with Peter MOLONEY, CEO, Loyalty Builders, Inc. NEDMA 2016 Conference

1. © Copyright Loyalty Builders Inc. 2016
The Next Frontier in Marketing
Automation: RevenueAutomation
Practical B2C Analytics
to Personalize Recommendations
CONFIDENTIAL
Peter Moloney
CEO, Loyalty Builders, Inc.
peterm@loyaltybuilders.com

2. © Copyright Loyalty Builders Inc. 2016
Personalizing Product Offers Works
Case Study: CPG Retailer
Problems: Too many products and customers
to analyze individually
Prior Solu@ons: Promote best selling products
Personalized Emails: Three 1:1 product
recommenda@ons, ~115K customers,10-days
Results: 1:1 product recommenda@ons produced
11% more revenue than best sellers
2
Revenue LiK per Email: $0.97 (11%)
Revenue LiK: $111,983 over 10 day campaign

3. © Copyright Loyalty Builders Inc. 2016
Moments of Customer AOen@on are
Rare:
Don’t waste them with irrelevant messages & offers
Are you showing the same
messages and offers to
all customers?
Are returns diminishing from over-
communica@ng?
How many customers are ignoring
you, or annoyed?
Colin Sebas@an, Robert W Baird & Co.
hOp://www.retaildive.com/news/wayfair-
reports-strong-sales-as-more-customers-return-
to-buy/414707/
Revenue up 86% 2015
Repeat customer business up 92%
“…due, in part, to a personalized shopping
experience… Wayfair sends more than 1
million different permuta@ons of outbound
emails on a daily basis.” (Baird & Co)
“The ‘response to buying sugges@ons’ is said to
generate an addi@onal 10-30% in
revenue.” (Econsultancy)

4. © Copyright Loyalty Builders Inc. 2016
B2C Scale Requirements
Retail, e-commerce, catalogs, consumer services, hospitality
Customers: 5K to 5M or more
Products: 100 to 100K or more
Regular purchasing by most loyal
customers

5. © Copyright Loyalty Builders Inc. 2016
The Last Mile of Marke@ng Automa@on
Automa@ng the Offers that Increase Buying
Next… “What”
Last 5 years… “When”
Last 10 years… “How”
Ironically, the more automated marke@ng has become,
the more customers try to protect themselves from it

6. © Copyright Loyalty Builders Inc. 2016
Common Techniques for “Personalizing”
Recommenda@ons & Offers
Product Affini@es
Best Selling Products per
Customer Segment or Persona
Purchases
Address
Gender
Age
Website Visits
Call Center
Cases
Sales Calls
Income
Family
Social Posts
Likes
Tweets
Clicks
GPS Location
Mobile Apps
E-Commerce
Page
Looks
Third Party Data Jobs
Social
Connections
Best Customers
per Product
Recently
Browsed

7. © Copyright Loyalty Builders Inc. 2016
Best Results:
Analyze Each Customer Individually
Who is ready to buy now?
Which customers are at risk?
Who is worth an expensive
campaign?
What specific products is each individual
customer most likely to buy next?

8. © Copyright Loyalty Builders Inc. 2016
Automa@ng the “What” – Let the Data Tell You
1. Limit source data to minimum (use best data)
2. Constrain objec@ves (e.g., exis@ng customers, best recommenda@on)
3. Use best data science (e.g., machine learning, automated modeling)
4. Deliver ac@onable results (e.g., campaign lists)
5. Package it as a “service”
Upload Purchase Data
to Cloud Service
Calculates Accurate
Buying Predictions for
ALL Customers
Download Offers & Lists On-Demand
Email
Offers
Web &
Mobile
Catalog
Depth
Direct
Mail List
Sales
Leads
The ul@mate goal is more buying (revenue), not just opens, clicks, etc.

9. © Copyright Loyalty Builders Inc. 2016
Purchase History (transac<ons):
BeOer Purchase Predic@ons & Far Simpler Process
First party data available on all customers
Typically available over many years
Most reliable, complete, and accurate data on customers
Most accurate predictors of future purchase behavior
No privacy concerns for first-party analy@cs
Easy to source – No data integra@on prerequisites
No ambiguity about what the data means
If you want the best predic@ons of buying behavior,
there is not beOer source data than past buying behavior

10. © Copyright Loyalty Builders Inc. 2016
Use Machine Learning to Automate
Data Modeling for Each Customer
Cust ID
Item ID
Date
Amount
Customer
Profiler
Recency
Duration
Retention
Total Spent
No. Orders
Distinct Items
Item 1 Totals
Item 2 Totals
Ÿ
Ÿ
Ÿ
Revenue
Module
Orders
Module
Category
Module
Time
Module
Customer
Loyalty Rank
Cross-Sell
Module
Re-Sell
Module
Item 1 Probability
Item 2 Probability
Ÿ
Ÿ
Ÿ
Item 1 Probability
Item 2 Probability
Ÿ
Ÿ
Ÿ
Likelihood to Buy per Period
Purchase Delay
Risk Score
Expected Value
Loyalty Segment
Ÿ
Ÿ
Ÿ
ProbabilisticStateModeler
Machine
Learning
Module
ScoringCalibrator
Probablis@c State Learning Machine

11. © Copyright Loyalty Builders Inc. 2016
Generate Predic@ons for Each Customer
Metric ExplanaSon
Life@me
Poten@al
Scores and ranks each customer
based on life@me value poten@al
Risk of
Defec@on
Probability customer will not
purchase before going “inac@ve”
Likelihood
to Buy
Probability each customer will
purchase within a @me horizon
Expected
Value
Expected amount of revenue from
each customer within a horizon
Metric ExplanaSon
Re-Sell
Probabili@es
Probability of customer to purchase
each item previously purchased
within a @me horizon
Cross-Sell
Probabili@es
Probability of customer to purchase
each item never before purchased
within a @me horizon
Product Rank
Rela@ve ranking of products based on
interest in purchasing items never
before purchased
Lifecycle PredicSons: Product RecommendaSons:

12. © Copyright Loyalty Builders Inc. 2016
Back Test First to Validate Accuracy
0%#
20%#
40%#
0&1%
#
1&2%
#
2&5%
#5&10%
#10&15%
#15&25%
#25&35%
#
35+#%
#
Re#sell&Back&Test&
ActualPurchasing
Predicted Probability

13. © Copyright Loyalty Builders Inc. 2016
Use It for Targe@ng and 1:1 Offers
Target & incent individuals by:
- Lifecycle Stage
- Recent loyalty/risk trend
- Interest in product promo@ons
Offer highest ranked products to
each individual customer
©2014EpsilonDataManagement,LLC.Private&Confidential
e
Customers are segmented by their Loyalty Rank and their
Risk Score to create Loyalty Segments
Loyalty Score components:
• revenue (total, also year to
date)
• likely buyer score
• category score
• service interval
• change in service interval
• purchase delay
• recency
• Loyalists
• “Best Customers”. High Value/Low Risk
• Nurturers
• Low Risk/Moderate Value. Come in
routinely, but don’t buy enough
• Underperformers
• High Value/Mid Risk. They buy a lot,
but are at risk to defect
• Faders
• Low Value/Mid Risk. These are
customer that never “took off”.
• Win-back
• High Risk. They haven’t bought in a
while and very like to go inactive
• 1 & 2’s
• Only came in one or two times.
Risk Score
Likelihood that the customer
will not make another purchase
before they go inactive
1 & 2’s
Lifecycle stages:

14. © Copyright Loyalty Builders Inc. 2016
Personalized Product
Recommenda@ons
Not everyone wants best sellers
Work with clients indicates “loyalty” is hard to stereotype
Classifica@on is a blunt instrument
Recent “browsing” is an inconsistent, flee@ng indicator
Analyze deeper; Make it truly “personal”

15. © Copyright Loyalty Builders Inc. 2016
Personalized Messages & Incen@ves
Determine lifecycle stage by predic@ng life@me value poten@al and risk to
defect based on:
– Exact @ming and paOerns established by each purchase
– Inter-order @mes, spend consistency, trending
– Range and types of products purchased
– Sequence of product purchases, items ordered together
– Each individual’s paOerns compared to others, en@re popula@on
– Abrupt changes in paOerns and longer-term trends
Lifecycle
Prospects 1-2x Buyers Win Backs Faders Underperformers Nurturers Loyalists
Welcome & Nurture Re-AcSvate Engage Retain
Timing & incen@ves Higher discounts, re-sell offers Low/no discounts, cross-sell offers

16. © Copyright Loyalty Builders Inc. 2016
RFM is too Simplis@c
Forces ar@ficial trade-offs between Recency, Frequency & Monetary Value
Both Customer X and Y have
the same RFM score
$0
$500
$1,000
$1,500
Jan Feb Mar Apr May Jun
$0
$500
$1,000
$1,500
Jan Feb Mar Apr May Jun
Purchases by Customer X:
Purchases by Customer Y:
Which customer is more
loyal? More valuable?
They are NOT the same!

17. © Copyright Loyalty Builders Inc. 2016
Data-Driven Content
Customer Image Text 1 Disc Product 1 Product 2 Product 3 Product 4
2246398 sd/img2 Thank you 15% Tote bag Paw pads RF Sweater Milk bones
1481677 sd/img1 We miss you 25% Rope toy Disc chaser EzDog Vest Sleep pad
1650265 sd/img4 Get ready 20% FP Leash EzDog Vest Wellness DF Milk bones
Data File from Analy@cs Service:
Individualized image
and message
Individualized
discount
Individualized product offers

18. © Copyright Loyalty Builders Inc. 2016
TradiSonal Approaches are Very Expensive
Est. Total Time & Cost by Step:
Marke@ng Campaigns
Data/Insight Interpreta@on
Campaign Planning
Offer Selec@on
Discount Level
Crea@ve Design
Campaign Lists
Execute Campaign
Translate scores and “insights” into
marke@ng ac@ons
Data Modeling
Data Mining
Data Sampling
Algorithm Selec@on
Scoring Func@ons
Rules
Tes@ng
Find “predictor” aOributes and
score customers
Sourcing
Extrac@on
Cleansing
Enrichment
Data Modeling
Transforma@on
Integra@on
Storage
Governance
Data Prepara@on
Integrate as much data about
your customers as possible
60% 20% 20%

19. © Copyright Loyalty Builders Inc. 2016
AutomaSon Save 80% of Time, Cost, Complexity:
Marke@ng Campaigns
Data/Insight Interpreta@on
Campaign Planning
Offer Selec@on
Discount Level
Crea@ve Design
Campaign Lists
Execute Campaign
Translate scores and “insights” into
marke@ng ac@ons
Data Modeling
Data Mining
Data Sampling
Algorithm Selec@on
Scoring Func@ons
Rules
Tes@ng
Find “predictor” aOributes and
score customers
Sourcing
Extrac@on
Cleansing
Enrichment
Data Modeling
Transforma@on
Integra@on
Storage
Governance
Data Prepara@on
Integrate as much data about
your customers as possible
Translate scores and “insights” into
marke@ng ac@ons
Data/Insight Interpreta@on
Campaign Planning
Offer Selec@on
Discount Level
Crea@ve Design
Campaign Lists
60% 20% 20%

20. © Copyright Loyalty Builders Inc. 2016
A Path to 1:1 Product
Recommenda@ons
1. Establish repeatable analy@cs process
2. Create a Trials Program
– Plan tests/campaigns over 2-3 months
– Define measurement and success criteria
3. Test personalized recommenda@ons “off-line” from main
marke@ng programs or with smaller customer sets
4. Layer personalized marke@ng into exis@ng marke@ng
systems and processes

21. © Copyright Loyalty Builders Inc. 2016
Example:
E-Commerce Apparel Company
Situa@on: Sta@c emails, limited analy@cs resources
Process:
1. Create variable content template
2. Upload transac@on records, all customers
3. Download formaOed list for email system
4. Send 1:1 offers specific to each customer
5. Measure sales vs. control group (sta@c emails)
21
Designs just for you. View this email in your brow ser
Sam Just For You!
Here is a collection of apparel tailored just for you.
Now that you‘ve selected a template to work with, drag in content blocks to define
the structure of your message. Don‘t worry, you can always delete or rearrange
blocks as needed. Then click —Design“ to define fonts, colors, and styles.
Need inspiration for your design? Here‘s what other MailChimp users are doing.
1534801
$6.00
BUY NOW!
1031032
$6.00
BUY NOW!
8522290
$6.00
BUY NOW!
1534801
$6.00
BUY NOW!
8035732
$6.00
BUY NOW!
1031032
$6.00
BUY NOW!
4518505
$7.00
BUY NOW!
8522290
$6.00
BUY NOW!
1010004
$8.00
BUY NOW!
Group Customers
Revenue per
customer
Revenue /
Buyer
% Buyers
Lib in
R/Buyer
Lib in
Buyers
Revenue Lib
per customer
Revenue
Lib
Target 3,526 $268.04 $1,878.94 14.3%
20% 22% $83.92 $295,906
Control 997 $184.12 $1,568.94 11.7%
Results in 1 Month:

22. © Copyright Loyalty Builders Inc. 2016
Even a LiOle LiK Makes a Big Difference
This $0.42 lift over previous
methods delivered $314,700
of additional revenue
At 500K individualized
emails per month, this
translates to $2.6 million
additional revenue per year
Average Over 6 Campaigns – 750K emails/group
Control Individualized Lib
29,149 orders 34,357 orders 5,208 orders
$3.09/email $3.51/email $0.43/email
Business as Usual Individualized

23. © Copyright Loyalty Builders Inc. 2016
Some@mes Results Astonish
Na@onal Auto Maintenance Retailer
Service transac@ons for
“luxe” segment: 1M customers
Campaign: Personalized offers,
mix of print & email
Mail
Volume
Purchase
Rate
Total Revenue
Average Revenue
per Responder
Average Revenue
per Touch
Revenue
Lift
Challenger 207,009 12.7% 17,746,240$ 676$ $85.73 375%
Champion 41,402 11.0% 747,594$ 163$ $18.06
Challenger = Loyalty Builders, Champion = Business as usual (control)
CALL US
(855) 781-4893
FIND TIRES SERVICE SPECIALS FIND LOCATION
Offer ends 7/6/2015 Offer ends 7/6/2015

24. © Copyright Loyalty Builders Inc. 2016
Unified Cross-Channel Recommenda@ons
Metrics DB
Predic@ons by
Customer
Customer Purchase
Data from Subscriber
Data Import
Data
Delivery
Service
Campaign & Program
Execu@on Lists
Summary Reports
Predicted Metrics for
Each Customer
Loyalty Builders Cloud Service Deliverables Any Plaxorm
Cust ID
TX Date
$ Amount
Product ID
Campaign Plaxorm
Analy@cs Plaxorm
E-Commerce,
CRM, Mobile App,
Triggered Email
Real-Time
Recommenda@on
Service

25. © Copyright Loyalty Builders Inc. 2016
Automated Cloud-Service
Best RecommendaSons & Offers
Typical
Approach
Loyalty
Builders
Uses only available, reliable, accurate first party data ✔
No data integra@on ✔
Automated data modeling and scoring ✔
No plaxorms to install, maintain ✔
Immediate access to ac@onable campaign lists ✔
Scores every customer on every product ✔
Immediate, easily measurable ROI ✔
Who will buy? When? How much? What products?

26. © Copyright Loyalty Builders Inc. 2016
QUESTIONS?
peterm@loyaltybuilders.com
Peter Moloney
Loyalty Builders, Inc.