Is Your SEM Attribution Model Lying to Your Business?

August 19, 2016
Sam Knapp, Head of Insights for [24]7 Predictive Search Bidding

With the recent announcements from Google broadcasting the ability to test out their shiny new "data-driven" attribution model, our customers have not been shy about sharing their impressions and asking [24]7.ai of ours. Before delving too far into that topic, we’d like take a step back and lay down the basics of SEM attribution and highlight some important points before getting into technical applications and the newest features.

What is an SEM attribution model? What are the implications of these choices? Why should I care?
What can I really learn by fine tuning my models? Will this cost me more? Will one model over another help illustrate the impact SEM marketing is having on driving my business? Does the type of attribution really matter in the end?

Existing Search Engine Marketing (SEM) attribution models each come with their own pros and cons, so the goal of this article is to lay out the foundation for available options with some considerations for how to best think about how they may impact your business. We will focus on attribution, specifically as it refers to SEM, and how keywords within your Accounts should be properly valued and bid appropriately. In future articles within this series of [24]7.ai Search Insights, the conversation on "Attribution" will continue to explore the technical as well as business topics, options and considerations to expertly address your paid search marketing efforts and opportunities.

Depending on your role within your company, attribution can affect the way data is presented from a reporting, optimization or forecasting angle, so it is important to understand the implications of the model with which you are working. There are biases to each attribution model which may influence the data you collect and use to make important business decisions. For example, if new customer acquisition is a recent initiative and your attribution model rewards keywords at the bottom of the funnel, you may be handicapping yourself from achieving your goals!

As Google Adwords controls the majority of SEM market share and is leading the SEM industry, let's take a look at the current standards for attribution as it pertains to the available models it offers:

  • By default, any new user will utilize last-click attribution. This approach assigns 100% of the credit for a Conversion to the last paid search click driving the user to the website. It places the emphasis on keywords towards the bottom of the funnel that drive the final action.
  • First-click attribution essentially takes the opposite approach by giving the entire Conversion to the first paid search click, and disregards all future clicks as long as the fall within the cookie window designated by your Conversion metric.
  • Linear attribution evenly distributes the Conversion between all paid search clicks that led up to the final Conversion.
  • Position-based attribution by default assigns 40% (this number can be adjusted, however) of the Conversion to both the first and last click, while spreading out the remaining 20% between all additional clicks that occurred between the first and last click. Another common name for this model is “u-shaped attribution”.
  • Time decay attribution awards the clicks closest to the Conversion the highest percentage of the credit.
  • Data-driven attribution is Google’s fancy new model that attempts to take a smarter approach to attribution by dynamically adjusting how value is attributed. This model uses Google’s Conversion tracking to identify the keywords, ad groups and campaigns that ultimately lead to the most conversions with a varying assignment of value across clicks.
    While a dynamic attribution has the potential to give the truest view of value distribution, the drawback is that unlike the other mentioned models, the logic is not transparent and it may be hard to know if it performs better or worse than other models.
    In a later post in this article series, we will dive deeper into our learnings at [24]7.ai since the Data Driven Attribution models were rolled out.

Now to best understand some of the pros and cons to each of the above models, let's examine a sample search click path. By reviewing the attribution models utilizing the same path, we can provide insights into which model option is best depending on the goals of specific Accounts.

In the sample, our potential customer is searching for a pair of shoes …..

  1. The user enters "running shoes" into Google, and clicks on an ad taking them to your site. Browsing your site, they find a few items they like, but decide to think more about it.
  2. The next day, they remember seeing a type of shoe from your specific site that they liked, but cannot remember the exact name for it. During lunch they enter your company’s name into Google and click on the Brand ad at the top of the page and navigate to their favorite shoe type. The shoe name is put to memory, but as lunch is wrapping up the user decides to post-pone the purchase decision to the evening.
  3. A few hours later, once home from work, they enter the exact shoe model name into Google and click on your ad that takes them directly to the product they want and add it to the cart.
  4. Doing several things in parallel, the user finds that they accidentally closed the tab before completing the purchase, so they once again enter the brand name into their search bar, click your brand ad to reach your site and then complete the purchase with their item already in their shopping cart.

While this is what we would consider a relatively standard click path, it can certainly get simpler if they convert on the first click or significantly more complicated when other channels and devices get involved.

Let's examine how each of the standard models would credit the above customer journey:

  • Last-click: all credit would go to the brand query
  • First-click: all credit would go to "running shoes," a rather generic search
  • Linear: each of the four keywords would receive 0.25 Conversions
  • Position-based: 0.4 Conversions would go to the first and last click, while the remaining 0.2 is split 0.1 and 0.1 to the two middle queries (brand + exact shoe model).
  • Time-decay: From first to last click, credit would be assigned in a fashion similar to 0.1, 0.2, 0.3 and 0.4 Conversions, depending on timing of events.
  • Data-driven: How much credit each click gets is decided by Google, based on their models.

           We will take a closer look at our findings around this logic in a later post.

Based on the above descriptions, which is "best?" This decision is not easy and depends on the goals of your account. Here are some things you should think about when choosing your model:

The Basic and Most Aggressive Approach

First- and Last-click models share the pattern that they are both "all-or-nothing" approaches. First-click rewards more top of the funnel keywords and focuses on the potentially first interactions with your brand. As we can see in the example above, this would give all credit to the first broad search the user made, even though the user searched across different shoes on the site before they made the decision on which shoes they would purchase. If an advertiser is convinced that users will always choose to return directly to their site by entering their website url, using a bookmark following the first interaction, or entering one of their brand terms that often guarantees the advertiser position 1 in the auction, they could choose to disregard all later clicks. But as most marketers have an average click path of >2 clicks to conversion, this approach is very aggressive and greatly risks giving the final conversion to a competitor since overemphasizing the initial "exploration" terms may leave yourself open to losing out on the later auctions when the user knows what they want and is ready to make a decision and complete a purchase.

Last-click has the opposite problem. In the above example, it ends up giving all credit to a brand keyword, but as can be observed, the brand would likely not have been searched if it were not for the earlier path. Last-click is the most common attribution model in SEM, but easily ends up missing important touch points that lead to the final clicks that are driving the conversion. Last-click often ends up shifting value from the keywords driving new users to the more navigational queries such as brand searches, that are commonly driven by previous clicks.

The Smarter Approaches

Linear, Position-based and Time-decay models all take a more balanced approach by rewarding each click along the journey.

A Linear approach is the simplest, sharing the value equally between all keywords, but thereby, lacks any prioritization of what part of the click funnel is important. In the above example, it may have been a reasonable approach, but the value assigned to the important last- and first-click become very low if there are many clicks in the path to conversion.

Time-decay can be considered an upgrade of the Last-click approach. It follows the theory that clicks occurring closest in time to the Conversion are considered the most important, while still factoring in previous clicks slightly. This model is a good option to switch to if you are making your first attribution model switch and moving away from a last-click model. It enables you to ease into another attribution model if you lack data supporting that early clicks in longer click paths result in more conversions.

Position-based attribution modeling is great option for an advertiser that sees the first- and last-click as the most important touch points and wants every click in the funnel to have some value assigned to it. The drawback of this approach, compared to Time-decay, can be seen in our example above. While the First-click is reasonable to receive 40% of the value, the third click only received 10% of the value, even though this was the search that made the user choose your business when they had decided on which model of shoes to buy. Without this click, the 4th search may not have occurred and a competitor could have sold this pair of shoes.

The Next Generation Modeling Approaches

Data driven models are among the next generation of attribution models. As you can see in our examples above, all above models have pros and cons. And while this will always be the case, there are repeatable patterns that a model can take into account to dynamically shift value between touch-points instead of using a static model. Google has taken their stab at figuring out how to do this in a smarter attribution modeling approach. It will be exciting to see if these models perform better than the standard static models.

Among our initial findings in efforts using a data-driven model, one of the things we have found surprising is that brand traffic is often stable or growing in value. Intuitively, shifting value from brand to generic traffic is often one of the goals of switching from a last-click model. This is because many users end up making their last click before conversion on a brand ad purely for navigation, having entered the brand name in the address bar of their browser, which may convert it to a search for the brand. A simple way to upgrade the "smarter" approaches mentioned above could be to exclude brand from click paths that contain generic queries. However, at the end of the day, a pattern like this may work great for some business models and badly for others. If you have competitors advertising on your brand, it may be dangerous to remove that last navigational search, which is hopefully something data-driven will start to pick up on. So far, the jury is still out on Google making the right decisions in these new models as it is still quite new and in beta form, but stay tuned and we will share our deeper findings in a later post.

Excited to start looking into attribution models for your business?

If you are new to attribution and are using Google conversion tracking in Adwords, a great first step is to start looking into the models Google serves through the interface.

To begin, we recommend taking a look at the Attribution Reports available within Adwords by navigating to Tools → Attribution → Attribution Modeling. From here, you can compare attribution models to see how various campaigns, ad groups and keywords would be valued depending on the model you select.

Excited to start looking into attribution models for your business?

For example, in the screenshot above, the top campaign would receive credit for 2.75% fewer conversions if using First-click attribution vs Last-click. When combined with recommendations from above on the various strengths of each model, this can help identify over or undervalued segments of your Account. Play around with the different models and drill down to some of your top keywords—you may see vastly different data than you expected!

Now that you have a baseline in Adwords attribution models, we can start getting into more advanced techniques the Search Insights Team at [24]7.ai uses to enhance our clients’ performance. Awareness is only the first step, so we will do our best to build your knowledgebase and technical toolbox of different approaches and techniques to get the most out of your Accounts.

As we continue to explore SEM Attribution topics, our next article in the series will focus on how reporting data based on Transaction Time or Click Time impacts your ability to make informed decisions on where to spend, how much to spend, how good your performance is and more. There are pros and cons to both approaches, but we want to make sure advertisers are aware of both sides.

[24]7.ai Search Insights Contributor:

Sam Knapp is a loyal member of the Insights team within the [24]7.ai Inc. Predictive Search Bidding division. He aims to help clients stretch their advertising budget further and grow their Accounts to make their managers look good. Find him on LinkedIn at: https://www.linkedin.com/in/knappsam

Sam Knapp, Head of Insights for [24]7 Predictive Search Bidding
Sam Knapp, Head of Insights for [24]7 Predictive Search Bidding

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