Quantitative user data refers to user data that expresses a certain quantity, amount, or range. Examples include metrics like page views, click rates, session durations, conversion rates, and bounce rates.
A quick and easy way to distinguish between quantitative user data and qualitative user data is to think numbers for the former and descriptions for the latter. Quantitative user data is objective and easy to analyze, while qualitative user data is subjective and can be more challenging to measure at scale.
Quantitative user data is essential for getting an overall view of user behavior and website performance. Digital teams use quantitative data to investigate things like most-viewed content, where errors are occurring, where most users drop out of the funnel, and so on. This enables them to form a broad impression of web or app performance, and reveals areas that need more attention.
Quantitative user data provides the ‘what’ of user behavior and website performance, but focusing only on reporting metrics like page views, bounce rates, and conversion rates results in a significant knowledge gap in understanding how to really measure and improve digital experiences. This leads to a reliance on conversion rate optimization as the dominant optimization philosophy – running time-consuming and expensive tests based on trial and error, rather than on genuine understand of customer behavior and sentiment.
In order to get this understanding and empathy, brands should combine quantitative user data with qualitative user data, which grants rich, deep insight into user sentiment.
To learn more about quantitative user data – and to see how leading companies harness data science to combine it with qualitative user data, maximizing visibility into digital experiences – download our report on digital experience transformation below now.