Data: It’s messy but get over it
This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.
It was busy week last week for the online marketer in London with the conference season getting into full swing. There was an inaugural Internet Retailing conference followed by two days of Ad-Tech. By all accounts both events were well attended both in the conferences and also on the exhibition floors. This is good news as its showing the industry is thriving and there are more people out there wanting to do more things.
I was at Ad-Tech last Wednesday running a couple of workshop sessions. One of the workshop sessions was entitled “Data integration: Why does it have to be so difficult?” where I moderated a panel made up of representatives from Tangozebra and Logan Todd here in the UK and from Sitebrand giving a North American perspective on the issue.
We didn’t really know what to expect from the audience and we were worried that we would fail to keep them engaged for long on what might be considered somewhat dry and turgid subject matter.
We needn’t have worried. After a cautious start (after all we are talking about Britain here) the audience soon warmed up to the theme which was basically this: “How do I make sense of all these numbers? Especially when I have different tools, supposedly showing measuring the same thing showing me different numbers!”
A show of hands revealed that the vast majority of end users were using at least two web analytics systems to track their online businesses as well as having other data coming in from search engine marketing systems, ad-server reports, affiliate systems and so on. Probably fairly typical of most online businesses today. So, how do you deal with it?
Probably the most consistent piece of advice that came back from the panel was that it’s messy and difficult, so don’t spend loads of time and effort trying to get it all absolutely right. But at the same time be absolutely clear about it is your measuring in the first place and so understand the difference between a click recorded on an advertising system and a visit recorded on a web analytics system. The two are not the same thing.
One of the biggest fallacies in online marketing is that’s totally measurable and accountable. I think we all know that it just isn’t true. Measuring online marketing performance is fuzzy and complicated. For example, how do you attribute sales properly? Typically you may have someone first visiting your site on a search referral, followed by a subsequent visit from an affiliate before converting on a later visit by coming to the site direct. Each of the acquisition marketing channel vendors may have their own rules claiming the sale. How do you avoid paying two or three times over?
The panels’ answer was to set your own rules and decide how you are going to pay for traffic and conversions. But to do this you also need good data, so you need to spend some time and effort getting your tracking systems sorted out so that you fully understand the dynamics of customer acquisition on your site.
As with a lot of things in life there isn’t a “silver bullet” to solving the problems of data integration and data validation. One key aspect that came through from the session was the importance of looking at relative performance rather than absolute performance all the time. We used to have a saying when I was at ACNielsen many years ago that “a trend is a friend”. If the data shows consistent in a certain direction, then it’s probably true. If there is a sudden change, then it’s either as a result of something happening or there’s an issue with the data. Either way, you will want to find out why is happening.
The same principal can be applied to online marketing data. Rather than worry too much about why “System A” is showing a different level of traffic then “System B”, look to make sure that the trends are at least in the same direction. Also, rather than focus to much on is it 10,000 visits or 12,000 visits, look at what’s happening between different visitor segments for example.
With such a data rich environment it’s potentially easy to get consumed by the numbers. The feeling from the session in London last week, that you just need to accept that it’s messy and horrible, get over it and move on. There’s a cost to perfect information that’s probably not worth everyone paying.