Customer segmentation - Games Analytics and Business Intelligence, Sep 2015
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Player Segmentation is a complex project that requires collaboration from various business units, not just analytics
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Customer segmentation - Games Analytics and Business Intelligence, Sep 2015
- 1. 1 2015 © All rights reserved to Player Segmentation: From 5 C’s of Marketing to Bonferroni Correction Volodymyr (Vlad) Kazantsev Head of Data Science at Product Madness volodymyrk
- 3. 3 volodymyrk 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
- 4. 4 volodymyrk - 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
- 5. 5 volodymyrk 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
- 6. 6 volodymyrk Technology Stack ETL orchestration Transformation & Aggregation SQL Data Products Reports Dashboards +
- 7. 7 volodymyrk Technology Stack ETL orchestration Transformation & Aggregation SQL Data Products Reports Dashboards +
- 8. 8 volodymyrk few examples .. A B A/B TestsCustomer Lifetime Value days $value Segmentation group 1 group 2 group 3 group 4
- 10. 10 volodymyrk MBA approach to Strategy Situation Analysis Plan of Action
- 11. 11 volodymyrk MBA approach to Strategy Situation Analysis Plan of Action
- 12. 12 volodymyrk 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
- 13. 13 volodymyrk 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
- 14. 14 volodymyrk 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
- 15. 15 volodymyrk 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,
- 16. 16 volodymyrk 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)
- 17. 17 volodymyrk Land-based Slots Player segmentation <10% >50%
- 18. 18 volodymyrk 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
- 19. 19 volodymyrk ..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
- 20. 20 How to profit from Segmentation? 2
- 21. 21 volodymyrk Clients of Segmentation ○ Strategy and Finance ○ Product development ○ Marketing operations
- 22. 22 volodymyrk Strategy and Finance This 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% LastMonth Made-up Data
- 23. 23 volodymyrk Strategy and Finance This 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% LastMonth Manage transitions, not churn Made-up Data
- 24. 24 volodymyrk Product Development New Slot Game Released Coins Spent
- 25. 25 volodymyrk Product Development Geo: Australia Value: Low-value Behaviour: Prefer Medium bet New Slot Game Released Coins Spent
- 26. 26 volodymyrk Marketing Behavioral RFM/CLV geo/demographic Lifecycle Sale Events Monetization campaigns Retention campaigns Re-engagement VIP management Objective Segmentation
- 27. 27 How to actually do segmentation? 3
- 28. 28 volodymyrk 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
- 29. 29 volodymyrk Top-down approach to segmentation 1. Define objectives and therefore customer characteristics a. dd 2. Choice method to split users a. d 3. Prioritise segments to target a. d 4. Operationalise segmentation a. s 5. ‘land’ the segmentation within the organization
- 30. 30 volodymyrk 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
- 31. 31 volodymyrk How to actually do segmentation? Just Look at Data Clustering Decision Trees Player Attributes de-correlate Normalise Scale
- 32. 32 volodymyrk de-correlate and normalise Player 1 more similar to Player 2 ? Player 3 more similar to Player 2 ? Weekly Play Summary ?
- 33. 33 volodymyrk de-correlate and normalise Weekly Play Summary (Euclidean)
- 34. 34 volodymyrk de-correlate and normalise Player 1 more similar to Player 2 !
- 35. 35 volodymyrk de-correlate and normalise Player 1 more similar to Player 2 !
- 36. 36 volodymyrk de-correlate and normalise Player 1 more similar to Player 2, isn’t he?
- 37. 37 volodymyrk de-correlate and normalise Player 3 more similar to Player 2 !
- 38. 38 volodymyrk What now? ● K-means ● Hierarchical Clustering ● Decision Trees ● .. and many more
- 39. 39 volodymyrk Decision Tree for Clustering All Payers 500 (next month>$100): 4.7% 10000 did not: 95.3% Last_months_dollars <=$2 2 (next month>$100): 0.04% 5000 did not: 99% Last_months_dollars >$2 498 (next month>$100) > $100: 9% 5000 did not: 91% Transactions <=10 243 (next month>$100): 5.5% 4200 did not: 94.5% Transactions > 10 255 (next month>$100): 24% 800 did not: 76%
- 40. 40 volodymyrk Decision Tree for Clustering All Payers 500 (next month>$100): 4.7% 10000 did not: 95.3% Last_months_dollars <=$2 2 (next month>$100): 0.04% 5000 did not: 99% Last_months_dollars >$2 498 (next month>$100) > $100: 9% 5000 did not: 91% Transactions <=10 243 (next month>$100): 5.5% 4200 did not: 94.5% Transactions > 10 255 (next month>$100): 24% 800 did not: 76% Low Value MediumValue High Value
- 41. 41 Segmentation at Product Madness 4
- 45. 45 volodymyrk Lifestage Segmentation On-Boarding Disengaged Engaged low risk high risk low risk high risk low risk high risk not played game churned churned Churned
- 46. 46 volodymyrk 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
- 48. 48 volodymyrk Infrastructure Data Warehouse Segmentation Engine CRM Email GAME Reporting Ad Hoc Analytics Predictive Analytics
- 49. 49 volodymyrk Segmentation for A/B tests A B
- 50. 50 volodymyrk Bonferroni correction: Bayesian Hierarchical Model Combine stats with Market Intuition! Adjustment for multiple testing adjustted = desired /M
- 51. 51 2015 © All rights reserved to Thank You! jobs.productmadness.com volodymyr.kazantsev@productmadness.com volodymyrk We Are Hiring ! jobs.productmadness.com