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Understanding Cause and Effect in Customer Behaviour
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Understanding Cause and Effect in Customer Behaviour

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A talk I gave at the Data Science London meetup on April 23, about how we at Causata use a combination of intelligent data structure, machine learning, and regulated experimentation to understand ...

A talk I gave at the Data Science London meetup on April 23, about how we at Causata use a combination of intelligent data structure, machine learning, and regulated experimentation to understand cause-and-effect in customer behavior

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Understanding Cause and Effect in Customer Behaviour Understanding Cause and Effect in Customer Behaviour Presentation Transcript

  • Understanding  Cause  &  Effect   in  Customer  Behaviour   Jason  McFall,  CTO   @jasonmcfall  
  • It’s  essen7al  to  connect  data  across  all  the  channels,  into  a  full  view  of  the  customer  
  • browser   cookie   online  browsing   delivery   address   credit   online  purchase   card   loyalty   card   store  purchase  store  purchase   store  purchase   Connec7ng  this  data  is  hard  –   there’s  ambiguity  
  • Some  organiza7ons  are  comfortable  with  some  uncertainty  about  user  iden7ty  
  • While  others  require  absolute  certainty  
  • Retailers  know  from  surveys  that  customers    oJen  research  online  and  buy  offline.  But  unless  they  connect  individuals’  ac7vity,  it’s  hard  to  aNribute  marke7ng  influence  
  • Arrow  of  >me  Website   Call  Center   Website   Loyalty  Card   Website   Loyalty  Card   Major  Product    Session   Ques7on   Session   Promo  Email   Session   Sign  Up   Purchase  in  Store    
  • !me  filter  customer  journeys  
  • before   aAer   rela!ve  !me   !me   customer  journeys   align  
  • scenario  record   7me   7me   interac7on  of  interest  
  • build  models  
  • experiment    put  some  science  into    data  science  
  • vs  The  simplest  form  of  experimenta7on  is  A/B  tes7ng   conversion  rate  
  • bandit  algorithms  
  • reinforcement  learning   Causata  must  choose  the  ac7ons  which  will  yield  the  greatest  reward,   where  reward  can  be  any  func7on  we  wish  to  op7mize,  and  the  rewards   may  be  deferred  to  some  7me  in  the  future   ac>ons   rewards   learning  agent   environment  
  • learn  a  complex  tree  
  • Visit  website  from   Research   Request   Speak  to  advisor   Sign   Ongoing   online  banking   credit  cards   applica7on  form   in  branch   agreement   rela7onship  Current  state  of  the  art  is  to  locally  op7mize  each  interac7on,  in  terms  of  immediate  next  step  With  all  the  data,  can  op7mize  over  true  long  term  business  goals  
  • Have  to  SCALE  
  • Have  to  be  REAL-­‐TIME  
  • We’re  working  in  very  exci>ng  >mes    Connected  datasets…  handle  ambiguity  Intelligent  decisions  with  machine  learning  Principled  experimenta>on  At  massive  scale  In  real  >me  
  • www.causata.com/careers