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Developing Dynamic Segmentation

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How to develop a dynamic segmentation.

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Developing Dynamic Segmentation

  1. 1. Developing Dynamic Segmentation Tim Wyatt Head of Data & Analytics, Sonovate
  2. 2. Contents 1 3 4 5 6 72Section A bit of background Types of segmentation Why make it dynamic? An exercise in visualisation Time for questions A practical demonstration Finally, to summarise
  3. 3. A bit of background Developing Dynamic Segmentation
  4. 4. Personal and Sonovate background Personal background Cardiff University • BSc Mathematics, Operational Research & Statistics Royal Statistical Society • Chartered Statistician (CStat) Held a number of statistics, analytics and data science positions across a number of different sectors: • GlaxoSmithKline • Sainsbury’s • dunnhumby • GoCompare • Babcock International Group • Sonovate Sonovate background Who they are • Sonovate is a FinTech company founded in 2013 by two former recruiters, and are one of the fastest growing FinTech businesses of the last 5 years. What they do • Recruitment / consultancy focused • Invoice factoring business • Timesheet application What I do • Responsible for the end-to-end data & analytics lifecycle
  5. 5. Analytical Maturity Model Data Descriptive Diagnostic Predictive Decision Support Analyse Understand Decision Automation Decide Act Source: Gartner Prescriptive
  6. 6. The Data Science Hierarchy of Needs ML, AI Segment, A/B Dashboard, analyse Cleanse, supplement, prepare Infrastructure, ETL, databases Instrumentation, sensors, tracking, user dataCollect Store Transform Analyse Optimise Source: hackernoon.com
  7. 7. The Data Science Venn Diagram Source: kdnuggets.com Data Science Maths / Stats Computer Science Domain Expertise Machine Learning Data AnalysisData Processing Segmentation
  8. 8. “A segment is a cluster of accounts that think and act in similar way. A segment should consist of accounts that behave similarly and in ways that are distinct from other segments. So, different segments can receive products, services, and marketing support that are designed for their unique needs and usage behaviour.” Source: marketresearch.com
  9. 9. Segmentation Requirements Accessible Actionable Differentiable Identifiable Substantial Source: hbr.org
  10. 10. Types of segmentation Developing Dynamic Segmentation
  11. 11. Types of Segmentation The 4 most common types are as follows: Source: Geographic • Location (country, county, city) • Climate / weather conditions • Population density • Average distance from store Demographic • Age / gender • Family lifestyle / lifestage • Occupation / income band • Ethnicity / religion / social ‘class’ Psychographic • Opinions / attitudes • Values & beliefs • Price sensitivity • Brand loyalty Behavioural • Time since last interaction • Regularity of interactions • Monetary value of interactions • Recency, Frequency, Value (R-F- V)
  12. 12. Geographic Segmentation Positives • Thorough understanding of differing performance across geographic locations • Works well for companies with wide market reach • Relatively easy to implement, with natural, pre- defined boundaries • Multi-level segmentation is simple • Easy for the business to understand Negatives • Sometimes too basic for the purposes of further understanding the customer-base • Targeting customers geographically not always possible Example • Supermarket salad sales
  13. 13. Demographic Segmentation Positives • The typical way of a company understanding their ‘average’ customer • Can help with product strategy, market research and marketing messaging • Often no need for additional data collection • Particularly useful for product/market positioning Negatives • Can suffer from ‘analysis paralysis’ • There’s much fewer factors for B2B demography Example • The automotive industry
  14. 14. Psychographic Segmentation Positives • Useful when deciding the vehicle for contacting customers of the business • Can help with pricing strategy and discounting • Enables a very deep understanding of your customer-base • Adds a qualitative angle to marketing analytics Negatives • Can be very complicated to build due to the nature of measuring the way consumers think • May require more complex analytical methods (cluster analysis, factor analysis etc.) • Data rarely readily available • Doesn’t tend to apply to B2B marketing Example • Promotional offers / pricing
  15. 15. Behavioural Segmentation Example Metric Definitions • Recency: # of days since last purchase • Frequency: # of purchases in last month • Value: Total value of purchases in last month R-F-V Segmentation Value Frequency Recency
  16. 16. Hierarchical Organisation Segmentation isn’t always a single tier: Customer Base Gold A 1 2 B 3 Silver C 4 5 6 D 7 E 8 9 Bronze F 10 G 11 12 Build Analyse Communicate
  17. 17. Why make it dynamic? Developing Dynamic Segmentation
  18. 18. What does ‘Dynamic’ mean?
  19. 19. Dynamic Segmentation Requirements Accessible Actionable Differentiable Identifiable Substantial Source: hbr.org
  20. 20. What makes a segmentation ‘dynamic’? Regularly moving Customers Part of building a dynamic segmentation is the acceptance that customers don’t stay in a static state. Regularly moving Boundaries The boundaries between segments will change over time, so dynamism is allowing them the freedom to do so.
  21. 21. Strengths and Weaknesses of Dynamic Segmentation Strengths • Useful for Growth businesses • Prevents collection of customers in extreme segments • Accounts for your company enabling the growth of your customers • Accounts for changes in business strategy • Accounts for weekends / bank-holidays • Prioritisation of ‘best’ or ‘worst’ customers • Removes bias from customers joining at different points in time Weaknesses • Difficult to see improvement/degradation in your overall customer behaviour • Hard to reverse-engineer historic segment boundaries • Requires a lot more effort in automation
  22. 22. Setting the Review Frequency Frequency Justifiability Automation Interpretability Practicality
  23. 23. A practical demonstratio n Developing Dynamic Segmentation
  24. 24. Step-by-Step Process 1 3 4 5 6 7 82Step Decide metrics for use in segmentation Recency, Frequency, Value Different ways of defining each metric Days since last visit, weekday purchases, profit/revenue Decide timeframe for analysis Business context and understanding is important ‘Lookback window’ will vary by organisation Decide on segmentation refresh rate Collect customer data Ensure metrics you wish to use are being collected by the business For larger datasets, take appropriate sample Derive percentiles for each metric Rank data points(remember; Recency is the other way around) Convert into percentiles Look into different ways of doing this Aggregate to higher- level segmentation It can be useful to ‘roll-up’ segments to give a bigger picture Could use minimum, mean, median, maximum etc. Cleanse customer data Apply business logic Treat outliers (removal/restriction) Remove/replace blanks and NULLs with appropriate values Break percentiles into segments Decide on the number of segments desired for each behavioural metric Consider how these segments multiply out when combining with other metrics Refresh / re-run process Decide on segmentation refresh rate Useful to automate as much of the process as possible
  25. 25. Identifying and Treating Outliers How do we determine what an outlier is? Lower Quartile Upper QuartileMedian Outlier Threshold = Median ± 1.5(Upper Quartile – Lower Quartile) Outliers Outliers
  26. 26. Deriving Percentiles How do we derive percentiles from our data? Data Method 1 Method 2 0% 0% 25% 25% 50% 50% 75% 75% 100% 100% = percentile.inc([data], k%) = (value – min[data]) / (max[data] – min[data])
  27. 27. An exercise in visualisation Developing Dynamic Segmentation
  28. 28. The ‘Rubik’s Cube View’ When overlaying Recency, Frequency and Value segmentations on top of one another, you can represent your segmentation as a cube. We control the following variables: • Review period • Layers within metrics • Method of aggregation Value Frequency
  29. 29. Layers within Metrics 4 layers 4 layers 64 segmentsx x = Layers Metrics = Segments 3 metrics 4 layers
  30. 30. Layers within Metrics 4 layers 4 layers 4 layers 64 segmentsx x = Layers Metrics = Segments 3 metrics
  31. 31. Method of Aggregation Maximum Median Mean Minimum Opportunity Risk
  32. 32. Method of Aggregation Maximum Median Mean Minimum Opportunity Risk
  33. 33. Customer Mapping over Time The Rubik’s Cube View can be used to visualise your customers over time: Customer-base Single Customer
  34. 34. Customer Mapping over Time The Rubik’s Cube View can be used to visualise your customers over time: Customer-base Single Customer
  35. 35. Finally, to summarise Developing Dynamic Segmentation
  36. 36. The Data Science Hierarchy of Needs ML, AI Segment, A/B Dashboard, analyse Cleanse, supplement, prepare Infrastructure, ETL, databases Instrumentation, sensors, tracking, user dataCollect Store Transform Analyse Optimise Source: hackernoon.com
  37. 37. Types of Segmentation The 4 most common types are as follows: Source: Geographic • Location (country, county, city) • Climate / weather conditions • Population density • Average distance from store Demographic • Age / gender • Family lifestyle / lifestage • Occupation / income band • Ethnicity / religion / social ‘class’ Psychographic • Opinions / attitudes • Values & beliefs • Price sensitivity • Brand loyalty Behavioural • Time since last interaction • Regularity of interactions • Monetary value of interactions • Recency, Frequency, Value (R-F- V)
  38. 38. Behavioural Segmentation Example Metric Definitions • Recency: # of days since last purchase • Frequency: # of purchases in last month • Value: Total value of purchases in last month R-F-V Segmentation Value Frequency Recency
  39. 39. Hierarchical Organisation Segmentation isn’t always a single tier: Customer Base Gold A 1 2 B 3 Silver C 4 5 6 D 7 E 8 9 Bronze F 10 G 11 12 Build Analyse Communicate
  40. 40. Dynamic Segmentation Requirements Accessible Actionable Differentiable Identifiable Substantial Source: hbr.org
  41. 41. Step-by-Step Process 1 3 4 5 6 7 82Step Decide metrics for use in segmentation Recency, Frequency, Value Different ways of defining each metric Days since last visit, weekday purchases, profit/revenue Decide timeframe for analysis Business context and understanding is important ‘Lookback window’ will vary by organisation Decide on segmentation refresh rate Collect customer data Ensure metrics you wish to use are being collected by the business For larger datasets, take appropriate sample Derive percentiles for each metric Rank data points(remember; Recency is the other way around) Convert into percentiles Look into different ways of doing this Aggregate to higher- level segmentation It can be useful to ‘roll-up’ segments to give a bigger picture Could use minimum, mean, median, maximum etc. Cleanse customer data Apply business logic Treat outliers (removal/restriction) Remove/replace blanks and NULLs with appropriate values Break percentiles into segments Decide on the number of segments desired for each behavioural metric Consider how these segments multiply out when combining with other metrics Refresh / re-run process Decide on segmentation refresh rate Useful to automate as much of the process as possible
  42. 42. Setting the Review Frequency Frequency Justifiability Automation Interpretability Practicality
  43. 43. Layers within Metrics 4 layers 4 layers 64 segmentsx x = Layers Metrics = Segments 3 metrics 4 layers
  44. 44. Method of Aggregation Maximum Median Mean Minimum Opportunity Risk
  45. 45. 72% agree Businesses with 1 segmentation Businesses with 2 segmentations Businesses with 4 segmentations Businesses with 3 segmentations More isn’t always Better Question: Do you agree that implementing segmentation helps you understand your customers better? 79% agree 84% agree 85% agree
  46. 46. “The goal is to turn data into information, and information into insight.” - Carly Fiorina, Former President of Hewlett-Packard
  47. 47. Thank you very much Developing Dynamic Segmentation

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