Www.decideo.fr/bruley Next Best Offer Extract from various presentations: Seng Loke, Peter Csikos,...

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Next Best Next Best OfferOffer

Extract from various presentations: Seng Loke, Peter Csikos , Aster Data …

[email protected]

February 2013February 2013


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


Personalized Offers via The Call Personalized Offers via The Call Center?Center?

< >

<>

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

Email

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

[email protected]

Joint account

Personalized OffersPersonalized Offers


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.


Why Recommendation Engine?Why Recommendation Engine?


HOW DOES IT WORK?HOW DOES IT WORK?


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


The RecommendationsThe Recommendations

Customer is looking for a product

Receive personal offerings

Receive tips


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


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


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


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


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


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


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


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?


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


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?


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


Team PowerTeam Power

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