www.decideo.fr/bruley next best offer extract from various presentations: seng loke, peter csikos,...
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www.decideo.fr/bruley
Next Best Next Best OfferOffer
Extract from various presentations: Seng Loke, Peter Csikos , Aster Data …
February 2013February 2013
www.decideo.fr/bruley
Next Best Offer Batch Use caseNext Best Offer Batch Use caseSmart Outbound Personal Banker Calls exampleSmart 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
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Personalized Offers via The Call Personalized Offers via The Call Center?Center?
< >
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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?
Personalized offers
Savings
Lending
Trigger X
Customer View
Cindy Bifano
1168 Barroilhet Dr.
Hillsborough, CA, 94010
555-954-5929
Customer Value score: 87
Attrition score: 32
Accounts
Household
Customer X
2181%6375
Hand offs
Sales$ TargetActualTarget
Offers Made
My Sales Targets & Scores X
Acct Age: 7 Last order: 01/15/07
Last offer: B707
Renewals: 07/02/09 Affinities: e-Nest3
Product links
04/21/07InboundCall Ctr
Customer History
04/18/07Outboundemail
03/02/07InboundCall Ctr
DateSummaryContact
X
!
!
708009838228
Joint account
Personalized OffersPersonalized Offers
www.decideo.fr/bruley
WHAT IS A RECOMMENDATION WHAT IS A RECOMMENDATION ENGINE?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.
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Why Recommendation Engine?Why Recommendation Engine?
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HOW DOES IT WORK?HOW DOES IT WORK?
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WHAT IT DOES?WHAT IT DOES?
• Data collection and processing
• Relevance & preference ordering
• Display recommendations• Self-learning & improving
capabilities
Recommender logic
• Mathematical models• Information systematization
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The RecommendationsThe Recommendations
Customer is looking for a product
Receive personal offerings
Receive tips
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SHORT SCIENCE RECOMMENDATION SHORT SCIENCE RECOMMENDATION ALGORITHMSALGORITHMS
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
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Early generation recommendation Early generation recommendation solutions…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 increaseLower conversion rateIncrease of customer satisfaction is questionable
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NEW GENERATIONAL RECOMMENDATION NEW GENERATIONAL RECOMMENDATION ENGINES: RELEVANT RECOMMENDATION BASED ENGINES: RELEVANT RECOMMENDATION BASED
ON THE ANALYSIS OF ALL SOURCESON THE ANALYSIS OF ALL SOURCES
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Teradata SolutionsTeradata Solutions
Technology and solutions
to drive greater
insights from new forms of
data (exploding
volumes and largely
untapped)
Integrated data
foundation for competing on analytics
Applications that utilize the data and insight to address
key business functions
BUSINESS BUSINESS APPLICATIONSAPPLICATIONS
BIG DATABIG DATAANALYTICSANALYTICSDATADATA
WAREHOUSINGWAREHOUSING
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Next Best Offer: customer centric Next Best Offer: customer centric marketingmarketing
• 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
www.decideo.fr/bruley
Understand Affinity between Understand Affinity between DepartmentsDepartments
Drive Sales by Cross-selling Products
Low Affinity between certain
departments
Low Affinity between certain
departments
Home & Garden, Bedding and Bath & Furniture have high
affinity
Home & Garden, Bedding and Bath & Furniture have high
affinity
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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.
Overview of Cross-Basket AffinityOverview of Cross-Basket Affinity
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
Transactional DB
Cross-Channel Transactions
X Customers X Marketing Campaigns
Retail EDW
UserId Address Phone
10001 10 Main St 555-3421
20001 24 Elm st 232-5451
30002 534 Rich 232-5465
Customer Loyalty
Item UPC Category Dept
83321 Heels Shoes-Womens
73910 Handbags Accessories
39503 Dresses Apparel-Womens
49019 Perfumes Cosmetics
Product/Item Hierachy
Date CampaignID UserId
11/12/24 3241 10001
11/12/28 2352 20001
11/12/24 3241 30002
11/12/30 2352 10001
Marketing/Promotions
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Barnes & Noble: Using Aster SQL-Barnes & Noble: Using Aster SQL-MapReduceMapReduce
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
Dynamic Consumer Personalized Recommendations
How to increase relevancy of cross-category offers?
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Increased Conversions from Increased Conversions from Personalized Recommendation EnginePersonalized 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
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Advanced Site Behavior and Advanced Site Behavior and PersonalizationPersonalization
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
Personalization
How to increase purchase size with personalized recommendations?
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Global Architecture Solution In Global Architecture Solution In Detail …Detail …
1. Observed patterns pushed to Channel
InboundChannel
Prioritized / Personalized Content, Message, Offer
4. Returns offer
2. Customer
Interacts with a Channel
5. Continuous
learning and updated models
3. Begin Processing
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
Dynamic Profiling
BusinessRules
Multi-dimensional
Analytics
Message Strategies
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Team PowerTeam Power