advanced statistical
models and optimization processes
• Even Marketers with no understanding in statistics can operate GSTAT NBO
Easy to Use
• Based on over 10 years of experience in Retail, providing integrated solution to most business challenges in
coupons allocation
• GSTAT is value oriented always looking for showing real monetary value for its customers
Practical
• We are not selling just a statistical tool; We are selling an end-to-end business solutions which include all is
needed for advanced promotions optimization – one stop shop (Software tools, consulting, PS, training)
End-to-end
solution

27. GSTAT Vs. Substitutes
GSTAT Solution Data Mining tools
Solution Concept An end-to-end business solution for A statistical development environment that
Promotions/coupons requires the work of statisticians and
recommendations based on out-of-the ETL/SQL experts for building predictive
–box automatic data management and processes such as Next Best Offer/Action
data mining processes
Data Management All data preparation for modeling and Data preparation for modeling and models’
models’ deployment processes are deployment are done outside of the DM
automatic and part of GSTAT software’s environment by coding.
GUI.
Users Marketing analysts with no DM or data Statisticians and data management
management knowledge can develop experts. Friendly data mining tools enable
and deploy models end-to-end marketers only to develop the model itself
(not to prepare the data and not to deploy)
which is 20% of all work required for real
modeling integration
User interface An intuitive designated user interface A standard modeling user interface for all
for retail marketers. A marketer just type of models. Complicated for marketers
needs to chose the products from the and business users.
product catalogue and population to be
contacted, and this is it.
Management of Managing and running constraints Requires coding which might take weeks
constraints (min/max promotions,…) in the GUI and months

28. GSTAT Vs. Substitutes
GSTAT Solution Data Mining tools
Quality of prediction Thanks to the capability to split a model Lower response rates
to several models for different segments
we can get potential lists with higher
response rates by up to 10%-50% as
compared to lists based on one data
mining model
Dependency on IT/ Minimal Full
consultants for changes
Time for development of Hours Months-years
1000 cross-sell & churn
prediction models
Time for deployment of Automatic Months-years
1000 models
Self learning models Because models development and Because models development and
deployment takes only hours, the deployment takes weeks, the
company can frequently update the company usually do not update
models what will bring to more relevant frequently the models what brings to
recommendations to customers and lower response rates over time
higher response rates
Implementation End-to-end implementation, based on Just a DM tool.
industry best practice - which will
enable Marketing analysts to run and
deploy thousands models in minutes

29. GSTAT NBO – the advantages of running a software
# Subject Services Provider GSTAT NBO
1 Targeting method Business rules or basic statistics Advanced propensity modeling –
leads to higher redemption rates
2 Dependency High dependency at services No dependency. Marketing
provider operates the system independently
3 User interface No user interface / minimum All functionality can be operated
functionality using a designated GUI for
Marketers
4 User Services provider with expertise Marketing analyst with no know-
in data mining how in data mining
5 Ability to analyze Black-box Ability to analyze each coupon’s
results model results – lifts and explaining
parameters
6 Time to execute Days-weeks hours
7 IT integration Sending data outside to external Integrated with aCRM components
servers (DWH, Campaign Management, …)
8 Cost effectiveness Periodic services Software licenses and set up
project, ROI within 2-3 months
and saving of millions of dollars

30. How to start?

31. Run a quick-win POC
 Prove we can increase its customers’ average basket by 1-3% in
a couple of months of work
1 week
2-3 weeks Reviewing
employees
Extracting data recommendations
according to design
paper
Optional – Running
a live campaign
(direct mail/print in
Running GSTAT the POS)
NBO on
customer’s
data
Business
and IT 1 week
Workshop
2 days

32. Thanks for
Listening !
Q & A…..