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Kyle Findlay           Constantin Michael     Jan Hofmeyr
Senior R&D Executive   Modelling Consultant   Chief Research Officer
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
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-
The Chaos
Beneath the
    Surface
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
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
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...
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
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?
How Do
People Really
    Behave?
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?
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?
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
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
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
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
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
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
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
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
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
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
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
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
Where’s the
    Value?
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
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
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
Managing
the Flows
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
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
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
Source: Coyles, S & Gokey, TC. 2002. Customer Retention
                               is Not Enough, The McKinsey Quarterly 2002 No.2




What about other categories?
What Does It
  All Mean?
Measures must
correlate at an
aggregate AND
respondent level
Markets are
dynamic. Most
people will change
their spend over
time
Many
customers
are transient
Brands lose and
gain fractions of
a person, not a
whole person.
Manage
the flows
“        …many very little living
    animalcules, very prettily a-moving



                                                     ”
                     ~ Anton van Leeuwenhoek, 1693




                   Thank you!

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Beneath the Surface: The Hidden World of Individual Buying Dynamics

  • 1.
  • 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-
  • 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?
  • 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
  • 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
  • 39. Brands lose and gain fractions of a person, not a whole person.
  • 41. …many very little living animalcules, very prettily a-moving ” ~ Anton van Leeuwenhoek, 1693 Thank you!