Veli-Pekka Julkunen

Head of Analytics, Co-Founder

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

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”

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

SO WHAT? - A BILLION DOLLAR CASE

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” featuresBasic usability,

no “sexy”

features

Own product

Competitor A

Competitor B

Competitor C Competitor D

Competitor E

1

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

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”

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”

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”

“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

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

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

A CASE FOR THE GAME INDUSTRY

Example: The hot spot for mobile strategy games

Casino

Fighting/competing -

Strategy

PuzzleFighting/competing –

Reaction focused

Word/trivia/boardDriving/steering

ThinkingReaction Emphasis of the gameplay

Number of

“layers” in

the game

A lot of

layers

Small

number

of layers

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

What the gaming industry can learn from the more mature industries in optimizing

their products?

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

Veli-Pekka Julkunen

Head of Analytics, Co-Founder

GameRefinery

Email: vp@gamerefinery.com

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

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