Abstract Background Constrained budgets within healthcare systems and the need to efficiently allocate resources often necessitate the valuation of healthcare interventions and services. However, when a technological product is developed for which no market exists it is a challenge to understand how to place the product and which specifications are the most sought after and important for end users. This was the case for a dashboard we developed, displaying analyses of patient experience survey free-text comments. Method We describe a customisation and evaluation process for our online dashboard that addresses this challenge, using a Discrete Choice Experiment (DCE). We were not interested in the exact content of the dashboard, which was determined in previous stages of our larger study, but on the availability of features and customization options and how they affect individuals’ purchasing behaviours. Results Our DCE completion rate was 33/152 (22%). Certain features were highly desirable - the search function, filtering, and upload own data - and would contribute significant added value to the dashboard. Purchasing behaviour was dependent on the dashboard features, going from a 10 to 90% probability to purchase when we moved from a baseline to a fully-featured dashboard. The purchasing behaviour elicited in this study assumes individuals already have buy-in to the online dashboard, so we assessed only how the various features of our dashboard influence the probability of purchasing the product. Results were used to inform development of a generic checklist of desirable healthcare dashboard features as well as to refine the dashboard itself. Our study suggests the development of the online dashboard and its roll-out in the market would result in a positive net benefit in terms of utilities. The cost-benefit analysis offers a lower bound estimate of the net benefit as it does not acknowledge or incorporate non-monetary benefits that would result from the use of the online dashboard, such as from improved healthcare management. Conclusion DCEs can be successfully used to inform development of an online dashboard by determining preferences for particular features and customisation options and how this affects individuals’ purchasing behaviours. The process should be transferable to the development of other technologies.