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Retail gstat nbo - september 5th finiper


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  • 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. IntroducingGSTAT 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 ofpropensity models in a few hours?How to take into consideration optimal promotionsallocation 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 GoalsSending all loyalty program members same Move from fixed coupons todiscount coupons led to very low personalized coupons based onredemption rate customers purchase behaviour analysisOnly statisticians can run DM models Enable marketers with no statistical know-how to run DM models
  • 13. Personalized Promotions Business Case – The SolutionGSTAT 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 • RecommendationCategory combine manufacturesManager 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 - ResultsMain 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 ROISegment Gold Silver Bronze# Customers 250,000 500,000 1,000,000Average Quarterly Basket (EUR) 500 200 30Increase in revenues due to 5 2 0.3personalized promotions – 1%(EUR)Total incremental revenues 1,250,000 1,000,000 300,000(EUR)Variable cost of personalized 125,000 250,000 500,000print – 0.5 EUR per customer(EUR)Quarterly Incremental 1,675,000Revenues (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 ETLGSTAT Next Best Offer is the and statisticians, for resolving personalized promotionsanswer 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 NBOMining Projects Months of hoursdevelopment •Increase customers’ Weeks of basket and revenues Automatic by up to 5% a monthdeployment Constant Self learning •Increase analytical models models team productivity by 100 times Need for Does not require Statisticians Statisticians •Shortening time-to market of providingComplicated friendly personalized recommendations from months to hours Room for Packaged mistakes Best Practice
  • 22. GSTAT NBO – Architecture Recommendation Inputs Engine Outputs1. Product 1. Identifying Catalogue customers with high2. Analytical Panel propensity to3. 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 NBOEasy 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 toolsSolution 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 processesData 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 Fullconsultants for changesTime for development of Hours Months-years1000 cross-sell & churn prediction modelsTime 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 NBO1 Targeting method Business rules or basic statistics Advanced propensity modeling – leads to higher redemption rates2 Dependency High dependency at services No dependency. Marketing provider operates the system independently3 User interface No user interface / minimum All functionality can be operated functionality using a designated GUI for Marketers4 User Services provider with expertise Marketing analyst with no know- in data mining how in data mining5 Ability to analyze Black-box Ability to analyze each coupon’s results model results – lifts and explaining parameters6 Time to execute Days-weeks hours7 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 forListening ! Q & A…..