Tips & Tricks

Key Things To Know About The Role Of Big Data In Validating & Measuring UX

Messy Desk with Big Data Related Notes

 

For companies, the quality user experience is crucial to gain competitive advantage. This is often ensured by monitoring and tracking user interactions. How far the quality of the experience can be delivered to customers remains a challenge for most businesses. The digital signature or traces left by business today tells a lot about their performance. Well, to measure the performance and customer experience in digital interaction, data analytics can offer a mine of resources. Big Data analytics besides offering data-driven insights about customers is helping us a lot in validating and measuring UX.

Big Data is a big scope

Big Data across devices

Big Data across devices

 

Big Data refers to the expansive digital data produced across platforms, devices and interfaces and they represent 5 crucial characteristics, namely, variety, volume, velocity, value, and veracity. The exponential fast paced growth of digital data in volume and variety is taken into account by this emerging field of data analysis. Obviously, data is increasingly becoming a valuable source for gaining business insights that traditional analytics often failed to obtain.

Big Data offers the most formidable scope of rigorous business analytics just because a diverse array of data sets belonging to both structured and unstructured data types from diverse sources now can be put under analytics to obtain deeper and more result driving insights. Big Data thanks to its robust potential in delivering business insights is widely regarded as the most important technology for the decision-making process in businesses.

All data is not insight

While large sources of data are always an asset, but their potential largely depends on upon extracting insights from them. There is a common misconception that all kinds of information contained in the operational process are needed for well-informed business decisions. Actually, understanding customers require more contextual data than dry numbers. When one is buying a product, all data corresponding to his preferences, time, and venue of buying, historical data concerning his buying habits, all these things matter as crucial data for drawing insights about the transaction and the customer. So, while operational data lurks insights, it is up to the integrated analytics to draw insights from them. Bridging the gap between the huge pool of data and insights is a big challenge for enterprises.

UX measurement and validation

Looking at diagram

Measureing UX

 

A web or app designer can feel content with a unique UX design. He may consider it out-of-the-box, engaging, fast-paced and performance driven. But all these claims have no meaning until and unless it is validated in quantifiable terms. To what extent the UX helps the business to drive customer engagement and conversion will be the final determinant factors. The business and outcome of customer’s interaction with the business should be clearly measured in quantified terms and that should be the principal determinant of the effectiveness of a UX.

UX validation and measurement is basically a streamlined approach with a set of measures for tracking business performance against the objectives and strategy. On the other hand, when working with Big Data, you need to start with a huge volume of data set and derive insight from them. But now, various businesses are showing interest in integrating this seemingly vast field of data to obtain insights on the performance of the UX.

The scope of analytics in measuring UX

While analytics tools have always been used for well-informed business decision making and strategy building, their utilization has been mostly restricted within marketers and strategists. With the increasing focus on UX performance for driving business growth in the recent times, it has now become a crucial area of focus for UX professionals as well.

Well, traditional analytics is limited in scope since its focus lies mostly within the operational business data and do not take other unspecified sources of data into account. For example, analytics can deliver insightful data in graphs and charts with the in-store sells, customer interaction, comparative study of various business outputs among stores, etc. But, it can totally miss the external factors related to customer’s mood-swings, buying habit over time and demographic insights on a various group of customers.

 

Measuring UX with Big Data

Now with the Big Data analytics, the overall picture is quite different. You not only have operational business data for immediate insights into customer interaction, but you have huge chunks of data from diverse sources to tell about customers and user interaction with more depth. With new and huge chunks of data assets, available decision-making can be more apt to take each and every influence factors into consideration. From customer intention, their emotional reactions to expectations all contribute to the measurement and validation of user experience now.

Actually, the objective of this multi-sourced analytical measure is tracking the effectiveness of UX measures from which your organization can have a deeper understanding of the customer interactions, engagement and potential loopholes that need to be addressed. Measuring UX against specific business insights help to know the areas that need change and positive focus to deliver business value.

UX and data visualization

 

Dashboard visulisation

Dashboard visualisation

 

User Experience (UX) design and Big data can make valuable interaction in data visualization. Data visualization refers to a type of data-oriented visual communication in which various sets of contextual data are presented in graphics or images. The complex of data in this way can thus be presented in an easily comprehensible way.

The information dashboards in the web, product specifications described in graphics, product shipping and movement described in pictorial graphics in websites, are some of the examples of this intersection between user experience and data visualization. Dashboards convert data into robust data visualizations by presenting them in graphs and charts. This allows easier understanding of the data and the statement that it wants to confer to the audience. When creating such data visualizations UX designers not only need to ensure that the data is presented in a comprehensive and sensible way, but they also need to ensure that the visual data drives business conversion.

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