33% & growing – According to Euromonitor International, that was Amazon’s 2016 Ecommerce Market Share in the U.S. – A scary thought for many ecommerce executives. Yet, it’s also a great opportunity for executives to learn Amazon’s strategies to acquire, grow & retain customers, to use for your own. While most retailers cannot compete with Amazon on operations and fulfillment, where you have opportunity is with advancing your approach to personalization and cross-channel campaign management.

1. Ecommerce Personalization Beyond Amazon
How to Beat Them at Their Own Game
#CPCStrategy
Acquire, Grow and Retain Your Customers

2. Today’s Logistics
Session Recording + Slides Will Be Sent Out
Join in the Poll Questions!
Submit Questions to Our Panelist

3. Overview
Founded in 2007
Recognized as a Google Premier Partner
400+ Active Retail Clients
Top 50 fastest growing company in San Diego
DELIVERING LASTING RESULTS FOR OUR CLIENTS
Solutions
Retail-focused PPC & Shopping
Amazon Sales Acceleration
Facebook Performance Marketing
About CPC Strategy

4. Today’s Event Guest Speaker
Jason Grunberg
Vice President of Marketing
● 15 years experience in B2C and B2B
marketing
● Specialist in digital strategy, content
marketing and engagement strategy
● Prior to Sailthru, spent 12 years in
strategy roles at agencies serving
clients such as Verizon, Siemens, Pfizer,
Johnson & Johnson, Mercedes-Benz
and other global brands

5. Winner of Facebook's 2016
Innovation Spotlight
for Personalized Marketing at
Scale
+21% increase in
customer lifetime value
-40% reduction in
customer acquisition cost
Positioned in 2017
Gartner Magic Quadrant
for Multichannel Campaign
Management
-46% reduction in
customer customer churn

6. Agenda
• Understanding Amazon
• Approaches & Use Cases for Product Recommendations
• Personalization Beyond Recommendations
• Cross-Channel Personalization
• Predictive-based Acquisition

8. How could any retailer have an edge over who is widely
considered to be a leader in personalization?

9. • Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like Music, TV,
Audible…
• Devices like Fire, Kindle,
Echo…
• Digital properties like
IMDb, Zappos, 6pm…
• And more…
How could any retailer have an edge over who is widely
considered to be a leader in personalization?
Challenge #1
Scale

10. • Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like Music, TV,
Audible…
• Devices like Fire, Kindle,
Echo…
• Digital properties like
IMDb, Zappos, 6pm…
• And more…
Amazon is not just focused on
building personalization products
to impact the consumer
experience, but also the seller
experience. They support over 2
million sellers worldwide.
How could any retailer have an edge over who is widely
considered to be a leader in personalization?
Challenge #1
Scale
Challenge #2
Audience

11. • Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like Music, TV,
Audible…
• Devices like Fire, Kindle,
Echo…
• Digital properties like
IMDb, Zappos, 6pm…
• And more…
Amazon is not just focused on
building personalization products
to impact the consumer
experience, but also the seller
experience. They support over 2
million sellers worldwide.
Amazon already has
relationships on lock because of
logistics and selection. You no
longer go to Google to search for
products you’re going to buy, you
go right to Amazon, you buy, you
leave.
How could any retailer have an edge over who is widely
considered to be a leader in personalization?
Challenge #1
Scale
Challenge #2
Audience
Challenge #3
Transactional
Focus

12. PROS CONS
Know what you’re up against – the pros and cons of Amazon’s personalization

13. PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendations, including wish list / watch list features as
inputs.
Ability to set preferences for recommendations and messaging on
additional channels.
Heavy focus on recent behavioral data,
rather than offering recommendations
based on predicted purchases and
historic interests
Know what you’re up against – the pros and cons of Amazon’s personalization

14. PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendations, including wish list / watch list features as
inputs.
Ability to set preferences for recommendations and messaging on
additional channels.
Heavy focus on recent behavioral data,
rather than offering recommendations
based on predicted purchases and
historic interests
Email
personalization
Recommendations using collaborative filtering or similar products
used in transactional emails.
Lack of welcome series, abandonment
emails, re-engagement campaigns, and
lifecycle optimization via email.
Know what you’re up against – the pros and cons of Amazon’s personalization

15. PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendations, including wish list / watch list features as
inputs.
Ability to set preferences for recommendations and messaging on
additional channels.
Heavy focus on recent behavioral data,
rather than offering recommendations
based on predicted purchases and
historic interests
Email
personalization
Recommendations using collaborative filtering or similar products
used in transactional emails.
Lack of welcome series, abandonment
emails, re-engagement campaigns, and
lifecycle optimization via email.
Mobile
personalization
Heavy focus on mobile messaging for app-enabled consumers.
Push notifications triggered by behavior; personalized product
recommendations in app and in notifications. Use of consumer name
and clear use of omnichannel profile data to improve the experience.
Lack of in-app messaging and lack of
message center to serve as home base
for push/in-app messages.
Know what you’re up against – the pros and cons of Amazon’s personalization

16. PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendations, including wish list / watch list features as
inputs.
Ability to set preferences for recommendations and messaging on
additional channels.
Heavy focus on recent behavioral data,
rather than offering recommendations
based on predicted purchases and
historic interests
Email
personalization
Recommendations using collaborative filtering or similar products
used in transactional emails.
Lack of welcome series, abandonment
emails, re-engagement campaigns, and
lifecycle optimization via email.
Mobile
personalization
Heavy focus on mobile messaging for app-enabled consumers.
Push notifications triggered by behavior; personalized product
recommendations in app and in notifications. Use of consumer name
and clear use of omnichannel profile data to improve the experience.
Lack of in-app messaging and lack of
message center to serve as home base
for push/in-app messages.
Digital
advertising
personalization
Diligent regarding retargeting when a consumer has displayed the
signs of purchase conversion.
Personalized product recommendations in display ads and across
social networks.
Relentlessly retargets.
Know what you’re up against – the pros and cons of Amazon’s personalization

17. /
Single
Message
Mailing
Field
Insertion
Segmentation/
Rules Based
Behavioral
Recommendations
Omnichannel
Optimized
Predictive
Personalization
REVENUE
PERSONALIZATION MATURITY
+5-10%
Lift in
Response Rate
+20-50%
Lift in
Conversion Rate
+20%
Lift in
Customer
Lifetime
Value

18. “Sailthru has really been the first company
that has been a true partner to us. Sailthru
has given us the ability to achieve our goals
with personalized marketing and to more
effectively retain customers and increase
lifetime value.”
Monica Deretich
VP of Marketing &
CRM
+39%
increase in
email revenue
+12%
increase in
percentage of
customers
who purchase
46%
decrease in
customer churn
+50%
increase in
email conversion

19. Approaches & Use Cases for Product
Recommendations

20. Collaborative Filtering
• “Wisdom of the crowd” made famous by Amazon
• Key Use Cases:
- Home & category pages for anonymous users on web and in app
- On product level pages
- In basket interface
- Transactional and post-purchase emails
• You don’t have to say “others who bought X also bought Y”
• Consider the approach to using description and specific data feed or use
“trending” and other similar terms
• Algorithm leverages historic purchase data from across customer base

22. Interest-Based
• Leverage your individual buyer’s preferences
• Key Use Cases:
- Home & category pages for known users on web and in app
- Regular cadence of email newsletters
- Re-engagement campaigns
• Approach requires structured product tagging strategy to ensure
recommendation accuracy
• Data needs include explicit and implicit interest / preference data from
omnichannel engagement

25. Item Predictions
• Leverage your individual buyer’s next purchases
• Key Use Cases:
- Home & category hero images
- Home & category pages for known users on web and in app
- Regular cadence of email newsletters
- Post purchase series
- Push notifications & in-app messages
• Requires algorithm that predicts the individual products every consumer
will purchase next
• Don’t creep!

27. Personalization beyond recommendations

28. Personalize email send time
Jamie typically
opens her email
at 8am

29. Personalize email send time
Jamie typically
opens her email
at 8am
Maya typically
opens her email
at 8pm

30. Personalize cadence & channel
• Email opens do far more than just allow for segmentation and
send time personalization
• Use historic data to identify individual trends and automate
cadence control
• Allow for user opt-down in an email preference center for
explicit control
• Get advanced:
• Predict the likelihood of an individual opening email
• Send only to those with a higher than average likelihood of
opening
• Suppress users not likely to open, preserving list and
improving deliverability
• Target users not engaging with email on other channels

31. Personalize offers & discounts
• Use historic data to identify trends in
customer segments and for
individuals
• Leverage predictions to determine
expected basket size and order
value
• Personalize offers to individuals to
drive conversion and optimize
revenue
• For an individual predicted to
spend $100; personalize offer
based on a $150 purchase floor
• For customers predicted to convert
at low value, incentivize with free
shipping

32. Cross-Channel Personalization

33. Omnichannel by Amazon
Consistent experience across channels using omnichannel cart and and wish list functions.

34. Omnichannel by Amazon
Inconsistent experience across channels in terms of personalization.

35. Tie it all together
Increase average
order value through
dynamic personalized
content
Increase conversion
rates with consistent
user experiences across
channels
Reduce friction for the
shopper with multiple
personalized approaches to
driving revenue
Drive repeat purchases
with offers that appeal to
shopper’s interests and
price points
Deliver
personalized
experiences to
your known loyal
customers and
unknown visitors
Maintain full control
of consumer
experiences
through
recommendation &
content settings
Drive more conversion
with personalized
discounts & offers
Personalize using
multiple methods
including item
predictions

36. Predictive Acquisition

37. Use retention data to optimize acquisition
Use predictive analytics to identify
highest-value customers by
predicting 1-year revenue for all
customers
Test, iterate and improve
creative & messaging of ads,
target segment and
onboarding experience.
Connect retention data and high
value customers segment to
Facebook, Google and other
acquisition platforms
Use prediction analytics to identify
users not likely to engage with email
and retarget across search and social
Build look-a-like models in
acquisition platforms based on
your future high value customers
Analyze and monitor the value of
subscribers acquired using cohort
reporting
Personalize the onboarding
experience for new subscribers
based on specific acquisition source

38. Initial results from this program were so successful that Rent the
Runway tripled their acquisition test spend and cited this program
as exceeding the results of their top performing customers
segment, and expanded their audience reach.
Use Case Detail
• Built lookalike for 30-day purchase prediction and tested versus
control lookalike derived from list of customers who purchased in
the last 180 days
• Tested strategies on both desktop and mobile
Audience Notes
• Overlap between prediction-based lookalike audience and
control lookalike audience was only 15%, meaning the Rent the
Runway team significantly increased overall reach
Through the use of Sailthru’s integration with Facebook,
Betabrand is anticipating meaningful lift in revenue from newly
acquired customers both in the short term and over the long term.
This program proves that leveraging retention to optimize
acquisition has long-lasting impact to growth.
Use Case Detail
• Built lookalike using 30-day purchase prediction
• Built lookalike using 30-day expected revenue prediction
• Compared against control group composed of customers
acquired from all acquisition campaigns that have historically
performed above Betabrand’s benchmarks
• Compared against segment from Mixpanel’s predictive tool
• Measured efficacy based on 7-day Return on Ad Spend (ROAS)
Desktop: 28% reduction in
subscriber acquisition cost; cohort
boasts 20% higher expected one-
year lifetime value than control
group.
Mobile: 40% reduction in subscriber
acquisition cost; cohort boasts 53%
higher expected one-year lifetime
value than control group
Results
30-day expected revenue
cohort will generate 17% more
revenue per customer in the
next 365 days
30-day purchase prediction cohort
will generate 21% more revenue per
customer in the next 365 days
Results The expected value per customer acquired through
Sailthru Predictive lookalikes proved materially stronger
than client control group:

39. Questions For Jason?
Jason Grunberg
Vice President of Marketing
jgrunberg@sailthru.com
Sailthru.com / success@sailthru.com