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Contextual product testing for small to medium sized enterprises (SMEs)
Contextual product testing for small to medium sized enterprises (SMEs)
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Rebecca N. Bleibaum*, Martin J. Kern†, Heather Thomas* *Dragonfly SCI, Inc., Santa Rosa, CA, United States, †SAM, Sensory and Marketing International, Munich, Germany
24.1
Introduction
Despite a worldwide trend in the globalization of products, a counter-trend has become recognizable in a growing share of consumers around the world with a preference towards natural, artisan, and craft products. Ingredient statements are read by the vast majority of consumers and nearly half of consumers read it frequently or almost always. In addition, there is an affinity for consumers to purchase natural, organic, and healthy products and furthermore to consider in the purchase decision whether it is from a local producer and not from a global player. These consumers purchase not simply based on their overall liking and traditional consumer preferences, instead there are many extenuating factors in play which, from a research perspective, form a specific environment and context to be considered when supporting this quickly growing sector in the development of wholesome products. Manufacturers are responding to this counter-trend by using innovation and technology to provide products serving the changing demand of their target groups, groups with an ever-evolving mindset. We are experiencing this significantly changed structure of demand and watching new opportunities form before our eyes. Today’s traditional brands cannot fully serve this new demand as they would alienate their brand signature recognized and appreciated by their loyal shoppers. This creates a gap on the shelf for new products and brands. This is highly attractive for start-ups and flexible, small and medium sized enterprises (SMEs) using the chance to close this gap, re-shaping the offer with natural, artisan, and craft products with a local origin. However, this opportunity hasn’t been overlooked by the global players, which recognize and serve this gap with innovations creating competitive forces in the niche market. The sensory and consumer insights community is responding to that change in consumer demand, SMEs, and local origin by creating or adapting current research methodologies. Start-ups who embody this artisan and craft segment do not always have the research budget required for multi-national testing with their targeted highly representative consumer segment. The new research approaches described in this chapter include ways to identify, qualify, and define driving factors that have high consumer appeal by measuring key features and benefits that provide predictability of success in Context. https://doi.org/10.1016/B978-0-12-814495-4.00024-6 Copyright © 2019 Elsevier Inc. All rights reserved.
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this new frontier. Against this background, what can the sensory and consumer science community offer to support this growing array of niche and consumer demand?
24.2
Body
What are the factors we need to consider beyond overall liking, strongly taking into account “environment” and “context” to ensure delicious foods and healthy options that can compete on a sensory basis with the more mainstream and best-selling products? l
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Product-Concept: It must be ensured that testing is conducted under the umbrella of the concept of the product that is targeting the artisan and craft consumer. Concepts that include words, statements, and imagery that identify the product as all natural, organic, sustainably harvested, regional, artisanal, etc., can create shifts in perception and expectations of the product’s sensory experience relative to more mainstream and best-selling products. Situations: Much of the current beer testing is conducted in a neutral white sensory booth that tests a very different experience compared to drinking the same beer in a bar. Eating in a local establishment is different to eating in a franchise chain or a fast-casual eatery. Consideration of situational context can lead to very different product evaluations. Socialization: Being together with friends and relatives also has an impact on perception that can be a source of bias. When socializing has an impact on product evaluation and is typical of the product consumption, sensory scientists must consider socializing needs when designing the testing scenario. Test protocol: How to prepare the products for proper evaluation: it is not realistic to test vinegar as a beverage—the typical usage has to be ensured, a realistic product preparation should be considered, including the right pairing e.g., cheese with bread, rum and cola, etc. It is important not to forget the volume in which a consumer tests: one sip or bite can be too little for a realistic evaluation—leading to a too high or too low rating. Packaging: Design, touch, and color of the package, label, graphics, etc., matter and oftentimes have a major impact on consumer perception of the whole product and brand experience. Such interactions must be considered in the research design to more fully guide the brand and marketing teams. Consumer: It goes without saying that products must be evaluated by its consumer, otherwise results are not valid or projectible to the target population.
When it comes to the consumer-oriented modification of products, we are tempted to believe that a product is defined as such and cannot be adapted or modified without losing its authenticity or specific character—especially when it comes to natural, artisan, and craft products. However, there are always options to adapt products to be different, even with challenging agricultural products. The authors of this chapter have global experience in many fast moving consumer goods (FMCG) categories over many decades including wines. Wine is a good example because there are many myths in the wine world, especially regarding sensory perception (Torri et al., 2013). Sensory science is a form of myth-busting. Based on empirical evidence and experience, we can demonstrate the following: l
the same vineyard (and any kind of plant) at the same location can produce very different qualities of juice, depending on how the viticulturist/farmer interacts with the vines,
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fertilizes, waters, trims the canopy, drops fruit, and harvests the grapes to guide its growth and development during the growing season. This point is true for grapes and it is also true for all kinds of agriculture products such as fruits, vegetable, grains, herbs, cereals, etc. the time of harvest and analytical parameters influence the final product experience. one single varietal of grape can be used to make very different styles of wine by altering a number of variables (yeast, time of fermentation, etc.). Winemakers have much more control over the final outcome of the finished wine than in years past.
These are only a few examples, showing that one can interact with, and have design intent, to adapt the products that are commercialized. Before going further in this chapter, we need to agree on the commitment that we are prepared to make an effort and adapt our products according to the needs of our appreciated consumer. At the end of the day, to be successful, the producer needs the consumer to buy their products. This being the undeniable reality, it truly makes the consumer the definer of the finished product quality.
24.3
The test environment
There are many ways and methods of performing sensory and consumer research. The key question is what can small and medium-sized businesses do to engage consumers in their product design and product development to be more successful in the marketplace. Sensory consumer research can help to define the quality and key characteristics of products. It can be conducted in some environments with one’s own customers/consumers without being cost prohibitive. All that is required is some fundamental knowledge of conducting sensory and consumer research, the motivation, scientific mindset, and the appropriate management support.
24.3.1 In-factory/on-premise testing Natural, artisan, and craft product producers are characterized by the fact that they have a direct relationship with their customers, oftentimes having at least one factory outlet and sometimes even well-equipped tasting rooms or gift shops. These already well-appointed rooms can be used as test rooms for consumers to allow the evaluation of products. Moreover, these types of rooms consider the typical situational context and environment for the authentic product consumption and interaction with the consumer: e.g., the breweries own bar/restaurant, wine bar, olive oil bar, market stand, etc. Doing so, even a small company can thoughtfully listen to its own customers and their voice can be integrated into current developments and marketing works. In such a case, the company’s customers contribute in a very valuable way to the success of business. The following surveys can be carried out in factory outlets in test rooms and by their customers (Fig. 24.1): l
The most important and recommendable action is the measurement of acceptance (e.g., 9-point hedonic) unbranded (blind) and branded with different products within a given category. This can include current products, new developments, and, of course, competitor
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Fig. 24.1 Example of the types of testing programs appropriate for direct sales SME’s.
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products. The findings support and enhance all efforts to be more successful and distinguish one’s product from the competitor and understand the beneficial uniqueness from a consumer point of view. When variants of products are developed, new processes are on trial, different technologies are implemented and discrimination tests can be a very helpful tool. The objective is to understand if differences between product variants are perceivable to the consumer, to what extent and their preference. The brand and product performance, especially for new packaging designs and claims, can be evaluated by the consumers. In addition to the product evaluation, important consumer views, usages, attitudes, likes and dislikes can be collected and these are relevant for sales. Taking this to the next level, consumers can be invited to do in-home evaluations, giving feedback about consuming/using current and competitor products in their home environment using more typical consumption behavior.
Some key points should be taken into consideration for proper implementation of the above to ensure valid results, as stipulated below: l
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The place for testing should ideally consider the situational context and environment of the product consumption, be a well-ventilated room, allow control of product servings, and an easy standardized product preparation (temperature, portion size, test protocols, instructions, data collection, adequate lighting and ventilation, and the absence of advertising messages unless that is part of your research). The number of products to be evaluated by the consumer must be limited to typical consumer consumption. A good estimate for the amount of time required is to allow about 10 minutes for the consumer to complete their evaluation of the product array.
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For the consumer: ο It is important to allow the consumer to evaluate with the least amount of interaction and as anonymously as possible. ο If there are groups of friends or acquaintances, depending on the product category, it can be recommended that they sample the products together as it may deliver the highly desirable socialization in a more natural situation. ο It goes without saying, that the larger a participant-sampling is, the better and more robust the results will be. About 50 participants is generally enough to obtain projectible data for one survey. For all in-house research, it is beneficial to have a trained sensory and consumer professional aid in the study design and all staff that are part of the research team must be trained in the test administration. The sensory scientist will ensure the quality of the resulting data, analysis of results, along with proper interpretation and recommendations based on the key finding.
Considering all the above allows the integration of the consumer in the entire idea and innovation management of even very small companies without large investment. As all the in-house research is done with their own clients, there is a certain level of bias towards your products. Conducting independent research with a representative sample of 120 target consumers from time to time, may be invaluable for confirming your own findings.
24.3.2 The casual bar setting (CBS) for context-sensitive products The Casual Bar test design was developed with the objective of incorporating context and the environment into the evaluation of products by consumers. It is an immersive approach, focusing on behavioral, acceptance, and non-declarative data (product choice and sequence, consumed volume, and consumption speed). The CBS approach allows for focusing on consumers’ behaviors in an immersive environment while at the same time fully controlling the setting, allowing consumers free choice of the products in the most natural way. There are key characteristics of such a setting to ensure as much reality as possible, while preserving control of the situation for data collection. 1) Environment: To enable a clean data set, the location for such a test should most resemble the ideal consumption setting while allowing full monitoring of test conditions by the test personnel. The key objective is to create a more natural environment (casual bar) for the selected variables to be tested while reducing non-variable impacts minimizing data-noise. Ideally the control of the setting is much less perceivable by the participant, unlike what you would find in a typical sensory booth situation. 2) Consumers: Ensure socializing as an integral aspect of context by recruiting participants who are well acquainted or types of close-knit groups, with typically 3 to 5 members. The objective is to avoid uncomfortable situations which can arise when people are put together who don’t know each other. 3) Product Preparation: The product preparation and presentation can vary broadly: unbranded or branded, with or without claim statements, with or without price points, etc., all of which
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may change consumer opinion. However, this flexibility allows adaptation of the design to any specific objective requested.
24.3.2.1 CBS for Radler beers: A case study To better illustrate the CBS design, a research study was conducted in 2017 among beer drinking consumers in the United States and Germany. Several objectives were identified, and the CBS methodology was used for data collection. Going into this study, the researchers had several questions and hypotheses, as follows: l
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How does this approach compare to a classical Central Location Test? Can sensory attribute data better predict liking in a CBS? Can the product choice sequence explain the final product choice? Is the first choice the most important one to predict the final choice, or is it the last one? Are consumption speed and consumed volume good predictors of choice? How much can the non-declarative data explain choice?
The methodology of the research followed the CBS protocol: l
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N ¼ 60 (4 groups of 15) in each country Similar setting in Munich, Germany and Redwood City, California, USA Test procedure: a) Start with warm-up drink of 150 mL of water to calibrate thirst; b) 500 mL servings total during the session in USA; unlimited in Germany; c) Tasting Menu: 5 cups with 20 mL of each beer (5 beers, 100 mL in total); at this phase collecting Overall Liking (9-point hedonic) for each product; d) In order to allow choice, the consumer is always offered the entire set of 5 products; e) First and each following choice will be 100 mL; f ) In USA, the number of choices will be limited to 5; in Germany the number of choices will not be limited, consumer is allowed to consume as much as s/he wants; g) When leaving the session, consumers will be asked about their final choice (no consumption) for the liquid and concept; h) Choices, consumed volume and left-over volume, consumption speed, and product sequence will be recorded; i) The objective of the study will not be revealed to the respondents.
24.3.2.2 How does this CBS compare to CLT? A direct comparison is possible with the Overall Liking scores that were collected in both independent sessions, CBS and CLT, with two independent consumer populations—one for the CLT, the other for the CBS. Consumers were recruited from similar demographic characteristics across countries with allowance for product/ brand usage differences. The CBS results indicate a different order of product liking than the CLT. Although the best liked and least liked products are the same in both the CLT and CBS, there is a rank order difference among the products rated in the middle. The best liked product, Product B, scored numerically similar in both tests but consumers in the CBS used a
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7.5 7.0 Product B Overall acceptance-9-point scale
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Product C
Product E Product D
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Fig. 24.2 Overall acceptance casual bar setting (CBS) versus traditional central location test (CLT).
Product D Product E Product C
5.5 5.0 Product A 4.5 4.0 3.5 Product A 3.0 Casual bar setting (n = 122)
Central location test (n = 129)
lower portion of the scale to indicate their dislike of Product A. Product C was better liked in the CBS while Product D was rated second among the CLT consumers. Consumers in the CBS were willing to rate products low—E, D, and A are all below 6.0, whereas in the CLT, most products were rated at 6.0 or above. In this study, the best liked products were more clearly defined in the CBS whereas the CLT provided less distinction among the top four products.
24.3.2.3 Using sensory attribute data to predict liking To help understand not only “what” consumers like, but “why” they like what they do, the same product set of 5 grapefruit-flavored beers/Radlers (A to E) were tested with a quantitative descriptive analysis (QDA) panel (Stone, Sidel, Oliver, Woolsey, & Singleton, 1974). The QDA panel consisted of a screened, qualified, and trained panel of consumers. As a group, they developed a sensory language with 36 total attributes—seven appearance, six aroma, 10 flavor, four mouthfeel/texture, and nine aftertaste/aftereffects attributes. The language is listed in Fig. 24.2. Once the language was developed by the panel as a group, they completed individual data collection by rating one product at a time, in sequence. The subjects rated the intensity of each attribute on a line scale and repeated measures were collected to provide a robust data set for analysis.
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Beer Definitions -
Appearance Judge Instructions: Pick up the cup and evaluate the following ORANGE COLOR (light-dark) GOLDEN COLOR (light-dark) PINK COLOR (light-dark) CLOUDY (slightly-very) CARBONATED (slightly-very) FOAMY (slightly-very) THICK (slightly-very)
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Intensity of the orange color, ranging from light, like a tangerine, to dark, like a blood orange. Intensity of the golden color, like that of apple juice, from light to dark.
Intensity of the pink color, like that of a pink grapefruit, from light to dark. Measure of how cloudy the liquid appears, from slightly, as if it is clear, to very, as if it is opaque. Measure of how carbonated or fizzy the liquid appears, referring to the amount of bubbles rising from the bottom, from slightly to very. Measure of how much foam or froth is on top of the liquid, from a slightly to very. A slightly foamy liquid may only have foam of the edges. A very foamy liquid may have foam covering the entire surface of the liquid. Measure of how thick the liquid appears, from slightly, similar to a light beer, to very, similar to a wheat beer.
Aroma Judge Instructions: Give the beer a gentle swirl before sniffing to evaluate the following GRAPEFRUIT (weak-strong) OTHER CITRUS (weak-strong) STONE FRUIT (weak-strong) OTHER FRUIT (weak-strong) BEER (weak-strong) SWEET (weak-strong)
Intensity of a grapefruit aroma, like that of a sweeter pink grapefruit, from weak to strong. Intensity of a citrus aroma, like that of lemon or lime, but not including grapefruit, from weak to strong. Intensity of a peach aroma, like that of a fresh peach, from weak to strong.
Intensity of a fruity aroma, like that of a mixed fruit aroma not including peach or apricot, from weak to strong. Intensity of a beer aroma, like that of a domestic beer and may contain subtle malt notes, from weak to strong. Intensity of sweet aroma, like that of fruit, from weak to strong.
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Flavor Judge Instructions: Take 1 or 2 sips and evaluate the following GRAPEFRUIT (weak-strong) OTHER CITRUS (weak-strong) STONE FRUIT (weak-strong) OTHER FRUIT (weak-strong) GINGER (weak-strong) BEER (weak-strong) SWEET (weak-strong) SOUR (weak-strong) TART (weak-strong) BITTER (weak-strong)
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Intensity of a grapefruit flavor, like that of a sweeter pink grapefruit, from weak to strong. Intensity of other citrus flavors, like that of lemon or lime, but not including grapefruit, from weak to strong. Intensity of a stone fruit flavor, like that of a fresh peach or apricot, from weak to strong. Intensity of other fruit flavors, like that of fresh apple or pear, from weak to strong. Intensity of a ginger flavor, like that of ginger ale, from weak to strong. Intensity of a beer flavor, like that of a domestic beer and may contain subtle malt notes, from weak to strong. Intensity of sweet flavor, like the sweetness of fruit, from weak to strong. Intensity of a sour flavor in the front of the mouth, like the sourness of a lemon, from weak to strong. Intensity of a tart flavor in the back of the mouth, like that of an unripe fruit, from weak to strong. Intensity of a bitter flavor, like that of the pith of a citrus fruit, from weak to strong.
Mouthfeel/texture Judge Instructions: Take 1 or 2 sips and evaluate the following CARBONATED (slightly-very) THICKNESS (slightly-very) TINGLY (slightly-very) PUCKERING (slightly-very)
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Measure of how carbonated the liquid feels in the mouth, from slightly to very. A slightly carbonated liquid would feel like a flat soda, while a very carbonated liquid would feel like a fizzy soda. Measure of the thickness of the liquid as felt in the mouth, from slightly similar to a light beer, to very similar to a wheat beer. Measure of how tingly the liquid makes the mouth and tongue feel, referring to light prickling on the tongue, from slightly to very. Degree to which the beer makes the mouth pucker, similar to that experienced when eating something tart, like a sour candy, from slightly to very.
Aftertastes/aftereffects Judge Instructions: Take one last sip, swallow, wait 10 s and evaluate the following
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GRAPEFRUIT (weak-strong) FRUITY (weak-strong) BEER (weak-strong) SWEET (weak-strong) BITTER (weak-strong) TART (weak-strong) MOUTHCOATING (slightly-very) TINGLING (slightly-very) LINGERS (shortlong)
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Intensity of a grapefruit flavor that remains, from weak to strong. Intensity of any other fruity flavor that remains, from weak to strong. Intensity of a distinct beer flavor that remains, from weak to strong. Intensity of a sweetness that remains, from weak to strong. Intensity of bitterness that remains, from weak to strong. Intensity of a tart or sour flavor that remains, from weak to strong. Degree to which a coating or film is left in the mouth after swallowing, as if the beer did not completely leave the mouth and a rinse is needed, from slightly to very. Degree to which a tingling is left in the mouth after swallowing, from slightly to very. Measure of how long any aftertastes or aftereffects linger, from a short to long time.
Results from the CBS and CLT were combined with the QDA data to understand relationships with sensory perceptions. Pearson correlation coefficients were calculated between acceptance and sensory attribute measures (Table 24.1). As typically observed with food products and especially beers, flavors and aftertastes are very good predictors of overall liking. Both the CBS and CLT methods resulted in very high correlations with sensory flavors, aftertastes, and mouthfeel characteristics. Products with high fruit and sweet flavors were well-liked (positive sensory correlations) while products with high beer, sour, and bitter notes were less wellliked (negative correlations). Differences were observed between the two test methods for appearance and aroma attributes. The CBS acceptance ratings were slightly more correlated for appearance attributes and the CLT acceptance ratings correlated better with some aroma attributes. These findings suggest that appearance characteristics may have a larger effect on liking perceptions in a natural consumption environment where all choices are viewed simultaneously versus the CLT format where the serving is sequentially monadic (e.g., one product at a time, in sequence). Among the beers, consumers may have noticed and preferred colors and foam characteristics more while they consumed the products in a casual use situation. The opposite was found for aroma attributes with consumers likely finding aroma differences to influence their liking decisions in a traditional CLT test where strict test conditions allow for increased perceptions of subtle sensory sensations. Attributes that were correlated with well-liked products in the CLT were also attributes that achieved similar correlations in CBS. In general, the correlations are the same, or close to the same value (e.g., 0.95 and 0.89), indicating similar sensory attributes appeal and these preferences are not dependent on the testing location. Product Choice and Sequence, Consumed Volume, and Consumption Speed
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Table 24.1 Correlation coefficients between acceptance and sensory attributes
ORANGE COLOR AP GOLDEN COLOR AP PINK COLOR AP CLOUDY AP FOAMY AP THICK AP GRAPEFRUIT AR STONE FRUIT AR BEER AR SWEET AR STONE FRUIT FL OTHER FRUIT FL BEER FL SWEET FL SOUR FL BITTER FL PUCKERING MF FRUITY AT BEER AT SWEET AT BITTER AT MOUTHCOATING AE LINGERING AT
Casual bar setting
Central location test
0.65 0.77 0.60 0.52 0.52 0.35 0.49 0.49 0.52 0.02 0.98 0.84 0.94 0.92 0.96 0.95 0.98 0.97 0.95 0.95 0.93 0.99 0.95
0.42 0.60 0.59 0.34 0.12 0.04 0.43 0.71 0.70 0.21 0.95 0.91 0.97 0.97 0.95 0.95 0.94 0.86 0.95 0.97 0.91 0.87 0.89
The shaded regions represent statistically significant correlations.
In addition to rationalizing the experience by completing questionnaires, CBS can collect behavioral, non-declarative data, i.e., product choice, sequence, consumed volume, and consumption time, therefore increasing the predictive power of the study and getting ever closer to the real product choice. 1) Observation: Enable implicit measurement by strict observation of the participant’s product choice and behaviors by the test personnel rather than requiring participants to complete a complex questionnaire. a) Consumers: On the consumer side, this is realized by offering the consumer a predefined choice set of products, from which the participant may choose. The portion of the product is oriented on an ad-libitum-intake, so it is consumed within a few minutes. Then the choice set is offered again so that the consumer can make another choice, repeating this procedure throughout the entire session length as much as he/she wants. b) Testing Staff: On the test staff side, a predefined set of criteria is monitored for observation of each participant. This could include and is not limited to (adapted to the specific objectives of the study and typical product-properties): i. Count and order of choice ii. Overall liking measures, server comments iii. Behavioral reactions, facial signs, gestures, eyebrow raises, winks, noises iv. Discussion among participants about the products and its characteristics, etc.
New choice
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Fig. 24.3 Return index illustrated.
This CBS approach was realized in the study described above with 5 grapefruitflavored beer-mixes with 60 consumers across two markets (Germany and USA), divided into 5 sessions with 3 to 5 persons per group and 3 groups participating per session. Consumers were invited as small groups of friends as well as being users within this respective product category. The findings were compared to a classical Central Location Test setup revealing the advantages of the CBS for Context Sensitive Products as follows: a) Choice measured under immersive conditions in a CBS leads to different results compared to Overall Liking in an abiotic CLT. This is especially apparent with context sensitive products. b) Predictive power on product success is higher with the CBS/immersive setup compared to the standardized CLT. c) The higher volume/portion intake results in a different evaluation of the product compared to a small portion or sip evaluation for both concepts, Overall Liking and Choice.
Further, the approach provides the opportunity to reveal a deeper and more meaningful understanding of the consumers behavior while conducting additional analysis: d) In a first step, choices are classified and tabulated as follows (see Fig. 24.3) i. First choice ii. Fidelity iii. New choice iv. Switch v. Return e) In a second step, it then becomes advantageous to understand the attractiveness of a product by looking at the relationship between switches and returns. If the number of returns to a specific product is greater than the number of net switches, a product is very attractive to the consumer (as s/he returns again and again to the product). In other words, the product is performing very well. This specific relationship is quantified by a return index, which is computed as follows:
Return Index ¼
Total Number of Returns : NetSwitchesð¼ Switches ReturnsÞ
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As a benchmark, well-performing products should achieve a Return Index of at least 0.75. (SAM Sensory and Marketing, research conducted in 2016/2017).
In context sensitive product categories, the casual bar setting (CBS) is a more realistic setup as it reveals the real-life choice of the consumer versus a CLT. The overall result is a higher predictive power for product success. The characteristics of the CBS are: l
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CBS can work without a questionnaire The CBS can be conducted in conjunction with other tools: ! Conjoint ! Shelf test ! Memory test
Consumers show a behavior of being curious in their choice. Some of the switches are caused by this behavior and must be taken into consideration as a noise of the setting. This leads to two main requirements: a) There must be enough choice opportunities—increasing with the number of products b) The number of products to be tested in one session is limited. Ideally there are three to four products.
There are limitations when testing alcoholic products based on the respective country, as the number of choices might be regulated by law. In such a case, the number of products must be limited to three or four in order to reduce the impact of the noise of natural human curiosity increasing the robustness of the study results.
24.3.3 Using conjoint approach to evaluate intrinsic and extrinsic product properties simultaneously “Conjoint analysis” is a survey-based statistical technique used in market research that helps determine how people value different attributes (features, functions, and benefits) that make up an individual product or service (Dauda & Lee, 2016; Enneking, Neumann, & Henneberg, 2007; Johnson, & Bryan, 1996; Orme, 2001, 2002, 2003; Sawtooth Software, 2002a, 2002b; https://www.sawtoothsoftware.com/download/ techpap/mbcconf2010.pdf). This approach can be used in Sensory and Consumer Research to complete product evaluation including in the context of packaging and branding with a focus on product choice. This applies to all products (i.e., not only context-sensitive products). Product packaging and advertising claims generate expectations on the consumption experience. Since the package and claims have an impact on product choice, it helps form the consumer’s context, which goes hand in hand with product choice. The choice made based on packaging is described as the first moment of truth (FMOT). This FMOT must be confirmed with the product experience itself, which then is the second moment of truth (SMOT). To make products really perform, they have to satisfy the expectation of the FMOT generating re-purchase.
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Fig. 24.4 Example of extrinsic and intrinsic choice research.
In cases when there are many opportunities to examine extrinsic and intrinsic variables, a specific test design can help to simultaneously develop both together. The test design may include the specific context of the brand, the packaging design or designs, and the specific claim(s) in the evaluation of the products. This can be realized by having either different images or real pack variants (mockups) involved in the product evaluation procedure with the consumer. The setting itself can be varied to a broader extent—being done in a sensory booth, a Central Location Test, or an intercept with consumers—the only condition is, to ensure standardized test setup, integrating multiple and appropriate evaluations. This approach was realized in a study with carbonated soft drinks. The objective was to understand how important extrinsic product attributes (brand, price, and claim) affect purchase decision, in particular sugar reduced products—including the 4 different product-variants (standard product, reduced sugar content, new sweetener, and conventional sweetener—see Fig. 24.4). The findings showed that this approach helped in one setting to reveal market potentials as follows: l
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That there is a commercial potential with a new consumer segment The definition of the most successful product recipe (the one with the new sweetener) Which extrinsic factors (brand, price, claim) are the most appropriate and predictive to be successful in the specific segment.
24.3.3.1 Future development Casual Bar Studies provide an alternative approach to the more traditional Central Location Test and allow sensory scientists to include context and social situations in a research environment. The overall objective is to provide robust tools to artisan and craft producers in ways that are easy to implement and analyze, and also provide the robustness and predictive relationships that can help select products that have high marketplace potential. These methods can easily be adapted to a wide array of products across the globe. There are several emerging trends for future development. Direct data entry and portable systems, along with more sophisticated and rapid data analysis and reporting tools, will continue to be further refined. These tools can incorporate observational data along with simple inputs from consumers or testing staff in the casual or more natural settings. Having a QDA panel evaluate products in conjunction with the CBS can provide the predictive relationships and help understand why consumers like what they do.
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24.3.3.2 QDA panels at SMEs Descriptive analysis has been challenged by some organizations as being expensive and many SMEs lack the necessary resources to create and maintain a descriptive panel. However, there is also a shift in this thinking and many SMEs are requesting this internal capability. Most companies taste and evaluate their products on a regular basis, but the discipline of sensory science is less understood. It is the role of sensory professionals to help train and guide companies with robust and scientific tools that can be used for decision making in new product development, product improvement, ingredient substitution, plant to plant variability, quality control, and many other uses. The company that manufactures the product should fully understand the sensory characteristics of their products, i.e., their sensory signature attributes. Traditional descriptive techniques require subject screening and lengthy training for specific product categories. They are oftentimes considered too costly or time consuming. However, there is an emerging trend to use culinary professionals, winemakers, brew masters, fromager, cosmetologist, etc. We refer to these as Master QDA panels. These subjects must still be screened and qualified to be part of a sensory panel and demonstrate a commitment to the category through work experience or hobbies (e.g., home brewers, wine club members, etc.). Although these panelists have a sensitivity to the products being evaluated and do not represent the naı¨ve consumer population, their experience and commitment to the products being evaluated are in fact an advantage in the rapid QDA approach. Their approach to evaluating and interpreting their perceptions has already been well established; their interest in the category is clearly evident; and their knowledge about the products is unmatched. Following in the QDA methodology, these highly skilled panelists are not taught about perception but are used to understand perception (Stone et al., 2012) from a highly skilled viewpoint. Developing language and collecting data from the Master QDA panel can require less time than traditional naı¨ve panels, especially as they gain experience and confidence in the scientific methodologies. As with any quality scientific test methodology, repetitions are included, as well as timed rest intervals given between evaluations. Data collection should be conducted in appropriate test environments following best sensory practices. If the key sensory attributes that enhance or detract from consumer liking or choice behavior are understood, then the development team can make appropriate changes to enhance marketability of their product. These descriptive analysis tools, such as QDA, should be in the hands of the development team and they also can be conducted at home or in a more typical product usage environment. The sensory data allows for considerably more detail as to how a product performs. Data collection out of the sensory laboratory/booth location is currently used globally and will continue to expand and be enhanced in the future (Lindstrom, 2016). In the future, the CBS methodology will align more closely with the corporate business strategy. Beyond guiding product development, results will increasingly be used by marketing and innovation teams to manage or create brands that target specific consumer preferences and preference segments. The context method will continue to expand into new categories with more focus on the total product experience, including emotions associated with packaging, consumer communication, and advertising. The development and use of predictive models of consumer product behavior will continue to have a strategic impact for businesses (Table 24.2).
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Table 24.2 QDA means tables—Grapefruit beers ORANGE COLOR AP Product Product Product Product Product
A D C E B
GOLDEN COLOR AP 30.14 21.64 18.48 9.36 8.75
A B B C C
B D C A E
26.55 24.98 21.93 17.86 12.98
A A AB B C
THICK AP Product Product Product Product Product
D B C A E
A A A B C
Product Product Product Product Product
D C A B E
22.27 21.11 20.86 14.00 13.30
A A A B B
Product Product Product Product Product
B C D A E
A AB ABC BC C
Product Product Product Product Product
E D C B A
C A D E B
27.45 10.30 5.20 4.50 4.32
A B C C C
25.68 23.45 23.23 17.23 6.05
A A A B C
FOAMY AP 27.18 26.68 26.09 25.45 23.14
Product Product Product Product Product
B D C E A
OTHER CITRUS AR 38.98 35.61 30.25 29.27 23.80
OTHER FRUIT AR 17.05 16.45 13.98 13.39 13.14
Product Product Product Product Product
A A B B C
Product Product Product Product Product
E D C B A
19.07 18.98 18.64 17.98 16.77
BEER AR 15.50 14.05 13.39 13.18 13.09
Product Product Product Product Product
A B D E C
21.20 21.07 20.07 18.32 16.23
A A A AB B
Context
C D A B E
28.05 25.64 25.59 14.93 8.59
GRAPEFRUIT AR
STONE FRUIT AR Product Product Product Product Product
B E D A C
CARBONATED AP
CLOUDY AP Product Product Product Product Product
Product Product Product Product Product
PINK COLOR AP
Product Product Product Product Product
C B E D A
24.57 22.25 22.20 20.57 19.16
A AB AB BC C
STONE FRUIT FL Product Product Product Product Product
C A D E B
B A D E C
17.64 15.48 14.73 14.73 9.48
E B D A C
32.02 29.02 27.11 26.16 24.52
Product Product Product Product Product
A A A A B
Product Product Product Product Product
C E D A B
30.05 21.55 21.20 19.61 17.70
A B B B B
Product Product Product Product Product
C E D A B
16.32 16.02 13.91 13.43 9.93
A A A A B
Product Product Product Product Product
B A D E C
20.98 20.34 20.27 20.05 17.27
Product Product Product Product Product
E A B C D
17.89 15.75 14.41 14.00 13.73
SOUR FL 27.57 21.57 20.48 19.82 10.07
A B B B C
Product Product Product Product Product
B E A D C
19.41 14.18 12.25 12.23 10.34
A B B B B
CARBONATED MF
BITTER FL 15.61 15.43 14.75 14.61 14.36
E C A D B
GINGER FL
SWEET FL
TART FL Product Product Product Product Product
C A D B E
OTHER CITRUS FL
OTHER FRUIT FL
BEER FL Product Product Product Product Product
Product Product Product Product Product
Contextual product testing for SMEs
GRAPEFRUIT FL
SWEET AR
29.41 12.48 11.89 11.68 10.07
A B B B B
Product Product Product Product Product
B D C A E
28.09 25.30 25.02 24.77 21.84 517
Continued
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Table 24.2 Continued THICKNESS MF Product Product Product Product Product
B D C E A
TINGLY MF 20.07 18.70 18.20 15.55 15.45
Product Product Product Product Product
GRAPEFRUIT AT Product Product Product Product Product
C B A D E
C E A D B
26.89 24.07 23.57 22.52 19.07
Product Product Product Product Product
C A D E B
22.91 20.05 18.98 18.82 11.23
A A A A B
Product Product Product Product Product
B A D C E
A B B B B
Product Product Product Product Product
B D A C E
22.00 14.64 13.68 13.20 12.45
A B B B B
27.50 17.93 17.55 16.30 15.14
A B B B B
BEER AT 19.93 19.55 17.89 16.61 13.41
A A AB AB B
Product Product Product Product Product
B D A E C
TART MF 25.86 11.55 11.55 11.32 10.77
TINGLING AE 25.02 19.36 17.30 16.30 15.70
B E D A C
A B B B B
Product Product Product Product Product
E B D C A
15.00 14.77 14.66 13.32 12.11
LINGERING AT 18.68 16.41 14.75 14.45 14.14
Product Product Product Product Product
B E C D A
31.43 21.32 20.52 20.39 19.55
A B B B B
Context
B D E A C
Product Product Product Product Product
BITTER AT
MOUTHCOAT AE Product Product Product Product Product
20.20 19.18 17.86 17.50 16.95
FRUITY AT
SWEET AT Product Product Product Product Product
B A C E D
PUCKERING MF
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For conjoint, especially services: l
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Orme, B., 1996. Which Conjoint Method Should I Use, Sawtooth Software. Sawtooth Software (ed.), 2001. Choice-based Conjoint (CBC) Technical Paper. Sawtooth Software (ed.), 2002. ACA 5.0 Technical Paper. Sawtooth Software (ed.), 2002. Conjoint Value Analysis (CVA) Version 3.0. Sawtooth Software (ed.), 2002. Orme, B., Interpreting Conjoint Analysis Data. Sawtooth Software (ed.), 2003. Johnson, R., Orme, B., Getting the Most from CBC. https:// www.sawtoothsoftware.com/download/techpap/mbcconf2010.pdf
References Dauda, S. Y., & Lee, J. (2016). Quality of service and customer satisfaction: A conjoint analysis for the Nigerian bank customers. International Journal of Bank Marketing, 34(6), 841–867. Enneking, U., Neumann, C., & Henneberg, S. (2007). How important intrinsic and extrinsic product attributes affect purchase decision. Food Quality and Preference, 18, 133–138. Johnson, R. M., & Bryan, K. (1996). How many questions should you ask in choice-based conjoint studies? Orme, Sawtooth Software, Inc. (360/681-2300), Copyright 1996, ART Forum, Beaver Creek. Lindstrom, M. (2016). Small data: the tiny clues that uncover huge trends. New York: St. Martin’s Press. Orme, B. (2001). Assessing the monetary value of attribute levels with conjoint analysis: Warnings and suggestions. Copyright 2001, Sawtooth Software. Orme, B. (2002). Perspectives on recent debate over conjoint analysis and modeling preferences with ACA. Copyright 2002, Sawtooth Software. Orme, B. (2003, August). Special features of CBC software for packaged goods and beverage research. Copyright 2002, Sawtooth Software. SAM Sensory and Marketing (2016/2017). Research experience on Return Index of 0.75 Reference. Sawtooth Software. (2002a). The CVA/HB Technical Paper. Copyright 2002, Sawtooth Software. Sawtooth Software. (2002b). A full-profile conjoint analysis system from Sawtooth Software. Copyright 2002, Sawtooth Software. Stone, H., Bleibaum, R.N., & Thomas, H.A. (2012). Sensory evaluation practices (4th ed., pp. 233–289). Elsevier: Oxford. Stone, H., Sidel, J. L., Oliver, S., Woolsey, A., & Singleton, R. C. (1974). Sensory evaluation by quantitative descriptive analysis. Food Technology, 28, 11, 24, 26, 28, 29, 32, 34. Torri, L., Dinnella, C., Recchia, A., Naes, T., Tuorila, H., & Monteleone, E. (2013). Projective mapping for interpreting wine aroma differences as perceived by naive and experienced assessors. Food Quality and Preference, 29(1), 6–15.
Further reading Frost, M. B., Giacalone, D., & Rasmussen, K. K. (2015). Alternative methods of sensory testing: Working with chefs, culinary professionals, and brew masters. In J. Delaru, J. B. Lawlor, & M. Rogeaux (Eds.), Rapid sensory profiling techniques: Applications in new product development and consumer research.
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Mecredy, J. M., Sonnemann, J. C., & Lehman, S. J. (1974). Sensory profiling of beer by a modified QDA method. Food Technology, 28, 36–41. Rogers, C. (1959). A theory of therapy, personality and interpersonal relationships as developed in the client-centered framework. In S. Koch (Ed.), Psychology: A study of a science. Vol. 3. Formulations of the person and the social context. New York: McGraw-Hill. Schutz, H. (1983). Multiple regression approach to optimization. Food Technology, 37, 46–48 62. Sidel, J. L., & Stone, H. (1983). Introduction to optimization research – Definitions and objectives. Food Technology, 37, 36–38. Stewart-Knox, B., & Mitchell, P. (2003). What separates the winners from the losers in new food product development? Trends in Food Science & Technology, 14(1–2), 58–64. Stone, H., & Bleibaum, R. N. (2009). Sensory evaluation. In G. Campbell-Platt (Ed.), Food science and technology (pp. 323–331). Chichester: Wiley-Blackwell. Stone, H., & Sidel, J. L. (1981). Quantitative descriptive analysis in optimization of consumer acceptance. In: Presented at eastern food science and technology conference on ‘strategies of food product development,’ Lancaster, Pennsylvania.
24
Rebecca N. Bleibaum*, Martin J. Kern†, Heather Thomas* *Dragonfly SCI, Inc., Santa Rosa, CA, United States, †SAM, Sensory and Marketing International, Munich, Germany
24.1
Introduction
Despite a worldwide trend in the globalization of products, a counter-trend has become recognizable in a growing share of consumers around the world with a preference towards natural, artisan, and craft products. Ingredient statements are read by the vast majority of consumers and nearly half of consumers read it frequently or almost always. In addition, there is an affinity for consumers to purchase natural, organic, and healthy products and furthermore to consider in the purchase decision whether it is from a local producer and not from a global player. These consumers purchase not simply based on their overall liking and traditional consumer preferences, instead there are many extenuating factors in play which, from a research perspective, form a specific environment and context to be considered when supporting this quickly growing sector in the development of wholesome products. Manufacturers are responding to this counter-trend by using innovation and technology to provide products serving the changing demand of their target groups, groups with an ever-evolving mindset. We are experiencing this significantly changed structure of demand and watching new opportunities form before our eyes. Today’s traditional brands cannot fully serve this new demand as they would alienate their brand signature recognized and appreciated by their loyal shoppers. This creates a gap on the shelf for new products and brands. This is highly attractive for start-ups and flexible, small and medium sized enterprises (SMEs) using the chance to close this gap, re-shaping the offer with natural, artisan, and craft products with a local origin. However, this opportunity hasn’t been overlooked by the global players, which recognize and serve this gap with innovations creating competitive forces in the niche market. The sensory and consumer insights community is responding to that change in consumer demand, SMEs, and local origin by creating or adapting current research methodologies. Start-ups who embody this artisan and craft segment do not always have the research budget required for multi-national testing with their targeted highly representative consumer segment. The new research approaches described in this chapter include ways to identify, qualify, and define driving factors that have high consumer appeal by measuring key features and benefits that provide predictability of success in Context. https://doi.org/10.1016/B978-0-12-814495-4.00024-6 Copyright © 2019 Elsevier Inc. All rights reserved.
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this new frontier. Against this background, what can the sensory and consumer science community offer to support this growing array of niche and consumer demand?
24.2
Body
What are the factors we need to consider beyond overall liking, strongly taking into account “environment” and “context” to ensure delicious foods and healthy options that can compete on a sensory basis with the more mainstream and best-selling products? l
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Product-Concept: It must be ensured that testing is conducted under the umbrella of the concept of the product that is targeting the artisan and craft consumer. Concepts that include words, statements, and imagery that identify the product as all natural, organic, sustainably harvested, regional, artisanal, etc., can create shifts in perception and expectations of the product’s sensory experience relative to more mainstream and best-selling products. Situations: Much of the current beer testing is conducted in a neutral white sensory booth that tests a very different experience compared to drinking the same beer in a bar. Eating in a local establishment is different to eating in a franchise chain or a fast-casual eatery. Consideration of situational context can lead to very different product evaluations. Socialization: Being together with friends and relatives also has an impact on perception that can be a source of bias. When socializing has an impact on product evaluation and is typical of the product consumption, sensory scientists must consider socializing needs when designing the testing scenario. Test protocol: How to prepare the products for proper evaluation: it is not realistic to test vinegar as a beverage—the typical usage has to be ensured, a realistic product preparation should be considered, including the right pairing e.g., cheese with bread, rum and cola, etc. It is important not to forget the volume in which a consumer tests: one sip or bite can be too little for a realistic evaluation—leading to a too high or too low rating. Packaging: Design, touch, and color of the package, label, graphics, etc., matter and oftentimes have a major impact on consumer perception of the whole product and brand experience. Such interactions must be considered in the research design to more fully guide the brand and marketing teams. Consumer: It goes without saying that products must be evaluated by its consumer, otherwise results are not valid or projectible to the target population.
When it comes to the consumer-oriented modification of products, we are tempted to believe that a product is defined as such and cannot be adapted or modified without losing its authenticity or specific character—especially when it comes to natural, artisan, and craft products. However, there are always options to adapt products to be different, even with challenging agricultural products. The authors of this chapter have global experience in many fast moving consumer goods (FMCG) categories over many decades including wines. Wine is a good example because there are many myths in the wine world, especially regarding sensory perception (Torri et al., 2013). Sensory science is a form of myth-busting. Based on empirical evidence and experience, we can demonstrate the following: l
the same vineyard (and any kind of plant) at the same location can produce very different qualities of juice, depending on how the viticulturist/farmer interacts with the vines,
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fertilizes, waters, trims the canopy, drops fruit, and harvests the grapes to guide its growth and development during the growing season. This point is true for grapes and it is also true for all kinds of agriculture products such as fruits, vegetable, grains, herbs, cereals, etc. the time of harvest and analytical parameters influence the final product experience. one single varietal of grape can be used to make very different styles of wine by altering a number of variables (yeast, time of fermentation, etc.). Winemakers have much more control over the final outcome of the finished wine than in years past.
These are only a few examples, showing that one can interact with, and have design intent, to adapt the products that are commercialized. Before going further in this chapter, we need to agree on the commitment that we are prepared to make an effort and adapt our products according to the needs of our appreciated consumer. At the end of the day, to be successful, the producer needs the consumer to buy their products. This being the undeniable reality, it truly makes the consumer the definer of the finished product quality.
24.3
The test environment
There are many ways and methods of performing sensory and consumer research. The key question is what can small and medium-sized businesses do to engage consumers in their product design and product development to be more successful in the marketplace. Sensory consumer research can help to define the quality and key characteristics of products. It can be conducted in some environments with one’s own customers/consumers without being cost prohibitive. All that is required is some fundamental knowledge of conducting sensory and consumer research, the motivation, scientific mindset, and the appropriate management support.
24.3.1 In-factory/on-premise testing Natural, artisan, and craft product producers are characterized by the fact that they have a direct relationship with their customers, oftentimes having at least one factory outlet and sometimes even well-equipped tasting rooms or gift shops. These already well-appointed rooms can be used as test rooms for consumers to allow the evaluation of products. Moreover, these types of rooms consider the typical situational context and environment for the authentic product consumption and interaction with the consumer: e.g., the breweries own bar/restaurant, wine bar, olive oil bar, market stand, etc. Doing so, even a small company can thoughtfully listen to its own customers and their voice can be integrated into current developments and marketing works. In such a case, the company’s customers contribute in a very valuable way to the success of business. The following surveys can be carried out in factory outlets in test rooms and by their customers (Fig. 24.1): l
The most important and recommendable action is the measurement of acceptance (e.g., 9-point hedonic) unbranded (blind) and branded with different products within a given category. This can include current products, new developments, and, of course, competitor
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Fig. 24.1 Example of the types of testing programs appropriate for direct sales SME’s.
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products. The findings support and enhance all efforts to be more successful and distinguish one’s product from the competitor and understand the beneficial uniqueness from a consumer point of view. When variants of products are developed, new processes are on trial, different technologies are implemented and discrimination tests can be a very helpful tool. The objective is to understand if differences between product variants are perceivable to the consumer, to what extent and their preference. The brand and product performance, especially for new packaging designs and claims, can be evaluated by the consumers. In addition to the product evaluation, important consumer views, usages, attitudes, likes and dislikes can be collected and these are relevant for sales. Taking this to the next level, consumers can be invited to do in-home evaluations, giving feedback about consuming/using current and competitor products in their home environment using more typical consumption behavior.
Some key points should be taken into consideration for proper implementation of the above to ensure valid results, as stipulated below: l
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The place for testing should ideally consider the situational context and environment of the product consumption, be a well-ventilated room, allow control of product servings, and an easy standardized product preparation (temperature, portion size, test protocols, instructions, data collection, adequate lighting and ventilation, and the absence of advertising messages unless that is part of your research). The number of products to be evaluated by the consumer must be limited to typical consumer consumption. A good estimate for the amount of time required is to allow about 10 minutes for the consumer to complete their evaluation of the product array.
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For the consumer: ο It is important to allow the consumer to evaluate with the least amount of interaction and as anonymously as possible. ο If there are groups of friends or acquaintances, depending on the product category, it can be recommended that they sample the products together as it may deliver the highly desirable socialization in a more natural situation. ο It goes without saying, that the larger a participant-sampling is, the better and more robust the results will be. About 50 participants is generally enough to obtain projectible data for one survey. For all in-house research, it is beneficial to have a trained sensory and consumer professional aid in the study design and all staff that are part of the research team must be trained in the test administration. The sensory scientist will ensure the quality of the resulting data, analysis of results, along with proper interpretation and recommendations based on the key finding.
Considering all the above allows the integration of the consumer in the entire idea and innovation management of even very small companies without large investment. As all the in-house research is done with their own clients, there is a certain level of bias towards your products. Conducting independent research with a representative sample of 120 target consumers from time to time, may be invaluable for confirming your own findings.
24.3.2 The casual bar setting (CBS) for context-sensitive products The Casual Bar test design was developed with the objective of incorporating context and the environment into the evaluation of products by consumers. It is an immersive approach, focusing on behavioral, acceptance, and non-declarative data (product choice and sequence, consumed volume, and consumption speed). The CBS approach allows for focusing on consumers’ behaviors in an immersive environment while at the same time fully controlling the setting, allowing consumers free choice of the products in the most natural way. There are key characteristics of such a setting to ensure as much reality as possible, while preserving control of the situation for data collection. 1) Environment: To enable a clean data set, the location for such a test should most resemble the ideal consumption setting while allowing full monitoring of test conditions by the test personnel. The key objective is to create a more natural environment (casual bar) for the selected variables to be tested while reducing non-variable impacts minimizing data-noise. Ideally the control of the setting is much less perceivable by the participant, unlike what you would find in a typical sensory booth situation. 2) Consumers: Ensure socializing as an integral aspect of context by recruiting participants who are well acquainted or types of close-knit groups, with typically 3 to 5 members. The objective is to avoid uncomfortable situations which can arise when people are put together who don’t know each other. 3) Product Preparation: The product preparation and presentation can vary broadly: unbranded or branded, with or without claim statements, with or without price points, etc., all of which
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may change consumer opinion. However, this flexibility allows adaptation of the design to any specific objective requested.
24.3.2.1 CBS for Radler beers: A case study To better illustrate the CBS design, a research study was conducted in 2017 among beer drinking consumers in the United States and Germany. Several objectives were identified, and the CBS methodology was used for data collection. Going into this study, the researchers had several questions and hypotheses, as follows: l
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How does this approach compare to a classical Central Location Test? Can sensory attribute data better predict liking in a CBS? Can the product choice sequence explain the final product choice? Is the first choice the most important one to predict the final choice, or is it the last one? Are consumption speed and consumed volume good predictors of choice? How much can the non-declarative data explain choice?
The methodology of the research followed the CBS protocol: l
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N ¼ 60 (4 groups of 15) in each country Similar setting in Munich, Germany and Redwood City, California, USA Test procedure: a) Start with warm-up drink of 150 mL of water to calibrate thirst; b) 500 mL servings total during the session in USA; unlimited in Germany; c) Tasting Menu: 5 cups with 20 mL of each beer (5 beers, 100 mL in total); at this phase collecting Overall Liking (9-point hedonic) for each product; d) In order to allow choice, the consumer is always offered the entire set of 5 products; e) First and each following choice will be 100 mL; f ) In USA, the number of choices will be limited to 5; in Germany the number of choices will not be limited, consumer is allowed to consume as much as s/he wants; g) When leaving the session, consumers will be asked about their final choice (no consumption) for the liquid and concept; h) Choices, consumed volume and left-over volume, consumption speed, and product sequence will be recorded; i) The objective of the study will not be revealed to the respondents.
24.3.2.2 How does this CBS compare to CLT? A direct comparison is possible with the Overall Liking scores that were collected in both independent sessions, CBS and CLT, with two independent consumer populations—one for the CLT, the other for the CBS. Consumers were recruited from similar demographic characteristics across countries with allowance for product/ brand usage differences. The CBS results indicate a different order of product liking than the CLT. Although the best liked and least liked products are the same in both the CLT and CBS, there is a rank order difference among the products rated in the middle. The best liked product, Product B, scored numerically similar in both tests but consumers in the CBS used a
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7.5 7.0 Product B Overall acceptance-9-point scale
6.5 6.0
Product C
Product E Product D
Product B
Fig. 24.2 Overall acceptance casual bar setting (CBS) versus traditional central location test (CLT).
Product D Product E Product C
5.5 5.0 Product A 4.5 4.0 3.5 Product A 3.0 Casual bar setting (n = 122)
Central location test (n = 129)
lower portion of the scale to indicate their dislike of Product A. Product C was better liked in the CBS while Product D was rated second among the CLT consumers. Consumers in the CBS were willing to rate products low—E, D, and A are all below 6.0, whereas in the CLT, most products were rated at 6.0 or above. In this study, the best liked products were more clearly defined in the CBS whereas the CLT provided less distinction among the top four products.
24.3.2.3 Using sensory attribute data to predict liking To help understand not only “what” consumers like, but “why” they like what they do, the same product set of 5 grapefruit-flavored beers/Radlers (A to E) were tested with a quantitative descriptive analysis (QDA) panel (Stone, Sidel, Oliver, Woolsey, & Singleton, 1974). The QDA panel consisted of a screened, qualified, and trained panel of consumers. As a group, they developed a sensory language with 36 total attributes—seven appearance, six aroma, 10 flavor, four mouthfeel/texture, and nine aftertaste/aftereffects attributes. The language is listed in Fig. 24.2. Once the language was developed by the panel as a group, they completed individual data collection by rating one product at a time, in sequence. The subjects rated the intensity of each attribute on a line scale and repeated measures were collected to provide a robust data set for analysis.
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Beer Definitions -
Appearance Judge Instructions: Pick up the cup and evaluate the following ORANGE COLOR (light-dark) GOLDEN COLOR (light-dark) PINK COLOR (light-dark) CLOUDY (slightly-very) CARBONATED (slightly-very) FOAMY (slightly-very) THICK (slightly-very)
-
Intensity of the orange color, ranging from light, like a tangerine, to dark, like a blood orange. Intensity of the golden color, like that of apple juice, from light to dark.
Intensity of the pink color, like that of a pink grapefruit, from light to dark. Measure of how cloudy the liquid appears, from slightly, as if it is clear, to very, as if it is opaque. Measure of how carbonated or fizzy the liquid appears, referring to the amount of bubbles rising from the bottom, from slightly to very. Measure of how much foam or froth is on top of the liquid, from a slightly to very. A slightly foamy liquid may only have foam of the edges. A very foamy liquid may have foam covering the entire surface of the liquid. Measure of how thick the liquid appears, from slightly, similar to a light beer, to very, similar to a wheat beer.
Aroma Judge Instructions: Give the beer a gentle swirl before sniffing to evaluate the following GRAPEFRUIT (weak-strong) OTHER CITRUS (weak-strong) STONE FRUIT (weak-strong) OTHER FRUIT (weak-strong) BEER (weak-strong) SWEET (weak-strong)
Intensity of a grapefruit aroma, like that of a sweeter pink grapefruit, from weak to strong. Intensity of a citrus aroma, like that of lemon or lime, but not including grapefruit, from weak to strong. Intensity of a peach aroma, like that of a fresh peach, from weak to strong.
Intensity of a fruity aroma, like that of a mixed fruit aroma not including peach or apricot, from weak to strong. Intensity of a beer aroma, like that of a domestic beer and may contain subtle malt notes, from weak to strong. Intensity of sweet aroma, like that of fruit, from weak to strong.
Contextual product testing for SMEs
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Flavor Judge Instructions: Take 1 or 2 sips and evaluate the following GRAPEFRUIT (weak-strong) OTHER CITRUS (weak-strong) STONE FRUIT (weak-strong) OTHER FRUIT (weak-strong) GINGER (weak-strong) BEER (weak-strong) SWEET (weak-strong) SOUR (weak-strong) TART (weak-strong) BITTER (weak-strong)
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Intensity of a grapefruit flavor, like that of a sweeter pink grapefruit, from weak to strong. Intensity of other citrus flavors, like that of lemon or lime, but not including grapefruit, from weak to strong. Intensity of a stone fruit flavor, like that of a fresh peach or apricot, from weak to strong. Intensity of other fruit flavors, like that of fresh apple or pear, from weak to strong. Intensity of a ginger flavor, like that of ginger ale, from weak to strong. Intensity of a beer flavor, like that of a domestic beer and may contain subtle malt notes, from weak to strong. Intensity of sweet flavor, like the sweetness of fruit, from weak to strong. Intensity of a sour flavor in the front of the mouth, like the sourness of a lemon, from weak to strong. Intensity of a tart flavor in the back of the mouth, like that of an unripe fruit, from weak to strong. Intensity of a bitter flavor, like that of the pith of a citrus fruit, from weak to strong.
Mouthfeel/texture Judge Instructions: Take 1 or 2 sips and evaluate the following CARBONATED (slightly-very) THICKNESS (slightly-very) TINGLY (slightly-very) PUCKERING (slightly-very)
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Measure of how carbonated the liquid feels in the mouth, from slightly to very. A slightly carbonated liquid would feel like a flat soda, while a very carbonated liquid would feel like a fizzy soda. Measure of the thickness of the liquid as felt in the mouth, from slightly similar to a light beer, to very similar to a wheat beer. Measure of how tingly the liquid makes the mouth and tongue feel, referring to light prickling on the tongue, from slightly to very. Degree to which the beer makes the mouth pucker, similar to that experienced when eating something tart, like a sour candy, from slightly to very.
Aftertastes/aftereffects Judge Instructions: Take one last sip, swallow, wait 10 s and evaluate the following
510
GRAPEFRUIT (weak-strong) FRUITY (weak-strong) BEER (weak-strong) SWEET (weak-strong) BITTER (weak-strong) TART (weak-strong) MOUTHCOATING (slightly-very) TINGLING (slightly-very) LINGERS (shortlong)
Context
Intensity of a grapefruit flavor that remains, from weak to strong. Intensity of any other fruity flavor that remains, from weak to strong. Intensity of a distinct beer flavor that remains, from weak to strong. Intensity of a sweetness that remains, from weak to strong. Intensity of bitterness that remains, from weak to strong. Intensity of a tart or sour flavor that remains, from weak to strong. Degree to which a coating or film is left in the mouth after swallowing, as if the beer did not completely leave the mouth and a rinse is needed, from slightly to very. Degree to which a tingling is left in the mouth after swallowing, from slightly to very. Measure of how long any aftertastes or aftereffects linger, from a short to long time.
Results from the CBS and CLT were combined with the QDA data to understand relationships with sensory perceptions. Pearson correlation coefficients were calculated between acceptance and sensory attribute measures (Table 24.1). As typically observed with food products and especially beers, flavors and aftertastes are very good predictors of overall liking. Both the CBS and CLT methods resulted in very high correlations with sensory flavors, aftertastes, and mouthfeel characteristics. Products with high fruit and sweet flavors were well-liked (positive sensory correlations) while products with high beer, sour, and bitter notes were less wellliked (negative correlations). Differences were observed between the two test methods for appearance and aroma attributes. The CBS acceptance ratings were slightly more correlated for appearance attributes and the CLT acceptance ratings correlated better with some aroma attributes. These findings suggest that appearance characteristics may have a larger effect on liking perceptions in a natural consumption environment where all choices are viewed simultaneously versus the CLT format where the serving is sequentially monadic (e.g., one product at a time, in sequence). Among the beers, consumers may have noticed and preferred colors and foam characteristics more while they consumed the products in a casual use situation. The opposite was found for aroma attributes with consumers likely finding aroma differences to influence their liking decisions in a traditional CLT test where strict test conditions allow for increased perceptions of subtle sensory sensations. Attributes that were correlated with well-liked products in the CLT were also attributes that achieved similar correlations in CBS. In general, the correlations are the same, or close to the same value (e.g., 0.95 and 0.89), indicating similar sensory attributes appeal and these preferences are not dependent on the testing location. Product Choice and Sequence, Consumed Volume, and Consumption Speed
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Table 24.1 Correlation coefficients between acceptance and sensory attributes
ORANGE COLOR AP GOLDEN COLOR AP PINK COLOR AP CLOUDY AP FOAMY AP THICK AP GRAPEFRUIT AR STONE FRUIT AR BEER AR SWEET AR STONE FRUIT FL OTHER FRUIT FL BEER FL SWEET FL SOUR FL BITTER FL PUCKERING MF FRUITY AT BEER AT SWEET AT BITTER AT MOUTHCOATING AE LINGERING AT
Casual bar setting
Central location test
0.65 0.77 0.60 0.52 0.52 0.35 0.49 0.49 0.52 0.02 0.98 0.84 0.94 0.92 0.96 0.95 0.98 0.97 0.95 0.95 0.93 0.99 0.95
0.42 0.60 0.59 0.34 0.12 0.04 0.43 0.71 0.70 0.21 0.95 0.91 0.97 0.97 0.95 0.95 0.94 0.86 0.95 0.97 0.91 0.87 0.89
The shaded regions represent statistically significant correlations.
In addition to rationalizing the experience by completing questionnaires, CBS can collect behavioral, non-declarative data, i.e., product choice, sequence, consumed volume, and consumption time, therefore increasing the predictive power of the study and getting ever closer to the real product choice. 1) Observation: Enable implicit measurement by strict observation of the participant’s product choice and behaviors by the test personnel rather than requiring participants to complete a complex questionnaire. a) Consumers: On the consumer side, this is realized by offering the consumer a predefined choice set of products, from which the participant may choose. The portion of the product is oriented on an ad-libitum-intake, so it is consumed within a few minutes. Then the choice set is offered again so that the consumer can make another choice, repeating this procedure throughout the entire session length as much as he/she wants. b) Testing Staff: On the test staff side, a predefined set of criteria is monitored for observation of each participant. This could include and is not limited to (adapted to the specific objectives of the study and typical product-properties): i. Count and order of choice ii. Overall liking measures, server comments iii. Behavioral reactions, facial signs, gestures, eyebrow raises, winks, noises iv. Discussion among participants about the products and its characteristics, etc.
New choice
Switch a
b
c
Switch
a
Return
a
Fidelity
Fidelity
Return b
Switch
b
Fidelity
a
New choice
a
Switch
Fidelity a
Fidelity
Context
First choice
512
Fig. 24.3 Return index illustrated.
This CBS approach was realized in the study described above with 5 grapefruitflavored beer-mixes with 60 consumers across two markets (Germany and USA), divided into 5 sessions with 3 to 5 persons per group and 3 groups participating per session. Consumers were invited as small groups of friends as well as being users within this respective product category. The findings were compared to a classical Central Location Test setup revealing the advantages of the CBS for Context Sensitive Products as follows: a) Choice measured under immersive conditions in a CBS leads to different results compared to Overall Liking in an abiotic CLT. This is especially apparent with context sensitive products. b) Predictive power on product success is higher with the CBS/immersive setup compared to the standardized CLT. c) The higher volume/portion intake results in a different evaluation of the product compared to a small portion or sip evaluation for both concepts, Overall Liking and Choice.
Further, the approach provides the opportunity to reveal a deeper and more meaningful understanding of the consumers behavior while conducting additional analysis: d) In a first step, choices are classified and tabulated as follows (see Fig. 24.3) i. First choice ii. Fidelity iii. New choice iv. Switch v. Return e) In a second step, it then becomes advantageous to understand the attractiveness of a product by looking at the relationship between switches and returns. If the number of returns to a specific product is greater than the number of net switches, a product is very attractive to the consumer (as s/he returns again and again to the product). In other words, the product is performing very well. This specific relationship is quantified by a return index, which is computed as follows:
Return Index ¼
Total Number of Returns : NetSwitchesð¼ Switches ReturnsÞ
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l
513
As a benchmark, well-performing products should achieve a Return Index of at least 0.75. (SAM Sensory and Marketing, research conducted in 2016/2017).
In context sensitive product categories, the casual bar setting (CBS) is a more realistic setup as it reveals the real-life choice of the consumer versus a CLT. The overall result is a higher predictive power for product success. The characteristics of the CBS are: l
l
CBS can work without a questionnaire The CBS can be conducted in conjunction with other tools: ! Conjoint ! Shelf test ! Memory test
Consumers show a behavior of being curious in their choice. Some of the switches are caused by this behavior and must be taken into consideration as a noise of the setting. This leads to two main requirements: a) There must be enough choice opportunities—increasing with the number of products b) The number of products to be tested in one session is limited. Ideally there are three to four products.
There are limitations when testing alcoholic products based on the respective country, as the number of choices might be regulated by law. In such a case, the number of products must be limited to three or four in order to reduce the impact of the noise of natural human curiosity increasing the robustness of the study results.
24.3.3 Using conjoint approach to evaluate intrinsic and extrinsic product properties simultaneously “Conjoint analysis” is a survey-based statistical technique used in market research that helps determine how people value different attributes (features, functions, and benefits) that make up an individual product or service (Dauda & Lee, 2016; Enneking, Neumann, & Henneberg, 2007; Johnson, & Bryan, 1996; Orme, 2001, 2002, 2003; Sawtooth Software, 2002a, 2002b; https://www.sawtoothsoftware.com/download/ techpap/mbcconf2010.pdf). This approach can be used in Sensory and Consumer Research to complete product evaluation including in the context of packaging and branding with a focus on product choice. This applies to all products (i.e., not only context-sensitive products). Product packaging and advertising claims generate expectations on the consumption experience. Since the package and claims have an impact on product choice, it helps form the consumer’s context, which goes hand in hand with product choice. The choice made based on packaging is described as the first moment of truth (FMOT). This FMOT must be confirmed with the product experience itself, which then is the second moment of truth (SMOT). To make products really perform, they have to satisfy the expectation of the FMOT generating re-purchase.
514
Context
Fig. 24.4 Example of extrinsic and intrinsic choice research.
In cases when there are many opportunities to examine extrinsic and intrinsic variables, a specific test design can help to simultaneously develop both together. The test design may include the specific context of the brand, the packaging design or designs, and the specific claim(s) in the evaluation of the products. This can be realized by having either different images or real pack variants (mockups) involved in the product evaluation procedure with the consumer. The setting itself can be varied to a broader extent—being done in a sensory booth, a Central Location Test, or an intercept with consumers—the only condition is, to ensure standardized test setup, integrating multiple and appropriate evaluations. This approach was realized in a study with carbonated soft drinks. The objective was to understand how important extrinsic product attributes (brand, price, and claim) affect purchase decision, in particular sugar reduced products—including the 4 different product-variants (standard product, reduced sugar content, new sweetener, and conventional sweetener—see Fig. 24.4). The findings showed that this approach helped in one setting to reveal market potentials as follows: l
l
l
That there is a commercial potential with a new consumer segment The definition of the most successful product recipe (the one with the new sweetener) Which extrinsic factors (brand, price, claim) are the most appropriate and predictive to be successful in the specific segment.
24.3.3.1 Future development Casual Bar Studies provide an alternative approach to the more traditional Central Location Test and allow sensory scientists to include context and social situations in a research environment. The overall objective is to provide robust tools to artisan and craft producers in ways that are easy to implement and analyze, and also provide the robustness and predictive relationships that can help select products that have high marketplace potential. These methods can easily be adapted to a wide array of products across the globe. There are several emerging trends for future development. Direct data entry and portable systems, along with more sophisticated and rapid data analysis and reporting tools, will continue to be further refined. These tools can incorporate observational data along with simple inputs from consumers or testing staff in the casual or more natural settings. Having a QDA panel evaluate products in conjunction with the CBS can provide the predictive relationships and help understand why consumers like what they do.
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24.3.3.2 QDA panels at SMEs Descriptive analysis has been challenged by some organizations as being expensive and many SMEs lack the necessary resources to create and maintain a descriptive panel. However, there is also a shift in this thinking and many SMEs are requesting this internal capability. Most companies taste and evaluate their products on a regular basis, but the discipline of sensory science is less understood. It is the role of sensory professionals to help train and guide companies with robust and scientific tools that can be used for decision making in new product development, product improvement, ingredient substitution, plant to plant variability, quality control, and many other uses. The company that manufactures the product should fully understand the sensory characteristics of their products, i.e., their sensory signature attributes. Traditional descriptive techniques require subject screening and lengthy training for specific product categories. They are oftentimes considered too costly or time consuming. However, there is an emerging trend to use culinary professionals, winemakers, brew masters, fromager, cosmetologist, etc. We refer to these as Master QDA panels. These subjects must still be screened and qualified to be part of a sensory panel and demonstrate a commitment to the category through work experience or hobbies (e.g., home brewers, wine club members, etc.). Although these panelists have a sensitivity to the products being evaluated and do not represent the naı¨ve consumer population, their experience and commitment to the products being evaluated are in fact an advantage in the rapid QDA approach. Their approach to evaluating and interpreting their perceptions has already been well established; their interest in the category is clearly evident; and their knowledge about the products is unmatched. Following in the QDA methodology, these highly skilled panelists are not taught about perception but are used to understand perception (Stone et al., 2012) from a highly skilled viewpoint. Developing language and collecting data from the Master QDA panel can require less time than traditional naı¨ve panels, especially as they gain experience and confidence in the scientific methodologies. As with any quality scientific test methodology, repetitions are included, as well as timed rest intervals given between evaluations. Data collection should be conducted in appropriate test environments following best sensory practices. If the key sensory attributes that enhance or detract from consumer liking or choice behavior are understood, then the development team can make appropriate changes to enhance marketability of their product. These descriptive analysis tools, such as QDA, should be in the hands of the development team and they also can be conducted at home or in a more typical product usage environment. The sensory data allows for considerably more detail as to how a product performs. Data collection out of the sensory laboratory/booth location is currently used globally and will continue to expand and be enhanced in the future (Lindstrom, 2016). In the future, the CBS methodology will align more closely with the corporate business strategy. Beyond guiding product development, results will increasingly be used by marketing and innovation teams to manage or create brands that target specific consumer preferences and preference segments. The context method will continue to expand into new categories with more focus on the total product experience, including emotions associated with packaging, consumer communication, and advertising. The development and use of predictive models of consumer product behavior will continue to have a strategic impact for businesses (Table 24.2).
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Table 24.2 QDA means tables—Grapefruit beers ORANGE COLOR AP Product Product Product Product Product
A D C E B
GOLDEN COLOR AP 30.14 21.64 18.48 9.36 8.75
A B B C C
B D C A E
26.55 24.98 21.93 17.86 12.98
A A AB B C
THICK AP Product Product Product Product Product
D B C A E
A A A B C
Product Product Product Product Product
D C A B E
22.27 21.11 20.86 14.00 13.30
A A A B B
Product Product Product Product Product
B C D A E
A AB ABC BC C
Product Product Product Product Product
E D C B A
C A D E B
27.45 10.30 5.20 4.50 4.32
A B C C C
25.68 23.45 23.23 17.23 6.05
A A A B C
FOAMY AP 27.18 26.68 26.09 25.45 23.14
Product Product Product Product Product
B D C E A
OTHER CITRUS AR 38.98 35.61 30.25 29.27 23.80
OTHER FRUIT AR 17.05 16.45 13.98 13.39 13.14
Product Product Product Product Product
A A B B C
Product Product Product Product Product
E D C B A
19.07 18.98 18.64 17.98 16.77
BEER AR 15.50 14.05 13.39 13.18 13.09
Product Product Product Product Product
A B D E C
21.20 21.07 20.07 18.32 16.23
A A A AB B
Context
C D A B E
28.05 25.64 25.59 14.93 8.59
GRAPEFRUIT AR
STONE FRUIT AR Product Product Product Product Product
B E D A C
CARBONATED AP
CLOUDY AP Product Product Product Product Product
Product Product Product Product Product
PINK COLOR AP
Product Product Product Product Product
C B E D A
24.57 22.25 22.20 20.57 19.16
A AB AB BC C
STONE FRUIT FL Product Product Product Product Product
C A D E B
B A D E C
17.64 15.48 14.73 14.73 9.48
E B D A C
32.02 29.02 27.11 26.16 24.52
Product Product Product Product Product
A A A A B
Product Product Product Product Product
C E D A B
30.05 21.55 21.20 19.61 17.70
A B B B B
Product Product Product Product Product
C E D A B
16.32 16.02 13.91 13.43 9.93
A A A A B
Product Product Product Product Product
B A D E C
20.98 20.34 20.27 20.05 17.27
Product Product Product Product Product
E A B C D
17.89 15.75 14.41 14.00 13.73
SOUR FL 27.57 21.57 20.48 19.82 10.07
A B B B C
Product Product Product Product Product
B E A D C
19.41 14.18 12.25 12.23 10.34
A B B B B
CARBONATED MF
BITTER FL 15.61 15.43 14.75 14.61 14.36
E C A D B
GINGER FL
SWEET FL
TART FL Product Product Product Product Product
C A D B E
OTHER CITRUS FL
OTHER FRUIT FL
BEER FL Product Product Product Product Product
Product Product Product Product Product
Contextual product testing for SMEs
GRAPEFRUIT FL
SWEET AR
29.41 12.48 11.89 11.68 10.07
A B B B B
Product Product Product Product Product
B D C A E
28.09 25.30 25.02 24.77 21.84 517
Continued
518
Table 24.2 Continued THICKNESS MF Product Product Product Product Product
B D C E A
TINGLY MF 20.07 18.70 18.20 15.55 15.45
Product Product Product Product Product
GRAPEFRUIT AT Product Product Product Product Product
C B A D E
C E A D B
26.89 24.07 23.57 22.52 19.07
Product Product Product Product Product
C A D E B
22.91 20.05 18.98 18.82 11.23
A A A A B
Product Product Product Product Product
B A D C E
A B B B B
Product Product Product Product Product
B D A C E
22.00 14.64 13.68 13.20 12.45
A B B B B
27.50 17.93 17.55 16.30 15.14
A B B B B
BEER AT 19.93 19.55 17.89 16.61 13.41
A A AB AB B
Product Product Product Product Product
B D A E C
TART MF 25.86 11.55 11.55 11.32 10.77
TINGLING AE 25.02 19.36 17.30 16.30 15.70
B E D A C
A B B B B
Product Product Product Product Product
E B D C A
15.00 14.77 14.66 13.32 12.11
LINGERING AT 18.68 16.41 14.75 14.45 14.14
Product Product Product Product Product
B E C D A
31.43 21.32 20.52 20.39 19.55
A B B B B
Context
B D E A C
Product Product Product Product Product
BITTER AT
MOUTHCOAT AE Product Product Product Product Product
20.20 19.18 17.86 17.50 16.95
FRUITY AT
SWEET AT Product Product Product Product Product
B A C E D
PUCKERING MF
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For conjoint, especially services: l
l
l
l
l
l
Orme, B., 1996. Which Conjoint Method Should I Use, Sawtooth Software. Sawtooth Software (ed.), 2001. Choice-based Conjoint (CBC) Technical Paper. Sawtooth Software (ed.), 2002. ACA 5.0 Technical Paper. Sawtooth Software (ed.), 2002. Conjoint Value Analysis (CVA) Version 3.0. Sawtooth Software (ed.), 2002. Orme, B., Interpreting Conjoint Analysis Data. Sawtooth Software (ed.), 2003. Johnson, R., Orme, B., Getting the Most from CBC. https:// www.sawtoothsoftware.com/download/techpap/mbcconf2010.pdf
References Dauda, S. Y., & Lee, J. (2016). Quality of service and customer satisfaction: A conjoint analysis for the Nigerian bank customers. International Journal of Bank Marketing, 34(6), 841–867. Enneking, U., Neumann, C., & Henneberg, S. (2007). How important intrinsic and extrinsic product attributes affect purchase decision. Food Quality and Preference, 18, 133–138. Johnson, R. M., & Bryan, K. (1996). How many questions should you ask in choice-based conjoint studies? Orme, Sawtooth Software, Inc. (360/681-2300), Copyright 1996, ART Forum, Beaver Creek. Lindstrom, M. (2016). Small data: the tiny clues that uncover huge trends. New York: St. Martin’s Press. Orme, B. (2001). Assessing the monetary value of attribute levels with conjoint analysis: Warnings and suggestions. Copyright 2001, Sawtooth Software. Orme, B. (2002). Perspectives on recent debate over conjoint analysis and modeling preferences with ACA. Copyright 2002, Sawtooth Software. Orme, B. (2003, August). Special features of CBC software for packaged goods and beverage research. Copyright 2002, Sawtooth Software. SAM Sensory and Marketing (2016/2017). Research experience on Return Index of 0.75 Reference. Sawtooth Software. (2002a). The CVA/HB Technical Paper. Copyright 2002, Sawtooth Software. Sawtooth Software. (2002b). A full-profile conjoint analysis system from Sawtooth Software. Copyright 2002, Sawtooth Software. Stone, H., Bleibaum, R.N., & Thomas, H.A. (2012). Sensory evaluation practices (4th ed., pp. 233–289). Elsevier: Oxford. Stone, H., Sidel, J. L., Oliver, S., Woolsey, A., & Singleton, R. C. (1974). Sensory evaluation by quantitative descriptive analysis. Food Technology, 28, 11, 24, 26, 28, 29, 32, 34. Torri, L., Dinnella, C., Recchia, A., Naes, T., Tuorila, H., & Monteleone, E. (2013). Projective mapping for interpreting wine aroma differences as perceived by naive and experienced assessors. Food Quality and Preference, 29(1), 6–15.
Further reading Frost, M. B., Giacalone, D., & Rasmussen, K. K. (2015). Alternative methods of sensory testing: Working with chefs, culinary professionals, and brew masters. In J. Delaru, J. B. Lawlor, & M. Rogeaux (Eds.), Rapid sensory profiling techniques: Applications in new product development and consumer research.
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Mecredy, J. M., Sonnemann, J. C., & Lehman, S. J. (1974). Sensory profiling of beer by a modified QDA method. Food Technology, 28, 36–41. Rogers, C. (1959). A theory of therapy, personality and interpersonal relationships as developed in the client-centered framework. In S. Koch (Ed.), Psychology: A study of a science. Vol. 3. Formulations of the person and the social context. New York: McGraw-Hill. Schutz, H. (1983). Multiple regression approach to optimization. Food Technology, 37, 46–48 62. Sidel, J. L., & Stone, H. (1983). Introduction to optimization research – Definitions and objectives. Food Technology, 37, 36–38. Stewart-Knox, B., & Mitchell, P. (2003). What separates the winners from the losers in new food product development? Trends in Food Science & Technology, 14(1–2), 58–64. Stone, H., & Bleibaum, R. N. (2009). Sensory evaluation. In G. Campbell-Platt (Ed.), Food science and technology (pp. 323–331). Chichester: Wiley-Blackwell. Stone, H., & Sidel, J. L. (1981). Quantitative descriptive analysis in optimization of consumer acceptance. In: Presented at eastern food science and technology conference on ‘strategies of food product development,’ Lancaster, Pennsylvania.