Attribution modeling 101, Mariia Bocheva
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The document discusses attribution models in marketing, explaining their importance for understanding the customer journey and making informed decisions. It compares various models such as last-click, first-click, and advanced models like Markov chains and Shapley values, emphasizing the need for businesses to choose models that accurately reflect their marketing impact. Key takeaways include the necessity of having a clear strategy, internal support, and utilizing high-quality data to optimize marketing efforts.
Attribution modeling 101, Mariia Bocheva
- 1. Про модели атрибуции простыми словами
- 2. Your personal marketing analytics assistant We help 20,000 analysts, marketing specialists and C-level executives to manage data and make the right decisions on time.
- 3. Agenda 1. What is attribution? 2. What attribution models are available at the market? 3. Comparison of models 4. How to choose the model that will benefit your business?
- 4. What is attribution? An example
- 5. A typical “customer 箩辞耻谤苍别测”...
- 6. A typical “customer 箩辞耻谤苍别测”...
- 7. A typical “customer 箩辞耻谤苍别测”...
- 8. A typical “customer 箩辞耻谤苍别测”...
- 9. A typical “customer 箩辞耻谤苍别测”...
- 10. So, who gets the credit?
- 11. So, who gets the credit?
- 12. So, who gets the credit?
- 13. So, who gets the credit?
- 14. Description Why do we need attribution? Interest / Awareness
- 15. Description Why do we need attribution? Interest / Awareness Consideration
- 16. Description Why do we need attribution? Interest / Awareness Conversion Consideration
- 17. Description Why do we need attribution? Interest / Awareness Conversion Retention Consideration
- 18. Description Why do we need attribution? Interest / Awareness Conversion Retention Consideration Less Targeted, Less Attributable
- 19. Description Why do we need attribution? Interest / Awareness Conversion Retention Consideration Less Targeted, Less Attributable Highly Targeted, Highly Attributable
- 20. In the context of (online) marketing...
- 21. Why do we need attribution? “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” - John Wanamaker, early 1900s
- 22. Questions business needs to answer 1. How do I achieve Sales plan? 2. How to allocate marketing budget? 3. How to decrease costs? 4. How to increase sales?
- 24. Position based models First Click
- 25. First Click Last Click Position based models
- 26. First Click Last Click Last Non-Direct Click Position based models
- 27. Linear Position based models First Click Last Click Last Non-Direct Click
- 28. Linear Time Decay Position based models First Click Last Click Last Non-Direct Click
- 29. Linear Time Decay Position Based Position based models First Click Last Click Last Non-Direct Click
- 30. Last Click attribution Source: Ad Roll 2017 44% of marketers still use last click attribution. Only 18% use algorithmic attribution.
- 31. 72.4% of marketers indicate that they ● don’t know why they chose their model ● selected the easiest attribution option available to them
- 32. Why is this happening??? 1. Lack of understanding of the potential attribution impact 2. No ownership of attribution or analytics 3. Scattered data
- 33. Other Google Attribution models Comprehensiveness Actionability Low Low High High Attribution 360Google Attribution Google Analytics Analytics 360 DCM AdWords DS
- 34. What if not LNDC? 1. Markov Chains 2. Shepley value 3. Funnel Based model 4. Custom algorithms
- 35. If not Last Click & Last Non-Direct, than what? 1. You have data from different Ad platforms (needed point) 2. You want to estimate the value of every step and session for particular user 3. You want to understand which bundles of ad channels work well together
- 36. Methodology 1. How is the value distributed? 2. Where is it used? 3. What data you can (and have to) use? 4. Which question helps to answer?
- 37. Let’s start with figures and formulas? Models: 1. Markov chains 2. Vector Shapley. The average contribution of all sources in the transaction 3. Funnel Based OWOX BI Attribution
- 38. Shapley value Lets analyze tr1 = 500$ & tr2 = 300 facebook direct 500 USD 300 USDdirect First transaction Second transaction
- 39. Shapley value (for geeks) Lets analyze tr1 = 500$ & tr2 = 300$ V1( {facebook} , {direct} ) = 500 V2( {direct} ) = 300 V3( {facebook} ) = 0 Ф1(facebook) = (1 - 1)! * (2 - 1)! / 2! * (0 - 0) + (2 - 1)! * (2 - 2)! / 2! * (500 -300) = 0 + 100 = 100 Ф2(direct) = (1 - 1)! * (2 - 1)! / 2! * (300 - 0) + (2 - 1)! * (2 - 2)! * (500 - 0)= 150 + 250 = 400
- 40. Example: N = 2 ● State Е ● State А Probability matrix: 0.3 0.7 0.6 0.4 Markov chains
- 41. 1 - Customer funnel 2 - How is it working 3 - Grouping by sources C1 -> C2 -> C3 -> conversion (start) -> C1 -> C2 -> C3 -> (conversion) (start) -> C1, C1 -> C2, C2 -> C3, C3 -> (conversion) C1 (start) -> C1 -> (null) (start) -> C1, C1 -> (null) C2 -> C3 (start) -> C2 -> C3 -> (null) (start) -> C2, C2 -> C3, C3 -> (null) Markov chains in Ecommerce Let’s see 3 simple examples of clients’ behaviour: C1 -> C2 -> C3 -> conversion C1 -> unsuccessful conversion C2 -> C3 -> unsuccessful conversion С - session (with particular source)
- 42. From To Probability General Probability (start) C1 1/3 66.7% (start) C1 1/3 (start) C2 1/3 33.3% Total from (start) 3/3 C1 C2 1/2 50% C1 (null) 1/2 50% Total from C1 2/2 C2 C3 1/2 100% C2 C3 1/2 Total from C2 2/2 C3 (conversion) 1/2 50% C3 (null) 1/2 50% Total from C3 2/2
- 43. Draw a chain on the graph
- 44. For evaluation, we use the delete effect P1 = (0,33 * 1 * 0,5) = 0,167 P2 = (0,33 * 0 * 0,5) = 0 P3 = (0,33 * 1 * 0) = 0 R1 = 1 - 0,167/0,33 = 0,5 R2 = 1 - 0 = 1 R3 = 1 - 0= 1 V1 = 0,5 / (0,5 + 1 + 1) = 0,2 V2 = 1 / (0,5 + 1 + 1) = 0,4 V3 = 1 / (0,5 + 1 + 1) = 0,4
- 45. Funnel Based OWOX BI 1. How is the value distributed? 2. Where is it used? 3. What data you can (and have to) use? 4. Which question helps to answer?
- 46. Step Users Probability Score Value Visit 100.0% Non-bounce visit 60.0% 60% 40 18% Product page 42.0% 70% 30 13% Add to cart 7.8% 19% 81 36% Purchase 2.1% 27% 73 33% 224 100% How is the value calculated? Visit Non-bounce Visit Product page Add to cart Purchase 100% 60% 42% 7.8% 2.1% 60% 70% 19% 27% 18% 13% 36% 33% = 40 / 224 = 30 / 224 = 8 / 224 = 73 / 224
- 47. But the funnel is not linear...
- 48. Comparison of how models work Funnel Based Data-Driven (Analytics 360) Markov chains 1.Allows you to assess the mutual influence of the channels on the conversion and advancement along the funnel 1.Allows you to estimate the mutual influence of channels on the conversion 1.Allows you to estimate the mutual influence of channels on the conversion 2.Allows you to find an inefficient channel and tell where exactly it is not effective. Resistant to nonlinearity. 2.Allows you to find an inefficient channel. High accuracy of calculations. 2.Evaluate which channel is the most significant. 3.Underestimates the first step of the funnel. 3. It does not evaluate progress on the funnel, you can not connect offline data from CRM 3.Underestimates the first link of the chain, is unstable to the order in the chains. Answering the question: How does the presence of a channel affect conversion and when is this the strongest influence? Answering the question: How will the presence of the channel affect the conversion? Answering the question: How will the absence of a channel affect the conversion?
- 49. Custom attribution models Answear story: 1. Multi-brand online store selling clothes, footwear and accessories 2. Founded in Poland in 2010 3. Operates in several different counties
- 50. Divided channels in logical groups ● Comparison — price comparison services: hotline, ceneo. ● Affiliate — affiliate websites: zanox. ● Retargeting — retargeting services: criteo, rtbhouse. ● Cpc — paid search: google brand and non-brand + social. ● Display — display ads: google with graphic ads, viva. ● Email campaigns: external.
- 51. Defined main KPIs and assigned values
- 52. Assigned value to each channel
- 53. Built reports
- 54. Optimized costs
- 55. Time to evaluate your budget
- 57. How did the presence of the channel in the chain affect the conversion?
- 58. How does the presence of a channel affect conversion and when is this the strongest influence? How did the presence of the channel in the chain affect the conversion?
- 59. How does the presence of a channel affect conversion and when is this the strongest influence? How did the presence of the channel in the chain affect the conversion? How did the lack of a channel in the chain affect the conversion?
- 60. How did the presence of the channel in the chain affect the conversion? How does the presence of a channel affect conversion and when is this the strongest influence? What indirect source before conversion was the last? How did the lack of a channel in the chain affect the conversion?
- 61. Without a bidding integration, attribution has no impact Optimize marketing campaigns based on data 70% of marketers struggle to act upon the insights of attribution. Source: Ad Roll 2017
- 63. Key takeaways 1. Start with a clear strategy and set of objectives 2. Get internal buy-in for attribution 3. Focus on defining the customer journey 4. Consider physical as well as digital touchpoints 5. Ensure the data quality 6. Use flexible technology 7. Test different models that align with your business goals 8. Act on the results
- 64. Useful links 1. Comparison of different attribution models in the OWOX BI Blog 2. Custom attribution model by Answear 3. Article on how OWOX BI uses attribution for decision making 4. Markov Chains 5. Shapley Values