The data science behind UX
October 4, 2019  

Supplied by entelect2013 Administrator from entelect2013
With the technological advancement in the 21st century, everybody wants to experience the best technology with the least amount of effort. By now, user experience (UX) design is recognised as a crucial part of customer satisfaction, but without the relevant data to validate the designs, we’re relying too much on intuition.

 

With the technological advancement in the 21st century, everybody wants to experience the best technology with the least amount of effort. By now, user experience (UX) design is recognised as a crucial part of customer satisfaction, but without the relevant data to validate the designs, we’re relying too much on intuition.

 

Design and data are a hard-to-ignore symbiosis. UX Engineers have a variety of research and problem-solving techniques at their disposal, but data is probably the most important. While design instincts are still valuable, data and analytics can help you refine your product understanding and ensure your decisions satisfy stakeholders.

 

Familiarity with users isn’t the same as listening to them. Here is how using targeted, data-driven research can help you listen to the needs of users to create seamless and memorable digital experiences:

 

1.    Find what truly resonates with users


If you’re designing a completely new product, you need user input, However, users are infamously bad at explaining their needs, often asking for the wrong features. Experience and good intuition are assets for UX Engineers, but without robust data, decisions would be based on educated guesses and some fairly intangible principles.

 

This is where data science comes in. Using data-driven insights, UX Engineers will better understand user behaviour and context, categorise users by behavioural patterns, and find direction in the design process.

 

2.    Focus on the correct problem


Being familiar with the product and deeply invested in its success, often affects the objectivity of the UX Engineer. Data-driven UX, provides insights to minimise guesswork, preventing UX Engineers from wasting time by circling back to research steps in the process.

 

It’s not necessarily about the data – but rather about gathering and analysing data properly in order to find the correct problems which will need creative solutions. The lack of data-supported evidence risks designing the wrong services, products and experiences.

 

3.    Explore other approaches


Assumptions are always a problem with the creative process of UX design, especially when it comes to relying on gut feeling. It’s hard to shake off usual approaches and best practices to create something that truly resonates with your audience. Data science can help guide UX Engineers in a more creative direction, tailoring the UX specifically to each target audience.  

 

Data may show user misunderstandings, how they take shortcuts, or how different groups of users fundamentally use our systems in very different ways – ultimately revealing unexpected findings that improve our understanding.

 

4.    Support bold design concepts


Data is more likely to encourage creative design than to supress it. When UX design principles are based on data-driven research, the outcome is highly personalised and optimised.

 

Design is often seen as subjective in the technology industry, but research findings can help gain buy-in from stakeholders and clients. Stakeholders ultimately just want a solution that works and works well. Making decisions based on evidence gathered from actual user research and data, will help them gain confidence in the proposed solution.

 

 

UX problems are complex (sometimes in their simplicity), and so solutions must be sought through evidence-based experimentation, practice and collaboration. Data provides the ultimate quantitative evidence to prove or falsify our instincts. Data should drive a responsive and improving UX, which then drives new data for measurement and observation. This is a valuable process which can create momentum of its own, resulting in an ever-evolving data-driven UX cycle.

 

 


 
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