In this presentation we will discuss what is a product metrics pyramid and how it's better to use it. First, we will start with events analytics. Second, we will discuss if there any problems to identify growth points using separated dashboards. At the end, we will summarize with a guide of how could we measure overall product health and how could we help to prioritize game improvement. Only real cases, no bullshit!)

1. How to identify key opportunities to
grow using product metrics pyramid:
f2p guide
Oleg Lapin
Head of Analytics, AppQuantum

2. SPEAKER
Oleg Lapin
analyst, team lead, father
PROF EXP
- more than 10 years in analytics
- building analytical department in AppQuantum
- built Product Analytics in Nexters
- before it, participated in Avito’s success
- before it worked in FMCG, science and consulting

3. team AppQuantum
WHO WE ARE
A free-to-play mobile game publisher
based in Moscow, Russia
WHAT WE DO
We assist developers with many
aspects of their apps, helping them
stay focused on making games and
delivering high quality content to users

4. Metrics pyramid

5. What is it “metrics pyramid”
● it is framework of using metrics
● my favourite author is Elena Seregina /
DataLatte “Иерархия метрик vs
пирамида метрик”
● an older article from Google “How to
choose the right UX metrics for your
product”
● or you could just Google))
* Cool viz from the Elena’s article mentioned

6. Simple metrics hierarchy
* ROI - return of investment, LTV - lifetime value, ARPU - average revenue per user, ARPPU - average revenue
per paying users, AOV - average order value, OPP - orders per payer, rv_per_user - rewarded videos per user
ROI = LTV / CPI
LTV = ARPU_inapp + ARPU_AdsMon
ARPU_inapp = ARPPU * %payers
ARPPU = AOV * OPP
ARPU_AdsMon = rv_per_user * ecpm
LTV
ROI
CPI
CPM CTR
ARPU_inapp ARPU_AdsMon
ARPPU %payers rv_per_user ecpm

7. ROI = LTV / CPI
LTV = ARPU_inapp + ARPU_AdsMon
ARPU_inapp = ARPPU * %payers
ARPPU = AOV * OPP
ARPU_AdsMon = rv_per_user * ecpm
Simple metrics hierarchy
* ROI - return of investment, LTV - lifetime value, ARPU - average revenue per user, ARPPU - average revenue
per paying users, AOV - average order value, OPP - orders per payer, rv_per_user - rewarded videos per user
LTV
ROI
CPI
CPM CTR
ARPU_inapp ARPU_adsmon
ARPPU %payers %payers ecpm
SO
WHAT???

8. We need to learn how
to use this tool!

9. First Story

10. Version N1 - classic
Event analysis / liveops:
- launch event
- LVL_1 uplift Revenue, ARPDAU
- LVL_2 - ARPDAU by cohorts
- LVL_3 - ARPDAU, currency balance,
AOV, %PU, %new payers, bundles,
completion funnels
- LVL_4 - LVL_3 + planning what
metric should we uplift, estimate
were we true
- LVL_5 - your variant))
* You could have an operational dashboard for this
purpose with all metrics calculated

11. We already have some metrics pyramide!
Your KPI
Revenue, ARPU, ARPPU, …
Spending Structure, баланс харды,
payers_ret
Bundle conversion

12. What for is metrics pyramid
● searching for right reasons
● analyzing small changes
● differentiating effects

13. Do you need smth
more interesting?

14. OK, let’s do it for the
whole project!)

15. Second Story

16. ARPU curve. Is it ok?
here we see that lifetime is more than 30 days
(users are still paying) and LTV is more than
$1,4 and in-app share is around 60%
- is this LTV is OK for this market /
optimization / genre?
- is this lifetime is OK for this genre?
- is this ARPU_inapp is OK for this type of
game?
- do we have a problem in early/mid/end
game in ARPU dynamics?
* just for an example
** some project, some cohort, some geography
*** lifetime 0, 1, 3, 14, 30,...

17. Retention by LT
here we see that Retention at first day is 28%
and retention_30 is 6%.
- is it OK?
- do we have any gaps at tutor / early
game?
- why relative retention (2nd graph) stop
increasing after 2 weeks? do we have any
lack of gameplay after 2 weeks?
* just for an example

18. ARPDAU by LT. Are they still paying in midgame?
here we see that ARPDAU is around $0.4 and
falling over time! (both in-app and ads, very
bad).
- why it’s falling?
- let’s decompose, is it falling because of
%payers or payers_churn or AOV?
- do we have any economic restrictions?
probably we don’t have enough content or
enough offers
* just for an example
** some project, some cohort, some geography
** lifetime 0, 1, 3, 14, 30,...

19. Daily payers by LT. Are they stop spending in game?
here we see that daily payers is around 5%
(seems it is purchase optimization traffic) but
falling over time!
- is there any problem with a first payment?
- do we have enough content in game to
spend much?
- do we have enough offers?
- do payers get an advantage in game, do
we have any VIP_lvl system??
- yeap, all questions are the same as for
ARPDAU in this game
* just for an example
** some project, some cohort, some geography
** lifetime 0, 1, 3, 14, 30,...

20. ARPPDAU by LT. Do they pay a lot?
here we see that is around $4
- is this ARPPDAU OK for this country /
optimization?
- why it’s not increases over time but
instead starting to decrease at second
week?
- do we have enough whales content? * just for an example
** some project, some cohort, some geography
** lifetime 0, 1, 3, 14, 30,...

21. Example of summary table
● We could summary all metrics in one table
and use weekly lifetime instead of daily
● it’s more useful when you do overall
conclusions
* just for an example
** some project, some cohort, some geography
** lifetime 1-2-3-4 weeks after registration

22. And then we go deeper and deeper
● Also we analyze time spending
● then we go deeper to economics, offers,
social interactions and so on
● At the end we’re trying to sum up all
problems we found and identify key
opportunities to grow (is it about mechanics
or economics or offers or smth else)
* the example of our report, the main part is
conclusions for sure))

23. FINAL Story

24. The main idea of this
presentation is not about
metrics itself

25. Expectations
● each feature should has its own expectations of outcomes in terms of metrics
and user segment
● yeap, we need data to calculate all metrics
● yes, we need some BI system to have useful dashboard
● for sure we need some work space to store all our expectations
● and finally we need some kind of analytical culture in company

26. Prioritization
we have a few instruments:
- our game design thinking or user voice ("it's good to have this feature/content
here")
- previous experience (smth like "this type of content is always increase this
metric)
- analysis of user behaviour / metrics that I described in presentation. So here
we could understand that based on metrics we see a growth opportunity in the
certain user segment
- the best thing when your team could use all these methods at once!

27. linkedin: https://www.linkedin.com/in/oleg-lapin-521891b5
tg me: Oleg_the_analyst
ping us at: hi@appquantum.com
Thanks!