Big data vs UX measurement

Big Data is being heralded as central to the future success of companies. But is Big Data complementary to UX measurement? Or is it a resource, which supersedes the need for traditional UX measures?

Businesses recognise that good customer experience is vital for competitive advantage.

Monitoring and tracking organisational performance is key to understanding the quality of the experience delivered to customers. Modern business operations leave digital traces, which tell a story about performance.

Big Data is being heralded as central to the future success of companies. But is Big Data complementary to UX measurement? Or is it a resource, which supersedes the need for traditional UX measures?

What is Big Data?

Big Data is the vast data sets generated across a business and commonly described using five V’s: variety, volume, velocity, value and veracity.

It is incidental by nature in that it is information accidentally collected rather than intentionally measured. Its existence leads to a bottom up approach to analysis, overlooking the entire data range and looking for patterns, problems or opportunities.

What makes Big Data an attractive proposition to business is the idea that previously disparate and unstructured data sets can be harnessed to create new insights and business capabilities through new software tools.

The keenest advocates see Big Data as the foremost driver of business decision-making in the future. Every piece of information a company needs to assess its performance and set direction is waiting to be extracted from reams of data already available but previously unexploited.

The gap between data and insight

Large data sources are clearly an asset. But the art of identifying and extracting useful, usable information to drive business decisions is still new. The idea that all information needed to inform business decisions is contained within pools of operational data is dangerous. Not everything you need to understand the customer experience can be collected from accidental or incidental data as it’s often lacking measures of customer sentiment. Someone may purchase an item, but that is not always a sure indication of their satisfaction and whether they intend to return in future.

What is UX measurement?

We describe it as a set of measures which describe and quantify behavioural and emotional outcomes of customers’ interaction with a business. Whereas Big Data is incidental in nature, UX measurement is inherently ‘Intentional’ in that it is collected and measured in line with a methodology. The insights may come from existing data sources, but these are deliberately picked from the set, or from additional measures, such as customer satisfaction surveys and Net Promoter Scores.

UX measurement is very much a top-down approach in that its a set of measures that help to track performance against business strategy and objectives. This is in contrast to the approach taken to Big Data whereby you start with a vast data set and attempt to draw out insight.

Measurement frameworks are a tool for decision-making: particularly prioritising aspects of UX that should be improved. Fundamentally, it helps organisations assign time, resources and attention to places where improved UX will improve business outcomes.

UX measurement and Big Data

Having new data assets available for MI and business decision-making is clearly useful. But Big Data tends to have blind spots in some important aspects related to UX. In particular, customer intention, expectation and their emotional reactions to their experience.

The answer of course is to put together UX measurement frameworks dashboards which draws on a mix of self-reported and observed measures at a qualitative and quantitative scale (which Big Data resources can clearly provide) alongside qualitative measures. Big Data sources can contribute but rarely contain all the answers.

The ultimate goal is to track a set of UX measures which give your organisation a clear sense of what it’s like to be a customer, and which areas of UX need attention in order to create business value. Where the data comes from is less important than how it is used and understood within the business – and in turn the impact it creates for customers.

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