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Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
Beneath the Surface: The Hidden World of Individual Buying Dynamics
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Beneath the Surface: The Hidden World of Individual Buying Dynamics

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This deck describes how people actually buy brands based on an analysis of several million purchase events over a period of 5 years across three different categories in the United Kingdom. …

This deck describes how people actually buy brands based on an analysis of several million purchase events over a period of 5 years across three different categories in the United Kingdom.

It was presented at the ESOMAR Congress 2012 conference in Atlanta, USA in September 2012.

I'm particularly proud of the illustrations in the presentation... although the fundamental nature of the research is pretty cool too :)

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  • 1. Kyle Findlay Constantin Michael Jan HofmeyrSenior R&D Executive Modelling Consultant Chief Research Officer
  • 2. Awareness metrics Usage metrics Brand strength metrics 0.96 0.94 0.96 0.95 0.95 0.86 0.87 0.86 0.84 0.73 0.56 0.51 0.51 0.48 0.38 0.51 0.25 0.19 0.21 0.22 0.12 0.14 Aided Other spontaneous Familiarity First mention Stated P3M/P6M Most often Constant sum Recommendation Satisfaction Purchase intention "Only one Id ever (Next 10) buy" Survey metric correlations with actual purchasing behaviour in subsequent 12 Aggregate level Respondent level months at a respondent (blue) and aggregate level (green). 6 datasets | Countries: USA, UK, China Categories: retailers, laundry detergentsTraditional metrics break down at an individual level
  • 3. Said that they wereSurvey daily drinkers Diary  Said drink daily  Said drink daily Said drink daily  Actually drink daily   Actually drink daily Actually drink daily Macro-Louw, A, Withington, V & Jansen, A. 2005. Self-reported Behavior: Fact or Fiction, paperpresented at the 2005 South African Marketing Research Association conference validity, micro-
  • 4. The ChaosBeneath the Surface
  • 5. Survey-derived UK laundry dual usage network demonstrating the amount of movement between brands ‘beneath the surface”. There is much more inter-brand usage than many marketers expect. How to read: node size represents brand penetration (% users in past 12 months); thickness of connecting lines represents % dual users of the two brands in past 12 monthsBuying behaviour is surprisingly complex
  • 6. 3 People are 1 surprisingly mobile 1 A two year purchase stream of an individual panelist buying chocolate 2Source: Hofmeyr & Bongers. 2010. A Critique of the Law ofDouble Jeopardy: Why Modeling Averages isn’t Good Enough 3
  • 7. Time period 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 Shampoo N/A 24,418 70,745 74,938 N/A Number of purchases Laundry N/A 537,521 210,948 202,320 N/A Soft drinks N/A 1,413,235 3,849,787 1,724,778 N/A Summary of number of active panellists and purchase instances in each year. Note: 2006 and 2010 data was used in defining active panellists in some cases and so was not analysed directly . Source: KWPMillions of transactions...
  • 8. Laundry detergent Soft drinks 160,000 180,000 140,000 160,000 140,000 120,000 120,000 100,000 100,000 80,000 80,000 60,000 60,000 40,000 40,000 20,000 20,000 0 0 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 1 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Shampoo 25,000 20,000Frequency distributions of repeat purchase cycles. Repeat purchase cycle lengthSource: KWP 15,000 10,000 5,000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
  • 9. 2008 SOW 0 SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100 2007 SOW 0 99.6 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SOW 10 78.0 10.4 7.5 2.1 1.0 0.3 0.3 0.1 0.0 0.0 0.1 SOW 20 75.7 5.4 8.6 4.9 2.1 1.1 1.0 0.5 0.3 0.2 0.3 SOW 30 68.4 4.2 9.7 6.9 3.4 1.7 2.6 1.4 0.4 0.3 1.0 SOW 40 62.8 3.3 7.7 7.5 6.7 3.1 3.2 2.5 1.1 0.6 1.7 SOW 50 47.1 2.7 9.3 9.5 6.8 5.4 7.3 2.9 2.9 2.4 3.7 SOW 60 56.2 1.3 5.4 8.0 5.4 4.0 7.3 4.1 1.9 2.6 4.0 SOW 70 43.6 2.0 6.5 8.8 3.9 4.9 9.1 7.5 4.6 1.6 7.5 SOW 80 31.0 2.3 7.8 10.1 6.2 4.7 9.3 8.5 6.2 4.7 9.3 SOW 90 17.0 0.0 4.3 5.3 5.3 9.6 13.8 7.4 6.4 12.8 18.1 SOW 100 44.7 0.2 2.7 4.5 4.0 1.1 8.1 4.5 3.8 4.5 21.8 Transition matrix for shampoo in the UK showing how people changed their share of wallet (SOW) from 2007 to 2008. Source: KWPHow do people really behave?
  • 10. How DoPeople Really Behave?
  • 11. Shampoo (2008 to 2009) Laundry (2008 to 2009) Soft drinks (2008 to 2009) 35 23 28 21 21 17 17 17 15 14 16 14 11 13 14 13 9 7 10 10 10 12 12 6 5 4 5 6 5 6 10% SOW 100% 10% SOW 100% 10% SOW 100% % steady Proportion of panellists that gave a brand the same SOW two years in a row. These patterns are consistent across all years. Source: KWPWhat proportion of people spend the sameafter a year?
  • 12. Shampoo Laundry Soft drinks 31 28 28 51 50 49 59 55 56 69 72 72 49 50 51 41 45 44 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 % users that stay % transient users Proportion of people that leave a brand after one year (defectors) versus those that stay with the brand (non-defectors). Source: KWPWhat proportion of people stay with a brandafter a year?
  • 13. Shampoo (2007 to 2008) Laundry (2007 to 2008) Soft drinks (2007 to 2008) 78 75 67 60 58 57 46 53 39 42 30 29 26 14 19 20 14 15 17 12 11 6 4 6 8 8 6 4 3 7 10% SOW 100% 10% SOW 100% 10% SOW 100% % defectors Proportion of each SOW group that stopped using the brand after a year. Source: KWPDefection rates by share of wallet
  • 14. Shampoo (2007 to 2008) Laundry (2007 to 2008) Soft drinks (2007 to 2008) 75 73 70 60 56 57 56 46 38 41 22 14 27 30 27 21 14 18 19 13 12 5 7 8 10 10 6 5 4 9 10% SOW 100% 10% SOW 100% 10% SOW 100% % new users Proportion of each SOW group represented by new users since last year. Source: KWPNew users by share of wallet
  • 15. 30 25 20 Year 1: 15 SOW 10 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 16. 30 25 20 Year 1: 15 SOW 20 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 17. 30 25 20 Year 1: 15 SOW 30 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 18. 30 25 20 Year 1: 15 SOW 40 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 19. 30 25 20 Year 1: 15 SOW 50 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 20. 30 25 20 Year 1: 15 SOW 60 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 21. 30 25 20 Year 1: 15 SOW 70 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 22. 30 25 20 Year 1: 15 SOW 80 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 23. 30 25 20 Year 1: 15 SOW 90 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 24. 30 25 20 Year 1: 15 SOW 100 10 5 0 Year 2: SOW 10 SOW 20 SOW 30 SOW 40 SOW 50 SOW 60 SOW 70 SOW 80 SOW 90 SOW 100Active panelists x Share of Wallet (SOW)UK shampoo 2007-2008. Source: KWP
  • 25. Where’s the Value?
  • 26. Shampoo (2008 to 2009) Laundry (2008 to 2009) Soft drinks (2008 to 2009) 31 21 19 19 16 14 14 11 10 11 11 12 12 10 6 7 9 9 8 7 8 3 3 5 6 6 5 3 2 1 10% SOW 100% 10% SOW 100% 10% SOW 100% % spend % of users (incidence) Proportion spend that comes from each SOW group. Source: KWPSpend by share of wallet
  • 27. Shampoo Laundry Soft drinks 22 21 26 26 24 18 12 12 12 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 % value from loyal customers Proportion of spend that comes from customers who give a brand 70-100% share of wallet in year 1. Source: KWPValue from loyal customers
  • 28. Shampoo Laundry Soft drinks 53 58 58 19 24 23 19 19 17 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 2007-2008 2008-2009 2009-2010 % value from transient customers Proportion of spend that comes from customers who stop using the brand from one year to the next. Source: KWPValue from transient customers
  • 29. Managingthe Flows
  • 30. Surf (laundry) Defection Slide down New users Slide up Year 1 -21% -16% 100% +29% +24% Year 2 115% 30% Defection Slide down New users Slide up Year 2 -21% -18% 100% +38% +22% Year 3 121% NOTE: Unweighted data might not accurately represent real-world brand movements. Source: KWPManaging the flows
  • 31. Persil (laundry) Defection Slide down New users Slide up Year 1 -19% -18% 100% +22% +15% Year 2 100% 20% Defection Slide down New users Slide up Year 2 -17% -14% 100% +20% +16% Year 3 103% NOTE: Unweighted data might not accurately represent real-world brand movements. Source: KWPManaging the flows
  • 32. Head & Shoulders (shampoo) Defection Slide down New users Slide up Year 1 -20% -16% 100% +23% +8% Year 2 94% 20% 70% Defection Slide down New users Slide up Year 2 -22% -13% 100% 21% +14% Year 3 100% NOTE: Unweighted data might not accurately represent real-world brand movements. Source: KWPManaging the flows
  • 33. Source: Coyles, S & Gokey, TC. 2002. Customer Retention is Not Enough, The McKinsey Quarterly 2002 No.2What about other categories?
  • 34. What Does It All Mean?
  • 35. Measures mustcorrelate at anaggregate ANDrespondent level
  • 36. Markets aredynamic. Mostpeople will changetheir spend overtime
  • 37. Manycustomersare transient
  • 38. Brands lose andgain fractions ofa person, not awhole person.
  • 39. Managethe flows
  • 40. “ …many very little living animalcules, very prettily a-moving ” ~ Anton van Leeuwenhoek, 1693 Thank you!

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