Actionable Analytics:
This presentation is the property of its rightful owner.
Sponsored Links
1 / 20

Jason Lee | Sr. Manager, Customer Success APAC PowerPoint PPT Presentation


  • 40 Views
  • Uploaded on
  • Presentation posted in: General

Actionable Analytics: User Economics in Game Development. Jason Lee | Sr. Manager, Customer Success APAC. AGENDA. What is Kontagent? What is data driven development? User Economics: The A.R.M. Model Deep Data Exploration Q&A. WHAT IS KONTAGENT?

Download Presentation

Jason Lee | Sr. Manager, Customer Success APAC

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Jason lee sr manager customer success apac

  • Actionable Analytics:

  • User Economics in Game Development

  • Jason Lee | Sr. Manager, Customer Success APAC


Agenda

AGENDA

What is Kontagent?

What is data driven development?

User Economics: The A.R.M. Model

Deep Data Exploration

Q&A


Jason lee sr manager customer success apac

  • WHAT IS

  • KONTAGENT?

  • We are the leading user analytics platform for the social and mobile web.

  • User-Centric Data

  • Accessibility

  • Domain Expertise

1


Jason lee sr manager customer success apac

Kontagent Facts

  • Founded in 2007

  • 100+ employees and growing

  • Locations around the globe

  • 1000’s of Apps Instrumented

  • Over 60 Billion Events/Mo Tracked

  • 200M+ MAUs tracked

  • Track $1 of every $4 spent in the social gaming industry

3


W hat is d ata driven d evelopment

What is data drivendevelopment?


User economics the a r m model

User Economics – The A.R.M. Model

  • Track users from point of acquisition through monetization

    • Acquire quality users at lowest cost

    • Keep users engaged as long as possible

    • Get them to spend as much as possible


A r m acquisition

A.R.M. - Acquisition

GOAL

WHAT TO MEASURE

Identify profitable user segments – acquire as many at the lowest cost possible

CAC

Retention

ARPU

* All of the above per channel and per campaign


Acquisition analyze in game behavior to optimize ad spend

Acquisition:Analyze in-game behavior to optimize ad spend

6


A r m retention and engagement

A.R.M. – Retention (and Engagement)

GOAL

WHAT TO MEASURE

Increase user lifetime playing games – highly retained and engaged customers are worth more

Avg. Session length and # of Sessions per day

DAU / MAU (Stickiness)

In-app funnel conversion

1, 7 and 30 day Retention


Retention retention rates important to monitor

Retention:Retention rates important to monitor

But need to dig deeper to take action on data collected…

6


Retention measure the actions that drive your game

Retention:Measure the actions that drive your game

6


Retention optimize user paths at key conversion points

Retention:Optimize user paths at key conversion points

6


A r m monetization

A.R.M. – Monetization

GOAL

WHAT TO MEASURE

Harvest retained users – maximize lifetime value of users over games’ lifecycle

Points of user monetization

A/B Test currency bundles

% Paying Users (PPU)

ARPU & ARPPU


Monetization identify trends in ppu arpu and arppu

Monetization: Identify trends in PPU, ARPU and ARPPU

6


Monetization optimize when users monetize

Monetization: Optimize when users monetize

6


Monetization a b test payment methods and packaged bundles

Monetization: A/B Test payment methods and packaged bundles

6


Data exploration

Data Exploration

GOAL

WHAT TO MEASURE

Gain competitive advantage by exploring edge cases and deep user behaviors specific to your games

Cross-app / Cross-platform user behaviors

Whale analysis – identify high spenders

Custom LTV models


Data exploration ad hoc queries across entire collected data set

Data Exploration:Ad hoc queries across entire collected data-set

6


Jason lee sr manager customer success apac

  • Conclusion

Continually collect and analyze data to validate design decisions

Identifying trends is important – but taking action requires deep understanding of game specific data


Jason lee sr manager customer success apac

  • QUESTIONS?


  • Login