While launching a UX process, we undertake the goal of finding a perfect solution for a certain problem. We use varied methods to understand the business assumptions better, get to know the users, recognize the environment for the product or service, the competition, and technological capacities. We apply cognitive methods which give us qualitative insights. We observe the users, conduct interviews, build personas, consider information architecture, perform different professional and heuristic analysis, build prototypes, and test them.
However, a perfect supplement of qualitative data are quantitative ones, too – used as soon as the concept part of the process starts. The word “analytics” is usually associated with charts full of digits that can only make us dizzy. Yet, first of all, there’s nothing to be afraid of. And secondly, analytics covers so much more than the charts :).
What does analytics give us?
Analytical data reduce the counter-productive discussions among a project team and with a client. And it’s not only a matter of what somebody feels. The data can show if the feeling is valid in a larger group of users or if it concerns only individuals. Checking the numbers, we’ll see that, e.g. banner A converts great, whereas banner B not so much. As a result, the arguments for version B are effectively brought down.
Analytics allows us to get to know our users better. We can verify the declared data based on behavioral ones which helps verify the information acquired in interviews with users. It’s quite common that people want to come out well embellishing their responses or they misrepresent the facts unconsciously, e.g. asked if they would be eager to use a Help tab in the service, they answer that they always do. And checking the quantitative data, we see that nobody has ever clicked the tab although it has been properly prepared. That’s why analytics helps track down all the imprecisions and build personas reflecting real users in the most accurate way.
Implementing analytics allows following various actions abreast, most commonly live. We can literally track the user through all the elements of interaction with our product and find out what is considered as a great solution and what bothers them the most.
We can control the source of traffic and analyze it for planning future actions in both developing the product and the marketing strategy. Access to analytics lets us verify all the implemented changes.
Every business owner wants to know what their competition’s up to. How to be a step ahead of it? What helps is the analytics connected with SEO operations.
Analytics doesn’t have to be linked to the users’ behaviour. There are also tests, e.g. performance, system’s speed, errors, restarts, and many other varied events that show the technical condition of our product.
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When and how to implement analytics in a UX process?
The scope of quantitative research should be established at the very beginning of the UX design process. After analysing the first material, we can determine areas which enable collecting quantitative data and provide value. First phase can include surveys and opinion polls which engage the user directly.
During the phase of prototyping and implementing new solutions, it’s worth analysing how to fulfill the established goals and whether they are even pursued.
After introducing the product into the product environment, we get many answers – how it works in comparison to the competition, how it functions over time, who the users are and what they do, and most importantly, if they fulfill the goals, and if not – why. There are many possibilities to analyze all these matters and, depending on the need, it’s worth choosing the most appropriate tool, e.g. heatmaps, clickmaps, session recordings, A/B tests, and analytical tools.
Can analytics be a threat?
It seems that analytical data that show real values provide us with only positive experience. Nevertheless they can interpose consternations, too.
Let’s assume that a team worked 100 hours in a project hoping to enhance a functionality or a user’s path and… nothing changed. Data showed that the number of conversions didn’t rise and actually nothing happened. What then? Who’ll answer for the lack of results?
Analytical data don’t reflect good will or expectations. They provide information about what’s happening with the product. If our team and clients are not ready for failures, analytical data can cause unpleasant situations.
It’s surely worth implementing quantitative research in a business process. Their scope should be adjusted to the project and its environment. The advantages are: a quick data analysis, low individual costs, short period of implementation, a possibility of analyzing the visual materials, and ensuring anonymity to research participants.
We don’t have to fear the numbers. They don’t oppose the UX process – they serve it. Combining qualitative and quantitative research gives us a broader spectrum of knowledge, the highest project value, and, in consequence, a valuable product.