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22 MIN. 60.3 MIN.AMOUNT OF
TIME PER DAY THE AVERAGE US MOBILE CONSUMER SPENDS WITH APPS. 00:22 The amount of time the average US mobile consumer spends per day with apps: AMOUNT OF TIME PER DAY THE AVERAGE US CONSUMER SPENDS ON THE MOBILE WEB. Nielsen & Comscore, 2014
19% 23% 29% 42% 48%
68% 71% Forced social logins Privacy concerns Intrusive ads Bad UI/UX Freezing Complex registration Annoying notifications TOP 7 REASONS WHY PEOPLE UNINSTALL MOBILE APPS* *AS A % OF ALL RESPONDENTS. EACH PARTICIPANT MENTIONED THREE REASONS.
Broadcast: Targeted: 3% of 100,000
users = 3,000 opened messages 7% of 100,000 users = 7,000 opened messages 15% of 3,000 opened messages = 450 converted users 54% of 7,000 opened messages = 3,780 converted users vs. Segment your audience vs
Bring them back and keep
them engaged with Push Motivate inactive users to return to your app with targeted, carefully timed, and well-written copy 88% MORE Users with push enabled have app launches. Source: Localytics, 2014
Bad Example - Ask them to
opt in immediately after launching the app for the first time Increase Push audience, increase success (first launch)
- Welcome your users with a
sequence of introductory, how-to screens to show value 1 2 32 3 Increase Push audience, increase success Good example
Good example - Welcome your users
with a sequence of introductory, how-to screens to show value - THEN, ask them to opt in with a unique, well-designed in-app message Increase Push audience, increase success
In-App Messages – Drive Conversions
Move users further along funnels to ultimate in-app action with beautiful, branded, in-app creatives 4X HIGHER In-app messages presented based on an event have conversion rates.
Remarketing – Reaching Existing Users
Source: Litmus, 2015 Show current users ads based on how they’ve previously engaged with your brand Great for reaching the who opt out of push notifications 48% OF USERS
Apps Create a New Opportunity
Apps generating massive amounts of data AND have marketing channels embedded Advances in computing have made machine learning more accessible Users Demand Better Experiences
Pillars of Predictive App Marketing
Predic5ve Segmenta5on • The dynamic grouping of users into segments which will behave in similar ways Marke5ng Auto-‐Op5miza5on • The automa8c tes8ng and op8miza8on of a marke8ng strategy across mul8ple channels Na5ve Personaliza5on • The 1:1 matching of users to content, products, with which they have the greatest aﬃnity
Keys to Successful Predictive App
Marketing Deﬁne the speciﬁcs of the objec8ve -‐ Churn Take ac8on via the app (via push, in-‐app msg, etc.) Establish Baseline and iden8fy user paIerns of user behavior and correlated characteris8cs
Deﬁne objec8ve – Churn =
users who have visited the app at least twice, but not in the last 30 days Predictive Churn Example for a Sports App
*Measured as % ac8ve users
with no ac8vity in past 30 days. Auto-‐segmented new users into the at risk buckets and sent personalized push messages to drive users back into the app Predictive Churn Example for a Sports App
Control Group Experimental Group Users!
190,930! 189,900! Returned! 115,243! 120,112! Churn %*! 39.3%! 36.8%! Improvement — 6.6% Users Rescued — 4,928 *Measured as % ac8ve users with no ac8vity in past 30 days. Predictive Churn Example for a Sports App
Control Group Experimental Group Users!
3,383,031! 381,723! Returned! 565,930! 102,500! Churn %*! 83.3%! 73.1%! Improvement — 14% Users Rescued — 38,644 *Measured as % ac8ve users with no ac8vity in past 30 days. Predictive Churn Example for a Lifestyle App