Fusing Marketing Research and Data Science by John Colias at @DecisionAnalyst

Live note-taking of the November 9, 2016 webinar. Any errors are my own.

  • Survey insights have been overshadowed in recent years, market research is struggling to redefine itself, there is an opportunity to combine big data and surveys
  • Preferences are not always observable in big data, includes social data, wearable data
  • Surveys can measure attitudes, preferences, and perceptions
  • Problem is organizational – isolation, compartmentalization of market research and big data functions
  • Started with a primary research survey about health and nutrition, one question is how often do you consume organic foods and beverages; also had block-group census data from American community survey five-year summary data with thousands of variables
  • Fused survey data and block group data using location of survey respondent from their address and matched to block group data, brought in geo data for that block group
  • Randomly split the data, built predictive model on training model, determined predictive accuracy using validation data (the hold-out data), 70% of data for model development, 30% for validation – independent objective model
  • Created a lift curve, predictive model identified consumers of organic foods more than 2.5 times better than chance
  • When predictive models bows out from random model, you have achieved success
  • Which variables were most predictive, not that they’re correlated but they predict behaviour – 26 or older, higher income, higher education, less likely Hispanic; this may be known but now they have a model to predict where these people are
  • Can map against actual geography and plot distances to stores
  • High-tech truck research
  • Used a choice modeling survey, design attributes and models to test, develop customer level preferences for features of the truck
  • Cargo space, hitching method, back up cameras, power outlets, load capacity, price
  • People chose preferred model from two choices, determined which people are price sensitive, or who value carrying capacity, biggest needs were price, capacity, and load
  • How to target to these groups of people
  • Fused in external data like previously, but now predicting based on choice modeling not based on survey attitudes, lift curve was again bowed to left, 1.8 times better than chance – occupation, education, income, and household size were the best predictors
  • [these are generic results – rich people want organic food and trucks, but point taken on the method. If there is a product whose users are not obvious, then this method would be useful]
  • Fusion can use primary and secondary data, also fuses technology like R dashboards and google maps, fuses survey and modeling, fuses consumer insights database marketing and big data analytics
  • Use this to find customers whose preferences are unobserved, improve targeting of advertising and promotions, optimize retail location strategies, predict preferences and perceptions of consumers, collaboration of MR departments with big data groups would benefit both entities
  • In UK and Spain, demographics are more granular, GPS tracking can be used in lesser developed countries
  • Used R to query public data set, beauty of open-source code and data
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