Ever wondered whether it makes sense for your company to build your own app analytics stack? This session will feature a case study about us doing just that, using tools like Google Firebase, BigQuery, and Data Studio. Martin Jelinek guided the audience through the whole process, including the initial decision making (why we chose to build, what tools to use), execution (events and metrics definitions, implementation and testing, cost control), and a final evaluation of pros and cons of such an approach & our learnings. Presentation run by Martin Jelinek (www.appagent.co) at 8th edition of GameCamp (www.gamecamp.io)

1. By Martin Jelínek
Google GameCamp, February 5th 2020
BUILDING COST-EFFECTIVE MOBILE ANALYTICS ON GCP

2. Martin Jelinek
• University of Economics
• 2007 – 2016: Independent Game Developer
• 2017 – 2018: Marketing Manager at AppAgent
• 2019 – Head of Marketing at AppAgent
• Designed and built AppAgent‘s in-house Marketing & Product
Analytics
• Speaker at App Promotion Summit, GIC and other events
INTRO

3. ● Since 2016
● 15 experts
● 50+ apps & games
● Built 5 analytics stacks
and infrastructures
INTRO

4. HOW TO
GET THE
DATA?
HOW TO
USE THE
DATA?
X

5. BEFORE WE START..
Who of you is using Firebase?

6. BEFORE WE START..
Who of you is using BigQuery?

7. BEFORE WE START..
Who of you uses Data Studio?

8. BEFORE WE START..
Who did built their own analytics?

9. 3 parts:
- General Thoughts
- Studycat Case Study
- Learnings
TODAY’S TALK - BUILDING COST-EFFECTIVE MOBILE ANALYTICS ON GCP

10. Everybody loves analytics!
WHY?

11. WHY?
Everybody loves analytics!
(but doesn’t want to pay for it)

12. - Many people wonder whether
building makes sense
- Lots of tools, many pricey
- Especially smaller companies can
bleed when scaling up
WHY?

13. PART 1
THOUGHTS ON ANALYTICS

14. 1. ALL Data in one place
2. Affordable
3. User Friendly
4. Customizable
5. (doesn’t exist :)
IDEAL
SETUP
WHAT IS AN IDEAL SETUP?

15. - Google Play, App Store Connect
- ASO Tools
- Apptweak, Appfollow...
- Attribution
- Ad Networks data
- Singular, Appsumer...
- Analytics SDK that collects data
- Database and vizualization tool
- Amplitude, Mixpanel, Localytics...
WHAT DATA SOURCES DO WE NEED?
PRODUCT
ANALYTICS
MARKETING
ANALYTICS
ASO
ANALYTICS
“What’s going on with the product?”
(retention, sessions, new users, active users, funnels,
feature usage..
“What campaigns perform the best?”
(LTV, ROI & pROI, impressions, clicks.. )
“How does our store listings perform?”
(impressions, pageviews, keyword rankings..)

16. BUILD
X
BUY
BUILD OR BUY?
- Buy what you need and
build the rest

17. PART 2
CASE STUDY - STUDYCAT

18. STUDYCAT - FUN ENGLISH

19. THE TASK
“Recommend tools for and help build
a unified (product, marketing, ASO)
dashboard in cloud.”

20. 1. Analyze Needs
2. Toolset & Setup
3. Events
4. Metrics & Dashboards Definition
5. Implementation
6. Setup, ETL, Orchestration
7. Visualization
ANALYZE
NEEDS

21. - 10M+ downloads
- 10+ apps
- 250K users
- Android & iOS
- subscriptions
- No analytics at all
- GDPR, expensive
- Product, marketing, ASO
- Tech Savvy
STUDYCAT - FUN LANGUAGE LEARNING FOR KIDS

22. N. of users? Scaling plans?
ANALYZE
NEEDS
Budget?
Amount of used UA networks?
Currently using any tools?
Required frequency of data processing
Short term / long term?
Able to do their own SQL?
Main KPI’s?
Timing?

23. SELECTING
THE
TOOLSET
1. Analyze Needs
2. Selecting the toolset
3. Events Definition
4. Metrics & Dashboards Definition
5. Implementation
6. Setup, ETL, Orchestration
7. Visualization

24. TOOLSET SELECTED FOR STUDYCAT

25. TOOLSET SELECTED FOR STUDYCAT
Data Collector SDK - Firebase analytics
- Free
- Part of the Google Cloud Platform
- Platform provides a lot of additional features
- Events are easily dumped to BQ

26. TOOLSET SELECTED FOR STUDYCAT
Attribution - ended up using Kochava
- Attribution is a must, cannot build in-house
- Kochava for more suitable pricing + raw data for
users from organic
- were not too happy with the choice
- events for purchases & installs go to BQ

27. TOOLSET SELECTED FOR STUDYCAT
Ad Network Data
- Connectors in Matillion vs. our own
- If more ad networks were used, we’d go for a third
party solution
- Impressions, clicks, costs - reports dlded to BQ

28. TOOLSET SELECTED FOR STUDYCAT
Subscription User Level Data - Own solution
- Unable to use RevenueCat due to used framework
- Built their own solution and send subscription status
for every user into BQ

29. TOOLSET SELECTED FOR STUDYCAT
ASO Data (keyword ranks, store data) - AppFollow
- Good pricing, good API, all info we needed
- Daily reports into BQ

30. TOOLSET SELECTED FOR STUDYCAT
Database - Google BigQuery
- Part of Google Cloud Platform
- Acceptable pricing

31. TOOLSET SELECTED FOR STUDYCAT
ETL and Orchestration - Matillion
- all data transformation written there
- SQL, Python, orchestration
- fuc*up control

32. TOOLSET SELECTED FOR STUDYCAT
Visualization - Data Studio
- currently in a pretty good state
- free, part of GCP

33. TOOLSET SELECTED FOR STUDYCAT

34. PRICING + BIGQUERY COSTS CALCULATOR
INPUTS:
- 250K MAUs
- 5 sessions / day
- 10 events / session
YEARLY COSTS: $2000
YEARLY COSTS: $365
Turned on once daily to
process the data. Priced by
hour, $1 /h.
YEARLY COSTS: $1200+Currently the lowest plan
for $100.

35. EVENT
DEFINITION
1. Analyze Needs
2. Selecting the toolset
3. Event Definition
4. Metrics & Dashboards Definition
5. Implementation
6. Setup, ETL, Orchestration
7. Visualization

36. EVENT
DEFINITION
We collect everything.
In case it became a problem cost-wise
in the future, we’d aggregate.

37. EVENTS DEFINITION SHEET
All events, parameters, triggers + user properties for implementation.

38. “How do you define a.. (session)?”
Before we start working on the ETL, we
make sure we have our metrics defined
well.
METRICS &
DASHBOARDS

39. METRICS DEFINITION
How will the events be processed to the final metrics? Sometimes tricky to nail.

40. Sometimes not trivial to change the dashboard.
We try to define the contents beforehand.
METRICS &
DASHBOARDS

41. DASHBOARDS DEFINITION
What should be included in the final dashboards?

42. IMPLEMENTATION
& TESTING
1. Analyze Needs
2. Selecting the toolset
3. Event Definition
4. Metrics & Dashboards Definition
5. Implementation & Testing
6. Setup, ETL, Orchestration
7. Visualization

43. Up to the developer team.IMPLEMENTATION
& TESTING

44. 1. Analyze Needs
2. Selecting the toolset
3. Event Definition
4. Metrics & Dashboards Definition
5. Implementation & Testing
6. Setup, ETL, Orchestration
7. Visualization
SETUP
ETL
ORCHESTRATION

45. - Setting up all the accounts and
connections
- Writing data transformation SQL
to get them ready for viz
- Data Aggregation
- Setting up the Matillion pipeline
SETUP
ETL
ORCHESTRATION

46. ORCHESTRATION
Process setup in Matillion

47. VISUALIZATION
1. Analyze Needs
2. Selecting the toolset
3. Event Definition
4. Metrics & Dashboards Definition
5. Implementation & Testing
6. Setup, ETL, Orchestration
7. Visualization

48. PRODUCT / ASO DASHBOARDS IN DATA STUDIO

49. MARKETING DASHBOARD IN DATA STUDIO

50. HOW LONG DOES
THE WHOLE
THING TAKE?
100 - 150 hours

51. PART 3
MAIN LEARNINGS

52. - Any data can be connected
and combined with others
- High level of freedom
- Easy to use (eventually)
LEARNINGS & THOUGHTS
Customizability
Ease of Use
Cost
Necessary Expertise
Batch processed vs. real time
Vendor Lock-in
How many people?

53. - Sufficient for marketing / ASO
- OK for product usage overview
- No advanced capabilities
- Combo with Looker
- Combo with Tableau / PowerBI
CLOSING THOUGHTS
Customizability
Ease of Use
Cost
Necessary Expertise
Batch processed vs. real time
Vendor Lock-in
How many people?

54. - Very cost efficient
- Data can be always aggregated :)
- Using off-the shelf packages can
be optimized too..
CLOSING THOUGHTS
Customizability
Ease of Use
Cost
Necessary Expertise
Batch processed vs. real time
Vendor Lock-in
How many people?

55. - There’s quite a lot of trial and error
+ a number of tools / API’s to
work with
- Nothing complicated - just quite a
lot of it
- > Building time can be a factor too
CLOSING THOUGHTS
Customizability
Ease of Use
Cost
Necessary expertise
Batch processed vs. real time
Vendor Lock-in
How many people?

56. - No one-size-fits all
- Firebase dumps data to BQ
multiple times a day in batches,
not real-time
CLOSING THOUGHTS
Customizability
Ease of Use
Cost
Expertise vs. Time
Batch processed vs. real time
Vendor Lock-in
How many people?

57. Customizability
Ease of Use
Cost
Expertise vs. Time
Batch processed vs. real time
Vendor Lock-in
How many people?
CLOSING THOUGHTS
- For off-the-shelf tools, pricing is
super friendly in the beginning, but
can become a real problem when
scaling

58. Customizability
Ease of Use
Cost
Expertise vs. Time
Batch processed vs. real time
Vendor Lock-in
How many people?
CLOSING THOUGHTS
- Always consider data analysts’
time

59. … SO DOES IT MAKE SENSE FOR ME?

60. As always… It depends.
… SO DOES IT MAKE SENSE FOR ME?

61. THANK YOU!
Martin Jelínek
E-mail: martinjelinek@appagent.co
LinkedIn: martinjelinek1

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