1.
Top app monetization
tactics…
Pratik Shah | Product Manager

2.
A bit
about
Myself..

3.
What do these entities have in
c o m m o n ?

4.
“The deeper the understanding
“We are an analytics company
we have about our customers
masquerading as a games
and our products, the better we
company”
can connect with them.”
Data. Insights. Actions.
Oakland A’s manager Billy
Citibank is exploring possible Beane based his winning
uses for IBM’s Watson strategy on rigorous data
supercomputer in mining analysis to acquire top baseball
customer data. players.

5.
This is all great…..
How the heck is this relevant for an app developer?

6.
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!

7.
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

8.
Best
Practices

9.
Monetization: Variety of monetization models
Consumer Advertiser
pays pays
Paid downloads, Banner,
in-app purchases, interstitial, cross
merchandizing, promote, offer
subscription.. walls..

10.
Embrace the power of Freemium model
The best part? Not limited to gaming apps..

11.
Metric driven? Don’t get lost in vanity metrics…
Did you catch the funny ones?

12.
Keep it simple: Focus on value maximization during ‘ARM’ cycle
Basics
‣ App value = Number of users * LTV of each user
Acquisition Retention LTV of each user
‣ Lifetime value
‣ LTV = value * engagement
USERS Value levers
‣ Monetization
‣ Virality
‣ Loyalty
Monetization ‣ UGC & Community
‣ Feedback
‣ Marketplace (Downloads, Ratings & Comments)
In order to focus on monetization, it is important to look beyond
monetization..

13.
Audience
Track key ‣ Daily Active Users (DAU) &
Monthly Active Users (MAU)
‣ Demography
metrics in Acquisition
‣ Cost per acquisition (CPA)
the ‘ARM’ ‣ ROI on campaigns (Value - CPA)
Retention
cycle ‣ Stickyness (DAU/MAU)
‣ Retention rate
Monetization
‣ Conversion rate
‣ ARPU & ARPPU

14.
Customer
segmentation

15.
Lets borrow an industry best practice..
Loyal 31%
Newly 24%
acquired
‣ Customer segmentation - a practice
Dormant 18% of:
‣ Dividing a customer base into buckets that are
Engaged similar in specific ways (spending, engagement
16% etc.)
‣ On which they can take targeted actions to
Socially 16% extract the maximum marketing value.
active
Advanced 13% ‣ Traditionally, retail marketers have
used segmentation as an important
Whales10% technique
‣ In order to maximize the value
levers, app developers need to adopt
the same sophisticated techniques.

16.
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

17.
Use cases

18.
Lets put it to use in the ‘ARM’ cycle?
Acquisition Retention
USERS
Monetization

19.
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

20.
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%

21.
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

22.
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
* Playnomics Q3 2012 report
‣ Localize content
‣ Gamification: Rewards, challenges etc…

23.
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

24.
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%

25.
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

26.
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

27.
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

28.
‣ Step 1: Deciding what data will
be collected and how it will be
How does a ‣
gathered
Step 2: Collecting data from
developer ‣
various sources
Step 3: Developing methods of
do all of big data analysis for
segmentation
‣ Step 4: Building in-house
this? message server - scaled
globally!
….Could this all be easier?

29.
Thank you Pratik Shah
Product Manager, InMobi
Pratik.shah@inmobi.com