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Next Best Offer
michel.bruley@teradata.com

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

February 2013

www.decideo.fr/bruley
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.
www.decideo.fr/bruley

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

LB@gmail.com
Household
Joint account

Summary

Date

Call Ctr

Inbound

03/02/07

Call Ctr

www.decideo.fr/bruley

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.

www.decideo.fr/bruley
Why Recommendation Engine?

www.decideo.fr/bruley
HOW DOES IT WORK?

www.decideo.fr/bruley
WHAT IT DOES?

Recommender logic

•
•
•
•

Data collection and processing
Relevance & preference
ordering
Display recommendations
Self-learning & improving
capabilities

www.decideo.fr/bruley

• Mathematical models
• Information systematization
The Recommendations
Customer is looking for a product

Receive tips

Receive
personal
offerings

www.decideo.fr/bruley
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

www.decideo.fr/bruley
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)
www.decideo.fr/bruley

Minimal revenue
increase
Lower conversion rate
Increase of customer
satisfaction is
questionable
NEW GENERATIONAL RECOMMENDATION
ENGINES: RELEVANT RECOMMENDATION BASED
ON THE ANALYSIS OF ALL SOURCES

www.decideo.fr/bruley
Teradata Solutions
Applications that utilize the data
and insight to address key
business functions
BUSINESS
APPLICATIONS

Integrated data
foundation
for competing
on analytics

www.decideo.fr/bruley

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
www.decideo.fr/bruley
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

www.decideo.fr/bruley
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.

www.decideo.fr/bruley

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

www.decideo.fr/bruley
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

www.decideo.fr/bruley
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
www.decideo.fr/bruley
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

www.decideo.fr/bruley

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

www.decideo.fr/bruley

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Big Data and the Next Best Offer

  • 1. Next Best Offer michel.bruley@teradata.com Extract from various presentations: Seng Loke, Peter Csikos , Aster Data … February 2013 www.decideo.fr/bruley
  • 2. 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. www.decideo.fr/bruley Impact • Scan 2.7M daily customer events • 3M annual opportunities • 500,000 relevant calls • >40% response rate
  • 3. 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 LB@gmail.com Household Joint account Summary Date Call Ctr Inbound 03/02/07 Call Ctr www.decideo.fr/bruley 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
  • 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. www.decideo.fr/bruley
  • 6. HOW DOES IT WORK? www.decideo.fr/bruley
  • 7. WHAT IT DOES? Recommender logic • • • • Data collection and processing Relevance & preference ordering Display recommendations Self-learning & improving capabilities www.decideo.fr/bruley • Mathematical models • Information systematization
  • 8. The Recommendations Customer is looking for a product Receive tips Receive personal offerings www.decideo.fr/bruley
  • 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 www.decideo.fr/bruley
  • 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) www.decideo.fr/bruley 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 www.decideo.fr/bruley
  • 12. Teradata Solutions Applications that utilize the data and insight to address key business functions BUSINESS APPLICATIONS Integrated data foundation for competing on analytics www.decideo.fr/bruley DATA WAREHOUSING BIG DATA ANALYTICS Technology and solutions to drive greater insights from new forms of data (exploding volumes and largely untapped)
  • 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 www.decideo.fr/bruley
  • 14. 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 www.decideo.fr/bruley
  • 15. 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. www.decideo.fr/bruley 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
  • 16. 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 www.decideo.fr/bruley
  • 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 www.decideo.fr/bruley
  • 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 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 www.decideo.fr/bruley
  • 19. 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 www.decideo.fr/bruley 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  

Editor's Notes

  1. Using Aster Discovery Platform, you can identify when customers are transitioning from one department to other and use this insight to better understand the affinity between departments. In the visual, lines represent number of visits going from one node to another across unique sessions. Home &amp; Garden, Bedding and Bath &amp; Furniture have high affinity as indicated by the thicker line connecting these departments. Low Affinity between certain departments – e.g. customers are not moving from Crafts to Luggage or vice versa. Such a affinity analysis can be used to: Strategically place ads in one department to drive cross sell of products from another department with high affinity As a retailer with a brick and mortar presence, you can also look into whether you should change the layout of the physical store. E.g. place Furniture and Home and Garden together.