Consumer product research assesses reactions to stimuli and informs about what is well liked and why in a very straightforward way. Most often, participants provide their opinion of the smell or taste using a rating scale, or check all that apply attribute list.  Gamification continues to gain popularity across a wide variety of research initiatives as a methodology to engage participants and to provide richer answers. The introduction of more interactive elements, such as a series of quests and rewards, characterize many gamification models and have been very successful with today’s demanding and tech savvy consumer.
 
Maximum Differential Scaling is a well-known technique used to identify the importance for multiple components as a contrast to standard rating scales and biases, used most commonly for product features, claims, benefits and advertising. 
 
We wanted to learn if combining Maximum Differential Scaling with gamification, could help to uncover even more product differentiation and thereby help guide product and concept development? And if so, in what form might this take?  Could gamification provide insights into hedonic and intensity measures as well as help to predict buying behavior?
 
What does gamification reveal?
Q Research Solutions (Q) conducted a study using gamification to see if this methodology would result in enhanced emotional profiles, better understanding of consumer preferences and subsequently provide better guidance for product development and to see how this could be introduced as an additional aspect to a traditional research program.
 
Q chose to conduct the study with 4 different tropical plug-in scented air fresheners, chosen because it is a lead olfactive direction within a well-developed category. The consumers were female, aged 18 – 60, who had purchased tropical or exotic fruits plug-in scented oil air freshener in the past six months.
The consumers evaluated the four products in a sequential monadic two stage study over two days, the fragrances were presented in 8 identical air flow controlled booths, which have positive airflow to eliminate cross contamination. The attributes – happy, contented, comforted, refreshed, sex, energized, irritated, relaxed, sad and boring – were assessed via a check all that apply list in stage 1.  These were selected to cover positive and negative valence as well as high and low arousal. 
 
In stage 2, Q introduced an element of interactive play with a Maximum Difference Scaling card game to determine if we better engage consumers leading to enhanced insights.  The process was to give the testers a deck of cards with the same attributes as used in stage 1.  Consumers were shown 4 attributes at a time and were tasked to choose which best described and least described the scent they were experiencing.  Each attribute chosen as “Best”, was moved to a “favorite’s hand”.  After 4 rounds, the favorite’s hand was fully populated, and consumers then chose the attribute which best and least described the scent they were experiencing from the favorite’s hand.

Which word best describes how this scent makes you feel?

Of the words selected, which BEST describes how this scent makes you feel?

More nuanced information
Check all that apply data was analysed using a simple frequency table, so that we could see the percent of consumers who selected each attribute per fragrance.  The higher the percent of consumers who selected an attribute, the better that attribute describes the fragrance.



To analyse the Maximum Difference Scaling card game, we counted the number of times each attribute was selected as “Best” and “Least” descriptive, and obtained a Net Score, by subtracting “Least” from Best”, so that the higher the Net Score, the better that attribute describes the fragrance.


This analysis showed which attributes the testers selected more frequently and identified which features elicited strong reactions.  With this study we found both many positive and negative mentions and were able to see which were more neutral.
 
All of the fragrances tested scored parity for overall liking, a familiar problem within product testing. Looking at the emotional profile from the check all that apply yielded key insights, namely most of the products were happy and refreshed.  By layering on gamification, we uncovered additional understanding of differentiation.  While the traditional and gamification methods associated specific attributes – happy and refreshed – with one of the fragrances, tested, for example, gamification showed how the profiles differed for others.
 
In the end, we saw how gamification can be a potentially important tool in product and consumer research, providing clarity on valuable data points for decision making. From our experience, we would recommend reducing the number of negative attributes from the three we used, so as to have more discrimination within positive attributes.  While the best methodology for each study needs to be considered and it is definitely not a case of having gamification be part of every study, there is tremendous scope for further refining its application to consumer sensory research.
 
About the Author:
Daniel Blatt is SVP, research services at Q Research Solutions.Daniel Blatt has over 10 years of consumer research experience in both FMCG manufacturing and the Flavor and Fragrance industry. He has worked across many categories including beverages, Ice Cream, Laundry, and Personal Care. Daniel believes that great consumer insights is the key to developing amazing products that delight consumers and build long term brand loyalty. He graduated from CUNY Baruch College with a MS in Industrial/Organizational Psychology.