Observational research and behavioral mapping is becoming more and more popular in consumer experience studies.
It covers anything from unobtrusive naturalistic observations in shops or in consumer homes to advanced multimodal lab studies, and from sensory research to marketing studies. A wide range of solutions is available, from easy to set up portable labs to fully integrated observation labs.
At the core of every solution, Noldus software is easy to use, developed to help you get the results you need:
- Viso for AV recording and immediate playback of consumer tests
- FaceReader for facial coding and assessment of interest, confusion, and boredom
- The Observer XT for perfect data integration and synchronization
Noldus helps you get to the results you need. We understand how to turn data into meaning. Our consultants have advanced degrees in the behavioral sciences, which provides a unique ability to uncover behavioral patterns and turn them into actionable results. Read more about consumer behavior consulting services.
Understanding Consumer Behavior
Observe behavior on-site or in an observation lab
Researchers might need to follow their subjects while they choose items in a store. Then they can code behavior with Pocket Observer and use eye tracking to follow the gaze of the participant. But researchers might also choose to work in an observation lab. They can use either Viso or Media Recorder for high quality, synchronized video recordings.
How to build a Consumer Experience Lab
A Consumer Experience Lab is designed to allow you to observe consumers unobtrusively, in an environment similar to your their natural surroundings.
Check out this ‘how to’ guide, providing you with the perfect tips & tricks!
Automatic facial expression analysis
In a Noldus lab you can integrate video, eye tracking, and physiological data, and you can assess emotions with FaceReader™. FaceReader offers you the possibility to analyze and report about participant responses to commercials or advertisements. By adding the Project Analysis Module to the basic FaceReader, you get a full-scale emotion analysis solution.
Spacial consumer behavior
You can use TrackLab to complete insight into the movement of customers in your retail environment. TrackLab is the new tool for recognition and analysis of spatial behavior and the design of interactive systems. In general, you can use it to measure and analyze customer traffic throughout your shop.
Restaurant of the Future
An interesting example of a living lab in which consumer research projects are carried out is the Restaurant of the Future. Together with our project partners, we created a facility for studying every aspect of food choice and eating.
- Andersen, B.V.; Hyldig, G. (2015). Consumers’ view on determinants to food satisfaction. A qualitative approach. Appetite, 95, 9-16.
- Danner, L.; Sidorkina, L.; Joechl, M.; Duerrschmid. Make a face! Implicit and explicit measurement of facial expressions elicited by orange juices using face reading technology. Food Quality and Preference, doi:10.1016/j.foodqual.2013.01.004
- Edelson, L.R.; Mokdad, C.; Martin, N. (2016). Prompts to eat novel and familiar fruits and vegetables in families with 1-3 year-old children: Relationships with food acceptance and intake. Appetite, 99, 138-148.
- Forde, C.G.; Kuijk, N. van; Thaler, T.; Graaf, C. de; Martin, N. (2012) (article in press) Oral processing characteristics of solid savoury meal components, and relationship with food composition, sensory attributes and expected satiation, Appetite.
- Garcia-Burgos, D.; Zamora, M.C. (2013). Facial affective reactions to bitter-tasting foods and body mass indez in adults. Appetite, 71, 178-186.
- He, W.; Boesveldt, S.; Graaf, C. de; Wijk, R.A. de (2012). Behavioural and physiological responses to two food odours. Appetite, 59 (2), 628.
- He, W.; Boesveldt, S.; Graaf, K. de; Wijk, R.A. de (2012). The effect of positive and negative food odours on human behavioural and physiological responses. Proceedings of the 5th European Conference on Sensory and Consumer Research, OP-1.
- Juodeikiene, G.; Basinskiene, L.; Vidmantiene, D.; Klupsaite, D.; Bartkiene, E. (2014). The use of face reading technology to predict consumer acceptance of confectionery products. Foodbalt, 276-279
- Mennella, J.A.; Forestell, C.A.; Morgan, L.K.; Beauchamp, G.K. (2009). Early milk feeding taste acceptance and liking during infancy. The American Jounal of Clinical Nutrician, 782- 786.
- Mozuriene, E.; Bartkiene, E.; Juodeikiene, G.; Zadeike, D.; Basinskiene, L.; Maruska, A.; Stankevicius, M.; Ragazinskiene, O.; Damasius, J.; Cizeikiene, D. (2016). The effect of savoury plants, fermented with lactic acid bacteria, on the microbiological contamination, quality and acceptability of unripened curd cheese. LWT - Food Science and Technology, 69, 161-168.
- Wanders, A.J.; Jonathan, M.C.; Borne, van den, J.G.C.; Mars, M. Schols, H.A.; Feskens, E.J.M.; Graaf, de C. (2013). The effects of bulking, viscous and gel-forming dietary fibres on satiation. British Journal of Nutrition, 109, 1330-1337.
- Wendin, K.; Allesen-Holm, B.H.; Bredie, W.L.P. (2011). Do facial reactions add new dimensions to measuring sensory responses to basic tastes? Food Quality and Preference, doi:10.1016/j.foodqual.2011.01.002
- Wijk, de, R.A.; Kooijman, V.; Verhoeven, R.; Holthuyzen, N.; Graaf, de, C. (2012). Autonomic nervous system responses on and facial expressions ot the sight, smell, and taste of liked and disliked foods. Food Quality and Preference, 26 (2), 196-203.
- Wilfinger, D.; Weiss, A.; Tscheligi, M. (2009). Exploring shopping information and navigation strategies with a mobile device. Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (Bonn, Germany, 15-18 September 2009).
- Zeinstra, G.G.; Koelen, M.A., Colindres, D.; Kok, F.J.; Graag, C. de (2009). Facial expressions in school-aged children are a good indicator of 'dislikes', but not of 'likes'. Food quality and preference, 20, pp.620-624.
Blog posts on consumer behavior research