When the conversion rate drops — we’re starting the finger-pointing process. Maybe it’s poorly set up remarketing campaign for abandoned carts? Maybe the design team chosen uninviting product’s photos? Or maybe marketing hired the wrong influencer to boost your limping sales again? Maybe. But it’s much more likely to find satisfactory answers by looking at Experience Metrics and analyzing user behavior.
Experience Metrics, i.e. data about the behavior, emotions and experiences of your users, is a relatively new concept. Yes, for years we have heard that customers want personalization and appreciate brands that understand their needs. But don’t we ignore the analysis of user behavior in our daily analytics?
What are Experience Metrics?
Experience Metrics is all information about the frustration of users visiting your website — be it compulsive content refreshing, zooming via mobile, or the so-called rage clicks. These are the situations that not only indicate critical points on the customer journey. By analyzing user behavior, you can understand why your consumers get angry when they visit your website and how their unexpected frustration is effectively killing your conversion.
The future of experiences
Constant technological progress, pademically accelerated digitization and the everyday life of digital products mean that we have less and less time for research. Analytics began to be a luxury. Or at least we think so.
Focusing on small data — ideally suited to our research needs — allows not only to save time, but also to reach conclusions and improvements more efficiently and effectively. Working with behavioral analysis allows you to catch the so-called quick wins — immediate and low-cost implementations affecting the conversion.
Automated goal-oriented analysis (e.g. sales) can lead you at an accelerated pace not only to the cosmetic changes that our customers expect, but also to optimizing the Customer Journey and improving the User Experience. Therefore, wise inference based on behavioral data has the potential to support conversion and detect sites that inhibit it.
The ubiquitous automation also influenced the analysis of behavior patterns. UX automation tools, thanks to the use of machine learning, are able to recognize patterns of specific user behavior. On their basis, algorithms learns and draws conclusions. Ultimately, they are able to predict which of these behaviors are influencing your business.
For example, you run an e-commerce store that sells footwear. Your overriding goal is to get the highest possible transaction-to-sales ratio. The CUX algorithm has already learned to detect rage clicks — angry mouse clicks on the page. The tool shows you that the “rage click” rate is extremely high on the checkout page. You check recordings of visits. It turns out that the shipping form does not work on mobile phones. For this reason, users abandon filled carts without completing the order. Thanks to automated behavior analysis, you are able to catch this error almost immediately, thus saving your conversion and increasing your sales.
I know what you feel
The analysis of behavior patterns is also a brilliant tool for marketers. Knowing the problems and pains of users, they are able to react appropriately. Personalize content (hyper-personalization), optimize user paths or even update sales personas based on data. By examining the correlation between user experience and shopping, marketers are able to construct advertising budgets that will not go down the drain.
Experience Metrics is not quantum physics. Thanks to tools such as CUX — which analyze automatically, using learning algorithms — it is accessible to everyone, uncomplicated and, above all, leading to immediate improvements in achieving a high level of conversion.