Big Data and the Next Best Offer

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Information about Big Data and the Next Best Offer
Business & Mgmt

Published on February 25, 2014

Author: MichelBruley



Next-best offer refers to the use of predictive analytics solutions to identify the products or services your customers are most likely to be interested in for their next purchase.
Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. This presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.

Next Best Offer Extract from various presentations: Seng Loke, Peter Csikos , Aster Data … February 2013

Next Best Offer Batch Use case Smart Outbound Personal Banker Calls example Situation Opportunity to analyze customer banking activity to detect opportunities for personal banker to cross- and up-sell. Problem Information in transactional systems needed to be pulled together and analyzed. Solution All customer activity is loaded into the AEI Warehouse. 300 business rule queries scan the customer database every night to direct significant customer events to trigger out the best opportunities. Information is driven to banker desktops for outbound calls. Impact • Scan 2.7M daily customer events • 3M annual opportunities • 500,000 relevant calls • >40% response rate

Personalized Offers via The Call Center? Personalized Offers Customer X Cindy Bifano Renewals: 07/02/09 Affinities: e-Nest3 Product links Trigger 1168 Barroilhet Dr. Savings Hillsborough, CA, 94010 555-954-5929 Customer Value score: 87 Attrition score: 32 Accounts 708009838228 Email Lending Household Joint account Summary Date Call Ctr Inbound 03/02/07 Call Ctr Inbound X 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? 04/18/07 04/21/07 My Sales Targets & Scores Offers Made Target 75 Actual 63 Sales $ Target 81% X Hand offs > < Personalized offers X Contact Outbound ! Acct Age: 7 Last order: 01/15/07 Last offer: B707 ! Customer History email < Customer View > 21

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.

Why Recommendation Engine?


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

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

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

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


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

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

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

Overview of Cross-Basket Affinity 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. Cross-Channel Transactions X Customers X Marketing Campaigns Transactional DB Customer Loyalty 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. TransID UserId Date/Time Item UPC 874143 10001 11/12/24 83321 543422 20001 11/12/28 73910 632735 30002 11/12/24 39503 452834 10001 11/12/30 49019 • Enables more accurate targeting of customer needs; reduce direct marketing spend, increase revenue yield. Address Phone 10001 10 Main St 555-3421 20001 24 Elm st 232-5451 534 Rich 232-5465 Retail EDW Product/Item Hierachy Item UPC Category Dept 83321 Heels Shoes-Womens 73910 Impact UserId 30002 • Handbags Accessories 39503 Dresses ApparelWomens 49019 Perfumes Cosmetics Marketing/Promotions Date CampaignID UserId 11/12/24 3241 10001 11/12/28 2352 20001 11/12/24 3241 30002 11/12/30 2352 10001

Barnes & Noble: Using Aster SQLMapReduce 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

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

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 between pairs 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

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

Team Power

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