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Performance 2010 KLM Frank Pon


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Performance 2010 KLM Frank Pon

  1. 1. KLM - Emerce PBA Cross channel attribution past the last click How to redistribute performance “weight” over media? Frank Pon December 2nd
  2. 2. KLM Promotions Team Goals
  3. 3. Steer customers via most (cost) effective e-acquisition channel Social media Meta Search E-mail Banners Affiliates Search engines
  4. 4. … but more importantly, approach/philosophy … <ul><ul><li>Goal: pure conversions, sales, revenue </li></ul></ul><ul><ul><ul><li>Primarily tickets </li></ul></ul></ul><ul><ul><li>Central approach, (mostly) central execution and budget </li></ul></ul><ul><ul><ul><li>Close cooperation with local establishments and budgets </li></ul></ul></ul><ul><ul><ul><li>Central expertise centres for advice and execution </li></ul></ul></ul><ul><ul><li>Optimisation across channels & countries </li></ul></ul><ul><ul><ul><li>High impact: central Affiliation activities in limited countries, E-mail 70 countries, etc </li></ul></ul></ul>
  5. 5. <ul><li>KLM Promotions Team Organization </li></ul>
  6. 6. KLM Promotions Team working together
  7. 7. KLM Promotions Team Results
  8. 8. Acquisition results: share For first 6 months 2010 on average 66% of all KLM bookings is attributable to one of the acquisition channels <ul><li>Source: DART, KLM only, apr – aug 10, fully de-duplicated on last click basis Top 13 </li></ul><ul><li>NOTE: Actual acquisition share is expected to be considerably higher: </li></ul><ul><li>Only structural acquisition activities are included: bookings resulting from campaigns (e.g. world Deals) are not included </li></ul><ul><li>Tracking problems with email since EBT7 (May 09) -> share likely higher. </li></ul>
  9. 9. E-acquisition share growth *Top 13 results
  10. 10. But how to get from ?
  11. 11. The «last touch» model, default attribution rules The last exposure before conversion gets the full credit, with clicks taking priority over impressions In other words, no matter how many different ads a consumer has previously been exposed to, the last click before purchase is attributed 100% of the revenue
  12. 13. Analysis of pre-booking behavior (30 days) *Display view (14 days) unless click
  13. 14. Dig Deeper
  14. 15. Considering touchpoints prior to last touch? % of touchpoints in the last 14 days prior to conversion /visit ignored in the “last touch” approach 1 st elements in 2009 showed a potential double digit gain in ROI through reallocation of budgets across channels and countries KLM-Artemis 2010, Feb. 01 – Apr.15
  15. 16. Towards a KLM proprietary attribution model <ul><li>Overall objectives </li></ul><ul><li>Defining methodological guidelines, implementation rules </li></ul><ul><ul><li>to attribute more “correctly” conversion credit to all channels analyzing cross-channel tracking data, </li></ul></ul><ul><ul><li>eventually, to create the framework for an optimization of holistic online strategy including investment allocation across channels </li></ul></ul>
  16. 17. Adding up an attribution model to include all ROI drivers
  17. 18. Modeling test period, input variables selection KLM data base 2010 Feb 01-Apr 15th 10+ millions cookies Sampling 176,461 cookies with a tagged activity on KLM web site @ each step of the EBT 1 Clustering the digital “journeys” Operational segments according to the outcome Analysis to determine key criteria to measure “assisting power” for each channel 2 Statistical modeling of individual paths and overall –aggregated time series to quantify contributions to response from each online channel, their interactions 3
  18. 19. We need more “mad” scientist! To break the rules & learn…
  19. 20. Each cookie described on a 6-dimension scale Channel (s) consumer was exposed to / interacted with Depth of on-site interaction measured as EBT max level reached Frequency or number of contacts of each type prior to on-site activity Recency or number of contacts per period of time prior to on-site activity Interaction type e.g. click vs. exposure for display ad, keyword entered for Search… on-site visits number of visits, max EBT level reach at each of them Engagement with KLM Contacts, interactions with online ad vehicles
  20. 21. Key factors in the “online consumer path” Multi-channel interactions at individual level In the 9 markets, 39% of purchases with Search as last touchpoint had an exposure to a Display ad in the 14 days prior to purchasing KLM-Artemis 2010, Feb. 01 – Apr.15
  21. 22. Key factors in the “online consumer path” Multi-channel interactions with a recency effect Purchase with Search as last touchpoint with previous display exposure KLM-Artemis 2010, Feb. 01 – Apr.15
  22. 23. Key factors in the “online consumer path” Multi-channel interactions with a frequency effect Purchase Search as last touchpoint with previous display exposure Distribution- nb of Display contacts prior to purchase KLM-Artemis 2010, Feb. 01 – Apr.15
  23. 24. What the heck does this tell us? What ingredients are we missing?
  24. 25. Key factors in the “online consumer path” Different interaction types In the 9 markets, 77% of purchases with Search as last touchpoint are done through “navigational” keywords – Ad Group KLM KLM-Artemis 2010, Feb. 01 – Apr.15
  25. 26. Key performance measurement factors Number of visits on the web site Cross-analyzing number of days with an on-site visit and eventual purchasing – Example of UK data KLM-Artemis 2010, Feb. 01 – Apr.15 Visit without purchase (95,6%) Eventual purchase (4,4%)
  26. 27. EBT7- Pre-purchase steps Interaction depth 1 Interaction depth 2 1 2 3 4 5 6 7 8
  27. 28. Re-allocation algorithm: inputs & KPIs Last touchpoint Other interaction in the last 7 days Assisting drivers Display in the last 14 days Display in the last 7 days Display in the last 2 days Exposure to KLM display ads # of contacts (grouped) Interaction with KLM Navigational Search Search other keywords Affiliation Meta-search Display Click Display Exposure <ul><li>KLM onsite performance measurement </li></ul><ul><li>Number of visits (in # of days) </li></ul><ul><li>“ Depth” of each </li></ul><ul><li>Eventual purchase </li></ul>e-mailing
  28. 29. What did we learn … earn!
  29. 30. Re-assigning credit to online tactics Based on online path patterns: weighted according to contribution to customers response (visits, multiple visits, purchases) – not to be generalized as fitted to BY0910-Q4
  30. 31. Potential gain in incremental bookings via re-allocation across channels and countries +5 to +8 % Based upon 2010, Feb. 01-Apr. 15 only Potential gain at same “offer attractiveness”
  31. 32. Key take-outs! <ul><li>1.) Although online marketing is perceived as sexy, innovative and creative, some of the real value is in good old boring analysis </li></ul><ul><li>2.) Don’t limit yourself to existing industry standards and models, dig deeper! </li></ul><ul><li>3.) Don’t stop being sexy and explore “single channel” options like BT to the fullest in parallel </li></ul><ul><li>..... Model = “ a simplified version of the truth” </li></ul><ul><li> .... so keep on testing & experimenting </li></ul>