Revenue Allocation (Attribution) Models in ClickEquations

One of the new features in the May Release of ClickEquations, is support for multiple revenue allocation models. A revenue allocation model defines how the revenues resulting from your paid search campaigns are allocated to the various keywords that were clicked as part of the purchase process. pieThe method of allocation is critical because the success or failure of keywords is generally judged on their ROAS (return-on-ad-spend) or ROI (return-on-investment), and what looks like a highly profitable keyword when using one method could turn out to be a very unprofitable one using a different method, or visa-versa. The paid search world – and online marketing in general – has been thus far largely based around ‘last-click’ allocation. In this method, the keyword that is clicked just before a purchase or conversion gets 100% of the revenue credit for the sale. To look at a simple example, suppose someone visits your website three times within 30 days before making a $100 purchase.

  • Visit #1: Search Query ‘Organic Dog Food’ | Keyword ‘organic dog food’ (Exact Match)
  • Visit #2: Search Query ‘Natural Dog Food Coupon’ | Keyword ‘Dog Food Coupon’ (Phrase Match)
  • Visit #3: Search Query ‘Dog House Pet Supplies’ | Keyword ‘Doghouse Pet Supply’ (Broad Match)

In AdWords, and most web analytic software, and most paid search reporting tools, the full $100 revenue from the purchase would be credited to the broad-match keyword ‘Doghouse Pet Supply’. The trouble with this, and the reason why multiple revenue attribution models are necessary is that this doesn’t really provide a full or accurate picture of what happened or why. Allocation Issues Each of the three keywords obviously played a role in this customer’s purchase process. We have no way of really knowing which was critical and which was incidental.

  • If they hadn’t visited our site during the initial ‘organic’ search would they have chosen our ad when they later did the ‘coupon’ search?
  • If they didn’t find us during the ‘coupon’ search would they have recalled our name when they did their final search?
  • If we didn’t purchase our brand term as a paid keyword would they have just clicked our organic listing in the same search results?

These and dozens of other questions can never be answered. First-Click Allocation The most common alternative revenue allocation method to Last-Click, is called ‘First-Click’. As the name suggests, this method gives 100% of the revenue credit for the ultimate sale to the first keyword a person clicks within the defined conversion time frame. In our example, the full $100 revenue credit would go to the exact-match keyword ‘organic dog food’. This method is based on the view that the initial visit, the first time the person becomes aware of your site, is the valuable one. It presumes that regardless of any subsequent steps and visits prior to conversion, the initial contact was ultimately responsible. Linear Allocation A more democratic approach is to simply divide the revenue up equally among all the paid search keywords which the user clicked within the target date range before converting. This ‘Linear’ method divides the $100 up giving $33.33 going to each of the three words in our example. Weighted Allocation The mathematical simplicity of first, last, or linear allocation makes them easy to understand, but for a variety of reasons many marketers don’t feel they distribute revenue in a way that fully represents the role and impact of the different keywords. Weighted allocation attempts to correct for this by shifting the revenue across the keywords in a way that more accurately reflects their contribution. Our weighting in ClickEquations is automatic, and based on the past performance of each keyword in the converting chains. The Right Allocation Model We’ll take a deeper look at the pros and cons of each allocation model in a future post. There clearly isn’t a universal ‘right answer’ as it depends upon your business, the sales cycle buyers go through, and your own views on what’s important or what you’re trying to encourage. We do however, share the agree with the growing industry consensus that last-click allocation is the worst choice (despite it being the industry standard). Our opinion is that a move to Linear allocation, while far from perfect, represents a major and simple step in the right direction. All of the Above banana-allOne of the many limitations to better revenue allocation solutions – beyond the lack of options – has been the fact that when choices are offered, it’s been a one-way-or-the-other choice. Switching allocations models has been, typically, a semi-permanent solution in that all revenue will be processed using the model you choose, and there will be no future way to reverse that change. This inflexibility is one main reason that even when given the choice many users haven’t yet left last-click allocation behind. In ClickEquations all allocation models are supported simultaneously. We calculate revenue, profit, and conversions using all four supported models every day. This means you can change allocation models at any time and all revenue numbers in all reports are immediately retroactively updated. So you can choose ‘Linear’ allocation and spend a few minutes looking at last months’ results, then choose ‘First-Click’ and go back and review those same reports. An even more powerful reporting option, is that you can access the revenue report for each keyword on all four allocation models together in Acquisio Analyst.

multiple-allocationClick To Enlarge

One warning about making allocation model changes: ClickEquations bid-rules run every night and will use the revenue numbers defined by the allocation model set at the time they run. So you can freely and quickly change models in the web application while browsing your reports, but don’t forget to set the model back to your ‘official’ model before the nightly bid calculation run. Other Allocation Issues There are many other issues relating to revenue allocation; the time frame considered, the dates of clicks and conversions, and the number and treatment of subsequent conversions, to name a few. We’ll take a look at these and other advanced allocation issues in the next post. NOTE: Part II of this post is now available.



The First Machine Learning Marketing Platform
Built to Scale Search for Local Resellers & Agencies

Automate, optimize and track more campaigns, more profitably.