An introduction to data-driven design
Nowadays, data is everywhere, and it is being collected constantly.
Data-driven design uses existing data to drive product development and design decisions. Because it does this, it doesn’t rely on predictions, assumptions or intuition. It should also enable the tweaking of products and offerings based on current input from users or customers.
Quantitative Data
Quantitative data is all of the numerical data that is available which relates to customer behaviour. This can be collected by methods such as surveys, funnel or path analysis, or event tracking. This type of data can be measured and analysed statistically.
Being able to analyse numerical data is particularly useful for measuring a product’s performance as it removes any subjectivity. Common metrics that are calculated this way include conversion rates (e.g. from trials to paid subscriptions), average browsing times and average number of visits and clicks on each webpage.
Data collection and tidying is an extremely important first step on the journey. If you feel you would benefit from using a professional data collection company, data specialists such as //shepper.com/ are able to offer both advice and services.
Qualitative Data
Qualitative data is useful for understanding the reasons behind people’s actions (which tend to be measured numerically). This data gives context and opens up the world of motivations and how products make people feel.
Qualitative data tends to be gathered via interviews, focus groups, user testing and ‘free text’ surveys where answers are not restricted. Some of these will involve direct interactions between companies and their clients and therefore will highlight real reactions and issues that may be missed in standard, qualitative surveys.
Other potential sources of qualitative data include customer reviews, social media comments and posts, and even support requests. Qualitative insights can be useful for creating more user-friendly designs.
Data-driven designs should incorporate both types of data so as to capture a fuller picture of consumer behaviour and desires.
