The document discusses mobile analytics and metrics. It emphasizes that analytics are part of a continuous learning loop involving measuring key metrics, learning from the data, and making changes based on insights. It outlines several frameworks for defining important metrics including the AARRR model for product usage and funnel analysis for understanding user flows and conversions. Finally, it provides examples of common mobile metrics categories and strategies for effective implementation of analytics.

1. Measurements and Metrics
MOBILE ANALYTICS

2. VALIDATED LEARNING LOOP
• Analytics and Metrics Process =
Build + Measure + Learn
• Best summarized by Eric Ries’s
Validated Learning Loop in Lean
Startup methodology
Not just code. Also alignment of
measurement and business
strategy
REFERENCE: Lean Startup by Eric Ries

3. OUTCOMES
• Analytics don’t end with
measurement
• Must translate data into desirable
outcomes
Test, measure, and take action
REFERENCE: Lean Startup by Eric Ries

4. BUSINESS REQUIREMENTS
• FUNDAMENTAL QUESTION: WHAT DEFINES
SUCCESS?
• All analysis start with a question
• Understand what metrics and data are needed to make
better decisions and perform better
• Analyze mobile app architecture
– Any constraints that may inhibit measurements
– How to leverage technology

5. PRODUCT USAGE METRICS
AARRR!
• What do you need to do to
build your product and
learn about your users?
• Dave McClure’s AARRR
model provides 5 useful
metrics to learning about
your product and how it is
used
Reference: dave McClure, 500 startups. Picture courtesy of walt Disney pictures

6. THE AARRR MODEL
Reference: dave McClure, 500 startups

7. FUNNEL ANALYSIS
• A funnel of steps that a user go through before meeting a
goal, for example
– Steps leading to contacting the company
– Steps leading to purchasing the product
– Steps leading to purchasing in-app modules/features
– Steps leading to purchasing merchandise or tokens (for games(
• Funnel analysis = understanding conversions
• A step in a funnel = a page view (web) = a screen or action
(mobile app)

8. WEB VS MOBILE
WEB MOBILE APP
Session tracking done primarily thru Session tracking done primarily thru
cookies and Javascript UDID
Human user interface is keyboard and Human user interface is gestural and
mouse based touch-based
Web measurement model is centered
Measurement model is less about
around page views, referrals, search,
referrals and search
and visits
Unique visitors are measured
Unique visitors are tied to individual or
differently because of gateway IPs of
server IP addresses
carriers

9. SOLUTIONS
• Flurry – http://flurry.com/
• Localytics – http://localytics.com/
• Webtrends –
http://webtrends.com/products/analytics/mobile/
• AppClix – http://www.appclix.com/
• Kontagent – http://kontagent.com/
• Bango – http://bango.com/
• Apsalar – http://apsalar.com/
• Claritics – http://claritics.com/
• Others that we may have missed…

10. MOBILE METRICS CATEGORIES
• Application
• Content
• User Behavior
• People/Location
• Technical

11. COMMON MOBILE METRICS 1
• Applications • Content
– #Downloads – Screens
– Conversions – Visits, unique visits
(Monetization) – In-app
– Engagement/loyalty – Ads
(over time) – Links
– User acquisition – Other events
– User retention
– Cohort analysis
(retention, engagement,
monetization)

12. COMMON MOBILE METRICS 2
• User Behavior • People/Location
– Screen flow (useful for – Users
navigation and usability) – Social identity
– Exits (how users are – Countries/Regions
exiting an app) – Languages
– Sessions – Marketplaces
(length, frequency, type
of users) – Carriers
– Age

13. COMMON MOBILE METRICS 3
• Technical
– Errors
– Devices
– Operating systems
– App Versions
– Connections

14. REPORT TERMINOLOGY
• Common report terminology
• Example: Breakdown of OS versions used to run the app
FILTER: Time Duration
DIMENSION: OS Version
METRIC: Session
Reference: Localytics report

15. STRATEGIES
• Define a few funnels to understand how user
usage drives towards a goal like
registration, purchase
• If possible apply some of the advanced tracking
and reporting features in the analytics tool to
provide deeper insights
– Filters (at the log and report levels)
– Funnel analysis
– Profiling
• Keep refining how metrics are tracked

16. EVENT TAGGING
• Data collection
• Mapping: Actions » Events » Metrics
• Need to define events to tag so that we can
measure the metrics
• Example:
– Start Time = Time when a player starts playing a
game
– End Time = Time when a player ends a game
– Defines the names for both events

17. Questions?
Picture: Sean Dreilinger -
http://www.flickr.com/photos/seandreilinger/2326448445/in/photostream/