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This is going down the whole attribution rabbit hole, which is arguably the most challenge issue in the advertising/marketing space today. Static attribution m

This is going down the whole attribution rabbit hole, which is arguably the most challenge issue in the advertising/marketing space today. Static attribution models like first touch, last touch, U-shaped, and even custom weighted models provided multiple lenses through which to view your data to see how it would be valued if more credit was given towards it (since historically things were viewed via last touch lenses for the most part). Google Analytics offers some great attribution tools for free if you're interested in checking those out. Beyond that, static models have their weaknesses, so dynamic models such as the data-driven model in Google Analytics Premium are a nice step up. There are other solutions that handle this too such as VisualIQ, Adometry, Convertro, etc. Those were all recently acquired by Google, and others btw. Then there is software that attempts to help you model incremental lift in addition to dynamic attribution models. I just demoed Adobe's econometric modeling tool in their Adobe Analytics package (that feature set might be in the higher-level version) and that was about the closest I've seen to date for doing it the way I consider the "right" way (ie. user-path level attribution and modeling for incremental lift using literally whatever external data you can feed into it). I'm an ad tech nerd so let me know if you have any other questions.

walterbell 11y ago
Do you know of any tools/studies/approaches which combine techniques from adtech attribution with techniques from online training, e.g. spaced repetition? E.g. are there timing models for saturation thresholds which cause users to ignore a message?
- shostack 11y ago
  Are you getting things like modeling "banner blindness" and the reduction (or increase) in value of a given creative/channel based on increasing or decreasing the frequency cap? I haven't seen anything really that handles the reporting of that specifically. Typically modeling ideal frequency caps is left as an exercise to the user, and banner blindness is typically looked at by CTR as certain channels like FB can see a steep drop in that pretty quickly after as little as 2 weeks if you have a relatively small audience you are saturating. This gets more interesting on the retargeting side as people often complain about constantly seeing ads for a company--yet the reality is higher frequency caps often work because it keeps the brand top of mind or reminds them they needed to do something. But again, not familiar with any specific technical solutions with features built around modeling that and providing recommendations. That is still largely a human effort that requires analysis of touch point data on the frequency cap side, and other directional metrics on the banner blindness side. Would love to know if you or others know of any solutions that handle that better. I'm sure DSPs/networks like Criteo and others have their own in-house solutions, but part of the challenge is that there's no great way to set global frequency caps across DSPs/networks if you use multiple platforms.

walterbell 11y ago

Do you know of any tools/studies/approaches which combine techniques from adtech attribution with techniques from online training, e.g. spaced repetition? E.g. are there timing models for saturation thresholds which cause users to ignore a message?

shostack 11y ago
Are you getting things like modeling "banner blindness" and the reduction (or increase) in value of a given creative/channel based on increasing or decreasing the frequency cap? I haven't seen anything really that handles the reporting of that specifically. Typically modeling ideal frequency caps is left as an exercise to the user, and banner blindness is typically looked at by CTR as certain channels like FB can see a steep drop in that pretty quickly after as little as 2 weeks if you have a relatively small audience you are saturating. This gets more interesting on the retargeting side as people often complain about constantly seeing ads for a company--yet the reality is higher frequency caps often work because it keeps the brand top of mind or reminds them they needed to do something. But again, not familiar with any specific technical solutions with features built around modeling that and providing recommendations. That is still largely a human effort that requires analysis of touch point data on the frequency cap side, and other directional metrics on the banner blindness side. Would love to know if you or others know of any solutions that handle that better. I'm sure DSPs/networks like Criteo and others have their own in-house solutions, but part of the challenge is that there's no great way to set global frequency caps across DSPs/networks if you use multiple platforms.