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Attribution: Do you give the last touchpoint all of the credit for a sale?

Discussion in 'Websites, SEO, SEM, Display, Social, Marketing' started by ed.brooks, Jun 27, 2016.

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  1. Alexander Lau

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    And...

    https://searchengineland.com/google...-features-and-store-visits-updates-307075/amp
    Store visits tools rolling out. Data-driven attribution and Smart Bidding, which includes Target CPA, Target ROAS, Maximize Conversions, and Enhanced CPC, will be available to all advertisers with access to store visits data in their accounts.

    Data-driven attribution is Google’s machine learning-powered attribution model. Based on probability modeling of all the touch points generated by ads across an account, the data-driven model assigns fractional conversion credit to each interaction along a conversion path. Google first added data-driven attribution in Google Ads (then AdWords) in 2016.
    ----
    It's worth mentioning that ROI is not ROAS and that is commonly mixed up in the auto digital world.
     
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    #101 Alexander Lau, Oct 30, 2018
    Last edited: Oct 30, 2018
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    Attribution modeling requires data on every user exposure to ads before a conversion occurs.

    While such touch point records in the form of web cookies used to be readily available for many digital ads in the early days of online marketing, touch point data from traditional channels, such as TV, out-of-home, radio and print, has always been harder to access.

    Beacon technology for mobile phones and TV set boxes create new opportunities to track individuals, but they require complex integrations to link lots of different identifiers into one meaningful list. While this is technically feasible, it creates extra costs and is rarely done well.

    In any case, a complete user touch point recovery across competing vendors that tend to avoid supporting each other is not the only challenge for advertisers. Google is not alone in its decision to exclude cookie IDs in downloadable files in the name of privacy: Other advertising powerhouses, such as Amazon, Facebook and Twitter, don’t share any of their identifiers either.

    Hence, it is fair to say that dealers that leverage several of the leading online platforms will always have many key touch point data missing in their attribution models, which is far from holistic.
     
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    #103 Alexander Lau, Nov 1, 2018
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  5. Alexander Lau

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    Every Tom, Dick and Harry now offer "attribution."

    AdRoll Attribution
    https://www.adroll.com/use-cases/attribute
    Evolve your measurement to the way consumers shop
    Consumers average 56 touchpoints before they buy.* Understand what marketing channels drive the most sales with the best attribution tool for e-commerce.

    [​IMG]
     
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  6. Alex Snyder

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  7. Alexander Lau

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    IOW, you can't use MTA tools alone.

    Combined forces: when attribution falls short, take a unified view

    https://www.thedrum.com/industryins...hen-attribution-falls-short-take-unified-view

    For many years, Multi-touch Attribution (MTA) has been held up as a magic bullet that will solve many challenges for marketers and show them the true worth of their efforts and be able to optimise in real time. But the truth is MTA on its own has been a source of disappointment.

    While MTA may be a powerful method for understanding the digital touch points that influence customers, it is not enough on its own to answer the demanding questions marketers face – especially in the age of privacy legislation and increasing difficulty accessing user level data. While granular, MTA only provides a partial view of marketing efficiency without accounting for factors like offline activities, baseline sales and external factors. That’s where Unified Measurement comes in.
     
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    The Math Problem With Digital Ad Targeting
    https://www.linkedin.com/pulse/math-problem-digital-ad-targeting-john-andrews

    Digital advertising has a glaring problem, at scale, there is little to no difference in data sets, resulting in increasingly homogenous performance for advertisers. To put it another way, brands relying on some magical targeting data have no advantage over their competitors. Take massive amounts of 'Big Data', crunch it up, use it to target people who are likely buyers, non-buyers (so give them a better deal), competitive buyers, etc. etc. and you win right? Nope, every brand has similar access to the same third party data, any advantages are short lived as they are copied by competitors; it's just simple math.

    If you don’t believe me, consider the ridiculousness that is the digital ad market today. Fully one half of digital advertising is never seen by a human. One half. And yet, digital spending continues to expand at a blistering pace. Compound that with the fact that the problem is getting worse, not better and the costs are increasing. It’s the ultimate head-scratcher for and industry, lower quality and higher prices. How is this even possible? The answer lies in capacity. There is simply too much advertising chasing too little attention. As spending pours into digital advertising, the problem simply gets worse. Math strikes again, a new solution is needed.

    [​IMG]

    Shoppers are only occasionally in 'buy' mode. Constantly bombarding them with conversion messaging based simply on inferred data reeks of desperation.

    What if marketers could add a new data set into the mix to better understand where shoppers are along the path to purchase. Instead of spamming people with endless messaging about products that aren’t wanted, needed or relevant, they could add value by offering messaging at known points along the shoppers journey. Thanks to a powerful new data source created by IRI, this is now possible.

    Through its relationships with regional and national retailers, IRI has assembled a behavioral data stream based on a combination of loyalty cards, point-of-sale and shopper panel data to create the largest database of actual shopper data. Using this dataset, marketer's can break free from the declining effectiveness of digital marketing by understanding where shoppers are along the path to purchase. This approach can also significantly reduce the crushing noise level that is not only interrupting shoppers needlessly but also turning them against social media platforms and brands. What's more, it works! This approach offers IRI clients 3-4 times sales uplift and up to 70% improvement on return on advertising spend.
     
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    So, can someone that currently works in attribution or any of the Google Analytics "experts" explain to me how they account for this?

    https://www.zerohedge.com/news/2018...onfirms-internet-traffic-metrics-are-bullshit
    http://nymag.com/intelligencer/2018/12/how-much-of-the-internet-is-fake.html
    "Also mobile user counts are fake. No one has figured out how to count logged-out mobile users, as I learned at reddit. Every time someone switches cell towers, it looks like another user and inflates company user metrics."

    "
     
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