Four Alternatives to Last-Click Attribution

Four Alternatives to Last-Click Attribution

Attribution Modeling

Advertisers have become accustomed to the belief that the final click that leads directly to the conversion is the most important click – hence the affinity for last-click attribution. But it’s important that businesses transition away from last-click attribution. That’s because last-click attribution fails to account for the value of the entire conversion path.

Most marketers would agree that their brand campaigns drive a large number of conversions and have very low costs per action (CPAs). Of course the cost per clicks (CPCs) in brand campaigns tend to be very low, but those campaigns are also benefiting from last-click attribution models.

Let’s think about a customer journey for a moment. With the holiday shopping season upon us, many of us will start our search for the perfect gifts with some online searching. Here’s how one of my searches might look:

Top electronic gifts 2018 -> Fitness Trackers -> Top Rated Fitness Trackers ->Apple Watch

In the example above, the brand campaign housing the keyword “Apple Watch” would get 100-percent of the conversion credit if you use the last-click model. Clearly, I did not start my search on a branded keyword, yet the brand campaign gets full credit. When marketers use last-click attribution, they generally see that non-brand keywords achieve low conversation rates and high CPAs, and brand keywords achieve high conversion rates and low CPAs. But is this approach really a fair way to evaluate our campaign and keyword performance?

Marketers have all seen non-brand keywords fail to work well in a campaign. They may be costly to run, and rarely do we see strong conversions. I have paused my fair share of non-brand keywords as I can’t justify their worth to my clients. Not surprisingly, I see search volume decline; and although my CPA often times improves, my overall number of conversions also begins to decline. What we have been missing is the ability to see the value of the entire conversion path.

Alternative Models

One of the main focuses for Google this year has been transitioning clients from last-click attribution into a model that gives credit to each paid click in the user journey. Currently, there several different attribution models available in Google Ads.

Let’s take a look at some of the choices:

Data-Driven Attribution

The model Google recommends most is data-driven attribution, which uses Google’s machine learning technology to determine how much credit to assign each click in the paid search journey. This attribution model is all based on an advertiser’s own data and continues to “learn” over time.

Data-driven attribution takes both converting and non-converting paths into account, and it’s powered by dynamic algorithms that assign credit to touch points based on fractional credit. Google recommends choosing data-driven attribution when available. Unfortunately, this attribution model is not always an option as it requires 15,000 clicks on Google search and 600 conversions over a 30-day period.  Although smaller advertisers will not have access to this attribution model, there are still some good options available.

Linear Model

The linear model distributes the credit for the conversion equally across all clicks on the conversion path. If it takes four clicks for a searcher to convert, each click receives an equal part of the total conversion credit.

Time Decay Model

The Time Decay Model gives more credit to clicks that happen closer in time to the actual conversion. For example, if the path to conversion takes five clicks, the time decay model would assign an increasing proportion of credit with each subsequent click, with the final click that led to the conversion receiving the most credit.

Position-Based Model

The Position Based Model gives 40 percent of the conversion credit to the first click, 40 percent to the last click in the conversion path, and the remaining 20 percent across the other clicks on the path.

A Recommended Approach

As mentioned above, if the data-driven attribution model is an option for your campaigns, always choose that. But if you don’t have enough data available for that option, how do you go about choosing among the other options? Google offers a few suggestions:

  • Choose a time decay model if your client has a conservative growth strategy, is a market leader, and has little competition. In this scenario, the final clicks in the conversion path will get more credit.
  • If your client is growth oriented, new to the market, and is facing a lot of competition, choose a position-based model where the first and last clicks in the conversion path will get the most credit while the clicks in between will receive a smaller portion.
  • If your client falls somewhere in between, you may opt for a linear model, giving equal credit to all the clicks on the conversion path.

There is no absolute right or wrong choice, and any of the models you choose will give you better insight into the complete conversion path more than the last-click model can. Google also offers an attribution modeling tool in Google Ads that allows you to change attribution models and compare results among the different model types.

Outcomes of Different Models

No matter what attribution model you choose, you should anticipate a decline in brand conversions and an increase in non-brand conversions. The actual number of conversions will remain the same regardless of the model you choose. But you will see fractional conversions reported, indicating each campaign/ad group/keyword that played a role on the conversion path.

So let’s revisit my holiday shopping search from above:

Top electronic gifts 2018 -> Fitness Trackers -> Top Rated Fitness Trackers -> Apple Watch

If I used a position-based attribution model, here would be the new breakdown for conversion credit:

  • 40 percent of the credit would be given to “top electronic gifts 2018.”
  • 10 percent of the credit would be given to “fitness trackers.”
  • 10 percent of the credit would be given to “top rated fitness trackers.”
  • 40 percent of the credit would be given to “Apple Watch.”

Using last-click attribution, I would see keywords “top electronic gifts 2018,” “fitness trackers,” and “top rated fitness trackers” appear to be poor performers, as all of the conversion credit would have gone to “Apple Watch.” Conversely, if I were to use the position-based model, I would see that all of those keywords together played a role in the conversion path — and I would have a better understanding of the value of my non-brand keywords. This insight would allow me to make smarter decisions when optimizing.

Without question, we are able to make smarter decisions when we have a better understanding of the full conversion path. I suggest taking some time to experiment with the various attribution models using the attribution modeling tool in Google Ads. Based on your findings, select the attribution model that best suits your goals. I have found the additional conversion path insight to be valuable.

For more insight into how to improve the performance of your online advertising, contact True Interactive. We’re here to help.

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