mobile analytics

17
MOBILE ANALYTICS Measurements and Metrics

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Quick introduction to mobile analytics.

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Page 1: Mobile Analytics

MOBILE ANALYTICS

Measurements and Metrics

Page 2: Mobile Analytics

REFERENCE: Lean Startup by Eric Ries

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

Page 3: Mobile Analytics

REFERENCE: Lean Startup by Eric Ries

OUTCOMES

• Analytics don’t end with measurement

• Must translate data into desirable outcomes

Test, measure, and take action

Page 4: Mobile Analytics

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

Page 5: Mobile Analytics

Reference: dave McClure, 500 startups. Picture courtesy of walt Disney pictures

PRODUCT USAGE METRICS

• 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

AARRR!

Page 6: Mobile Analytics

Reference: dave McClure, 500 startups

THE AARRR MODEL

Page 7: Mobile Analytics

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)

Page 8: Mobile Analytics

WEB VS MOBILE

WEB MOBILE APP

Session tracking done primarily thru cookies and Javascript

Session tracking done primarily thru UDID

Human user interface is keyboard and mouse based

Human user interface is gestural and touch-based

Web measurement model is centered around page views, referrals, search,

and visitsMeasurement model is less about

referrals and search

Unique visitors are tied to individual or server IP addresses

Unique visitors are measured differently because of gateway IPs of

carriers

Page 9: Mobile Analytics

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…

Page 10: Mobile Analytics

MOBILE METRICS CATEGORIES

• Application• Content• User Behavior• People/Location• Technical

Page 11: Mobile Analytics

COMMON MOBILE METRICS 1

• Applications– #Downloads– Conversions

(Monetization)– Engagement/loyalty

(over time)– User acquisition– User retention– Cohort analysis

(retention, engagement, monetization)

• Content– Screens– Visits, unique visits– In-app – Ads– Links– Other events

Page 12: Mobile Analytics

COMMON MOBILE METRICS 2

• User Behavior– Screen flow (useful for

navigation and usability)– Exits (how users are

exiting an app)– Sessions (length,

frequency, type of users)

• People/Location– Users– Social identity– Countries/Regions– Languages– Marketplaces– Carriers– Age

Page 13: Mobile Analytics

COMMON MOBILE METRICS 3

• Technical– Errors– Devices– Operating systems– App Versions– Connections

Page 14: Mobile Analytics

Reference: Localytics report

REPORT TERMINOLOGY

• Common report terminology• Example: Breakdown of OS versions used to run the app

DIMENSION: OS VersionFILTER: Time Duration

METRIC: Session

Page 15: Mobile Analytics

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

Page 16: Mobile Analytics

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

Page 17: Mobile Analytics

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

Questions?