ClickEquations Blog

A Serious Look at Paid Search Marketing Strategies, Tactics & Tools

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 ClickEquations Analyst, so you can see the impact of the different models side-by-side.

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.

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  • rimmkaufman
    Interesting!

    You've discussed different methods for allocating credit, but above don't mention the economic impact of this choice.

    We've looked at this issue across our client base of over 100 online retailers, mostly B2C, who in nearly every case take the search-as-direct-marketing vs. the search-as-branding perspective.

    Within the population of advertisers we serve, we find that varying the revenue allocation scheme for keywords in multi-step click streams is a second- or third-order effect, because the preponderance of paid click streams we track as are exactly one click long.

    Here's a post George Michie did on this last month:

    rkgblog: Paid Search Buying Cycle: More thoughts.

    A more significant decision is choosing how revenue is allocated across channels, not keywords. This is the case when the advertiser has to decide how how to divvy up credit when, close in time before an order, they have marketing interactions via search, email, print catalog, affiliate, etc with the same consumer. Again, in the studies we've conducted for clients, today a substantial number of online orders are single channel, making the this a smaller impact problem. Likely the multi-channel rates will continue to rise into 2010, which will increase the importance of getting it right.

    Two questions:

    * Can you give some description of the population of advertisers on which your conclusions are based?

    * Can you some sense of the economic impact (sales and profits) of the impact of getting the revenue allocation scheme "right" vs. doing it "wrong?" Would you say this is 2%, 5%, 10%, or what-sized benefit/detriment?

    It is great to see folks in the industry thinking carefully about how advertisers should count revenue.

    These sorts of discussions raise the bar for the industry on how paid search should be run. Good stuff!

    Cheers --

    Alan Rimm-Kaufman
    rkgblog
  • Alan - Thanks for visiting and for your post. I read George's post and commented on it back then - thought provoking and was run just when our dev system was able to allow me to build the first ClickEquations Analyst templates comparing performance between attribution models. There wasn't much data then, and while that very preliminary look showed some of what George and you mention - keywords where there is little impact, it also showed some keywords where there was dramatic impact.

    At this point, we have a little more data on those accounts that were running in our dev environment, but the majority of our clients didn't get this upgrade until last week, and we didn't rerun all attibution models historically, so we're just building up a data history to do more comprehensive studies. I promise we will and post real data as soon as it's practical.

    My general comments ("last click must die") are admittedly partially philosophical, but even looking at the data from those 'dev' clients now we do see pockets of keywords where the impact is +100% in terms of the revenue allocated to a keyword linear vs last. So even if it turns out to be 5% of the keywords, turning those off without realizing their impact would in my view be unfortunate.

    But mainly, the post and opinions were backdrop to our new ability to get and present rich flexible data to learn the truth - any CQ client can now see four different attribution views of their own results, and compare and decide for themselves. So let the analysis begin!

    On your cross-channel point, 100% agreement. One step at a time, but stay tuned!
  • Interesting read. But when a campaign is losing money, it's usually those underperforming keywords that get cut first.

    The conversion of a keyword is not always constant. It could be seasonal or just a million other things out of your control that affect why people are searching for it.

    maybe a TV ad is getting users to search for a specific term that converts well, then that ad is pulled after several months and your conversions drop with just the regular seraches, who knows ...
  • Yes, it's a very complicated subject. There's not an easy answer for the reasons in the post, the ones you mention, and those to be covered in the follow-on post. Like many other aspects of online marketing, a very simplistic view has taken hold and there are a lot of decisions being made without any clear understanding that they're being made based on very bad data. Awareness of the issue, and it's complexity, is the first step...
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