Adobe Summit - Data-Driven Marketing Attribution

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Adobe Summit - Data-Driven Marketing Attribution

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Adometry's presentation from Adobe Summit 2014 on Data-Driven Marketing Attribution. ...

Adometry's presentation from Adobe Summit 2014 on Data-Driven Marketing Attribution.

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  • Slide ObjectiveKey Talking Points
  • Slide ObjectiveKey Talking Points
  • Slide ObjectiveSet up key pain points with the status quoKey Talking PointsData is in silos, no one place to get to get answers to your questions, no system of record for marketing performance. Rather it is in:Web analytics toolAd serverEmail platformSocial platformsYour agencyUses long, established rule-based attributionEasy to do, supported by current tools setsPrimarily click-basedArbitrary rule setNo unified metrics across channelsPredominately a digital viewDoesn’t include digital impact on offline conversions (POS, branch, dealer, call-center)Doesn’t include impact of offline channels such as direct mailAs a result, marketers do not have the clarity and insights necessarily to excel in today’s hypercompetitive, complex multi-channel world
  • Slide ObjectiveTalk about the current state of marketing attributionKey Talking PointsLot’s of blogs, articles, and presentation claiming the demise of last clickHowever, depending on which studies you read, somewhere between 45% and 65% are still using it. In an IgnitionOne study late last year, they reported:24% aren’t doing attribution – which probably means they are double-counting or using activity based metrics58% are using last click18% are doing attribution and using something other than last clickGenerally we all agree, there are better approaches that will drive improved understanding and marketing performance
  • The task of assigning credit might sound simple, but in today’s omni-channel world, it is anything but – there are a lot of barriers to change. When I speak with companies, I consistently hear four main ones highlighted here on this study by AdAge and Nuestar
  • Slide ObjectiveKey Talking Points
  • Slide ObjectiveDiscuss advantages and disadvantages of two primary approaches to attribution data collectionKey Talking PointsViewability – Ad tags measures IAB standard of 50% in view for a minimum of 1 secData integration – foundation for extending the data to include a more comprehensive viewAudience DataCross device mappingOffline Conversion mappingDirect Mail mappingCookie Deletion1st and 3rd Party Cookies
  • Slide ObjectiveSetup background for methodology conversationKey Talking PointsTypical conversion path across display, email, and searchRules based models = pre determined. These are the models that ad servers and site analytics provideTypically only looking at click-events – may include view-throughFocused on conversion paths, does not consider non-converting (order of magnitude more data about what doesn’t work)
  • Slide ObjectiveKey Talking PointsKEY PLACE TO POINT OUT BENEFIT/ABILITY TO PROVIDE DAILY ATTRIBUTION – DAILY COUNTING EXERCISE, BETTER SUITED TO SCALETo understand the weight or credit due to an event in a particular sequence, we first find all examples of that sequence – both converting and non-converting.  From this we can calculate the conversion rate for that sequence.Next, we find a similar sequence of events that excludes one of the events from the prior sequence – for example, the first event.  Again, we find both converting and non-converting examples.  We then calculate the conversion rate for this (second) sequence.Now, by comparing the conversion rates of the sequences we can determine the weight or impact of the the missing event.  If the rates are identical or similar then the missing event deserves little or no credit whereas if the rates are different then that missing event had an impact and deserves credit.There are some points that you can make after this – the death is in the details stuff:Obviously simplified explanation.Number of sequence and length of sequences will varyWe normalize the weights such that they add up to one (1)Tons of events - "big data problem"If there is only one event in the sequence it will get 100% of the creditAn event is actually a complicated thing – has lots of attributes – site, campaign, placement, creative concept, tactic, etc.   we calculate at the lowest level and roll the results upAlso frequency and recency.But this is the stuff that the engineers have all worked out in a principled manner.  Machine learning, etc.
  • Slide ObjectiveKey Talking PointsKEY PLACE TO POINT OUT BENEFIT/ABILITY TO PROVIDE DAILY ATTRIBUTION – DAILY COUNTING EXERCISE, BETTER SUITED TO SCALETo understand the weight or credit due to an event in a particular sequence, we first find all examples of that sequence – both converting and non-converting.  From this we can calculate the conversion rate for that sequence.Next, we find a similar sequence of events that excludes one of the events from the prior sequence – for example, the first event.  Again, we find both converting and non-converting examples.  We then calculate the conversion rate for this (second) sequence.Now, by comparing the conversion rates of the sequences we can determine the weight or impact of the the missing event.  If the rates are identical or similar then the missing event deserves little or no credit whereas if the rates are different then that missing event had an impact and deserves credit.There are some points that you can make after this – the death is in the details stuff:Obviously simplified explanation.Number of sequence and length of sequences will varyWe normalize the weights such that they add up to one (1)Tons of events - "big data problem"If there is only one event in the sequence it will get 100% of the creditAn event is actually a complicated thing – has lots of attributes – site, campaign, placement, creative concept, tactic, etc.   we calculate at the lowest level and roll the results upAlso frequency and recency.But this is the stuff that the engineers have all worked out in a principled manner.  Machine learning, etc.
  • Slide ObjectiveKey Talking PointsTo understand the weight or credit due to an event in a particular sequence, we first find all examples of that sequence – both converting and non-converting.  From this we can calculate the conversion rate for that sequence.Next, we find a similar sequence of events that excludes one of the events from the prior sequence – for example, the first event.  Again, we find both converting and non-converting examples.  We then calculate the conversion rate for this (second) sequence.Now, by comparing the conversion rates of the sequences we can determine the weight or impact of the the missing event.  If the rates are identical or similar then the missing event deserves little or no credit whereas if the rates are different then that missing event had an impact and deserves credit.There are some points that you can make after this – the death is in the details stuff:Obviously simplified explanation.Number of sequence and length of sequences will varyWe normalize the weights such that they add up to one (1)Tons of events - "big data problem"If there is only one event in the sequence it will get 100% of the creditAn event is actually a complicated thing – has lots of attributes – site, campaign, placement, creative concept, tactic, etc.   we calculate at the lowest level and roll the results upAlso frequency and recency.But this is the stuff that the engineers have all worked out in a principled manner.  Machine learning, etc.
  • Slide ObjectiveKey Talking PointsTo understand the weight or credit due to an event in a particular sequence, we first find all examples of that sequence – both converting and non-converting.  From this we can calculate the conversion rate for that sequence.Next, we find a similar sequence of events that excludes one of the events from the prior sequence – for example, the first event.  Again, we find both converting and non-converting examples.  We then calculate the conversion rate for this (second) sequence.Now, by comparing the conversion rates of the sequences we can determine the weight or impact of the the missing event.  If the rates are identical or similar then the missing event deserves little or no credit whereas if the rates are different then that missing event had an impact and deserves credit.There are some points that you can make after this – the death is in the details stuff:Obviously simplified explanation.Number of sequence and length of sequences will varyWe normalize the weights such that they add up to one (1)Tons of events - "big data problem"If there is only one event in the sequence it will get 100% of the creditAn event is actually a complicated thing – has lots of attributes – site, campaign, placement, creative concept, tactic, etc.   we calculate at the lowest level and roll the results upAlso frequency and recency.But this is the stuff that the engineers have all worked out in a principled manner.  Machine learning, etc.
  • Slide ObjectiveKey Talking Points
  • Lays the foundation for our benefits.Introduction to how we approach the problem of looking at historical data and making changes to improve performance.
  • Slide ObjectiveKey Talking Points
  • Slide ObjectiveKey Talking Points
  • Slide ObjectiveKey Talking Points
  • Each session will have a survey winner selected at the end of the conference day who will receive a $10 Starbucks electronic gift card. In addition, you'll be entered into our grand prize raffle where a grand prize winner will be drawn at the end of each conference day for an opportunity to win exciting prizes including an autographed Richard Sherman jersey, a Summit bash experience package and ski gear from Park City! Each survey responded to will give you another opportunity to win!

Transcript

  • 1. ©2014 Adometry, Inc. All Rights Reserved. 1#AdobeSummit March 24-28, 2014 | Salt Lake City, UT Learn more at summit.adobe.com 1
  • 2. ©2014 Adometry, Inc. All Rights Reserved. 2#AdobeSummit DATA-DRIVEN MARKETING ATTRIBUTION making the leap to
  • 3. ©2014 Adometry, Inc. All Rights Reserved. 3#AdobeSummit Paul Pellman CEO Lewis Broadnax Executive Director Web Sales & Marketing @ppellman A Bit About Today’s Presenters
  • 4. ©2014 Adometry, Inc. All Rights Reserved. 4#AdobeSummit marketers need help answering TWO QUESTIONS… “How is my business doing?”
  • 5. ©2014 Adometry, Inc. All Rights Reserved. 5#AdobeSummit marketers need help answering TWO QUESTIONS… “What should we do differently?”
  • 6. ©2014 Adometry, Inc. All Rights Reserved. 6#AdobeSummit Measured and Managed in Silos No Unified View of Performance Uses Rules-Based Measurement Skews Results and is Click-Centric Predominately a Digital Only View Doesn’t Consider Offline Effects Today’s Approach to Digital Marketing Analytics Has Limitations
  • 7. ©2014 Adometry, Inc. All Rights Reserved. 7#AdobeSummit
  • 8. ©2014 Adometry, Inc. All Rights Reserved. 8#AdobeSummit barriers to change
  • 9. ©2014 Adometry, Inc. All Rights Reserved. 9#AdobeSummit Mapping Your Ecosystem at the User Level for Marketing Attribution CRM and Audience Marketing Events Cost and Reference Conversion Events
  • 10. ©2014 Adometry, Inc. All Rights Reserved. 10#AdobeSummit A Comprehensive Approach to Collecting User-Level Attribution Data All Major Ad Servers • Historical data • Simplicity • Validation • Greater channel flexibility • IAB Standard viewability • 1st and 3rd-party data integration • Cross-device mapping Log Files All Major Publishers All Major Tag Managers Page Tag Conversion Pixel Ad Tag Log File Advantages Tag Advantages
  • 11. ©2014 Adometry, Inc. All Rights Reserved. 11#AdobeSummit Simple vs. Data-Driven Attribution Simple Rules-Based 0% 0% 0% 100% LAST EVENT 25% EVEN 25%25%25% 40% AD-HOC 15%15%30%
  • 12. ©2014 Adometry, Inc. All Rights Reserved. 12#AdobeSummit Common Advanced Attribution Models Advantages • Well understood – “predicts the contribution” of each touch • Well suited for “periodic” model updates Multiple Regression Algorithmic Advantages • More accurate – actually “counts” the contribution of each marketing touch • Well suited for dynamic, i.e. daily model updates
  • 13. ©2014 Adometry, Inc. All Rights Reserved. 13#AdobeSummit Predictive Model Approaches to Attribution Have Blind Spots Discriminating the impact of granular details for each ad event CHALLENGE 1
  • 14. ©2014 Adometry, Inc. All Rights Reserved. 14#AdobeSummit Predictive Model Approaches to Attribution Have Blind Spots Discriminating the impact of granular details for each ad event Same marketing event in different sequence has same probability CHALLENGE 1 CHALLENGE 2
  • 15. ©2014 Adometry, Inc. All Rights Reserved. 15#AdobeSummit Data-Driven Attribution Algorithmic Methodology Data Driven A/B Testing 3.1% Conversion Rate 3.0% Conversion Rate
  • 16. ©2014 Adometry, Inc. All Rights Reserved. 16#AdobeSummit Data-Driven Attribution Algorithmic Methodology Data Driven A/B Testing 3.1% Conversion Rate 2.5% Conversion Rate
  • 17. ©2014 Adometry, Inc. All Rights Reserved. 17#AdobeSummit Attribution Performance and Insights at the Most Granular Level Paid Search • Campaign • Provider • Keyword Group • Keyword • Creative Display • Campaign • Site • Placement • Format • Creative Email • Campaign • Segment • Primary Message • Secondary Message
  • 18. ©2014 Adometry, Inc. All Rights Reserved. 18#AdobeSummit Attribution Optimization How did my marketing perform? How can we improve? Past FuturePresent Using Attribution to Fuel Predictive Optimization
  • 19. ©2014 Adometry, Inc. All Rights Reserved. 19#AdobeSummit Scenario Timeframe Target KPI Flexible Optimization Planning for Real-Life Scenarios Input Data Set Budget Constraints KPI Constraints Forecasted Results for Each Scenario with Confidence Intervals
  • 20. ©2014 Adometry, Inc. All Rights Reserved. 20#AdobeSummit Overall Optimization View optimization opportunities across channels Evaluating Scenarios Reallocate spend across and within channels For Each Scenario – Specific Recommendations Granular line item changes – “I/O ready” • Modify default recommendations • Rerun prediction • Evaluate revised KPIs Optimal Investment Across and Within Channels
  • 21. ©2014 Adometry, Inc. All Rights Reserved. 21#AdobeSummit Adometry and Adobe Marketing Cloud Integrations Integration Point Value Provided Log File Integration Eliminate need for new page and conversion tags by integrating ad server data with Adobe Analytics data. Dynamic Tag Management Speed time to value by reducing time required to implement page and conversion tags. Adobe Analytics Leverage Adometry attributed KPIs in dashboards and reports for a more complete view of cross- channel performance. Media Optimizer Improve keyword performance by leveraging fully attributed value as the target objective for optimization. AudienceManager View segment and audience performance based on fully-attributed results.
  • 22. ©2014 Adometry, Inc. All Rights Reserved. 22#AdobeSummit Achieving a Greater Return on Marketing ROI Value Drivers Areas of Impact Display eCPA 20% - 30% decrease in effective CPA for display and retargeting Optimization Within Channels 10% - 20% improvement by optimizing channel performance including PPC, Affiliate, Email, and Social Optimization Across Channels 10% - 20% improvement in performance by optimizing spend across various channels Reduction in Analysis and Reporting Costs 25% - 50% decrease in effort required to pull, aggregate, and report on marketing performance across channels Overall Marketing ROI 20% - 40% improvement in overall performance of marketing investments
  • 23. 24 15.3 M 17.3 M PC Tablet + Phone Lenovo’s Performance Lenovo Tablet and Smartphone Volume Exceeded PC Volume since Fiscal Q1 6.5% 8.2% 9.6% 13.1% 18.5% 2009 2010 2011 2012 2013 Lenovo WW PC Market Share 2011/12 2012/13 2013/14
  • 24. 25 Our Heritage and M&As – Creating a Branding Challenge 2013 LENOVO
  • 25. 26 For each computer sold…… 2013 LENOVO
  • 26. 27 Number of programs claiming credit…… 2013 LENOVO
  • 27. 28 State of the Business 2012 2013 LENOVO
  • 28. 29 The Inevitable ROI Discussion 2013 LENOVO
  • 29. 32 Our Goals for the Attribution Initiative View Programs Holistically Full Funnel View of Performance Optimize Spend Across Channels
  • 30. 33 Our Approach to Capturing Marketing Event Data Channel Impressions Clicks Primary Collection Method Brand Display ✔ ✔ DFA and Adobe Analytics Ecommerce Display ✔ ✔ DFA and Adobe Analytics Paid Social ✔ ✔ DFA and Adobe Analytics Email ✔ Adobe Analytics Direct Navigation ✔ Adobe Analytics Affiliate ✔ Adobe Analytics CSEs/GPLAs ✔ Adobe Analytics Preload ✔ Adobe Analytics Organic Search ✔ Adobe Analytics Paid Search ✔ Adobe Analytics Organic Social ✔ Adobe Analytics
  • 31. 34 So, What Have We Learned So Far?
  • 32. 35 How Prevalent are Multiple Touches and Channels?  Conversion Types  New and Repeat Orders  New and Repeat Orders – Systems  Total Orders  Total Orders – Systems Conversions Multi-Touch 73% Multi-Channel 45%
  • 33. 36 How Prevalent are Multiple Touches and Channels?  Conversion Types  New and Repeat Orders  New and Repeat Orders – Systems  Total Orders  Total Orders – Systems Conversions Multi-Touch 73% Multi-Channel 45% Revenue Multi-Touch 75% Multi-Channel 34%
  • 34. 37 5% 3% 5% 11% 21% 55% 6+ 5 4 3 2 1 Unique Converted Visitors Path Length What is the Breakdown of Conversion Paths?
  • 35. 38 5% 3% 5% 11% 21% 55% 6+ 5 4 3 2 1 Unique Converted Visitors Path Length What is the Breakdown of Single Channel Conversions? 3% 3% 4% 20% 20% 50% Other CSE Paid Search Organic Search Affiliate Direct Unique Converted Visitors Single Channel Paths
  • 36. 39 What is the Predominate Role of Each Channel? Introducer Channels • Brand Display • Direct Navigation
  • 37. 40 What is the Predominate Role of Each Channel? Introducer Channels • Brand Display • Direct Navigation Promoter Channels • Brand Display • Paid Social • Paid Search • Organic Social • eCommerce Display
  • 38. 41 Introducer Channels • Brand Display • Direct Navigation Promoter Channels • Brand Display • Paid Social • Paid Search • Organic Social • eCommerce Display Closers Channels • Affiliate • Organic Search • Comparison Shopping Engines (CSEs) What is the Predominate Role of Each Channel?
  • 39. 42 50% 28% 12% 3% 3% 1% 1% 0% Direct Organic Search Display - Ecommerce Email Paid Search CSE Display - Brand Other Revenue by Channel What is the Revenue Performance by Channel?
  • 40. 43 50% 28% 12% 3% 3% 1% 1% 0% Direct Organic Search Display - Ecommerce Email Paid Search CSE Display - Brand Other Revenue by Channel What is the Revenue Performance by Channel? Revenue/User by Channel Organic Search Direct Email Display - Ecommerce Display - Brand Paid Search CSE Affiliate
  • 41. 44 CSE Display - Ecommerce Paid Search Affiliate Display - Brand Return on Ad Spend (ROAS) What is the Efficiency of Each Channel?
  • 42. 45 CSE Display - Ecommerce Paid Search Affiliate Display - Brand Return on Ad Spend (ROAS) What is the Efficiency of Each Channel? Conversion Rate Paid Search Email Affiliate Organic Social Organic Search Paid Social Direct Nav
  • 43. 46 Average Lift on TOTAL order conversions to eCommerce programs when Brand Display precedes lower funnel (click based) programs SEO: +353.39% SEM: +25.45% Email: +70.68% Affiliate: +18.46% CSE: +40.72% Average Lift on NEW order conversion to eCommerce programs when Brand Display precedes lower funnel (click based) programs SEO: +420.40% SEM: +10.76% Email: +7.85% Affiliate: NO LIFT CSE: +21.04% Average Lift on TOTAL order conversions to eCommerce programs when eCommerce Display precedes lower funnel (click based) programs SEO: +769.15% SEM: +82.03% Email: +65.32% Affiliate: +49.78% CSE: +100.46% Average Lift on NEW order conversion to eCommerce programs when eCommerce Display precedes lower funnel (click based) programs SEO: +616.08% SEM: +16.02% Email: +65.19% Affiliate: NO LIFT CSE: +17.98% Brand and eCommerce Display Lift
  • 44. 47 Next Areas of Focus  Applying initial insights to drive optimization  Global rollout  Key countries and regions  Integrate call-center data  Evaluate methods for integrating in-store purchase data
  • 45. ©2014 Adometry, Inc. All Rights Reserved. 48#AdobeSummit
  • 46. © 2014 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. Tuesday Grand PrizeEvery Session Wednesday Grand Prize Thursday Grand Prize Take Survey & Win Sweet SWAG (“surveys” section of mobile app) Signed Jersey Richard Sherman$10 Gift Card Swag Pack Band Ski Swag
  • 47. ©2014 Adometry, Inc. All Rights Reserved. 50#AdobeSummit Paul Pellman paul.pellman@adometry.com Lewis Broadnax lewismb@lenovo.com