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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..…