Download - Acquire, Grow & Retain Customers, Fast
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Acquire, Grow & Retain Customers, Fast
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@eporres
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What % of consumers say that retail and media brands don’t
mean anything to them?
0-20%
21-40%
41-60%
61-80%
81-100%
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Fewer than 6% of consumers say that “brands
don’t mean anything to them.”
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Loyalty is a Two-Way Street
of brand loyalists say they want the brand to acknowledge their participation in loyalty programs.40%
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
For publishers, loyalty can’t be boughtLess than a quarter of consumers (23%) say their loyalty to media publishers would be improved by changes in prices or discounts, and just as many said they best way to encourage their loyalty was to improve content.
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Are there any media outlets, publishers, or brands you consider
yourself loyal to? If so, please list up to three.
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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US, Canada, Western Europe
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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US
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Three out of four retail brand loyalists (76%) have interacted with a favorite brand in some way that
could have been personalized over the past
12 monthsSource: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Loyalists Interact Digitally
Enabled push notifications from the mobile appSigned up for/received Text (SMS) Messages or alerts
Interacted with their official social media accountsDownloaded or used a mobile app
Received direct (physical) mailReceived/downloaded a digital coupon/code
Signed up for/participated in loyalty programReceived a physical coupon
Received an online messageSigned up for or received an e-mail newsletter
Visited a physical store locationVisited their website
7%8%
12%17%
22%22%22%23%24%
26%51%
61%Loyalist Brand Interactions, past 12 months
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Almost every interaction with a media publisher can be can be personalized in some way
for the reader
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Loyalists Interact Digitally
Signed up for/participated in a loyalty programSigned up for/received Text Messages/Alerts
Received physical mailEnabled push notifications from a mobile app
Received an online messageInteracted with official social media accounts
Signed up for email newsletterDownloaded/used mobile app
Visited the websiteRead/watched/listented to content
10%11%
15%16%18%
21%21%
35%66%
70%Loyalist Brand Interactions, past 12 months
Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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We open 49% of all emails on our mobile devices
Source: Sailthru Open Rate Benchmarks, 2015 - 2016
12% Tablet29% PC 10% Other
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“My Tesla read my emails for me while
running autonomously.”
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Loyalty, Retention and Personalization
of retail brand loyalists say they only want to be shown products or services that are relevant to them.31%
of those retail brand loyalists want their purchase history to be used to tailor messaging content for them.41%of those retail brand loyalists want to be addressed by name.34%of those retail brand loyalists want those brands to acknowledge something specific about them, such as their hobbies and interests
20%Source: Sailthru Q3 Consumer Loyalty Study, n=1,005
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Personalization Methods
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How to personalize
v
Improve Make a decision
Learn CustomerPreferences v
v
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Method: Attribute-Based Personalization
Attribute-based systems look at attributes assigned to a given item. When a person shows interest in or purchases that item, these systems find other items with similar attributes to recommend to that customer.
Generally, attribute-based recommendation systems are Product-based.
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Method: Collaborative FilteringCollaborative filtering systems use data from multiple customers to fill in “gaps” in a person’s profile, identifying things they might like based on their similarities to other customers.
Contextual: similarities based on what the person is doing or looking at right now
Historic: similarities based on what the person has done throughout his or her lifetime
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Method: Model-Based Collaborative Filtering
Applies machine-learning to the product and person datasets and uses statistical techniques to predict the probability of a customer acting on a given recommendation (or set of recommendations) using the entire dataset.
Model-based approaches are both product- and user-based (A person like X is most likely to respond to products A, B, and C)
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Examples of Each
Attribute-Based
Collaborative Filtering“Because you watched…”
“People who bought this also bought…”
Model-BasedCollaborative Filtering
Discover Weekly
“Top picks for you…”
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Why Personalize?
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Consider the alternative: Disengagement!
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Personalized communications reduce opt-
outs by 45%
Source: Sailthru Customer Benchmarks, 2015 - 2016
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Personalized messaging performs 27% better than
generic messaging
Source: Sailthru Customer Benchmarks, 2015 - 2016
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Personalization Drives Higher Engagement
Source: Sailthru Customer Benchmarks, 2015 - 2016
No Personalization Personalized
Interaction Rates with Emails, Non-Personalized vs. Personalized
+27%
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The Real Cost of Not Personalizing Content
Sent by media publishers between October 2015 and September 2016
28 Billion Emails5.3 Billion Opens
211 Million Lost Interactions6.5 Billion additional lost page views
Source: Sailthru Customer Benchmarks, 2015 - 2016
No Personalizati
on
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Loyalty matters more than everLoyalists interact digitally more than everThose interactions can be personalizedPersonalization methods vary; choose wisely(Person)alization drives real results
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“And don’t forget about
mobile.”
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IGNITION
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By 2020,50% of all brand
interactions will be personalized
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www.sailthru.com [email protected]@sailthru
NYC HQ160 Varick St.,12th FloorNew York, NY 10013
San Francisco360 Clementina StreetSan Francisco,CA 94103
Los AngelesWeWork LaBrea925 N. LaBrea AveLos Angeles, CA 90038
London6 Ramillies Street2nd Floor, London W1F 7TY, United Kingdom
Wellington31 Dixon StreetLevel 2, Wellington 6011, New Zealand
Pittsburgh100 South Commons Pittsburgh, PA 15212
Thank You@eporres