Your Best Next Business Soluti on

GSTAT Next Best Offer –Optimal Personalized Promotions Recommendations

August, 2012

Company Profile

The benefits of personalized promotions

Business cases

Introduction to GSTAT Next Nest Offer

Demo

How to start?

Q & A

Agenda

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

GSTAT Profile

Professional Services

Software Development

ACRM and Targeted Marketing

CoE (100+ consultants)

Credit Risk and Basel II CoE (80+

consultants)

GSTAT Profile

Subsidiary – GSTAT Software

Development

Selected Customers

Introducing

GSTAT Next Best Offer

Call Center

Direct Mail

eMail, mobile

Loyalty Program Management

Goals :

Increasing Customers’ basket

Retaining customers through unique offers

Contact Channels : Direct Mail, SMS, POS, email

The name of the game : segmentation and personalization

The challenge : giving the Marketing tools for recommendations on

the right personalized offer that will increase customers’ revenues

DWH

Loyalty Program

members’ data analysis

1-to-1 Communication with the Customers

Personalized promotions

Communication using generic promotions

No 1-to-1 communication

Personalized promotions based on data mining and statistical analysis

of customers’ purchase history, compared to fix generic promotions :

Increase the average basket by 2%-5%

Increase redeem rates by 3-4 time

Lead to higher customers satisfaction

Personalized Promotions

1-to 1 targeting based on statistical propensity modeling, per item

1-to-1 targeting based on statistical basket analysis methods

1-to-1 targeting based on business rules

Segmental targeting

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 …

The Challenges of Executing Personalized Promotions

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

Personalized Promotions Business Case - Shufersal

Challenges Goals

Sending all loyalty program members same discount coupons led to very low redemption rate

Move from fixed coupons to personalized coupons based on customers purchase behaviour analysis

Only statisticians can run DM models Enable marketers with no statistical know-how to run DM models

Personalized Promotions Business Case - Goals

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

Personalized Promotions Business Case – The Solution

Coupons Pool

Creative

Tests

Campaigns Manager (trade/ marketing)

Loyalty program manager:

• Project manager• Designer• Legal consulting

GSTAT NBO

Analytics

Chain’s Employees

Print &direct

mail, emails

Loyalty Program Members

1 Day 1-2 Days 1-2 Days

Personalized Promotions Business Case – The Process

Coupon

Category Manager / Buyers

Coupon

Coupon

Coupon 400 coupons

• The chain manages as a bridge between manufactures (who sponsor the discounts) and customers

• Recommendation combine manufactures requirements and customers’ preferences

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)

Personalized Promotions Business Case - Results

An Example Personalized Promotions ROI

# of Customers Segment

1,000,000 Non Customers

1,000,000 Bronze

500,000 Silver

250,000 Gold

Bronze Silver Gold Segment

1,000,000 500,000 250,000 # Customers

30 200 500 Average Quarterly Basket (EUR)

0.3 2 5 Increase in revenues due to personalized promotions – 1% (EUR)

300,000 1,000,000 1,250,000 Total incremental revenues (EUR)

500,000 250,000 125,000 Variable cost of personalized print – 0.5 EUR per customer (EUR)

1,675,000 Quarterly Incremental Revenues (EUR)

An Example Personalized Promotions ROI

Introducing GSTAT NBO

GSTAT Suite for Finance•Next Best Offer

•Customers Retention Optimization•Customers Segmentation Analyzer

•Credit Risk Analyzer

GSTAT Suite for Retail•Next Best Offer (Personalized Promotions)

•Customers Retention Optimization

GSTAT Suite for Telecom •Next Best Offer

•Rate Plan Optimization•Customers Retention Optimization

•Customers Segmentation Analyzer

• GSTAT NBO – a software solution for planning and optimal allocation of personalized recommendations

• Based on automatic data mining models which analyze the basket purchase history of each customer and recommends on the right offers for each customer

• Operated by marketing analysts – now need for statistical know-how

GSTAT – Automatic Data Mining Solutions

GSTAT Next Best Offer is the

answer for companies

looking for an end-to-end

business solution for

personalized promotions

optimization, based on

advanced data mining and

optimization processes

GSTAT NBO IS not a data mining tool

GSTAT NBO is a software solution which

automatically performs processes executed by

ETL and statisticians, for resolving personalized

promotions allocation business challenges

GSTAT NBO provides recommendations

supporting automatic decision making

Performs automatically all processes of data

mining and optimization models development

and deployment

Saves resources of statisticians and

integration experts or increasing productivity

Shortens time for development and deployment

of personalized promotions optimization projects

from months to hours

No need in any statistical know-how – all

work is done by marketer using friendly GUI

What is GSTAT NBO?

Months ofdevelopment hours

Weeks ofdeployment Automatic

Constant models

Self learning models

Need for Statisticians

Does not requireStatisticians

Complicated friendly

Room for mistakes

Packaged Best Practice

• Increase customers’ basket and revenues by up to 5% a month

• Increase analytical team productivity by 100 times

• Shortening time-to market of providing personalized recommendations from months to hours

Classic Data Mining Projects

GSTAT NBO

GSTAT Differentiators Compare to Classic DM Projects

22

1. Developing and running DM models for propensity of each offer customer-product combination

2. Optimal Allocation under constraints

RecommendationEngine

1. Product Catalogue

2. Analytical Panel 3. RFM Table

Inputs Outputs

1. Identifying customers with high propensity to purchase an item for the first time

2. Identifying customers with high propensity to re-purchase an item

GSTAT NBO – Architecture

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)

…

GSTAT NBO – Retailers Functionality

GSTAT Automatic DM Engine

Data extraction, data management and

Sampling

Variable Selection

Modeling and Validation

Scoring and Optimization

Implementing periodic scoring

process

• The system samples customers who have/haven’t bought the product in the last months

• The system prepares the data for modeling, including target and explanatory parameters

• The system runs a variable selection process using GSTAT proprietary algorithms based on chi square statistics for multi-dimension reduction and prevention of over-fitting

• The system builds periodic scoring processes for re-building the models or updating the scores and running allocation every selected period (day/week/months,…)

• The system uses Regression methods for estimating customers’ propensity to buy the product

• The system runs validation processes and present Lift and Captured Response charts as well as the main explaining parameters

• The system calculates propensity scores for each customer per product

• The system runs Optimal allocation process for re-prioritizing customers-products based on different constraints

Example – GSTAT Next Best Offer Architecture

DWH

DWHGSTATServer

Unique Advantaged of GSTAT NBO

Softwar

e

•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 recommendations and higher response rates

Easy to

Use

•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

Practical

•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

End-to-end solution

•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)

GSTAT Solution Data Mining tools

Solution Concept An end-to-end business solution for Promotions/coupons recommendations based on out-of-the –box automatic data management and data mining processes

A statistical development environment that requires the work of statisticians and ETL/SQL experts for building predictive processes such as Next Best Offer/Action

Data Management

All data preparation for modeling and models’ deployment processes are automatic and part of GSTAT software’s GUI.

Data preparation for modeling and models’ deployment are done outside of the DM environment by coding.

Users Marketing analysts with no DM or data management knowledge can develop and deploy models end-to-end

Statisticians and data management experts. Friendly data mining tools enable 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 for retail marketers. A marketer just needs to chose the products from the product catalogue and population to be contacted, and this is it.

A standard modeling user interface for all type of models. Complicated for marketers and business users.

Management of constraints

Managing and running constraints (min/max promotions,…) in the GUI

Requires coding which might take weeks and months

GSTAT Vs. Substitutes

GSTAT Solution Data Mining tools

Quality of prediction Thanks to the capability to split a model 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

Lower response rates

Dependency on IT/ consultants for

changes

Minimal Full

Time for development of 1000

cross-sell & churn prediction models

Hours Months-years

Time for deployment of 1000 models

Automatic Months-years

Self learning models Because models development and deployment takes only hours, the company can frequently update the models what will bring to more relevant recommendations to customers and higher response rates

Because models development and deployment takes weeks, the company usually do not update frequently the models what brings to lower response rates over time

Implementation End-to-end implementation, based on industry best practice - which will enable Marketing analysts to run and deploy thousands models in minutes

Just a DM tool.

GSTAT Vs. Substitutes

GSTAT NBO – the advantages of running a software

GSTAT NBO Services Provider Subject #Advanced propensity modeling – leads to higher redemption rates

Business rules or basic statistics Targeting method 1

No dependency. Marketing operates the system independently

High dependency at services provider

Dependency 2

All functionality can be operated using a designated GUI for Marketers

No user interface / minimum functionality

User interface 3

Marketing analyst with no know-how in data mining

Services provider with expertise in data mining

User 4

Ability to analyze each coupon’s model results – lifts and explaining parameters

Black-box Ability to analyze results

5

hours Days-weeks Time to execute 6

Integrated with aCRM components (DWH, Campaign Management, …)

Sending data outside to external servers

IT integration 7

Software licenses and set up project, ROI within 2-3 months and saving of millions of dollars

Periodic services Cost effectiveness 8

How to start?

Business and IT Workshop

Extracting data according to design paper

Running GSTAT NBO on customer’s data

Reviewing employees recommendations

Optional – Running a live campaign (direct mail/print in the POS)

1 week

2-3 weeks

2 days

1 week

Run a quick-win POC

Prove we can increase its customers’ average basket by 1-3% in

a couple of months of work

Thanks for Listening!

Q & A..…