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Superweek - Marketing attribution after 2024
- 2. 2 How did you find out about Superweek? ⓘ Start presenting to display the poll results on this slide.
- 3. 3 The breadless future is coming #SPWK
- 4. 4 Is our current attribution that good? #SPWK
- 5. 5 Is our current attribution that good? #SPWK • Unmeasurable interaction points • Not all conversions are measured • Does not capture “buying intent” • Cross-device usage
- 6. Why even talk about it? 6
- 7. 7 What money is wasted on ads? <Hoe werkt campagne tracking binnen een digitale context?>
- 8. 8 Measuring marketing effectiveness #SPWK How attribution contribute to business goals Customer insights Attribution provides insights on your customers by uncovering at which point in the customer journey they are most influenced. Marketing ROI Attribution enables you to understand where your advertising budget produces the highest incrementality per Euro spent.
- 9. 9 Attribution modeling #SPWK Two main types Touch-based • Bottom-up • Focus on individual customer journey • Need for detailed individual-level data • Distributes credit for conversions among touchpoints based on position or time decay. Correlation-based • Top-down • Macro-level overview • Need for aggregated data • Utilizes statistical techniques to quantify the impact of marketing on sales or other KPIs
- 11. 11 Touch-based attribution models #SPWK Ignores direct traffic and attributes 100% of the conversion value to the last channel that the customer clicked through before converting. Cross-Channel Last Click Cross-Channel Position Based Cross-Channel First Click Ads-Preferred Last Click Cross-Channel Linear Cross-Channel Time Decay Gives all credit for the conversion to the first channel that a customer clicked before converting. Attributes 100% of the conversion value to the last Google Ads channel that the customer clicked through before converting. If there is no Google Ads click in the path the attribution model falls back to Cross-channel last click. Distributes the credit for the conversion equally across all the channels a customer clicked before converting. Gives more credit to the touchpoints that happened closer in time to the conversion. Credit is distributed using a 7-day half-life: a click 8 days before a conversion gets half as much credit as a click 1 day before a conversion. Attributes 40% credit to the first and last interaction, and the remaining 20% credit is distributed evenly to the middle interactions. Machine learning algorithms to evaluate both converting and non-converting paths. The resulting Data- driven model learns how different touchpoints impact conversion outcomes. Data-Driven attribution
- 12. 12 Shapley (game theory) In cooperative situations, something known as the Shapley value is used to fairly distribute credit to each individual player/participant. #SPWK Image: https://clearcode.cc/blog/game-theory-attribution
- 13. 13 Shapley (game theory) It takes into account the order in which each touchpoint occurs and assigns different credit for different path positions. #SPWK Image: https://clearcode.cc/blog/game-theory-attribution
- 14. 14 Shapley (game theory) The average value of all those possible combinations is the “Shapley value” for that player/participant. #SPWK Image: https://clearcode.cc/blog/game-theory-attribution
- 15. 15 Markov chains Looks at the customer journey as a sequence of states (touchpoints) that transition from one to another with certain probabilities. #SPWK Image: https://adequate.digital/en/markov-chain-attribution- modeling-complete-guide/
- 16. 16 Markov chains It calculates the probability of conversion given the removal of a particular channel. #SPWK Image: https://adequate.digital/en/markov-chain-attribution- modeling-complete-guide/
- 17. 17 Markov chains Removal effect: the difference in conversion probabilities with and without a channel provides an estimate of that channel's importance. #SPWK Image: https://adequate.digital/en/markov-chain-attribution- modeling-complete-guide/
- 18. 18 Touch-based attribution #SPWK Typical outputs Channel First touch Last touch Shapley Markov Organic Search 100 90 105.0 95 Paid Search 150 160 155.0 152 Email 120 110 115.0 118 Paid Social 130 140 135.0 132 Organic Social 110 105 107.5 108 Absolute impact. The conversions obtained via this channel. Estimated impact. Higher value = greater impact on driving conversions.
- 19. 19 Touch-based attribution #SPWK Pros and cons Pros • Tracking on the “user level” and insights into the customer journey • Understand the role that each channel plays within the customer journey (awareness vs closing) Cons • Ignores person’s history with the brand • Rules don’t fully reflect complexity • Hard to track every touchpoint • Cross-device • Cookie lifetime
- 20. 20 Favors “directly measurable” channels Compared to MMM, touch-based attribution models overestimate the impact of paid search by 2x. And they underestimate brand advertising by 10x. #SPWK
- 22. 22 Marketing Mix Modeling (MMM) #SPWK
- 23. 23 Interaction effects #SPWK Macro-economic factors Sales Media investments (marketing channels) Top of mind (brand awareness) MMM is much more than correlation between “ad budget” and “sales”. Many possible interaction effects need to be considered when looking for true causation.
- 24. 24 Point of diminishing returns After a certain point, also called the saturation point, each dollar you spend has a relatively lower impact. #SPWK
- 25. 25 Adstock effect Or carryover effect. A past ad could influence present sales. Not all people that see your ad will buy the same day, some will buy your product the day after, or the week after. #SPWK
- 26. 26 Marketing Mix Modeling #SPWK Typical outputs 53% 7% 6% 4% 2% -28% Base + Distribution TV Promotions Digital spend Print Price/ Competition Contribution chart Response curves Linear = constant returns Concave = diminishing returns Convex = exponential growth S-shaped = low impact in beginning, nice returns once traction, with an eventual saturation point
- 27. 27 Marketing Mix modeling #SPWK Pros and cons Pros • No need for individual user level data • Can incorporate complexity of multiple variables influencing sales. • Customizable to specific business context. Cons • Need for a lot of historical data • Need for variation in the data • Actively experiment! • Less granularity • Slower • More complex to interpret
- 28. 28 Post Purchase Surveys help improve MMM Surveys help to get an idea of ”unmeasurable channels” like word-of-mouth. #SPWK
- 29. 29 Split tests help improve MMM A typical example is to run different creatives / campaigns / channels per region. Example: would there have been a difference in how #SPWK was promoted in the Netherlands vs Belgium? #SPWK
- 30. Innovations in attribution Solutions for the “cookieless future” 30
- 31. 31 Conversion Modeling #SPWK Touch-attribution with a bit of AI
- 36. 36 Conversion Lift Test #SPWK Image: https://www.searchenginejournal.com/conversion-lift- attribution/298073/
- 37. 37 Data Clean rooms #SPWK 1st Party data 2nd Party data Data Clean Room Stitch and share data in a privacy compliant way with double-blind joins Your customer data Customer data from another organisation Audience insights Ad Targeting
- 38. 38 Data Clean room #SPWK 1st Party data Publisher data Example Use Case Samsonite wants to understand how many sales can be attributed to impressions of its recent banner campaign.
- 39. What I hope you will remember 39 Key Take Aways
- 40. “ ” 40 Attribution was always and will remain complex and imperfect.
- 41. “ ” 41 Touch-based & correlation-based models should be used together.
- 42. “ ” 42 Adopt an experimentation mindset and dare to take (calculated) risks.
- 43. “ ” 43 Before you start: think about what you want to achieve. What is the business result you’re aiming for?
- 44. Let’s get Stitchd! Any questions? Get in touch. www.stitchd.be | jente.deridder@stitchd.be
Notas del editor
- Bad data => bad decisions. How to handle attribution reporting?
- Are cookies the only problem with attribution?
- Skepticism around the value of attribution reports, regardless of the depreciation of cookies.
- Most known from Google Analytics. What do all these heuristics have in common? Well… we made them up Rule based instead of measuring the true intent of the user. Oversimplification: Position / time are the only factors taken into account.
- The data-driven model used within Google Analytics. You first start by identifying each player’s contribution when they play individually, when 2 play together, and when all 3 play together.
- Then, you need to consider all possible orders and calculate their marginal value – e.g. what value does each player add when player A enters the game first, followed by player B, and then player C.
- Now that we have calculated each player’s marginal value across all 6 possible order combinations, we now need to add them up and work out the Shapley value (i.e. the average) for each player
- It calculates the probability of conversion given the removal of a particular channel, allowing marketers to see how likely it is that any given channel influences the path to conversion. The difference in conversion probabilities with and without a channel provides an estimate of that channel's importance.
- It calculates the probability of conversion given the removal of a particular channel, allowing marketers to see how likely it is that any given channel influences the path to conversion. The difference in conversion probabilities with and without a channel provides an estimate of that channel's importance.
- It calculates the probability of conversion given the removal of a particular channel, allowing marketers to see how likely it is that any given channel influences the path to conversion. The difference in conversion probabilities with and without a channel provides an estimate of that channel's importance.
- Shapley: The number is a measure of its contribution to achieving conversions. A higher Shapley Value indicates a greater impact on driving conversions, while a lower value suggests a lesser impact.
- MMM is designed to measure the impact of advertising and promotions across channels while controlling for external factors outside a brand’s control. Better suited to grasp the complexity that comes with attribution than the touch-based models.
- The highest-intent audience (the low hanging fruit) has already been converted in the first few runs of the ad Expansion requires reaching less relevant or more expensive audiences to reach, harming average performance Ad fatigue sets in for audiences that are repeatedly exposed to your ads, hence they stop paying attention Background (mathematical): https://www.toppr.com/guides/business-economics/theory-of-production-and-cost/the-law-of-diminishing-returns/
- Up until when will people buy from you because of the impression/interaction they had with an ad?
- Out of the 100 units sold, 53 units would be sold even if the marketer doesn’t invest in any form of advertisement. Basically, these 53 units are sold because of the brand’s equity in the market and the awareness it had created in the customer’s mind in the past. Similarly, 7 units are sold due to TV advertisements and 3 units are sold due to Consumer promotions and BTL promotions each. Interpreting price correctly is the key to understand the MMM contributions. Many times, people are misled due to the negative sign present when it comes to representing price. Notice that, when we sum up the contributions in the above chart with the negative sign on price, the sum is 44 not 100. If we ignore the negative sign on the price, the contributions would sum up to 100. Since sales and price have a negative correlation for most of the brands (damn you Apple!), the price contribution is represented with a negative sign to show the quantity of loss in sales it can cause. Here, negative sign of the price indicates that 28 units of sales was lost, due to increase in price. This is a notional concept which depicts that 28 additional units of sales could have been gained, had there been no increment in price.
- The complexity of econometric models can also be a challenge, particularly for marketers who may not have a strong background in statistics or programming. It's essential to work with experienced analysts or data scientists who can help to develop and interpret these models correctly.
- So, should our conclusion be to forget about touch-based attribution and go back to the correlation based analysis methods? Offcourse, we have Google and Meta looking out for us…
- Nothing that AI can’t solve.
- Ad interactions and conversions are grouped in two groups. One group contains ad interactions that have a clear, observable link to a conversion. The other group contains ad interactions that don't have a clear, observable link to a conversion.
- The observed conversions are divided into subgroups based on shared characteristics (a variety of dimensions, including location, time, and browser), and key metrics are calculated for each. For example, conversions observed in the morning in France are found to have a certain conversion rate, whereas this rate may be different in the evening.
- Those subgroups are used for sorting unobserved ad interactions and conversions.
- Using the known conversion rates and other characteristics from the observed subgroups, machine learning links unobserved ad interactions and conversions, where appropriate. The observed and modeled conversions are then integrated into your conversion data to help you make informed decisions about ad performance reporting and fed into bidding.
- A few year ago, Facebook and Google made available conversion lift tests. This feature makes it possible to conduct a controlled experiment and to measure the incremental value of marketing campaigns. Conversion lift test creates a control group out of the pool of users that would have seen our ad (because we have won the auction) but instead of showing the ad, they show the next ad in the ranking.
- With the security and access controls that data clean rooms provide, media companies and publishers can provide detailed reporting, and advertisers can track attribution more effectively.
- Ads Data Hub from Google is a good example. But note that Big Query also offers DCR capabilities now.
- Snapshot: we take a snapshot and start drawing conclusions from it. However, the context in which we operate is constantly changing. So that snapshot is always only partially useful to explain the future. The end of the third party cookies does not change this. It will make our data even less trustworthy, but it also forces us to rethink what we were relying on.
- Each model and approach has their own advantages and disadvanteges. The value lays in understanding how different models compare and forming your conclusions based on that.
- Lot’s of innovations happening. Try things out and learn on what works and doesn’t.
- As a scorekeeper, to show the overall incremental impact of marketing investments on the business As a forecaster, to predict the outcome that raising or lowering marketing budgets will have on marketing’s contribution to the overall budget As a coach, to suggest shifts to current marketing investments that improve performance