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Marketing Attribution Training Workshop

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Marketing Attribution Training Workshop

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Marketing Attribution Training Workshop

  1. 1. ATTRIBUTION TRAINING For Thomas Cook, Mallorca, May 2017 By Christian Bartens, MD, Datalicious May 2017 (c) Datalicious Pty Ltd 1
  2. 2. QUICK OVERVIEW qAbout Datalicious and myself (5 min) qPart A: Dispelling myths (10 min) qMA vs. MMM qCustom attribution qVendor vs. internally driven qPart B: Attribution training (60 min) qAttribution is worthwhile (the benefits) qAttribution is difficult (things to consider) qAttribution is easier together (group efficiencies) qDiscussion: Attribution roadmap (15 min) May 2017 (c) Datalicious Pty Ltd 2
  3. 3. SUMMARY OF TRAINING GOALS 1. Measuring true contribution of media touch points on sales 2. Measuring online marketing contribution to offline sales 3. Challenges of data silos and multiple attribution solutions 4. Customer journey optimization for specific audiences 5. Making data more actionable May 2017 (c) Datalicious Pty Ltd 3
  4. 4. ABOUT DATALICIOUS May 2017 (c) Datalicious Pty Ltd 4
  5. 5. ABOUT DATALICIOUS / MYSELF qMy background is in tourism marketing (analytics came later) qFounded Datalicious in 2007 as web analytics consultancy qNow full (marketing) data science shop with focus on attribution qTelstra, Bupa, News Corp, Lego, Expedia, Intrepid, GoIbibo, KoreaHotel.com, etc q50 people around the world with main offices in Sydney/Bangalore qWeb analysts, data engineers, data scientist, software developers q3rd party platform resales, software development and research qOver time implemented most analytics platforms under the sun q1 of 3 companies with access to Facebook cross device data qBiggest Google 360 Premium reseller in APAC region May 2017 (c) Datalicious Pty Ltd 5
  6. 6. PART A: DISPELLING MYTHS May 2017 (c) Datalicious Pty Ltd 6
  7. 7. WHICH ONE IS BEST, MA OR MMM? qMoving away from inaccurate simplistic view (of last click) qAttribution all about accurately giving credit where credit is due qAttributing value of conversion across all touch points leading up to it qAcross all and any channels involved to get holistic channel view qImpossible to accurately attribute credit based on partial picture qMA and MMM are just different terms for the same thing qMA = Media attribution to establish optimal mix for online channels qMMM = Media mix model to establish optimal mix for offline channels qDo we need a holistic MA (online) and MMM (offline) view? qOf course we do, just logic, no special knowledge required May 2017 (c) Datalicious Pty Ltd 7
  8. 8. CUSTOM ATTRIBUTION (MODELS) ARE BEST? qSelecting an out-of-the-box model is the same as selecting last-click qSomeone has to make a subjective call which will always be inaccurate qSimply impossible for a human to consider all the variables involved qEven if consumer behavior (model part A) is similar across industries qThe individual levers available to your company are always different qDifferent levers require different models (attribution model part B) qPlus major part of effort lies in data collection and not just modelling qDo we need an accurate customer attribution model fit for purpose? qOf course we do, simple logic, no special knowledge required May 2017 (c) Datalicious Pty Ltd 8
  9. 9. VENDOR OR INTERNALLY DRIVEN IS BEST? qAny attribution vendors will more or less promise you the world qUsually not malicious but function of prolonged negotiations where it is either working with unrealistic cost and scope expectations or saying no and loosing deal qVendors will never understand your business as well as you do qVendors will never care about your business as much as you do qVendors might also disappear tomorrow (many still need funding) qVendors can help build, accelerate and support internal capabilities qBut businesses need to drive and own core competencies in house qDo we need to own processes that drive our marketing optimization? qOf course we do, just logic, no special knowledge required May 2017 (c) Datalicious Pty Ltd 9
  10. 10. PART B: ATTRIBUTION TRAINING May 2017 (c) Datalicious Pty Ltd 10
  11. 11. PURCHASE PATH + ATTRIBUTION BASICS May 2017 (c) Datalicious Pty Ltd 11 Touch point Touch point Touch Point Online Sale Offline Conversion Touch point Touch point Touch Point Online Conversion Offline Sale Touch point Touch point … Online Conversion Offline Conversion
  12. 12. ATTRIBUTION IS WORTHWHILE qProves return, justifies spend and helps increase budgets qWho would not invest more if every additional $1 generates $1.5? qAttribution essential step to introducing unlimited advertising budgets qWhy not continue spending as long as return on ad spend is positive? qNote: Important to bring CFO along on the attribution journey for this qEliminates waste and generates more revenue with the same spend qIdentifies and eliminates ad placements that do not drive sales qCase studies: Suggest approx. 10-30% advertising cost savings from this in the 1st year qMeaning if your investment is $1m, you can save 100-300k spend while maintaining revenue qThe saved 100-300k can then of course also be re-deployed to further increase revenue qCase study: Telco using attribution to optimize bids saw >30% revenue increase in test May 2017 (c) Datalicious Pty Ltd 12
  13. 13. ATTRIBUTION IS WORTHWHILE qEnables scenario planning, forecasting and test learn culture qOnce obvious waste eliminated conversation naturally turns to ‘what if’ qHow much revenue can we expect from $X overall/additional spend? qWhat happens if we shift budget from this channel/activity to this one? qLet’s test always on vs. activity spikes followed by reduced activity qOpportunity cost of not doing attribution can be very expensive qCase study: ROI of SEM for Bank having used last-click to justify high SEM spend/CPC actually turned out to be negative with accurate attribution qUnderstand and improve optimization of customer journey qTracking all touch points leading up to conversion = single customer view qOpens up entirely new use cases such as predicting churn, next best action, etc qCase study: Telco using attribution data to predict next best ad and develop cookie pools for better ad targeting saw >10% increase in ad response rates May 2017 (c) Datalicious Pty Ltd 13
  14. 14. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 14 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > >
  15. 15. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 15 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Sourcing of cost data from various channels q Some manual some via API q Some daily, some weekly, etc q Some estimation, i.e. SEO q Sourcing of campaign classification data q Categorization and taxonomy q Alignment across agencies q Alignment across markets q Changes or fixes to above data sources in retrospect may require (frequent) data re-processing
  16. 16. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 16 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Garbage in, garbage out principle applies to tag implementation and ongoing maintenance q Impact of display ad blocking, ad view-ability and ad fraud q Research: Ad blocking affects approx. 5-25% of users/impressions, average ad view-ability is approx. 40-50% and approx. 5-15% of impressions may be fraudulent (i.e. non-humans) q Use of multiple devices requires cross device stitching of purchase paths q Deterministic or probabilistic approaches can compress number of visitors by 20-80% q Research: Cross-device attribution in finance on average increase path length by >30% thus significantly changing the results for mobile by >200% for some channels q Sourcing and standardizing spot schedules for offline ads across markets q Combining MA (online) and MMM (offline) q Injecting offline touch points into online paths for user max. granularity or summary level only
  17. 17. TOPLEVELTAGAUDIT May 2017 (c) Datalicious Pty Ltd 17 Tag Present % Pages thom ascook.com neckerm ann-reisen.de jettours.com neckerm ann.be neckerm ann.nl spies.dk ving.se Google Universal Analytics (analytics.js) 50.9% 96.3% 99.8% 98.1% 100.0% 99.8% 96.5% Bing Ads Universal Event Tracking (UET) 8.9% 97.9% 99.9% 2.0% Google Web Font 5.6% 8.1% 26.3% 83.9% 99.9% 17.3% 23.8% Google Tag Manager 54.0% 98.2% 99.9% 98.1% 99.9% 96.5% Optimizely 1.8% 98.6% 83.9% 97.0% 91.6% DoubleClick 13.5% 8.8% 11.1% 98.6% 85.8% 98.3% 0.3% Facebook Pixel 49.8% 88.9% 90.3% 98.0% 77.3% 97.9% 96.5% Facebook Social Plugins 84.8% 77.5% 89.9% 98.0% 77.4% 97.7% 96.3% Google Adwords Conversion 2.2% 89.2% 90.7% 83.9% 97.9% Hotjar 90.1% 94.5% 97.9% 96.5% DoubleClick Floodlight 88.8% 88.1% 24.5% 97.7% 94.5% 97.8% Crazy Egg 34.2% 57.4% 97.8% 96.4% Google Adwords Remarketing 3.9% 68.6% 90.5% 84.9% 2.3% 97.5% 76.5% Google AdSense 16.6% 1.6% 92.0% 72.8% 97.4% 0.0% Adform 96.5% Turn 43.1% 63.8% 83.9% 94.5% 0.1% ADTECH 0.1% 1.0% 3.3% 25.1% 94.3% 0.0% Salesforce 93.4% Ve Interactive 88.9% 0.0% Criteo 40.7% 37.2% 84.8% 71.3% 0.1% Quantcast 82.1% 0.2% 0.1%
  18. 18. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 18 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Transaction value vs. lifetime value q One sale worth $1,000 vs. initial 1 sale followed by 9 others worth $10,000 – resulting ROI will be different and potentially shift some negative ROI figures into the positive (completely changing results) q Offline sales driven by online activity (not all tools allow import after the fact) q Case study: Telco established for every 1 online sale digital campaigns also influence another 1-2 offline sales (4 years ago it was 1 to 20-25) q Customer journey impact in addition to ROI q Some modelling approaches (HMM) can not only determine campaign impact on final success, the sale, but also how well activities are moving consumers along the funnel from disinterest > awareness > consideration
  19. 19. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 19 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Logistic Regression: Does not include temporal dynamics (changes over time) or diversity amongst consumers, "assumes" same impact of an ad across any and all time of a user’s journey q Latent-Class Models: Accounts for differences in consumer behavior and identifies latent consumer journey stages but “assumes” consumer behavior does not change over time q Shapely: Makes linearity assumptions leading to multicollinearity (variables seem more correlated than they actually are) and game theory outputs can be debated to be less predictable q Hidden Markov: More realistic less linear approach that requires less assumptions, uses temporal dynamics to adjust value of ads based on both position and ultimate contribution and identifies hidden user intent (disinterest > awareness > consideration > $) via visible observable user activity
  20. 20. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 20 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Measuring incremental requires establishment of a baseline first q Limit exposure geographically, by segment, over time, etc
  21. 21. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 21 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Out-of-the-box vs. ability to customize q Ability to integrate into existing reports q Mandate single source of truth to avoid ongoing data accuracy debates q Frequency and granularity
  22. 22. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 22 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Requires sufficient dedicated resources (not a part-time project) q Embarking on cultural change process which takes time and dedication q Cultural change should ideally extend to team structure and KPIs q Need to bring everyone on the journey including external stakeholders q Need for documentation and training to ensure continuity q Establishment and maintenance of ongoing optimization meetings key
  23. 23. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 23 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Optimization of channel mix q Optimization of individual bids q Optimization of ad targeting q Optimization of ad creative q Optimization of user journey q Developing and testing ideas
  24. 24. ATTRIBUTION IS DIFFICULT May 2017 (c) Datalicious Pty Ltd 24 Investment Purchase Path Return ROI Baseline ROAS Reporting Process Optimization Automation Taking Action The Modelling + + = > > > q Bid management automation q Segment creation and export
  25. 25. ATTRIBUTION IS EASIER TOGETHER qEconomies of scale apply to attribution as with everything else qDevelop documents, processes, reports, etc once but use multiple times qImprove average cost by markets by sharing technology and resource costs qEnable markets to benefit from attribution that otherwise could not qShared approach reduces key ‘man’ risk and helps with continuity qNew resources joining can learn from others and transferees know already qBringing attribution in-house long-term requires a dedicated attribution team with multiple specialized skill-sets and it probably requires the scale of more than one market to justify investment q[People + Process + Structure] > [Data + Technology] May 2017 (c) Datalicious Pty Ltd 25
  26. 26. ROLES REQUIRED TO BRING IN-HOUSE Web Analysts Collect Data Data Engineers Process Data Data Scientists Analyze Data May 2017 (c) Datalicious Pty Ltd 26
  27. 27. DISCUSSION: ATTRIBUTION ROADMAP May 2017 (c) Datalicious Pty Ltd 27
  28. 28. ROUGH ATTRIBUTION ROADMAP May 2017 (c) Datalicious Pty Ltd 28 LowEffortHigh Now Timing Later Streamlining of Platforms Online Only Attribution Cross-Device Attribution Offline Channels Online to Offline Sales Bid Automation Basic Forecasting Next Best Action Ad Blocking, View-Ability Tag, Data, Tech Audit Complex Scenarios
  29. 29. RECAP OF TRAINING CONTENT 1. Measuring true contribution of media touch points on sales 2. Measuring online marketing contribution to offline sales 3. Challenges of data silos and multiple attribution solutions 4. Customer journey optimization for specific audiences 5. Making data more actionable [People + Process + Structure] > [Data + Technology] May 2017 (c) Datalicious Pty Ltd 29
  30. 30. THANK YOU! QUESTIONS? https://www.linkedin.com/in/cbartens/ cbartens@datalicious.com M +1 347 873 4647 May 2017 (c) Datalicious Pty Ltd 30

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