customer segmentation - games analytics and business intelligence, sep 2015
TRANSCRIPT
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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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?
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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?
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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
51 2015 © All rights reserved to
Thank You!
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