When  the  fog  clears…

a  simple  truth  about  data  emerges  …

There  are  two  types  of  data…

&

inferred  data observed  data

One  type  of  inferred  data  is  panel  data

Panel  data  use  has  proliferated  in  recent  years

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1980 1985 1990 1995 2000 2005 2010 2015 2020

Panel  D

ata  Stud

ies*

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

Nielsen  Homescan  Panel

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)

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

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

CASE  STUDY Measuring  Retail  Performance

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

No  surprise,  it’s  sales!

But,  retailers  are  reluctant  to  give  up  sales  data

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

Performance  measurement  alternatives  are  within  reach

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

•  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

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

Trip  Driver  Customer  Deciles

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

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

Media  Effectiveness

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      

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