1.
Veli-Pekka Julkunen
Head of Analytics, Co-Founder

2.
Background
• 10+ years and 200+ projects: to helping global blue
chip companies to optimize their brands, products and
services by using quantitative analytics
• Econometrics, optimization/simulation, machine
learning

3.
Too often the situation was this….
• Answering WHAT, not why
• Data AFTER the brand/product is
launched – too late to correct mistakes
…but of course not all the companies are
thinking like that
“As you can see, we seem to be benefitting from
consumers purchasing our products”

4.
The maturity ladders for product optimization
“WHAT”
• “What is happening”
• Followed KPIs: sales,
preference, retention etc.
“WHY”
• “Why products are
successful”
• Followed KPIs: contribution of
features on success, feature
effects in different situations
“WHAT IF…”
• “What would happen to a
product if…
• Followed KPIs: scenario /
estimated sales, preference,
retention etc.1
2
3

5.
SO WHAT? - A BILLION DOLLAR CASE

6.
Market leader in one specific consumer electronics
category with sales value of over $30 billion had a
problem…
“WHAT”
• Retention was decreasing and
didn’t know why
• Strong in basic features
• However products seen as
“vanilla ice cream”
Strong in basic features
Weak in basic features
Good usability,
“sexy” features
Basic usability,
no “sexy”
features
Own product
Competitor A
Competitor B
Competitor C Competitor D
Competitor E
1

7.
3
“WHAT”
• “What is happening”
• Followed KPIs: sales,
preference, retention etc.
“WHY”
• “Why products are
successful”
• Followed KPIs: contribution of
features on success, feature
effects of different situations
“WHAT IF…”
• “What would happen to a
product if…
• Followed KPIs: scenario /
estimated sales, preference,
retention1
2

8.
Quantitative modeling methods enables to
understand the reasons behind retention 1/3
“WHY”
Features RetentionStatistical
model
Base retention
incremental from feature A
incremental from feature B
incremental from feature C
incremental from feature D
Retention
A “formula for retention”

9.
Quantitative modeling methods enables to
understand the reasons behind retention 2/3
“WHY”
Features RetentionStatistical
model
Feature A “value”
HighLow
Retention
Not much to gain
by improving this
feature!
A “formula for retention”

10.
Quantitative modeling methods enables to
understand the reasons behind retention 3/3
“WHY”
Features RetentionStatistical
model
Feature D “value”
HighLow
Retention
A lot to gain by
improving this
feature!
A “formula for retention”

11.
“WHAT”
• “What is happening”
• Followed KPIs: sales,
preference, retention etc.
“WHY”
• “Why products are
successful”
• Followed KPIs: contribution of
features on success, feature
effects of different situations
“WHAT IF…”
• “What would happen to a
product if…
• Followed KPIs: scenario /
estimated sales, preference,
retention
3
1
2

12.
Quantitative simulation allows to make scenarios and
assess the outcomes 1/3
“WHAT IF…”
RetentionStatistical
model
Simulation &
optimization
Features
Own brand
Competitor A
Competitor B
Competitor C Competitor D
Competitor E
Strong in basic features
Weak in basic features
Good usability,
“sexy” features
Basic usability,
no “sexy”
features
Hotter the color,
the higher the
estimated retention
Retention
“hot spot” for
the own
product

13.
Quantitative simulation allows to make scenarios and
assess the outcomes 2/3
Base retention
incremental from feature A
incremental from feature B
incremental from feature C
incremental from feature D
Base retention
incremental from feature A
incremental from feature B
incremental from feature C
incremental from feature D
Retention Retention

14.
A CASE FOR THE GAME INDUSTRY

15.
Example: The hot spot for mobile strategy games
Casino
Fighting/competing -
Strategy
Puzzle
Fighting/competing –
Reaction focused
Word/trivia/board
Driving/steering
ThinkingReaction Emphasis of the gameplay
Number of
“layers” in
the game
A lot of
layers
Small
number
of layers

16.
Base
Mechanics
Brand & Publisher
Social elements
Example: optimizing Pokémon Go’s feature set
Feature set’s fit to market
Base
Mechanics
Brand & Publisher
Social elementsFeature set’s fit to market
Detailed feature level
results available to make
the results actionable

17.
What the gaming industry can learn from
the more mature industries in optimizing
their products?

18.
Key takeaways
Don’t be satisfied only on “what” -questions – why and what next
matters
Competition is getting tougher  if you want to win also in the future,
“climb the ladders”
This can help analytics to become more than “live ops”, but strategic
asset that is in the core of the business

19.
Veli-Pekka Julkunen
Head of Analytics, Co-Founder
GameRefinery
Email: vp@gamerefinery.com

20.
Make better product related decisions
with help of our online tool &
information database
“WHAT IS HAPPENING”
“WHY”
“WHAT IF…”
Game feature set related
performance
Most important features
Commercial Potential
Test ideas and concepts
• Retention, ARPDAU, player
demographics etc.
1
2
3
www.gamerefinery.com

21.
www.gamerefinery.com
• Access feature level analysis for 700+
mobile games
• Follow feature level market trends
• Validate games commercial potential
before soft launch
• See how your game’s feature set
benchmarks against competitors
Make better product related decisions
with help of our online tool &
information database