App Lifecycle Engagement
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App Lifecycle Engagement Josh Todd, CMO
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Transcript of App Lifecycle Engagement
- App Lifecycle Engagement JoshTodd, CMO
- Agenda Mobile Trends App Lifecycle Predictive App Marketing
- OUR APP-ETITE IS GROWING.
- 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
- 48,000APPS ARE DOWNLOADED FROM THE APPSTORE EVERY 60 SECONDS. Mashable, 2014
- Nielsen, 2014 41APPS ARE INSTALLED ON THE AVERAGE US SMARTPHONE.
- 25% THE PERCENTAGE OF USERS WHO ONLY OPEN AN APP ONCE. Localytics, 2015
- 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.
- Agenda Mobile Trends App Lifecycle Predictive App Marketing
- The App Lifecycle Acquire Engage & Grow Retain
- Acquire
- App Store Optimization Gain visibility in app store searches Optimize your app store listing
- Organic Channels Website Redirect Redirect mobile website traffic to your app
- Organic Channels Email Encourage email subscribers to download your app 53% of emails are opened on a mobile device. Source: Litmus, 2015
- Organic Channels Social Media Promote your app on social platforms
- Paid Channels Mobile Ads Source: Litmus, 2015 Work with a mobile advertising company to place targeted ads in other apps
- NOT EVERYONE WHO DOWNLOADS YOUR APP WILL BECOME A USER.
- Source: Localytics, 2015 Of users only use an app ONCE. 25%
- Source: Localytics, 2014 60%The likelihood that an app user who doesnt return within 7 days will NEVER COME BACK.
- Paid Channels Attribution Use an app analytics platform that partners with major ad networks to track user acquisition campaigns
- The App Lifecycle Acquire Engage & Grow Retain
- Engage&Grow
- Maximize user value through engagement Segmentation Channels to the customer Push In-App Remarketing Email
- (your entire userbase) Sports Apparel App Segment your audience
- 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
- Imagine an app with 100,000 users Segment your audience
- 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
- Maximize user value through engagement Segmentation Channels to the customer Push In-App Remarketing Email
- 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
- Increase Push audience, increase success 52% of app users have push enabled on their phones Industry Averages
- 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
- 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 theyve previously engaged with your brand Great for reaching the who opt out of push notifications 48% OF USERS
- Email Cross Channel Marketing Treat users with richer, longer form content Source: Copyblogger, 2014
- The App Lifecycle Acquire Engage & Grow Retain
- Retain
- 5yearsago theworldwasawashinBigData
- Data Scientists to the Rescue
- Still not fulfilling the promise of big data But still 50% of all Data Science Projects Fail
- 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 anity
- 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
- 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
- *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
- *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
- 52 Predictive App Marketing Across the Lifecycle Acquire Engage & Grow Retain
- 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
- Thank you
- Day of the week
- Day of the week
- Time of day
- Time of day
- Length of your message
- Length of your message
- Reactive Proactive User Engagement Historical Data Machine Learning Predic5ons FinallyShiftingusTowardProactiveMarketing
- 62 AppsaretheSelfContainedUnit
- 63 Understand your app users intent before he or she acts.
- 64 Adjust your app marketing accordingly to reduce churn risk and improve conversions.
- 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