We are coming into a digital / data age by all accounts, and have been for a while. Everything is based on data, data data and making decisions based on that data, especially for ecommerce sites. Really though, do we know what we’re looking at? Do we understand the numbers? The differences in conversion rates, visitor sources and the like?

The truth is that data has two major caveats that everyone needs to be aware of and needs to put their statistical hat on to understand:

Data can tell any story

Depending the on the way you cut your data, the way you include or exclude certain groups and the metrics you use, data can tell wildly varying stories to people. The data, of course, is always the same (hopefully). One pool of data, and we attach meaning to that data. It’s the meaning we attach that can differ so much!

If we go into data wanting to draw a particular conclusion, then we can find a way to do it! Be careful.

Data needs to be statistically relevant

The good old AB test. A dangerous tool at times. We’re comparing two different landing pages. One converts the visitor 10% better than the other one. We’re all happy with the work done. Is this number real? ‘What do you mean is it real?’ I hear you cry – how can numbers lie? They can’t. But they can hide the whole truth.

First of all, sample size. Did you test enough visits and conversions. It ran over 100 visits? Not enough! To get statistical relevance we need a large sample size, over a decent time period.

Secondly, if you try an AA test, comparing exactly the same creative to itself, in theory they should perform exactly the same. In practice, this does not always happen. In fact, over at Get Elastic they performed an AA test and one version outperformed the other by 4.97%! Shocking. So that 10% difference could of been 5%, or 15% – who knows!

If you perform tests make sure you have statistically relevant numbers, and that the results you get show a very clear difference between the two. If you can, perform an AA test also.

Most companies are collecting data for no reason. Far too often we’re coming across companies that have some form of analytics but no idea why. They have had it put in (mainly because it’s free) and someone has normally shown them how to log in and told them what some of the numbers mean. They have never logged in again, let alone actually done any analysis of the data.

Data is useless without meaning. Meaning is useless without action. Companies need to be analysing their website statistics, finding out why certain trends are appearing or not appearing, and take action to change what they are seeing. Only then are you truly using web analytics, and not just being a data collector for no reason.

Here are some things you might want to know about your visitors:

• Where are they coming from? Which of these sources is the most profitable? What’s the trend of these visitor sources? Which are declining or increasing over time?
• What is my bounce rate? Which content gives the worst bounce rates (and therefore should be fixed)
• What content are they looking at on my site? Which pieces of content are the most popular? Which produce the most exits from the site?
• What keywords are people using to visit my site? Are they all brand led? If so what can I do to encourage people to visit my site that haven’t already heard of me?

There’s a ton of questions you can ask yourself. So why not start asking yourself right away. Stop collecting, start analysing.

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