Next Best Offer

1. Next Best Offer michel.bruley@teradata.com Extract from various presentations: Seng Loke, Peter Csikos , Aster Data … February 2013

2. Next Best Offer Batch Use caseSmart Outbound Personal Banker Calls example

3. LB@gmail.com 708009838228 Joint account Customer History Contact Summary Date Call Ctr Inbound 03/02/07 email Outbound 04/18/07 Call Ctr Inbound 04/21/07 ! ! < > < > X X X X Personalized Offers via The Call Center? Personalized Offers Customer View Acct Age: 7 Last order: 01/15/07 Last offer: B707 Renewals: 07/02/09 Affinities: e-Nest3 Product links Customer Cindy Bifano 1168 Barroilhet Dr. Hillsborough, CA, 94010 555-954-5929 Customer Value score: 87 Attrition score: 32 Accounts Email Household Trigger Personalized offers Savings Lending I see you made a large deposit 4/13/07. Do you have any plans for this? Can I suggest a high yield bond? Did you know you are near your overdraft limit? Would you like to consolidate this into a term loan? My Sales Targets & Scores Offers Made Sales $ Target Hand offs Target Actual 75 63 81% 21

4. What is a recommendation engine? Recommendation engines form a specific type of information filtering system technique that attempts to present information items that are likely of interest to the user.

5. Why Recommendation Engine?

6. How does it work?

7. WHAT IT DOES? Recommender logic • Data collection and processing • Relevance & preference ordering • Display recommendations • Self-learning & improving capabilities • Mathematical models • Information systematization

8. The Recommendations Customer is looking for a product Receive tips Receive personal offerings

9. Short Science Recommendation Algorithms • Recommendation in general: • Possible to use a wide palette of recommendation algorithms • The best fitting algorithms are selected – after careful analysis of the data – to the given recommendation problem and the corresponding optimization task • Overview of recommendation algorithms: • Collaborative filtering (CF): Based on events generated in your service (Vod purchase, Live channel watching event), finds similar behavior on users, and similarity on items (VoD content, live schedule, etc.) • Content based-filtering (CBF): Using only user/item metadata. Recommendations are based on matching keywords. • Measuring Recommendation Quality: • Average Relative Position (ARP): The distance between the prediction and the user’s choice • Top 10 Recall: the probability of hitting the chosen item from the top 10 items of the personalized list

10. Early generation recommendation solutions… … Did not offer really personalized recommendations for each and every user… Not personalized Only based on part of the available information Low customer retention (if any) Minimal revenue increase Lower conversion rate Increase of customer satisfaction is questionable

11. New generational recommendation engines: Relevant recommendation based on the analysis of all sources

12. Teradata Solutions Applications that utilize the data and insight to address key business functions BUSINESS APPLICATIONS DATA WAREHOUSING BIG DATAANALYTICS Technology and solutions to drive greater insights from new forms of data (exploding volumes and largely untapped) Integrated data foundation for competing on analytics

13. Next Best Offer: customer centric marketing Action can take multiple forms Purchase recommendation Pricing recommendation Advertising recommendation Promotion recommendation … Recommendations can be based on multiple factors Product affinity Pricing affinity Behavior affinity Lifecycle affinity Attribution analysis … Ability to customize actions to get more favorable outcomes

14. Understand Affinity between Departments Drive Sales by Cross-selling Products Home & Garden, Bedding and Bath & Furniture have high affinity Low Affinity between certain departments

15. Overview of Cross-Basket Affinity Cross-Channel Transactions X Customers X Marketing Campaigns Challenge • Difficult to do in a relational DB due to the sheer size of the combinatorial permutations of the various purchasing sequences. • Requires good customer recognition via a credit card database or a customer loyalty card program. With Teradata Aster • Use nPath/Sessionization to identify “super” baskets within a time window. Tighter time window implies higher affinity. • Run Basket Generator to identify the most frequent affinity items & subcategories. Impact • Enables more accurate targeting of customer needs; reduce direct marketing spend, increase revenue yield. Transactional DB Customer Loyalty Retail EDW Product/Item Hierachy Marketing/Promotions

16. Barnes & Noble: Using Aster SQL-MapReduce Dynamic Consumer Personalized Recommendations How to increase relevancy of cross-category offers? • Analyze Cross-Channel Consumer Data • Both “known” members and non-Members • Purchases and browsing behavior online, in-store, and mobile • Rapidly change targeting strategies & models • Drive personalized recommendations across products and categories through any in-bound or out-bound delivery • Co-purchase analysis and category affinity scoring • Customer recommendations:186 million product pairs • Keep scoring models updated across changes in both customer and aggregate actions • Ensure that model output is available to all consumer communication channels: in-bound and out-bound

17. Increased Conversions from Personalized Recommendation Engine Aster Data Business Impact and ROI Increase conversions from recommendations; analyze patterns across eBook (Nook) customers; 360 degree view of customer across in-store and .com behavior Build revenue attribution models to link every purchase to a site feature Analytics Efficiencies: Payment processing and analytics; from 1 day to 1 minute processing with SQL-MR eBook analysis (downloads, reader preferences…); from 4-5 hours to 1-3 minutes Web log data processing: from 7 hours to 20 minutes Web Analytics data loading from Coremetrics: from 4 hours to 30 minutes including geographical IP look-up

18. Advanced Site Behavior and Personalization Personalization How to increase purchase size with personalized recommendations? Interpret individual user site visit behavior • Customer example: Growing from 10TB to 20TB of semi-structured clickstream data • Capture behavior patterns in a site visit using Aster Data Sessionization operator • Determine who put what in their cart and if they checked out • Deeper, personalized recommendations cross-product and cross-category with graph analysis • Improve recommendations beyond “people like you” • Identifies relationships betweenpairs of product types, association and direction of relationship • Behavioral pattern analysis for site optimization • Discover order in which customers add/remove items to/from carts

19. 1. Observed patterns pushed to Channel Prioritized / Personalized Content, Message, Offer Inbound Channel 2. Customer Interacts with a Channel 4. Returns offer 3. Begin Processing 5. Continuous learning and updated models Multi- dimensional Analytics Dynamic Profiling BusinessRules Message Strategies • 360 degree view • Demographics • Transaction data • Contextual • No data replication • Campaigns activation and qualification • Offers governance • Offers history • Automatic real-time targeting • Likelihood estimation • Response prediction • Aligns customer interests and organization objectives • Balances channel and marketing Global Architecture Solution In Detail …

20. Team Power