1. App Lifecycle Engagement JoshTodd, CMO
2. Agenda Mobile Trends App Lifecycle Predictive App Marketing
3. OUR APP-ETITE IS GROWING.
4. 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
5. 48,000APPS ARE DOWNLOADED FROM THE APPSTORE EVERY 60 SECONDS. Mashable, 2014
6. Nielsen, 2014 41APPS ARE INSTALLED ON THE AVERAGE US SMARTPHONE.
7. 25% THE PERCENTAGE OF USERS WHO ONLY OPEN AN APP ONCE. Localytics, 2015
8. 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.
9. Agenda Mobile Trends App Lifecycle Predictive App Marketing
10. The App Lifecycle Acquire Engage & Grow Retain
11. Acquire
12. App Store Optimization Gain visibility in app store searches Optimize your app store listing
13. Organic Channels Website Redirect Redirect mobile website traffic to your app
14. Organic Channels Email Encourage email subscribers to download your app 53% of emails are opened on a mobile device. Source: Litmus, 2015
15. Organic Channels Social Media Promote your app on social platforms
16. Paid Channels Mobile Ads Source: Litmus, 2015 Work with a mobile advertising company to place targeted ads in other apps
17. NOT EVERYONE WHO DOWNLOADS YOUR APP WILL BECOME A USER.
18. Source: Localytics, 2015 Of users only use an app ONCE. 25%
19. Source: Localytics, 2014 60%The likelihood that an app user who doesnt return within 7 days will NEVER COME BACK.
20. Paid Channels Attribution Use an app analytics platform that partners with major ad networks to track user acquisition campaigns
21. The App Lifecycle Acquire Engage & Grow Retain
22. Engage&Grow
23. Maximize user value through engagement Segmentation Channels to the customer Push In-App Remarketing Email
24. (your entire userbase) Sports Apparel App Segment your audience
25. 3% of broadcast push messages are clicked 7% of targeted push messages are clicked 15% of users converted 54% of users converted Broadcast: Targeted: Segment your audience vs
26. Imagine an app with 100,000 users Segment your audience
27. 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
28. Maximize user value through engagement Segmentation Channels to the customer Push In-App Remarketing Email
29. 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
30. Increase Push audience, increase success 52% of app users have push enabled on their phones Industry Averages
31. Increase Push audience, increase success 52% of app users have push enabled on their phones 48% of app users dont have push enabled on their phones Industry Averages
32. Bad Example -Ask them to opt in immediately after launching the app for the first time Increase Push audience, increase success (first launch)
33. -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
34. 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
35. 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.
36. Remarketing Reaching Existing Users Source: Litmus, 2015 Show current users ads based on how theyve previously engaged with your brand Great for reaching the who opt out of push notifications 48% OF USERS
37. Email Cross Channel Marketing Treat users with richer, longer form content Source: Copyblogger, 2014
38. The App Lifecycle Acquire Engage & Grow Retain
39. Retain
40. 5yearsago theworldwasawashinBigData
41. Data Scientists to the Rescue
42. Still not fulfilling the promise of big data But still 50% of all Data Science Projects Fail
43. 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
44. 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 anity
45. Keys to Successful Predictive App Marketing Dene the specics 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
46. Dene 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
47. *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
48. 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
49. *Measured as % ac8ve users with no ac8vity in past 30 days. Predictive Churn Example for a Sports App
50. 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
51. 52 Predictive App Marketing Across the Lifecycle Acquire Engage & Grow Retain
52. 53 How we got here and where we are going 2012 2013 2014 2015 2016 2017 Personalized Content & UI Deep Automa8on & Lifecycle Management Personalized Messaging (Push, In-app, Email, Remarke8ng) Behavioral Analy8cs (Mobile, Web, 3rd Party, Cross-app) User Insights (Proles, Segments, User Acquisi8on) Machine Learning Predic8on & Op8miza8on 2009 - 2011 2018 Op5mized Engagement Rich Data
53. Thank you
54. Day of the week
55. Day of the week
56. Time of day
57. Time of day
58. Length of your message
59. Length of your message
60. Reactive Proactive User Engagement Historical Data Machine Learning Predic5ons FinallyShiftingusTowardProactiveMarketing
61. 62 AppsaretheSelfContainedUnit
62. 63 Understand your app users intent before he or she acts.
63. 64 Adjust your app marketing accordingly to reduce churn risk and improve conversions.
64. In 2008, everyone thought apps were a fad. They couldnt have been more wrong. Apps have become the dominant way we interact with information and the world. Raj Aggarwal CEO, Localytics