User experience is more than data collection. It’s about understanding the motivation behind user needs and striking a strategic balance between expectations and business needs
Joshua Garity explains the easy way to understand your customers
U ser experience is not magic. You don’t run a simple test that Becky the marketing intern read a blog about once and uncover quick-fix solutions to generate huge growth. UX strategy is a science: a science that has been around since long before the first computer blipped into existence and long before UX became a buzzword in the Nineties.
All scientific theories begin as a hypothesis. The assumption of purpose. Why are these events happening? Then you test the hypothesis by collecting data to validate, or invalidate, the hypothesis. It then becomes a theory.
A theory is a validated explanation of why something is happening. A theory is not based on bias nor is it based on what the loudest person in the room is saying; it’s based on factual data collected through a replicable method. You know. Because science. Without that structure, it’s easy to run a test and fallback on confirmation bias, or data manipulation, to get the feedback you want. That’s not how this works. We don’t control the outcome. We find a means to communicate the complex nuance of user behaviour in a simple way. Sometimes the data proves us wrong and that’s okay. The goal isn’t to always be right; it’s to uncover the facts.
User data solutions like Google Analytics rely heavily on assumption. You can export records and use a service like IBM Watson to find correlating trends. However, don’t confuse data with fact. Predictive modelling or assumptions are the first step, but they don’t answer the golden question of why. Why a user is motivated to take an action is the central focus of UX.
This is the inherent problem with user experience. Everyone thinks they have all
the answers. UX then becomes guided by perception bias.
Think of it this way. The sales team thinks they know what customers want to buy and the marketing team thinks they know how to convince customers they want it. Management has an approved budget based on what they assumed the teams would need a year ago and it likely didn’t include budget for UX research. Sound familiar?
Each organisation, department or employee has their own perspective on what should be done based on their own experience with customers. The problem is they’re all right. The bigger problem is that they’re all wrong too.
Organisations that fall into this perception trap often find themselves avoiding the conflict of a heated debate and try to serve everyone. The problem with trying to serve everyone is that you’re not serving anyone.
The job of user experience is to remove that bias and help the group to understand a bigger picture: the needs and expectations of the customer. So how can we reframe the conversation and make it less about opinion?
Let data do the talking. The process of validating different data can provide different perspectives misunderstood by the vast majority of people. It does not need to be devoid of emotion nor does it need to focus strictly on usability. What it needs to have is a purpose. What kinds of data are you collecting and why? There are two core types of data to collect: 1. Qualitative: emotional feedback Qualitative research gathers nonnumerical feedback from participants. Think first reactions or personal opinionbased feedback. What you liked and why, and descriptions instead of numbers. Qualitative = quality. 2. Quantitative: scientific data Quantitative research gathers numerical feedback. Perform this action and rate the ease of completing the action on a scale of one to ten. This is the basis for systems like Net Promotor Score (NPS). Quantitative = quantity. What you need to analyse should determine what data you need to collect For example, if you’re tasked with creating a baseline for customer satisfaction on member sign-up or checkout in a shopping cart you’re going to need quantitative data. This lets you collect unbiased numbers that show a clear progression from where you began to where you ended months or years later. This is crucial in showing the importance of investing in UX within an organisation.
Many organisations will see the initial improvement and not understand the value in retesting.
Seeing an increase in sign-ups, revenue or drop in support requests is fantastic but there are many variables that could influence results. Attribution is your friend. It’s also the friend of the departments that you will be working with to showcase explicitly that the testing performed and subsequent changes were validated.
This goes back to the scientific validation we discussed earlier. Collect the data, make the change and validate that the change was accurate. If it wasn’t, create a hypothesis as to why it wasn’t and begin again. The trick is to always try to prove something wrong.
If you’re redesigning a consumer facing website without a long-term UX plan it may be okay to focus on qualitative feedback: descriptions and emotions. This works well for design-centric UX like landing pages for marketing or blogs. This does not work well for long-term strategy as trends are fluid. What works today for a tested demographic may not work well next year, so be careful.
Qualitative feedback is harder to distil into strategy because what users say they want and what they actually want are two completely different things in most cases. It requires a lot of foresight into when to peel back the layers of feedback and dig deeper with follow-up questions or facilitation.
Without the context of motivation, you become trapped in a feedback loop. This tends to lead down the perception trap again. If you’re stuck without direction you will try to find meaning in the data by applying bias. Once that happens, you focus on the wrong meaning and the data becomes useless. How focusing on the wrong meaning can derail a project Let’s take a look at another example: tenants in a New York office building would complain because, in their opinion, there was too much time in-between pressing the button and when the elevator would arrive, ding and open. Several tenants threatened to move out. They wanted a faster elevator to solve the problem. This is qualitative feedback and emotional responses. Management requested a feasibility study to determine cost and effectiveness, which means hard numbers and quantitative data.
A different perspective from someone in the psychology field focused on the tenants’ core needs by digging deeper than their initial feedback. They ignored the numeric feedback of the financial study because it was not costeffective to replace the elevator and rebuild the structure to accommodate the tenant’s suggestions.
The psychologist determined that finding a way to occupy the tenants’
An increase in sign-ups, revenue or drop in support requests is fantastic but there are many variables that could influence results