customer segmentation - games analytics and business intelligence, sep 2015

1 2015 © All rights reserved to

Player Segmentation: From 5 C’s of Marketing to Bonferroni Correction

Volodymyr (Vlad) KazantsevHead of Data Science at Product Madness

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What we do?

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Heart of Vegas in (public) Numbers

US (games) Australia

* source: App Annie, top grossing list, 13th of September

iPad 12

iPhone 30

Android 35

iPad 1

iPhone 1

Android 1

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- Head of Data Science at Product Madness- Product Manager at King- MBA, London Business School- Visual Effect developer (Avatar, Batman, ...)- MSc in Probability Theory

About myself

Now

2004

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Data Impact Team

● Ad-hoc analytics and daily fires; dashboards

● Deep dive analysis; Predictive analytics

● ETL, Data Viz tools, R&D, DBA

Analytics

Data

Science

Data

Engineering

7 people; 4 in London office

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Technology Stack

ETL orchestration

Transformation& Aggregation

SQL

Data Products

Reports

Dashboards

+

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Technology Stack

ETL orchestration

Transformation& Aggregation

SQL

Data Products

Reports

Dashboards

+

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few examples ..

A B

A/B TestsCustomer Lifetime Value

days

$ va

lue

Segmentation

group 1 group 2 group 3 group 4

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Segmentation Basics

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MBA approach to Strategy

Situation Analysis Plan of Action

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MBA approach to Strategy

Situation Analysis Plan of Action

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Successful segmentation is the product of a detailed understanding of your market and will therefore take time

- Market Segmentation: How to Do it and Profit from it, 4th edition: Malcolm McDonald

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Basics

Customers have different needs and means

Segmentation can help to understand those differences

Which can help to deliver on those needs

And drive higher profitability

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What is a Player Segment?

A segment is a group of customers who display similar attributes to each other...

Customers move in and out of segments over time

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How many segments are there?

There is no one right way to segment (not should there be):

● Many different approaches and techniques

● Mix of art, science, common sense, experience and practical knowledge

● Depends on business needs and availability of data

● Don’t aim to build one holistic model to meet all needs,

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Strategic Management

Product Development

Marketing Operations

Comments

Geography /Demographics ✭✭ ✭✭ ✭✭

Separates players by country, city, city-district, distance from land-based casinos. By generational profile: boomers, Gen-Y, Gen-X.

Loyalty / Length of Relationship ✭✭✭ ✭ ✭✭

New players, on-boarding, engaged, lapsed, re-engaged, cross-promoted.

Behavioural ✭ ✭✭✭ ✭✭✭

Based on identifying player’s behaviour characteristics that help to understand why customer behave the way they do

Needs-based ✭ ✭✭✭ ✭Divide customers based on needs which are being fulfilled by playing Online Slots

Value Based ✭✭✭ ✭ ✭✭Based on present and future value of the customer (RFM/CLV)

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Land-based Slots Player segmentation

<10%

>50%

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Segmentation = building a taxonomy

All Players

New(<28 days)

Established (>28d)

Payer Non Payer0-2 days 3-7d 8-27

<30 spins >30 … High V Med V Low V Engaged Casual…VIP Concierge

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..and simplifying it

All Players

New(<28 days)

Established (>28d)

Payer Non Payer0-2 days 3-7d 8-27

<30 spins >30 … High V Med V Low V Casual…

New High Value Med Value Low Value Engaged Casual

Engaged

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How to profit from Segmentation?

2

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Clients of Segmentation

○ Strategy and Finance

○ Product development

○ Marketing operations

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Strategy and FinanceThis Month

high-value med-value low-value super free-rider casual slotter recently lapsed

high-value 55.27% 30.06% 4.81% 5.54% 2.00% 2.32%

med-value 11.11% 42.50% 25.25% 10.92% 6.20% 4.02%

low-value 0.59% 7.72% 36.02% 30.59% 17.12% 7.96%

super free-rider 0.04% 0.30% 2.76% 70.50% 22.22% 4.18%

casual slotter 0.01% 0.10% 0.96% 8.98% 51.37% 38.58%

recently lapsed 0.05% 0.22% 1.01% 8.93% 13.00% n/a

New 0.01% 0.08% 0.67% 3.22% 31.05% 64.97%

This Month 0.15% 0.54% 2.13% 21.56% 31.22% 23.03%

Last Month 0.11% 0.43% 2.03% 21.09% 37.19% 27.20%

Last

Mon

th

Made-up Data

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Strategy and FinanceThis Month

high-value med-value low-value super free-rider casual slotter recently lapsed

high-value 6.80% -0.45% -1.66% -2.39% -1.07% -1.24%

med-value 3.09% 2.60% -2.81% -2.12% -0.60% -0.16%

low-value 0.11% 0.90% -1.63% 1.99% -0.54% -0.82%

super free-rider 0.01% 0.05% -0.05% -2.05% 2.58% -0.54%

casual slotter 0.00% 0.02% 0.05% -1.26% 2.71% -1.54%

recently lapsed -0.01% -0.05% -0.35% -4.21% -8.43% N/A

New 0.01% 0.04% 0.36% 1.59% 16.17% 1.21%

Last

Mon

th

Manage transitions, not churnMade-up Data

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Product Development

New Slot Game Released

Coins Spent

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Product Development

Geo: AustraliaValue: Low-valueBehaviour: Prefer Medium bet

New Slot Game Released

Coins Spent

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Marketing

Behavioral RFM/CLV geo/demographic Lifecycle

Sale Events

Monetization campaigns

Retention campaigns

Re-engagement

VIP management

ObjectiveSegmentation

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How to actually do segmentation?

3

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Pillars of Successful Segmentation Project

Business knowledge

Data knowledge

Analytical skills People

Process

Technology

ETL

Machine Learning

Business Intelligence

Product Integration

Marketing

Product

Data Services

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Top-down approach to segmentation

1. Define objectives and therefore customer characteristicsa. dd

2. Choice method to split usersa. d

3. Prioritise segments to targeta. d

4. Operationalise segmentationa. s

5. ‘land’ the segmentation within the organization

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Bottom-up approach

360o player view

Segmentation

Player transitions

Tailored interventions

Prioritisation and testing

● Build database to provide 360o view of the customer● Demographic, behavioural, payments, etc.● Add predictive attributes, such as conversion probability, churn risk, LTV, etc.

● Segment customers by desired attributes: more than one approach● Use robust statistical techniques for clustering or validation of empirical segmentation● Ensure segmentation is intuitive for the business and can be used across business functions

● Identify how players are moving from one segment to another (segment transition matrix)● Determine value levers and identify potential improvement ideas

● Create tailored interventions (CRM, push ..), aimed at moving customers to more valuable segments● Build predictive models to detect best offer and prevent undesirable transitions

● Prioritise interventions based on expected LTV uplift and ease of implementation● Test interventions through experimentation

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How to actually do segmentation?

Just Look at Data Clustering Decision Trees

Player Attributes

de-correlate

Normalise Scale

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de-correlate and normalise

Player 1 more similar to Player 2 ?Player 3 more similar to Player 2 ?

Weekly Play Summary

?

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de-correlate and normalise

Weekly Play Summary

(Euclidean)

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de-correlate and normalise

Player 1 more similar to Player 2 !

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de-correlate and normalise

Player 1 more similar to Player 2 !

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de-correlate and normalise

Player 1 more similar to Player 2, isn’t he?

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de-correlate and normalise

Player 3 more similar to Player 2 !

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What now?

● K-means● Hierarchical Clustering● Decision Trees● .. and many more

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Decision Tree for Clustering

All Payers500 (next month>$100): 4.7%

10000 did not: 95.3%

Last_months_dollars <=$22 (next month>$100): 0.04%

5000 did not: 99%

Last_months_dollars >$2498 (next month>$100) > $100: 9%

5000 did not: 91%

Transactions <=10243 (next month>$100): 5.5%

4200 did not: 94.5%

Transactions > 10255 (next month>$100): 24%

800 did not: 76%

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Decision Tree for Clustering

All Payers500 (next month>$100): 4.7%

10000 did not: 95.3%

Last_months_dollars <=$22 (next month>$100): 0.04%

5000 did not: 99%

Last_months_dollars >$2498 (next month>$100) > $100: 9%

5000 did not: 91%

Transactions <=10243 (next month>$100): 5.5%

4200 did not: 94.5%

Transactions > 10255 (next month>$100): 24%

800 did not: 76%

Low Value

Medium ValueHigh Value

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Segmentation at Product Madness

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Lifestage Segmentation

On-Boarding

Disengaged

Engaged

not played game

Churned

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On-boarding segment

On-Boarding

Disengaged

Engaged

not played game

Churned

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On-boarding segment

On-Boarding

Disengaged

Engaged

not played game

Churned

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Lifestage Segmentation

On-Boarding

Disengaged

Engagedlow risk

high risk

low risk

high risk

low risk

high risk

not played game

churned

churned

Churned

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Behavioural Segmentation

● Average Bet● Gifts per Day● Bonuses per Day● Machine Stickiness● Days Played● Spins per Day● Preference for New Machines● %% of spin on High-Roller machines● Big Win Stickiness● etc.

Hierarchical Clustering

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Behavioural Segmentation

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Infrastructure

Data Warehouse

Segmentation Engine

CRM Email GAME Reporting Ad Hoc Analytics

Predictive Analytics

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Segmentation for A/B tests

A B

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Bonferroni correction:

Bayesian Hierarchical Model

Combine stats with Market Intuition!

Adjustment for multiple testing

adjustted = desired/M

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Thank You!

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