•Compare internal preference mapping through hedonic scaling and preference ranking.•Preference ranking linked to open comments proved as very good alternative.•The alternative approach gave similar preference maps than traditional ones.•Elicit drivers of liking and disliking directly from consumers with their own words. In classic consumer science, liking has generally been measured with the 9-point hedonic scale. In recent years, signal detection procedures where consumers rank products in terms of preference have been used, together with an R-index that measures the distance in preference. Ranking has been found to be friendlier for consumers, being a more “natural” exercise than scaling. However, scaling has the advantage of quantifying liking, resulting in data sets that can be treated further, for example through preference mapping, together with sensory data from a trained panel or from consumers. Preference mapping is very useful for product development and as a communication tool. This study compared two preference mapping approaches, one using a data set from hedonic scaling plus intensity questions and the other using preference ranking data coupled with open comments. Preference ranking tests plus open comments by consumers proved a very promising method as it produced very similar internal preference map results to “traditional” preference mapping from liking scales. This quicker and easier method in terms of practical implementation has the added advantage of eliciting drivers of liking and disliking directly from consumers, as these cannot be obtained through attribute intensity assessment or by using a trained panel.