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 :)

2. Kyle Findlay Constantin Michael Jan Hofmeyr
Senior R&D Executive Modelling Consultant Chief Research Officer

3. 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 I'd 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 detergents
Traditional metrics break down at an individual level

4. Said that they were
Survey
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, paper
presented at the 2005 South African Marketing Research Association conference
validity, micro-

5. The Chaos
Beneath the
Surface

6. 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 months
Buying behaviour is surprisingly complex

7. 3
People are
1
surprisingly
mobile 1
A two year purchase stream of an individual
panelist buying chocolate
2
Source: Hofmeyr & Bongers. 2010. A Critique of the Law of
Double Jeopardy: Why Modeling Averages isn’t Good Enough 3

8. 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: KWP
Millions of transactions...

9. 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
20,000
Frequency distributions of repeat purchase cycles. Repeat purchase cycle length
Source: 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

10. 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: KWP
How do people really behave?

11. How Do
People Really
Behave?

12. 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: KWP
What proportion of people spend the same
after a year?

13. 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: KWP
What proportion of people stay with a brand
after a year?

14. 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: KWP
Defection rates by share of wallet

15. 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: KWP
New users by share of wallet

16. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

17. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

18. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

19. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

20. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

21. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

22. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

23. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

24. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

25. 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 100
Active panelists x Share of Wallet (SOW)
UK shampoo 2007-2008. Source: KWP

26. Where’s the
Value?

27. 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: KWP
Spend by share of wallet

28. 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: KWP
Value from loyal customers

29. 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: KWP
Value from transient customers

30. Managing
the Flows

31. 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: KWP
Managing the flows

32. 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: KWP
Managing the flows

33. 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: KWP
Managing the flows

34. Source: Coyles, S & Gokey, TC. 2002. Customer Retention
is Not Enough, The McKinsey Quarterly 2002 No.2
What about other categories?

35. What Does It
All Mean?

36. Measures must
correlate at an
aggregate AND
respondent level

37. Markets are
dynamic. Most
people will change
their spend over
time

38. Many
customers
are transient

39. Brands lose and
gain fractions of
a person, not a
whole person.

40. Manage
the flows

41. “ …many very little living
animalcules, very prettily a-moving
”
~ Anton van Leeuwenhoek, 1693
Thank you!