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Getting Dirty in the Domain Data

Getting Dirty in the Domain Data

We’ve recently been working with a client to better understand the underperformance of a number of their domain names compared to a few months earlier. What we discovered when we conducted an internal forensic analysis of the data was quite surprising.

The domain we will look at in this article has been renamed to A.COM for privacy purposes. Since the beginning of the year it has had a monthly revenue ranging from $183 to $1221 and a normalised RPM (revenue per thousand visitors) of $75 to a high of $488. So what was going on with this domain?

Upon closer inspection we found that A.COM was a domain from the travel industry. People were looking for the services the domain offered from May to July and this dramatically pushed RPM rates higher as advertisers competed more aggressively for the traffic during this time. In fact, the peak numbers were achieved by a direct advertising travel company.

So let’s imagine you bought the domain based on those very high July numbers? It wouldn’t be long before you discovered your investment was under water as the typical monthly revenue retreated to 20% of the peak! Our client isn’t unscrupulous so they would never do this…..but always remember, buyer beware!

So now we know we are dealing with a domain that is seasonal but the second question that needs answering is whether it is being properly optimised during the entire year? The only way to answer that question is to look at where the raw traffic was sent and examine the revenue it generated.

The first table below shows the traffic sent to the different monetisation providers (Ad networks are aggregated) over the period of twelve months.  Remember this is the raw unfiltered traffic and is not impacted by any filtering algorithms the different monetisation providers may apply before displaying the views in their interface.

The two letter codes at the tops of the columns represent the parking companies the traffic for this domain was sent through to. In order they are; Sedo Domain Sponsor, Voodoo, Bodis and Parking Crew.


The second table is the Revenue the traffic generated from the above traffic and the third table is the normalised RPM. Don’t forget the definition of a normalised RPM is the revenue divided by the raw traffic multiplied by one thousand. This metric will allow us to accurately compare one monetisation provider against versus another and know beyond a shadow of doubt who is performing the best.

For simplicities sake, I’ve removed all mention of the direct advertising networks from the Winner table and concentrated on the traditional parking solutions that were sent traffic.


Starting in January this year we can see that Voodoo was winning with normalised RPM (nRPM) of 68.94 and close on its heels was Parking Crew with $65.69. Logically, Voodoo received the lion share of the traffic followed by Parking Crew while Sedo and Domain Sponsor received enough for sampling purposes.

In February, circumstances have really changed with Sedo shooting to the winning position and snagging a lot more of the traffic for both February and Mark. And so it goes, month after month we can examine the numbers and see who is winning the traffic at every stage until we have the below table.

Sadly, for A.COM, Domain Sponsor wasn’t the overall winner in any month but it’s not uncommon to have one monetisation company not win for a long time and then suddenly spring up. What is clear in the tables of data is that the “winner” is constantly moving. I didn’t do the analysis but my guess is if I got down to the daily level then the flow of traffic between winners would be even more dynamic.

So how did this analysis help the client? They needed to report to their board with confidence that everything that could be done was being done to optimise their domain traffic to the highest paying solution at any point in time. This data proved this was the case.

The data also provided them with the necessary information to back-up the supposition that some domains fluctuate all over the place. It’s one thing to suggest seasonality or variability and quite another to prove that it’s the case. Once again, the data provided the necessary information to support this hypothesis.

The final question that needed answering was whether A.COM was actually performing less than twelve months previously. The answer was a resounding no. The RPM twelve months ago was $115 while now it was $146. What triggered the investigation was the previous month the RPM had dropped to $95. Remember it’s a seasonal domain, no one wants to purchase travel services at the end of summer. So this domain clearly experience a post-summer slump and then quickly rose out of it.

So analysis is a lot more than just mobilising a lot of numbers. It’s also about interpreting what they are telling you about a domain so you can understand whether you are actually getting the best performance at any point in time.

Greenberg and Lieberman

Maximising Domain Revenue
Saturday Musings - When Someone Spoils Your Week.....

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Wolftalker on 25 October 2016
Good investigation.

Congrats on good detective work.

Congrats on good detective work.
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