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Ecommerce Personalization Beyond Amazon
How to Beat Them at Their Own Game
#CPCStrategy
Acquire, Grow and Retain Your Cust...
Today’s Logistics
Session Recording + Slides Will Be Sent Out
Join in the Poll Questions!
Submit Questions to Our Panelist
Overview
Founded in 2007
Recognized as a Google Premier Partner
400+ Active Retail Clients
Top 50 fastest growing company ...
Today’s Event Guest Speaker
Jason Grunberg
Vice President of Marketing
● 15 years experience in B2C and B2B
marketing
● Sp...
Winner of Facebook's 2016
Innovation Spotlight
for Personalized Marketing at
Scale
+21% increase in
customer lifetime valu...
Agenda
• Understanding Amazon
• Approaches & Use Cases for Product Recommendations
• Personalization Beyond Recommendation...
How could any retailer have an edge over who is widely
considered to be a leader in personalization?
• Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like ...
• Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like ...
• Amazon supports more
than just Amazon.com,
which carries nearly 500
million products
• Amazon has digital
products like ...
PROS CONS
Know what you’re up against – the pros and cons of Amazon’s personalization
PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendati...
PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendati...
PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendati...
PROS CONS
Website
personalization
Among the first to use collaborative filtering and 360-degree profile
based recommendati...
/
Single
Message
Mailing
Field
Insertion
Segmentation/
Rules Based
Behavioral
Recommendations
Omnichannel
Optimized
Predic...
“Sailthru has really been the first company
that has been a true partner to us. Sailthru
has given us the ability to achie...
Approaches & Use Cases for Product
Recommendations
Collaborative Filtering
• “Wisdom of the crowd” made famous by Amazon
• Key Use Cases:
- Home & category pages for anonymo...
Interest-Based
• Leverage your individual buyer’s preferences
• Key Use Cases:
- Home & category pages for known users on ...
Item Predictions
• Leverage your individual buyer’s next purchases
• Key Use Cases:
- Home & category hero images
- Home &...
Personalization beyond recommendations
Personalize email send time
Jamie typically
opens her email
at 8am
Personalize email send time
Jamie typically
opens her email
at 8am
Maya typically
opens her email
at 8pm
Personalize cadence & channel
• Email opens do far more than just allow for segmentation and
send time personalization
• U...
Personalize offers & discounts
• Use historic data to identify trends in
customer segments and for
individuals
• Leverage ...
Cross-Channel Personalization
Omnichannel by Amazon
Consistent experience across channels using omnichannel cart and and wish list functions.
Omnichannel by Amazon
Inconsistent experience across channels in terms of personalization.
Tie it all together
Increase average
order value through
dynamic personalized
content
Increase conversion
rates with consi...
Predictive Acquisition
Use retention data to optimize acquisition
Use predictive analytics to identify
highest-value customers by
predicting 1-ye...
Initial results from this program were so successful that Rent the
Runway tripled their acquisition test spend and cited t...
Questions For Jason?
Jason Grunberg
Vice President of Marketing
jgrunberg@sailthru.com
Sailthru.com / success@sailthru.com
Ecommerce Personalization Beyond Amazon
Ecommerce Personalization Beyond Amazon
Ecommerce Personalization Beyond Amazon
Ecommerce Personalization Beyond Amazon
Ecommerce Personalization Beyond Amazon
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Ecommerce Personalization Beyond Amazon

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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.

Published in: Retail
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Ecommerce Personalization Beyond Amazon

  1. 1. Ecommerce Personalization Beyond Amazon How to Beat Them at Their Own Game #CPCStrategy Acquire, Grow and Retain Your Customers
  2. 2. Today’s Logistics Session Recording + Slides Will Be Sent Out Join in the Poll Questions! Submit Questions to Our Panelist
  3. 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. 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. 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. 6. Agenda • Understanding Amazon • Approaches & Use Cases for Product Recommendations • Personalization Beyond Recommendations • Cross-Channel Personalization • Predictive-based Acquisition
  7. 7. How could any retailer have an edge over who is widely considered to be a leader in personalization?
  8. 8. • 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
  9. 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… 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
  10. 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. 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
  11. 11. PROS CONS Know what you’re up against – the pros and cons of Amazon’s personalization
  12. 12. 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
  13. 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 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
  14. 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. 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
  15. 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. 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
  16. 16. / 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
  17. 17. “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
  18. 18. Approaches & Use Cases for Product Recommendations
  19. 19. 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
  20. 20. 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
  21. 21. 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!
  22. 22. Personalization beyond recommendations
  23. 23. Personalize email send time Jamie typically opens her email at 8am
  24. 24. Personalize email send time Jamie typically opens her email at 8am Maya typically opens her email at 8pm
  25. 25. 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
  26. 26. 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
  27. 27. Cross-Channel Personalization
  28. 28. Omnichannel by Amazon Consistent experience across channels using omnichannel cart and and wish list functions.
  29. 29. Omnichannel by Amazon Inconsistent experience across channels in terms of personalization.
  30. 30. 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
  31. 31. Predictive Acquisition
  32. 32. 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
  33. 33. 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:
  34. 34. Questions For Jason? Jason Grunberg Vice President of Marketing jgrunberg@sailthru.com Sailthru.com / success@sailthru.com

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