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You can think of attitudinal data as data that conveys (customers') attitudes about a topic. For market research, this could be about a product, a service, a market, a trend, whatever. You can also think of attitudinal data as sentiment data, centered around capturing the way the customer feels about something. Attitudinal data can be very useful at quantifying 'feelings,' but it's usually a good idea to juxtapose or support these sentiments with more objective data points in order to be able to explain or illustrate [why] a customer feels 'satisfied,' 'confident,' or 'disappointed' for instance. While more subjective, attitudinal data is important because, well, attitudes are important. Humans are creatures who (more often than not) tend to make decisions based on emotions and feelings, thus it's instructive for researchers to attempt to capture data that reflects them (and how they relate to the topic in question).
Attitudinal data is used to measure how important customers consider certain attributes of a product or service to be. Businesses generally use surveys to collect the data, and the information is then reviewed so that the business owner has a good idea of what customers like and dislike about their product or service.
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