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Lucie Sperkova - Pioneering multi-channel attribution for the lack of comprehensive solutions

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Marketing Festival 2016

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Lucie Sperkova - Pioneering multi-channel attribution for the lack of comprehensive solutions

  1. 1. Why do we attribute (1838 - 1922)
  2. 2. Why do we attribute Budget Allocation Media Plan Channel Performance and Value Customer Journeys Data Source: Google Analysis <Marketing Attribution: Valuing the Customer Journey>
  3. 3. Separate silos SEARCH AD BUDGET SEARCH CLICKS & IMPRESSIONS SEARCH CONVERSIONS DISPLAY AD BUDGET DISPLAY CLICKS & IMPRESSIONS DISPLAY CONVERSIONS PROGRAMMATICPPC SOCIAL AD BUDGET SOCIAL SOCIAL CLICKS & IMPRESSIONS SOCIAL CONVERSIONS
  4. 4. Uniform data ADVERTISING BUDGET CLICKS & IMPRESSIONS CONVERSIONS PROGRAMMATICPPC SOCIAL
  5. 5. Why own solution? all impressions full browsing history paths which did not make conversion cross-device paths in their whole length (Google cuts them to 4 channels) sophisticated methods CRM data
  6. 6. Last-click (heuristic) problem more information in: John Murphy, 2014
  7. 7. Last-click (heuristic) problem
  8. 8. Last-click (heuristic) problem
  9. 9. First-click (heuristic) problem
  10. 10. Display campaigns matter
  11. 11. Channel dynamics
  12. 12. Customer behaviour (consumer funnel) matter Was the conversion caused by this channel?
  13. 13. logistic regression models (Shao & Li 2011; Klapdor 2013) Data-driven models
  14. 14. logistic regression models (Shao & Li 2011; Klapdor 2013) game theory-based models (Berman, 2015; Dalessandroet al. 2012) Bayesian models (Li & Kannan 2014; Nottorf 2014) mutually exciting point process models (Xu, Duan, & Whinston, 2014) hidden Markov models (Abhishek, Fader, & Hosanagar 2015; Anderl et al. 2014) Data-driven models
  15. 15. logistic regression models (Shao & Li 2011; Klapdor 2013) game theory-based models (Berman, 2015; Dalessandroet al. 2012) Bayesian models (Li & Kannan 2014; Nottorf 2014) mutually exciting point process models (Xu, Duan, & Whinston, 2014) hidden Markov models (Abhishek, Fader, & Hosanagar 2015; Anderl et al. 2014) VAR models (Kireyev, Pauwels, & Gupta 2016) multivariate time-serie models (Anderl et al. 2015) survival models Data-driven models
  16. 16. Simple Probabilistic Method Shao and Li, 2011 Shapley Value Aspa Lekka, 2014 Hidden Markov Model Anderl et al., 2014 Science behind the models
  17. 17. Criteria / Model Heuristic Simple probabilistic Shapley value Markov Objectivity and fairness No Yes Yes Yes Predictive accuracy No Partly - Yes Carryover and spillover effects No Partly Yes Yes Data-driven No Yes Yes Yes Interpretability Yes Yes Partly Partly Customers’ heterogeneity No Partly Partly Yes Robustness No Partly - Yes Algorithm efficiency Yes Satisfactory for lower orders No Satisfactory for lower orders Versatility Yes Yes Yes Yes
  18. 18. Criteria / Model Heuristic Simple probabilistic Shapley value Markov Objectivity and fairness No Yes Yes Yes Predictive accuracy No Partly Yes Yes Carryover and spillover effects No Partly Yes Yes Data-driven No Yes Yes Yes Interpretability Yes Yes Partly Partly Customers’ heterogeneity No Partly Partly Yes Robustness No Partly Yes Yes Algorithm efficiency Yes Satisfactory for lower orders No Satisfactory for lower orders Versatility Yes Yes Yes Yes
  19. 19. “We have no place to grow; PPC campaigns has used up its potential.” “Effective revenue share is smaller than was the goal so that we could spend more money, but it was not where to spend… We put more money to Google in Slovakia market, and ERS got even cheaper.” How to get from last-click trap
  20. 20. Methodology Our clients are heterogeneous, but we have to be able to maintain uniform solution. Data collection Data pre–processing Run models Budget reallocation Results testing and validation Descriptive analysis Data cleaning Data selection Paths reconstruction
  21. 21. Technology:
  22. 22. Data Collection Data collection all raw data including all clicks, impressions, web entrances Data granularity channel - campaign - media - placement Channels free channels are taken into account
  23. 23. Data preparation: 80% success Data cleaning exclude robotic transactions exclude disabled cookies exclude not visible impressions exclude repeated actualisations of websites combine impressions in 30-minute interval Transformation to journeys non-conversion taken in account exclude paths longer than treshold Data: > 1,5 TB Rows: > 3,2 billions
  24. 24. Consecutive impressions visualisation: Crossmasters
  25. 25. Consecutive impressions visualisation: Crossmasters
  26. 26. Consecutive impressions visualisation: Crossmasters
  27. 27. Modelling Period analysed monthly basis Time window 1 month
  28. 28. Reporting CPA ROAS (%) channel cost number of channel conversions channel weight channel cost weight ROAS > 100 % channel is undervalued channel cost weight = channel cost sum of all cost Proposed Budget actual budget * ROAS= = =
  29. 29. Return of advertising spends (ROAS) channel weight channel cost weight channel cost first-click last-click linear-touch shapley value simple probabilistic markov SKLIK 193 000 CZK 162 % 194 % 165 % 147 % 193 % 35 % RTB 13 000 CZK 110 % 70 % 90 % 70 % 60 % 189 % RTB 2 10 000 CZK 1057 % 319 % 663 % 561 % 369 % 14 %
  30. 30. Proposed Budget = actual budget * ROAS channel cost first-click last-click linear-touch shapley value simple probabilistic markov SKLIK 193 000 CZK 313 000 CZK 375 000 CZK 320 000 CZK 284 000 CZK 373 000 CZK 68 000 CZK RTB 13 000 CZK 15 000 CZK 9 000 CZK 12 000 CZK 10 000 CZK 8 000 CZK 25 000 CZK RTB 2 10 000 CZK 108 000 CZK 33 000 CZK 69 000 CZK 57 000 CZK 38 000 CZK 148 000 CZK ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... SUM 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK
  31. 31. Proposed Budget = actual budget * ROAS channel cost first-click last-click linear-touch shapley value simple probabilistic markov SKLIK 193 000 CZK 313 000 CZK 375 000 CZK 320 000 CZK 284 000 CZK 373 000 CZK 68 000 CZK RTB 13 000 CZK 15 000 CZK 9 000 CZK 12 000 CZK 10 000 CZK 8 000 CZK 25 000 CZK RTB 2 10 000 CZK 108 000 CZK 33 000 CZK 69 000 CZK 57 000 CZK 38 000 CZK 148 000 CZK ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... SUM 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK
  32. 32. Budget optimalization is an iterative process budget shiftbudget shift The optimal budget is reached when a channel reaches its maximum conversion.
  33. 33. Customer Journeys RTB212636 RTB2157216 SEARCH SKL2079081 RTB2125178 DIR visualisation: Crossmasters
  34. 34. RTB and Display drive PPC and Search conversion rate remained 24 % CPA remained 0,019 CZK 2x more conversions 2,5x conversion value
  35. 35. Conclusion: last-click is a barrier of any growth Data-driven attribution has sense with channels which shift customer in consumer funnel Data-driven attribution gives immediate answers we couldn’t otherwise measure High technology costs will return The results are visible after some time (the need of enough data!) Different marketing mix needs different model scalability all data at one place ad-hoc reporting transparency
  36. 36. At the end it’s a human job “THE ONLY SOURCE OF KNOWLEDGE IS AN EXPERIENCE.” ALBERT EINSTEIN (1879 - 1955)

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