Face it: data is boring and confusing. We’ve all been in meetings to review a PowerPoint presentation or reams of data where your eyes cross at the sheer volume of information. I know, because I’ve been on the other side of the equation–the lone web analyst in front of tough marketers. Inevitably, all questions can be rolled up into one of three categories:
- What happened?
- Why did it happen?
- What should we do about it?
Obviously, there’s no substitute for a talented web analyst (that’s where you should be spending 90% of your budget) or a web analytics process. I’ve also found that context is a crucial element. Here are 4 simple ways to improve your web analysis.
- Set a Target – Revenue went up 10%. Is that good? Is it expected or unexpected? The natural inclination for most people when they look at numbers is to want them to go up and to the right. Set expectations by establishing a target. The simplest way to do this is to use last year’s data plus some expectation of year over year improvement.
- Include Historical Data – Seasonality can play a big factor, especially in ecommerce businesses. Providing year over year and month over month data can help you get a sense of whether trends reflect actual changes or just natural variation
- Note External Events – Your data craves context. Chances are that everyone reviewing your dashboards will not know about all the promotional and marketing events that influence how you read the numbers. Even if your web analytics program doesn’t include a way to add this right with the data, simply keep a table by month of the notable events that color your analysis.
- Index Against Yourself – At the very least, your campaign performance should be comparable to your own campaigns. One clever email vendor has a built in function that records all past performance and creates an index to tell you how much higher or lower key metrics, such as open rate, are than past emails. You could do the same thing by simply recording all of the data in Excel and calculating current vs. past performance. It’s a rough way to start setting expectations.
So there you have it, 4 simple ways to improve your web analysis and make it easier for everyone to interpret the data. What are your tips?