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With the holiday season nearing, is your app monetization strategy geared up to get the most out of your users? Crafting an effective monetization strategy involves understanding and influencing your user's lifetime value (LTV). In this 1 hour webinar, you'll learn: What is LTV and how to apply it to your app business effectively -- metrics that you need to monitor and measure constantly. How to go beyond analytics & metrics -- apply advanced user segmentation to design clever strategies that can help you engage and monetize your users better. Some ideas to increase your app's monetization this holiday season. This session is led by Pratik Shah, Product Manager at InMobi.
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Pratik Shah | Product Manager
Top app monetization tactics…
A bit about Myself..
What do these entities have in c o m m o n ?
Data. Insights. Actions.
“The deeper the understanding we have about our customers
and our products, the better we can connect with them.”
“We are an analytics company masquerading as a games
company”
Oakland A’s manager Billy Beane based his winning strategy on rigorous data
analysis to acquire top baseball players.
Citibank is exploring possible
uses for IBM’s Watson supercomputer in mining
customer data.
This is all great….. How the heck is this relevant for an app developer?
700K iOS & Android apps 60% app developers don’t profit 30% apps used only once
It’s a tough world out there.. Only the intelligent app businesses will win!
Agenda: Improve App monetization by focusing on your users
Best practices ‣ Monetization models ‣ Key metrics & ARM cycle Customer segmentation ‣ Why ‣ How Use cases ‣ Acquisition
‣ Retention ‣ Monetization
Best Practices
Monetization: Variety of monetization models
Advertiser pays Banner,
interstitial, cross promote, offer
walls..
Consumer pays
Paid downloads, in-app purchases, merchandizing, subscription..
Embrace the power of Freemium model
The best part? Not limited to gaming apps..
Metric driven? Don’t get lost in vanity metrics…
Did you catch the funny ones?
Retention Acquisition
Monetization
USERS
Keep it simple: Focus on value maximization during ‘ARM’ cycle
In order to focus on monetization, it is important to look beyond monetization..
Basics ‣ App value = Number of users * LTV of each user LTV of each user ‣ Lifetime value ‣ LTV = value * engagement Value levers ‣ Monetization ‣ Virality ‣ Loyalty ‣ UGC & Community ‣ Feedback ‣ Marketplace (Downloads, Ratings & Comments)
Track key metrics in the ‘ARM’ cycle
Audience ‣ Daily Active Users (DAU) &
Monthly Active Users (MAU) ‣ Demography Acquisition ‣ Cost per acquisition (CPA) ‣ ROI on campaigns (Value - CPA) Retention ‣ Stickyness (DAU/MAU) ‣ Retention rate Monetization ‣ Conversion rate ‣ ARPU & ARPPU
Customer segmentation
10%
13%
16%
16%
18%
24%
31% Loyal
Newly acquired
Dormant
Engaged
Socially active
Advanced
Whales
Lets borrow an industry best practice..
‣ Customer segmentation - a practice
of: ‣ Dividing a customer base into buckets that are
similar in specific ways (spending, engagement etc.)
‣ On which they can take targeted actions to extract the maximum marketing value.
‣ Traditionally, retail marketers have used segmentation as an important technique
‣ In order to maximize the value levers, app developers need to adopt the same sophisticated techniques.
Customer segmentation: How does it work?
Basics ‣ Use a rule engine to define user behavior & attributes to
define a segment
Dimensions ‣ Purchase history
‣ Time spent
‣ Session length
‣ Advancement
‣ Session frequency
‣ Country, Carrier, Device
‣ ….
Examples ‣ S1: IF purchase history > 25 percentile of my app
‣ S2: IF purchase history > $10
‣ S3: IF purchase history > $10 & Time spent < 5 minutes in last month
Need to track key metrics with the prism of each segment
Use cases
Lets put it to use in the ‘ARM’ cycle?
Retention Acquisition
Monetization
USERS
Acquisition: Leverage organic techniques
Basics ‣ Expensive to pay to acquire users unless you
have a well oiled positive ROI engine (LTV > CPA)
Measurement ‣ Cost per acquisition (CPA) ‣ ROI on campaigns (LTV/CPA) Techniques ‣ Internal cross promote (Keeping users within
your app portfolio) is the best but needs to be done properly..
‣ Viral is very cost effective, but also very difficult
‣ Performance networks (display, cross promote) are widely used to acquire further users
Identify pattern: Highly engaged users from USA are most likely to give you viral uplift Segment using rule engine: IF (time spent > 300 hours) & (country == USA) Incentivize virality Segment: Social influencers
Reduce your CPA by as much as 50%
Identify pattern: Advanced users in your top app don’t have other apps in your portfolio Segment using rule engine: IF (levels crossed > 25) & (! Using omegajump)
Smart cross promote Segment: ‘ripe’ users
Increase ROI by acquiring known users
Retention: Use a variety of techniques at different user stages
Basics ‣ Difficult.. but certainly most important Measurement ‣ Stickyness (DAU/MAU) ‣ Retention rate (% of returning users across
months) ‣ Cohort analysis ‣ Measure how many users return for 2nd time, 3rd
time and so on… Techniques ‣ Clean early experience ‣ Localize content ‣ Gamification: Rewards, challenges etc…
* Playnomics Q3 2012 report
Identify pattern: New users are likely to be delighted to see a tailored message Segment using rule engine: IF (App launches < 5) & (country == China) Localized ‘welcome’ Target segment: New Chinese users
Increase retention beyond day 1
Identify pattern: User engagement can be improved with a social taunt Segment using rule engine: IF (user time spent in last month < 50% of average time spent) Social ‘taunt’ Target segment: Waning users
Increase engagement by 30%
Monetization: Use tiered pricing
Basics
‣ Price goods along the curve based on capacity of each customer
Measurement ‣ Conversion rate (% paying) ‣ ARPU & ARPPU ‣ Customer profile split
‣ Whales (10% users, 60% revenue) ‣ Dolphins (30% users, 30% revenue) ‣ Minnows (60% users, 10% revenue)
Techniques ‣ Holiday & event specific ‣ Timely offers
Identify pattern: Hardcore users would pay a lot for certain features Segment using rule engine: IF (user time spent == high) & (app section == ‘tough’) Timely unlocks Target segment: Hardcore users
Display offers at right time
Identify pattern: High paying users in developed economies tend to purchase a lot during holidays Segment using rule engine: IF (user purchase history == high) & (date == 31st Oct) & (country == USA || UK) Holiday promotion Target segment: High paying US and UK users
Add cyclic bursts to your sales
How does a developer do all of this?
‣ Step 1: Deciding what data will
be collected and how it will be gathered
‣ Step 2: Collecting data from various sources
‣ Step 3: Developing methods of big data analysis for segmentation
‣ Step 4: Building in-house message server - scaled globally!
….Could this all be easier?
Thank you Pratik Shah Product Manager, InMobi
Pratik.shah@inmobi.com
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