You have come to expect a certain amount of unique visitors to your website every month. You know that 30% of website hits bounce and the average time spent on the company site is around 1 minute, 10 seconds. Your overall traffic has increased by 48% in the last month (which is great news) and it’s only the start of February, so you and your team must be doing something right. Right? 

Many of us use web analytics to monitor online traction and believe that the data it presents is accurate enough to conclude things like:

  1. An increase in unique visitors coming to the site demonstrates that the last direct mail campaign had a positive impact on the organisation
  2. A decrease in bounce rate means our new home page design is working wonders for engaging customers and prospects
  3. While the number of repeat visitors has increased, these are not as important as the new visitors, which has also increased this month

 There are many other things we can assume from our web analytics data but I think the above gives a good sample of deductions most of us come to at some time in our digital marketing careers. The point of raising this today is, however, to explore the theory that much of what we deduce from our web analytics data is inaccurate and I’ll use the above assumptions to demonstrate how we might be getting it wrong.
 

Assumption #1: An increase in unique visitors coming to the site demonstrates that the last direct mail campaign had a positive impact on our organisation

Just because you’re unique visitor data shows an increase in traffic around the time of your last direct mail campaign doesn’t mean that your only marketing effort for that month had a positive impact on the organisation. For example, most of your unique visitors may have been steered to your site via an old magazine ad you took out a few months ago. Or, perhaps your involvement in an upcoming trade event has pushed an unexpected amount of new visitors your way. The point is, you don’t know exactly how your direct mail campaign had an impact (i.e. directly correlate the campaign to the individual visitors) and therefore can only assume an impact.

Assumption #2: A decrease in bounce rate means our new home page design is working wonders for engaging customers and prospects

It’s not that this assumption is necessarily incorrect; the problem lies more in the fact that there could be many reasons why the bounce rate has dropped. Just because the bounce rate reduction coincides with the month that the home page changes were made, doesn’t necessarily mean that the increase in traction is a result of the grand new design and layout.
 

The positive change may be the result of your last PR campaign whereby more traffic arrived at your site looking for specific information. Or you may have had less casual browsers that month which means people were conducting purposeful searches and therefore were less likely to land on the home page, get distracted and navigate away.
 

Food for thought…

Assumption #3: While the number of repeat visitors has increased, these are not as important as new visitors, which has also increased this month

There’s a tendency to view the new visitor figures each month as one of the single most important areas of data. And there’s good reason – it means that X amount of people who’d never previously engaged with your company online decided to pay you a visit. 

There’s no denying that it’s an important element of your web analytics data but don’t forget how valuable your repeat visitors are.  In fact, in the one week a new visitor may have arrived at your site, navigated away, seen one of your new banner ads, returned to your site and made a purchase or enquiry. This person shows up in your data as 1 new visitor and 1 repeat visitor which makes it really difficult to work out exactly how and why that person converted. Without knowing the full story of each visitor, you’ll never understand exactly how your marketing efforts are impacting the organisation.

Finally…

Simple web analytics services are a fantastic starting point for all businesses with an online presence. They give insight into overarching details like traffic data and which search terms people are plugging into Google or Yahoo! to find you. There’s so much they don’t give you though.

Without knowing an entire visitor’s history with your organisation, there’s no real way of understanding exactly what marketing tactics are working and what’s not. You can’t actually know for sure why a repeat visitor converted. Was it a result of your new ad or the partner event that he or she recently attended? How many marketing ‘touches’ did it take for you to win them over? How are you segmenting your customers so that you can communicate with them on a personal level, and how are you then tracking this group of customers online? The list of questions goes on. 

Remember the old adage: Assume makes an ass out of ‘u’ and me!  In web analytics this means dealing in facts – measuring full visitor activity (not just clicks or hits) in the context of the web site. 

This is my personal blog.  The views expressed here are my own and do not represent those of my employer, Coremetrics.