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Design of multichannel attribution model using click stream data

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slides from MeasureCamp 2015

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Design of multichannel attribution model using click stream data

  1. 1. Design of multichannel attribution model using click- stream data MeasureCamp Prague 2015 Lucie Šperková
  2. 2. Everything you need to know (about me) I used to work in bank. The only language I can use is SQL. I have never worked directly with GA, just extract the data from it. Somebody said: “Without data you are just a person with an opinion” I say in addition: “… but with data, which are messy nad shitty, you are a clear liar.”
  3. 3. Data overload? Lack of data -> incomplete decisions Too much data -> overload and still lack of knowledge (What I should focus on?!) Basement / garage problem I store big volume of data just for case, but will probably never use it. -> Ask yourself why you will need them (have a target)
  4. 4. Why? costs and revenues expenses and benefits income and spending profit and loss customer loyalty/satisfaction
  5. 5. Target Create exponential model that takes into account all the inputs into the conversion funnel. With the use of AdForm metadata: for every cookie (user) on the particular trackingpoint calculate number of interactions for the particular time period and assign weights to campaign channels.
  6. 6. What I do / will do with the data... - calculation of the weights and share of channels in conversions - budgeting the total cost to the individual channels according their share - visualize the shares of the channels - drill down the channels - to medium, campaign,... - slice according to refferer type, device type, customer segments … - find the right campaign mixture (how to achieve particular number of conversions for the lowest price) - prediction of the future development and setting the right campaign mixture - observe the conversional / non-conversional rates (how many interactions didn’t lead to conversion) - intregration of data from other sources (GA, sklik, CRM, budgets, etc.) - revenues from conversions - customers data - ...
  7. 7. seen the banner 1 seen the banner 2click PR click PPC click Organic click banner1 Web - Conversion 1point 1point2points 2points2points 3 points Weights assigned according to: basic division: conversion click (triggered the trackingpoint) last impression (triggered the trackingpoint) direct entry click impression refining the weights: ● by mouse overs, mouse over time, visibility time, refferer type, medium etc. ● on the web there are many trackingpoints cookie has visited (not interested about the move through websites) ● focus on conversion points or points foregoing conversions (e.g. where customer left the action)
  8. 8. Trackingpoint A
  9. 9. Trackingpoint B
  10. 10. Trackingpoint C
  11. 11. Trackingpoint D
  12. 12. Trackingpoint E conversion
  13. 13. metadata calculations extract extract transform
  14. 14. Process of basic transformation data cleaning - delete robotic transactions - transactions, which happened in less than 30 minutes from the last transaction (same cookie, same trackingpoint, same session) - avoid refresh joins - for every cookie at the trackingpoint find all interactions which happened during the time between trigger of the last trackingpoint and today’s trackingoint (for more conversions of single cookie) - every cookie can have interaction with different campaign: calculation for every campaign (avoid multipletimes counting of the same add - banners etc) - the campaign of the conversion interaction is known (higher weight) weights calculation and refining
  15. 15. !
  16. 16. Predictions costs conversions (revenues) more investments to this campaign mix won’t help right campaign mix for acceptable price 100 300 330
  17. 17. Thanks. Let’s talk! mail: lucie.sperkova@gmail.com linkedin: https://cz.linkedin.com/in/luciesperkova twitter: https://twitter.com/pihatka