ClickEquations Blog

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

The Crazy World of Revenue Attribution

Revenue attribution is how you (or the software tracking your online marketing activities) decides where to place the credit for the sales that occur on your website.

attributionIf someone who has never been to your website before does a paid search, clicks an ad caused by a keyword you bought, then makes a purchase, the attribution is easy. They keyword that you paid for to get the click, should get credit for the revenue generated by the purchase.

But very often, this is not the scenario that leads to conversions which take place on your site.

  • People come multiple times before purchasing.
  • They often come from different sources each time the come, occasionally repeating sources along the way.
  • They sometimes make a series of purchases, either after all of their visits or interlaced among their visits.

These are just a small sampling of the issues and don’t begin to define or describe the complexities.

And this is not the post where I’ll try to do either. (Those will come.)

But was we work to sort out the right way to handle revenue attribution within ClickEquations, we’re capturing some data that I thought it would be interesting to share.

The following images document real-life ‘click-chains’ – sequences of visits to a website with resulting or intersperced conversions. They are a tiny tiny fraction of the sequences found in one account in a 30 day period.

  • Rows with green ‘P’ cells are visits that came from paid search keyword clicks.
  • Rows with white ‘N’ cells are visits from organic search, email, affiliate links or other sources.
  • Rows with yellow ‘C’ cells are conversion events.
  • The number in the first column represents the visit number for that person over all time.
  • Only visits within a 30 day window are included although the visit count may have begun far earlier.

And if you’re into this kind of thing, they’re very interesting.

Click-Chain Histories

#1 – Our first contestant is a frequent visitor (note we’re starting with visit 37), loves those paid search ads, but does buy at least occasionally.

att1

Four more after the jump (as they say)

#2 builds up a head of steam with a bunch of Dec 30th visits and a purchase, then wham-wham, two more buys with no more paid-click-charges over the next few days.

att2

#3 is another guy an online marketer could love. One paid search, three purchases in a row.

att3

#4 is a jack-rabbit, clicking six paid searches over two days, but coming through with the purchase in the end.

att5

#5 is another consistent visitor and shopper, with purchases before and after their two paid clicks.

att6

For each of these, assuming for a moment that we’re only concerned with paid search, how would you allocate the (yellow) revenue to the (green) keywords?

Now make it harder – assume that for each (white) ‘N’ we can tell you if it was a bookmark or email or affiliate link. How would you allocate revenue across all the visits?

Hint: There are no right answers. Much more later.

  • Greg

    Like painting a house, my experience with these projects is that 90% of the job is prep work. After six months of getting a data set ship shape, relatively simple statistical analysis will produce great results.

    As I believe Jeff Bezos once said, Amazon's greatest asset is it's database. As the provider of an application, your greatest asset may be having multiple databases, one for each customer, with all databases structured identically.

    Once you do the hard work for one customer, the prize will be a database that has been restructured many times, with additional database tables of derived data. With data that's just right you can run the statistics and show the results.

    The payoff will be that all customers will have the new database, so the usual need for a custom job and expensive prep work will be gone. Each customer will only need to let their account run and their database fill, and the built in statistics formula will present some wonderful attribution reports.

    The results may be surprising. Rigorous analysis may reveal that if the first click goes to a specific ad, the person is 20% less likely to buy. Pausing that ad may boost sales by 5%.

    I'm skeptical of CRM – Conference Room Marketing. Things don't need to make sense when presented to the group. Ideas that are clear and quickly accepted may actually hurt the business. Ideas that everyone rejects as nonsense and flat out wrong may actually work quite well.

    I like This Old House because in this complex world it is relaxing and comforting to watch skilled people succeed in an environment where reasonably simple rules, like measure once and cut twice, will do the trick.

    My job involves imponderables, complexities beyond my comprehension. When the spreadsheets go up for the senior execs, I sit quietly, waiting to talk with the consultant at lunch, where we will talk shop and reveal how little we really know, where we will talk about what the spreadsheets don't contain, where we will acknowledge that our different approaches are often mere personal preferences and not the solid conclusions the black and white numbers on the spreadsheets make them out to be.

    Months of work often results in incremental improvements. While glancing at a few customer records occasionally yields insights and it's fun to offer up a guess, I am skeptical, since this approach often produces results that give new meaning to your final statement – there are no right answers. When the data is so complex, how can it be otherwise?

  • http://clickequations.com Craig Danuloff

    Thanks Greg – Interesting thoughts well expressed. We can and will do the analysis, but I worry that there aren't universal truths even in deep analysis – in other words there are all kinds of visitors who behave all kinds of ways, so there is no right answer. The real 'right answer' is to be able to show multiple angles on what the attribution means, so that a keyword doesn't have one number – the revenue it gets credit for – but many numbers which taken together give the impression/weight of that keyword and they are all considered when a person or an algo decided what to do next about that keyword. Much left to do.

  • Greg Moore

    Brian Clifton has a Google Analytics hack in his book, p. 203, “Changing the Referrer Credited for a Conversion.”

    Do you happen to know anyone who has tried this?

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