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Page 1: The real mc coy 8

When  the  fog  clears…

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a  simple  truth  about  data  emerges  …

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There  are  two  types  of  data…

&

inferred  data observed  data

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One  type  of  inferred  data  is  panel  data

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Panel  data  use  has  proliferated  in  recent  years

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Panel  D

ata  Stud

ies*

*Social  Sciences  Citation  Index  1986,  2004,  and  2005  –  keywords  “panel  data”  or  “longitudinal  data”  

Nielsen  Homescan  Panel

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Panel  data  may  be  the  best  option

Panel  data is  suited  to  large  longitudinal  studies

United  States •  National  Longitudinal  Surveys  of  Labor  Market  

Experience  (NLS) •  University  of  Michigan’s  Panel  Study  (MPS)

Europe •  Netherlands  Socio-­‐‑Economic  Panel  (SEP) •  German  Social  Economics  Panel  (GSOEP) •  Luxembourg  Social  Panel  (PSELL) •  British  Household  Panel  Survey  (BHS)

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Warning:  Panel  data  have  limitations

•  Panel  membership  may  not  be  representative •  Breaks  down  at  lower  levels  of  aggregation •  Low  sample  size  issues  are  common •  Modeling  accentuates  bias •  Panel  members  may  report  wrong  data •  Not  timely •  Costly

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As  noted  by  independent  reviews

Nielsen  Homescan  Data “The  different  data  give  different  results.  Out  of  the  20  slope  parameters,  5  have  different  signs,  9  do  not  agree  on  their  statistical  significance,  and  13  are  statistically  different.” USDA  On  the  Accuracy  of  Nielsen  Homescan  Data

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CASE  STUDY Measuring  Retail  Performance

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What  is  the  best  source  for  measuring  a  retailer’s  performance?

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No  surprise,  it’s  sales!

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But,  retailers  are  reluctant  to  give  up  sales  data

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Don’t  despair,  there’s  another  way  to  measure  performance  

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Performance  measurement  alternatives  are  within  reach

Foot  traffic  and  distance  travelled  are  observable  events  that  can  be  measured  and  analyzed

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•  Deliver  timely  and  relevant  marketing  offers  by  offering  location-­‐‑based  ad  triggers  

•  Improve  decisions  by  an  improved  understanding  of  customers  using  time  and  location  analytics  

•  Rethink  competitive  strategy  based  on  competitor  sets  derived  from  actual  visitations

•  Enhance  financial  reporting  by  benchmarking  competitor  performance

Benefits  of  visitation  data

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Performance  management  redefined

•  Place  Visit  Rates  –  PVR •  Total  site  visits •  Visits  by  distance  travelled •  Visits  by  time  of  day •  Days  since  last  visit •  Visits  to  top  competitors •  Profile  of  best  customers •  Market  share  by  territory •  Competitive  benchmarking •  Rank  of  top  pre-­‐‑visit  locations

Performance  Management  2.0

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Trip  Driver  Customer  Deciles

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Trip  Driver  Model

•  The  trip  driver  model  predicts  future  visitations  based  on  historical  visitations,  average  distance  traveled,  demographic  acributes,  and  other  explanatory  variables

•  Methodologies  considered  include:  generalized  linear  models  (GLM),  random-­‐‑forest,  and  other  classification  models

•  The  model  is  validated  on  a  hold-­‐‑out  sample  as  well  as  a  test-­‐‑sample  using  rigorous  standards  to  ensure  model  robustness  and  conformity  to  modeling  assumptions

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The  model’s  score  is  grouped  into  deciles  to  highlight  differences  in  customer  behavior  

Trip  Driver  Model  A.ributes   1   2   3   4   5   6   7   8   9   10  

Visits  per  Month   60%  of  Visits 40%  of  Visits

Distance  Traveled  (Miles) <  3 3  -­‐‑  5 >  15

Age 25-­‐‑35 35-­‐‑45 45-­‐‑55

Household  Income   $85K  to  $120K $65K  to  $120K

Grocery  Visits  per  Month <  1.8 >  1.8

Visits  to  Top  3  Competitors  per  Month <  2x 2  –  4x >  4x

60%  of  visits  come  from  30%  of  customers

Distance  macers

TRIP  DRIVER CUSTOMER  DECILES      

Best  customers  

skew  younger Best  

customers  skew  affluent

Best  customers  are  not  active  

grocery  shoppers Best  

customers  are  loyal

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Media  Effectiveness

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Customer  Deciles  is  overlaid  onto  PVR  lift  to  identify  marketing  opportunities

1 2 3 4 5 6 7 8 9 10

Exposed  to  Ad  in  Period  1  &  Converted  in  Period  2

Exposed  to  Ad  in  Period  1  &  Did  Not  Convert  in  Period  2

Not  Exposed  to  Ad  in  Period  1  &  Converted  in  Period  2

Not  Exposed  to  Ad  in  Period  1  &  Did  Not  Convert  in  Period  2

PVR  Lift

Exposed  PVR

Control  PVR

TRIP  DRIVER CUSTOMER  DECILES      

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For  example

CUSTOMER)DECILES)))-

1" 2" 3" 4" 5" 6" 7" 8" 9" 10"

Exposed(to(Ad(in(Period(1(&(Converted(in(Period(24

Exposed(to(Ad(in(Period(1(&(Did(Not(Convert(in(Period(24

Not(Exposed(to(Ad(in(Period(1(&(Converted(in(Period(24

Not(Exposed(to(Ad(in(Period(1(&(Did(Not(Convert(in(Period(24

PVR(Lift4

Exposed(PVR4

Control(PVR4