InMobi inDecode - How to Acquire & Retain High LTV Users
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InMobi is a global mobile advertising platform that helps app developers build, grow, and monetize their apps. It provides tools to acquire and retain high lifetime value users through optimizing user acquisition campaigns, measuring the right key performance indicators, and leveraging user data and machine learning to continually improve targeting. InMobi processes data from over 1.5 billion monthly active users globally to generate insights that help advertisers find their ideal audiences.
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InMobi inDecode - How to Acquire & Retain High LTV Users
- 1. by David Maciel February, 29th 2016 How to Acquire & Retain High LTV Users who Truly Find Value in your App
- 2. Build Awesome Apps Turn your idea into a killer app having all the monetization tricks already on mind. Grow your App Take your app to the next level, now it’s all about optimization, localization & scalability. Monetize your app Mix n’ match the right ad format with the right ad placement, A/B testing for the win! inDecode is InMobi’s global developer community. Our mission is to connect developers and help them decode how to build, grow and monetize their apps. indecode.inmobi.com – indecode@inmobi.com
- 3. 50000 Publisher apps 2.6 billion App downloads tracked 200 countries of operation 140 billion Monthly ad impressions 17 officesAll over the globe 1.5 billionMonthly active users Founded in 2007, InMobi is the world's largest independent mobile ad platform About InMobi For making mobile ads you actually want to see
- 4. click - install - open daily - buy once - uninstall try - engage - buy - keep buying - often & a lot - tell friends Average User Quality User = High LTV User DEFINITION OF LTV Thinking in terms of user life-cycle and lifetime value
- 5. PREDICTING LTV Knowing the most important signals in a user flow Signup Login Search Browse View Product Add to cart Proceed to Payment PURCHASE Purchase Value INSTALL
- 6. • Drive quality users based on LTV data • Optimize based on user profiles, segments & lifecycle stages LT V • Use product & service discovery powered by LTV data to drive purchase • Re-engage existing users and nudge them further down the lifecycle • Projected retention is a good proxy for LTV • Acquiring quality users results in higher downstream retention ANCHORING AROUND LTV Knowing the most important signals in a user flow
- 7. KPI CPQ Cost per Quality User Cost per Registration, Cost per Order, Cost Per First Ride, Cost per Purchase LTV metric as % of installs Tutorial completion rate Registration Rate, Purchases/install ROAS Return on ad spend Revenue to Cost Ratio GMV contribution Cost per ‘State Change’ Dormant to active, Non-buyer to buyer MEASURING THE RIGHT GOALS Define your KPI metrics to drive and optimize for quality users
- 8. InMobi builds holistic user-level insight for UA customers using first, second and third party user profile data Device Hardware OS and Carrier Download behaviour User Interests Demographic Footprint Behavior Socio-economic App category ownership USER CENTRIC DATA SCIECE INSIGHTS The engine that powers it all
- 9. • Automate & Monitor post install events. Then let goal metrics dictate. • by monitoring and analyzing post install events against your specific goals -eg. bookings or purchases with accurate attribution data from InMobi Certified MTAP partners. • Data integration and flow is always-on • Data flow in real-time – recent data, frequency • User events are leveled – optimized at each step • Data is safeguarded, with customer control LTV OPTIMIZATION IS A NON-STOP PROCESS Optimizations that never sleeps
- 10. InMobi UA CAMPAIGN SAMPLE PUBLISHER Swagbucks TV (video app). (BRAND) ADVERTISER Lyft looking to drive brand awareness. AD FORMAT Pre-roll 15 second video without CTA.
- 11. InMobi UA CAMPAIGN SAMPLE PUBLISHER Team Stream by Bleacher Report (sport news app). ADVERTISER UBER looking to drive app downloads. AD FORMAT Native ad in the news feed with CTA ”Install”
- 12. APPOGRAPHIC TARGETING User profiling based on lifestyle interests Build audience profiles based on app ownership data Target quality converting users for maximum efficiency
- 13. LOOKALIKE MODELLING & TARGETING Finds potential users similar to your current high value users
- 14. Your high value users Advertiser engagement metrics Device/app ownership Geo location context User history Demographic data User attributes (over 2000) Lookalike modelling works by using machine learning to combine over 200 user attributes.
Notas do Editor
- 1 - [Connect the idea of user lifecycle and lifetime value] Some truths we know about mobile users, they may click on your ad they may install your app they may open the app everyday they may even buy once BUT the very next day, they may also disengage & uninstall AND every user behaves differently 2 - [Define quality users as those with high lifetime value] Every marketer’s dream, users that try -> engage -> buy -> keep buying -> often & a lot -> tell their friends. In short - quality users quality users = valuable, loyal users with longevity = users with high lifetime value 3 – [Example from the gaming world to illustrate the point] 750 new games are released every day Less than one third of all users will pay for an app Less than 3% of mobile gamers make an in-game Purchase But only 0.15% of mobile gamers account for 50% of in-game revenue
- 1 - [Distinguish actual vs predictive LTV] For existing users - we can measure lifetime value based on actual behavior For new users - we look at how they behave soon after installing the app and approximate their future behavior. In other words, we analyze a user’s in-app install and post-install events to predict the potential dollars that a user will contribute across their lifetime, i.e. their lifetime value. 2 - [Explain the value of LTV based acquisition & optimization, and sharing post-install data] Tracking that post-install user behavior provides insight into the user segments that are more likely to be higher quality, and passing that to channel partners means those cohorts can then be more precisely targeted. The key is understanding the behavior signals that are most likely to indicate your quality users, in other words, figuring out the most important proxies to predict LTV.
- LTV can be leveraged for both, Install = UA – drive quality users through discovery, optimize all elements of your acquisition campaign and adapt your spend Post-Install = UE/UR - set additional campaign goals that drive users further down the lifecycle towards engagement, re-engagement and retention So today’s smart marketing strategy has 2 equally essential parts, identifying the best characteristics of an entire population to drive acquisition of quality users. Here you start by having a few data points on a large number of users. optimizing based on individual users, leveraging all the install and post install data available to drive optimal user behavior. Here you analyze a huge number of data points on a small number of users. Both parts have to be seamless. Optimize on the basis of one unified user journey funnel as opposed to two perceived independent funnels (awareness/install vs. post-install). Because getting more relevant users to click-through the ad and install the app can translate into higher retention downstream, and by proxy, high LTV users. Key question: how do you choose whether to acquire new users or re-engage existing users? => must be capable of measuring and optimizing to individual user Lifetime Value How do you know whether to spend on Installs or Reengagement? “Easy.” ROAS = Total Revenue per Day [ / Week / Month ] divided by UA Cost [ ave by time ] => maybe “Our new users are buying more than our older users” => maybe you need to more effectively engage your existing users [ while you can ] Best answer = UA ROI <vs> RE ROI UA ROI UA Cost * Ave Lifetime Value * factor for Optimizing Future Incremental Revenue * longer-term time value of money RE ROI RE Cost * REmaining LTV * short-term factor for time value of money UA Cost high vs RE Cost low Ave LTV vs REmaining LTV => how big a factor is user age / existing purchase history? => how good are you at identifying / predicting / incenting “whales”
- -CPQ vs RoAS -Evolving towards Cost per state-change. Retargeting in this context is different from affiliate traffic drives, more CRM.
- Optimization is a continuous process, so the data integration and flow needs to be, automated, always-on process. It is also highly time-sensitive, so the flow needs to be real-time or near real-time, elements like recency & frequency are critical. Post-install events need to be leveled, so that optimization can happen at every level, not just at purchase, thus reducing spend waste at every step in the user journey, ultimately pushing up RoAS Trusted partners like InMobi safeguard your data, give you comprehensive control over it, and have clear data protection policies and systemic processes to enable them. Ultimately, you remain firmly in control of your data, sharing it only to unlock higher value