Adobe Summit - Data-Driven Marketing Attribution

Editor's Notes

1. Slide ObjectiveKey Talking Points
2. Slide ObjectiveKey Talking Points
3. 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
4. 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
5. 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
6. Slide ObjectiveKey Talking Points
7. 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
8. 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)
9. 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.
10. 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.
11. 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.
12. 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.
13. Slide ObjectiveKey Talking Points
14. Lays the foundation for our benefits.Introduction to how we approach the problem of looking at historical data and making changes to improve performance.
15. Slide ObjectiveKey Talking Points
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18. 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!