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Mark Jeffery Agile Insights LLC and the Kellogg School of Management [email protected] © Agile Insights LLC, 2012, All Rights reserved
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Mark Jeffery keynote at MassTLC marketing analytics summit

Oct 22, 2014

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Page 1: Mark Jeffery keynote at MassTLC marketing analytics summit

Data-Driven Marketing: Leveraging analytics for 5X performance gains in marketing

Mark Jeffery Agile Insights LLC and the Kellogg School of Management [email protected]

©  Agile  Insights  LLC,  2012,  All  Rights  reserved  

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•  Mo<va<on  •  Research  •  The  15  essen<al  metrics  •  Analy<cs  case  examples  

–  Retail  (B2B),  Manufacturing  (B2B),  Pharmaceu<cal  Sales  (B2B)    

•  ROI  for  analy<c  marke<ng  •  Where  do  you  start?  

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1.  Brand  Awareness    2.  Test  Drive  3.  Churn  4.  CSAT  =  Customer  Sa<sfac<on    

Essen%al  Opera%ons  Metric  

Essen%al  Financial  Metrics  

Essen%al  Value-­‐Based  Marke%ng  Metric  

Essen%al  Non-­‐Financial  Metrics  

10.  CLTV  =  Customer  Life<me  Value  

6.  Profit  7.  NPV  =  Net  Present  Value  8.  IRR  =  Internal  Rate  of  Return    9.  Payback  

5.  Take  Rate  

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Data-Driven Marketing: Leveraging analytics for 5X performance gains in marketing

11.  CPC  =  Cost  Per  Click  12.  TCR  =  Transac<on  Conversion  Rate  13.  ROA  =  Return  on  Ad  dollars  Spent  

Essen%al  Web  Site  Performance  Metric  

Essen%al  Search  Engine  Marke%ng  Metrics  

14.  Bounce  Rate  

15.  WOM  =  Word  of  Mouth     Essen%al  Social  Media  Metric  

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Data-Driven Marketing: Leveraging analytics for 5X performance gains in marketing

•  The  Business  –  Major  US  general  merchandise  retailer  with  over  $30  Billion  

revenues  –  Direct  mail  more  than  250  Million  catalogs  per  year.  Targe<ng  

14  -­‐  18  Million  customers.  Spread  over  18  separate  mailings.    –  Genera<ng  incremental  sales  of  $900  Million  per  year.  

•  Business  Challenge  –  Exis<ng  campaign  selected  the  top  40%  of  customer  

households,    •  Based  on  Recency,  Frequency  &  Monetary  values    •  Geographic  Versioning    

–  The  mailings  generated  significant  sales  volume…  –  However,  the  ROI  was  NOT  mee,ng  the  company's  

expecta,ons  

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•  New  strategy    –  Split  the  targeted  customers  into  25  dis<nct  segments,  based  on  a  robust  

series  of  variables,  ahributes  and  purchase  characteris<cs    –  Version  the  mailers,  based  on  products  &  categories  relevant  to  the  various  

groups  –  Eliminated  strict  cutoff  that  included  only  top  40%  of  customers  (RFM),  but  

instead  captured  "up-­‐sell"  opportuni<es  among  lower  value  customers  with  upside  poten<al  

•  Results  –  Increase  in  mailer  revenue  of  $215  Million  dollars  per  year,  based  on  improved  

targe<ng  and  campaign  management  •  1%  improvement  in  number  of  trips  generated  among  mailed  customers  •  5%  improvement  in  average  purchase  dollars  per  trip  •  2%  improvement  in  gross  margin,  as  "right"  product  featured  captured  sales  without  

reliance  on  'off-­‐price'  promo<ons  

–  Catalog  Targe<ng  project  alone  had  an  NPV  exceeding  $40MM  

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Source:  Russel  Walker,  Kellogg  School  

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•  We  don’t  know  how.    •  We  don’t  have  the  right  metrics.    

•  We  don’t  know  where  to  start.  

•  The  problem  is  not  too  lihle  data;  quite  the  opposite,  we  have  lots  of  data  but  none  of  it  is  useful.  

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•  A  marke<ng  divide  exists  between  organiza<ons  that  do  data-­‐driven  marke<ng,  use  marke<ng  metrics  and  analy<cs,  and  those  that  do  not.  

•  The  15  essen<al  metrics  quan<fy  the  vast  majority  of  marke<ng  ac<vi<es  

•  Research  shows  that  firms  that  ‘keep  score’  for  marke<ng  and  use  analy<cs  have  significantly  beher  financial  and  market  performance  compared  to  those  that  do  not  

•  Measurement  combined  with  analy<cs  drives  5X  or  more  performance  gains  

•  There  is  a  systema<c  approach  to  develop  a  data-­‐driven  marke<ng  strategy:  You  don’t  need  100%  of  the  data  to  get  started  

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