We rely on web analytics software to measure and dissect user activity, marketing effectiveness and performance metrics across our online media. Powerful tools such as Google Analytics and Omniture help us build an increasingly detailed picture of our visitor’s journey from their point of origin to their point of departure. Eye tracking software, heatmaps and multivariate testing add further to our understanding of user interaction and help us tune performance.
But they only give us half the picture.
That’s because your customers are not numbers. They have emotions and desires and frustrations that we cannot measure with code snippets and conversion funnels. We can track what customers are looking at or where they click on a page or which headline they respond to but it is near impossible to ascertain what they are feeling by studying graphs and heat maps.
For example, usage statistics can reveal that 26% of the visitors to your shiny new landing page clicked on a sales promotion. This is great, now you have analytics to report. But what you would really like to know is the reason the other 74% didn’t. Unfortunately, while traffic analysis does an excellent job of telling you ‘what’ is happening, it falls short when it comes to understanding the ‘why’. Which is where user experience (UX) research comes in.
Web analytics can inform, there is no doubt about that. The difference with usability testing is, the users will show you WHY they abandoned the process before completion. Armed with that information, the onus is now on the site owner, mobile app developer or Ecommerce manager to see if there is anything they can do about it.
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To give you an example, a company set up UX tests with 5 participants asking them to visit their website and complete a number of specific and relevant tasks. The video clips showed very clearly where the problem lay. At a critical point in the payment process, the customer was confused by the final cost of her purchase. An optional extra that was inadvertently selected, was added to the total cost but it was not clear to the customer, why that this had happened. This confusion was sufficient to cause a lost sale.
Now the company has diagnosed exactly what the problem was and WHY users were abandoning their baskets in the process. The next step was to remove the confusion and make it easier for customers to complete their intended tasks.
Matching quantitative web analytics with qualitative customer insights in this way gives us a much broader and deeper understanding of customer interaction and engagement with our online media.
Statistical analysis answers questions like What?, How?, Where?, and When?
But UX research and testing can tell us WHY.