Digital analytics has become increasingly important as an integrated discipline in our design processes. Data analytics and user research have joined paths to offer a holistic view of the insights we get from users. This gives us a more exhaustive understanding of the customer journey, all in a single research experience.
User Experience professionals bring various techniques into play, such as user tests, ethnographic studies, interviews, focus groups, and so on. But this type of research—albeit praiseworthy for helping us figure out users’ needs, frustrations and expectations as they relate to the use of digital products and services—is no longer enough. We need to support our research with quantitative data in order to make a more assertive design decision.
Until recently, analytics was only linked to specific departments or to Marketing exclusively, but it’s gradually becoming a transversal discipline and is already part of our UX strategy or CRO (Conversion Rate Optimization).
To do this, we apply data-driven or data-based design methodologies in any project, be it software development or sites for eCommerce or lead capture.
Incorporating a data culture into our work creates countless advantages:
In addition, data becomes more important in projects that require CRO strategies—finding conversion problems requires constant measurement of our digital assets. The qualitative and quantitative data will help us form hypotheses that are geared toward making decisions and improvements, as you will see explained soon.
Designing based on data requires adapting design methods and techniques, guiding them to achieve business objectives. Before starting to design the solutions, we must answer the questions:
Without going into detail, a good methodological basis includes these activities:
CRO stands for Conversion Rate Optimization.
We can find many definitions for what it means to do CRO in a digital business model, but to me, this best describes it:
When we talk about CRO, we are talking about a discipline and methodology aimed at improving the commercial and business efficiency of any digital asset (web, application, landing, etc.) through the 1). identification of improvement points 2). generation of hypotheses that explain the inefficiencies detected 3). design of actions to correct them and 4). implementation and monitoring of the effectiveness of those actions
Furthermore, all of this assumes one clear objective: to improve the profitability of whatever digital business we work with.
The development of the comprehensive UX strategy for these conversion-focused projects goes, once again, through the analytical management of data, segmenting and identifying those elements and/or pages with the greatest contribution to the conversion.
Any company that 1. has a website, application, SaaS models, eCommerce, etc., and 2. That wants users to end up carrying out a beneficial action for the digital business portion of the organization (purchase, registration, contact, etc.).
Of course. First, we need to understand that making optimization strategies involves following a methodology that encompasses a fair amount of effort.
This work proposal goes through a several phases:
It’s a continuous cycle that we can integrate into an agile methodology.
Monitoring the results of our strategy is essential for the success of our projects. Knowing the results helps us analyze and make decisions that correct the course of the strategy followed.
In our UX discipline (as in any other), we have a great toolbox to use depending on the project and strategy to follow. Our traditional UX work tools are joined by others specific to data analytics, such as Google Analytics, Adobe Analytics or more specific ones like VWO, Optimizely, Mixpanel, to name a few. With them, we must be able to convert data into useful information that helps us understand the user, find out what our customers like the most, discover potential market segments, identify patterns of behavior, identify vanishing points in our conversion funnels, and so on.
What I named are some tools that I currently like quite a bit, but as we know, in the world of technology, what is relevant today is no longer relevant tomorrow 😉.
Hotjar/Clicktale/CrazyEgg are tools specifically designed for a class of tasks pertaining to checking where users get stuck, where they decide to abandon and the time it takes for users to complete forms.
Typeform (both NPS [Net Promoter Score] and general)
Optimizely allows you to create different versions of a website quickly without the need to program them in order to analyze them and decide which is the most suitable core for further development.
Google Optimize allows the creation of different variants in different types of tests, as well as HTML and CSS customization of the site to be tested.
Mixpanel is a simple but different approach to the world of web analytics. It has interesting online marketing functions to improve site visitor engagement.
KISSmetrics is a cloud platform for web analytics whose main characteristic is displaying intelligence associated with each person or client, monitoring in real time the actions users take while navigating the site and collecting information on reference, search, behavior and conversion funnel sites.
One of the most important parts of the data analysis process is showing results. Data Visualization is the discipline responsible for identifying the best ways to graphically represent data, using instruments that turn the data into insight that’s accessible at any time. Dashboards are responsible for showing all our KPIs at a glance.
User experience analytics helps us identify new user needs and respond with new services to meet them. It also helps us detect, correct and optimize our applications, functionalities or websites continuously, which impacts conversions and improves profitability in the ambit of digital business, because we can figure out where to invest our efforts.