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Emotional Profiles of a Frozen Food BrandDiscovering Evident and Hidden Feelings for Improved Emotional Marketing

February 2010

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PREFACERecent research in neuroscience and psychology confirms that emotions strongly, if not exclusively, drive people’s behaviorNote1, including their behavior as consumers. Also, studies convincingly show that people being researched via traditional market research techniques often can’t or won’t convey their emotionsNote2, including how their emotions affect their behavior.

Evident emotions are those that people can and will disclose. They are also called “explicit.” Hidden emotions are those that people can’t or won’t disclose. They are also called “implicit.”

Recognizing the importance of emotions, as well as the need to assess both explicit and implicit emotions, this study used a technique that effectively revealed how moms with school-aged children explicitly and implicitly felt about a frozen food (FF) brand. Furthermore, the study showed how the respondents’ explicit and implicit emotions related to (i.e., “drove”) their brand preference and purchasing behavior.

This information can be used by the FF brand to develop strategies that identify specific emotions to activate to improve consideration and purchase of the FF branded products.

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CONTENTSABOUT experiEmotive® analytics ………. 4

APPLICATIONS AND INFORMATION OBJECTIVES ………. 6

METHODS ………. 7

KEY RESULTS ………. 10

The FF Brand’s Preferences and Purchase Behavior 11The FF Brand’s Emotional Profile (Total Sample) 12

The FF Brand’s Emotional Profile (Higher Minus Lower Share of Purchases ) 14The FF Brand’s Emotional Profile (Total Sample/1-2 Second Average Implicit Responders) 16

The FF Brand’s Emotional Profile (Higher Minus Lower Share of Purchases/1-2 Second Average Implicit Responders) 17Emotions Most Impacting the FF Brand’s Share of Sales 19

Summary 20

RECOMMENDATIONS FOR THE FF BRAND………. 21

HOW TO CONDUCT AN EMOTIONAL PROFILING STUDY ………. 22

NOTES AND REFERENCES ………. 23

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ABOUT experiEmotive® analytics

experiEmotive® analytics (EMA) is a consumer research company that conducts emotional research to discover the “experience-to-emotional motive chains” that drive consumer behavior.

EMA’s work is fundamentally based on the experiEmotive® model, diagrammed at right. The experiEmotive® model says that experiences initiate conscious and unconscious cognitive, emotional, and physiological processing that drives behavior; and emotions are the fuel that does the serious driving.

EMA uses a variety of techniques, all designed to effectively assess emotional dynamics. Because emotions are often “implicit” (i.e., either unconsciously operating or consciously guarded), special techniques are needed. All of EMA’s work intends to support more effective emotional marketing, particularly more emotionally engaging product designs, brand identities & positionings, advertising, and customer service experiences, if not more.

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In essence, experiEmotive® analytics works to…

Find the emotions that move (stimulate, compel, entice, inspire, stir,

arouse, provoke, incite, etc.) people to buy products and services.

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APPLICATIONS AND INFORMATION OBJECTIVES

Help the FF Brand’s brand/marketing executives…

• Get new ideas for what emotions to trigger to optimize the frozen food (FF) brand’s preference and/or purchase among moms with school-aged children (AKA target)...

By answering the following questions:

1. “Net-net” how strongly does the target feel about the FF Brand and are their feelings generally more positive or more negative?

2. What specific feelings does the target have about the FF Brand and how strong are those specific feelings?

3. Are there important feelings for the FF Brand that the target has that are operating unconsciously or are deliberately being hidden for some reason? If so, what are they?

4. Which of these feelings is/are most impacting preference for or purchase of the FF Brand among the target?

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METHODS

• Recruited moms with school-aged children from a national online consumer panel (e-Rewards) for an online study positioned as “How Well Different Visuals Convey Different Feelings Under Different Levels of Distraction.”

• Asked respondents a series of questions and had them complete “feelings” tasks.– Order of questions/tasks: Demographics, implicit feelings task, explicit

feelings task, brand awareness, brand purchase, brand preference.

• 127 completed “surveys” were processed for analysis.

• Converted implicit and explicit feelings task responses to standardized scores.

• Performed (regression-based) driver analyses to identify emotions most impacting brand preference and purchase.

• The next two slides contain more information about the implicit and explicit feelings tasks.

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IMPLICIT FEELINGS TASK• Based on well-founded

“implicit assessment” techniques in social/cognitive psychologyNote3

• Present a “prime” (brand representation)

• Misdirect respondent to symbol/emotion word task

• Emotions (implicitly) associated with prime disproportionately “leak” onto symbol task

FF BRAND NAME

Compared to designs like this in general, how much more or less does this design convey this feeling? (4-pt. scale)

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EXPLICIT FEELINGS TASK

• More straightforward

• Present a brand representation

• Ask respondent to rate how much the brand image conveys a certain feeling

• Baseline control images worked in with brand representation images

Compared to images like this in general, how much more or less does this image convey this feeling? (4-pt. scale)

FF BRAND NAME

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KEY RESULTS

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THE FF BRAND’S PREFERENCES AND PURCHASE BEHAVIOR

• The FF Brand showed high preference rankings, particularly for family- and party-size meals.

• On average, the respondents claimed to have purchased almost five FF Brand frozen meals in the past 6 months.

• These purchases calculated to an average 41% share of frozen meal purchases across the five brands included.

Average FF Brand’s Preference Ranks and Purchase Behavior

General Preference 1.72

Recommend Preference 1.69

Family-Size Meal Preference 1.52

Party-Size Meal Preference 1.43

Average Overall Preference 1.59

Number of Purchases (6 mo.) 4.80

Share of Purchases (6 mo.) 41%

All were in the context of “frozen meals.” Preference ranks range from 1 (Most Preferred) to 5 (Least Preferred) within a 5 brand competitive set. Share of Purchases were relative to the same competitive set.

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THE FF BRAND’S EMOTIONAL PROFILE (Total Sample)

Red areas and numbers within represent standardized degrees of explicit emotion (i.e., conscious and willing), blue areas and numbers within the same for implicit emotions (i.e., unconscious or unwilling). Numbers just to the right of each emotion indicate the total degree of that emotion (explicit score + implicit score). Positive scores for positive emotions are desired, as are negative scores for negative emotions (so negative scores for negative emotions are added). Pooled positive and negative emotion scores are derived from grouping the five emotions in each category. The Overall Emotionality scores (OES) are the (1) additions of the averaged individual positive and negative emotions (adding a negative score for negative emotions) and (2) additions of the pooled positives and pooled negatives (again, adding a negative score for pooled negatives).

Among all respondents, the FF Brand showed moderate to low overall emotionality. (This is based on two previous EPs where the brands’ overall emotionality scores were 119 and 80, vs. 77 for the FF Brand. ) Most noticeable were a relatively strong feeling of happy (58) and a relatively weak feeling of worried (-40). Explicit emotions were stronger than implicit; but notable implicit emotions were good in general and (because they were in an undesired direction) loving and guilty.

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WorriedGuiltyBored

BadPooled Negatives

ResponsibleProudLovingHappy

ComfortedGood

Pooled Positives

Standardized Emotion Score

FF Brand's Emotional Profile

(Total Sample:OES = 77/78)

Implicit Explicit

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Base: Total Sample N = 127

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CLOSER EXAMINATION – WHAT’S IMPORTANT?

Although the EP among the total sample is interesting and perhaps informative, it is not optimally useful. Getting people to feel a certain way is a means to an end. And that end is higher brand preference and, even better, higher sales and/or share of sales. To cut right to the chase (i.e., higher share of sales), we compared the EPs of people who claimed a higher share of the FF Brand’s sales to the EPs of people who claimed a lower share of the FF Brand’s sales. The results of this analysis at least start to isolate emotions that “drive” people to buy the FF Brand’s vs. the competition. The EP on the next page presents the results of that comparison.

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THE FF BRAND’S EMOTIONAL PROFILE (Higher Minus Lower Share of Purchases)

In this chart, we show the “emotional profile” differences between those who claimed higher share (35%+, N=52) vs. lower share (<35%, N=51) of the FF Brand’s purchases. We chose to present share comparisons (vs. preference) because share represented a more important “outcome” and because results were clearer. All keys, colors, and emotion scores have the same meaning as in the previous chart. The emotion scores in this chart were calculated by subtracting the lower-share respondents’ emotion scores from the higher-share respondents’ emotion scores. Results indicate which emotions differentiated the two groups, signaling emotions that need attention to optimize the FF Brand’s share of purchases.

When examining the differences between the FF Brand’s Higher Share vs. Lower Share respondents, we found that the strongest differentiating emotions were feeling implicitly loving and happy. (The yellow backgrounds also indicate significant regression impacts, presented later.) Also notable were relatively strong levels of explicitly feeling responsible, comforted, happy, and loving, countered by a relatively strong level of implicitly feeling bored. Finally, a lack of feeling implicitly comforted emerged.

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WorriedGuiltyBored

BadPooled Negatives

ResponsibleProudLovingHappy

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Pooled Positives

Standardized Emotion Score

FF Brand's Emotional Profile

(Higher Minus Lower Share of Purchases)

Implicit Explicit

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Base: Total Sample N = 127

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AN INVESTIGATED CAVEATImplicit emotional assessment studies have shown that implicit emotions most reliably emerge in a relatively short period of time (i.e., less than 2 seconds). To minimize respondent frustration, we did allow longer periods of time to respond in the implicit task (up to 5 seconds). After seeing relatively weak implicit responses, we wondered whether the longer period of time resulted in a more explicit “thinking” response. So, we examined only respondents whose average implicit response time was 1-2 seconds. (We also eliminated less than 1 second responders to minimize those who were just pushing keys to hurry through, as opposed to sincerely focusing on the implicit task.) This included 83 of the 127 respondents (65%). Their results follow on the next two slides.

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THE FF BRAND’S EMOTIONAL PROFILE (Total Sample/1-2 Second Average Implicit Responders)

Again, red areas and numbers within represent standardized degrees of explicit emotion (i.e., conscious and willing), blue areas and numbers within the same for implicit emotions (i.e., unconscious or unwilling). Numbers just to the right of each emotion indicate the total degree of that emotion (explicit score + implicit score). Positive scores for positive emotions are desired, as are negative scores for negative emotions (so negative scores for negative emotions are added). Pooled positive and negative emotion scores are derived from grouping the five emotions in each category. The Overall Emotionality scores (OES) are the (1) additions of the averaged individual positive and negative emotions (adding a negative score for negative emotions) and (2) additions of the pooled positives and pooled negatives (again, adding a negative score for pooled negatives).

Compared to the “total-total” sample, the total sample of those with ideally more conducive implicit response times showed somewhat stronger implicit scores (+7 dif. on average). Overall, happy remained the highest scoring positive emotion (62), with bored (-39) and worried (-34) the lowest. Implicit feelings were stronger (vs. all respondents), particularly for feeling bad (6, +14 dif.), comforted (9, +11 dif.), good (22, +8 dif.) and happy (15, +7 dif.).

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WorriedGuiltyBored

BadPooled Negatives

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Pooled Positives

Standardized Emotion Score

FF Brand'sEmotional Profile

(Total Sample: OES = 75/75)

Implicit Explicit

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Base: 1-2 Second Avg. Implicity Latency N = 83

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THE FF BRAND’S EMOTIONAL PROFILE (Higher Minus Lower Share of Purchases/1-2 Second Average Implicit Responders)

Again, in this chart, we show the “emotional profile” differences between those who claimed higher share (35%+, N=52) vs. lower share (<35%, N=51) of the FF Brand’s purchases, but this time among the ideal implicit response latency respondents. We chose to present share comparisons (vs. preference) because share represented a more important “outcome” and because results were clearer. All keys, colors, and emotion scores have the same meaning as in the previous chart. The emotion scores in this chart were calculated by subtracting the lower-share respondents’ emotion scores from the higher-share respondents’ emotion scores. Results indicate which emotions differentiated the two groups, signaling emotions that need attention to optimize the FF Brand’s share of purchases.

Again, when examining the differences between the FF Brand’s Higher Share vs. Lower Share respondents, we found that the strongest differentiating emotions were feeling implicitly happy and loving, particularly loving. (The yellow background indicates a significant regression impact, presented later.) Also notable were strong differences in feeling implicitly bored and bad, but explicitly feeling not bored and not bad.

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WorriedGuiltyBored

BadPooled Negatives

ResponsibleProudLovingHappy

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Pooled Positives

Standardized Emotion Score

FF Brand's Emotional Profile

(Higher Minus Lower Share of Purchases)

Implicit Explicit

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Base: 1-2 Second Avg. Implicity Latency N = 83

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ADDITIONAL “EMOTIONAL DRIVER” RESULTSVIA MULTIPLE REGRESSIONSNOTE4

Another way to assess the impact of each discrete emotion on the important outcome variable – share of the FF Brand’s sales – is via multiple regression. A multiple regression statistically examines the degree to which variance in an outcome variable (i.e., OV; share of the FF Brand’s sales) is accounted for in a series of predictor variables (i.e., PVs; discrete emotions toward the FF Brand). The PV/OV model is examined for significance and “standardized betas” are returned as the amount of impact significant PVs have. The results that follow present the key results of multiple regressions conducted to investigate the impact of various combinations of emotions on share of the FF Brand’s sales. Furthermore, these regressions were conducted among the total sample and among the 1-2 second average implicit responders.

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EMOTIONS MOST IMPACTINGTHE FF BRAND’S SHARE OF SALES

• When only explicit emotions were in the model as PVs, none significantly predicted share of the FF Brand’s sales.

• When only implicit emotions were in the model as PVs, feeling implicitly loving significantly predicted share of the FF Brand’s sales (for the total sample and for the 1-2 second average implicit responders).

• When both explicit and implicit emotions were in the model as PVs, feeling implicitly loving (primarily) and happy significantly predicted share of the FF Brand’s sales. No explicit emotions were significant predictors.

• It is also noteworthy that implicit emotions – the detection of which was a differentiating feature of the technique used – improved overall predictability of share of the FF Brand’s sales (shown by higher R2s).

The top table shows the impacts of explicit emotions only, implicit emotions only, and both explicit and implicit emotions together on share of the FF Brand’s purchases among all respondents (N=127). The bottom table shows the same for respondents with average implicit responses between 1 and 2 seconds (N=83). R2 = the degree of variance in the outcome variables explained by the emotions in the model. p = the significance of the model. “i” = implicit. PV = Predictor Variable (the emotions); OV = Outcome Variable (share of purchases).

Multiple Regression Analyses - Total SampleFeelings Toward the FF Brand’s (PVs)/Share of the FF Brand’s Purchases (OV)

PVs in Model R2/p Significant Betas

Explicit Only No significant predictors

Implicit Only .041/.039 iLoving .203Explicit & Implicit .110/.009 iLoving .260; iHappy .219

Multiple Regression Analyses - 1-2 Second Avg. Implicit RespondersFeelings Toward the FF Brand’s (PVs)/Share of the FF Brand’s Purchases (OV)

PVs in Model R2/p Significant Betas

Explicit Only No significant predictors

Implicit Only .059/.045 iLoving .243Explicit & Implicit .091/.024 iLoving .302

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SUMMARY• “Net-net” how strongly does the target feel about the FF Brand and are their feelings generally more positive or

more negative?

– Feelings for the FF Brand were definitely more positive than negative, but with low to moderate intensity.

• What specific feelings does the target have about the FF Brand and how strong are those specific feelings?

– Explicitly the target felt most strongly happy and comforted by the FF Brand, and least worried. Implicitly the target felt most strongly good (in general) and happy and least worried. All of these emotions were at low to moderate levels of intensity. Also, slight indications of feeling implicitly guilty emerged.

• Are there important feelings for the FF Brand that the target has that are operating unconsciously or are deliberately being hidden for some reason? If so, what are they?

– Yes. As mentioned above, happy and (slightly) guilty about the FF Brand were notable implicit feelings. In addition, feeling implicitly responsible about the FF Brand was relatively high.

• Which of these feelings is/are most impacting preference for or purchase of the FF Brand among the target?

– Feeling implicitly loving (primarily) and happy toward the FF Brand were the feelings that most impacted the FF Brand's share of sales.

– Although not detailed in the main part of this document, and not as consistent as impacts on sales, implicitly feeling proud, responsible, and loving and explicitly feeling good, responsible, and not worried most impacted measures of the FF Brand's brand preference.

RECOMMENDATIONS FOR THE FF BRAND

• Look for ways to increase the FF Brand's positive emotional intensity.

• Assuming higher share of sales is your goal, for this target…

– Strongly consider building your brand around feeling LOVING.• That is, explore ways to activate this feeling when this target is exposed to

the the FF Brand brand (particularly implicitly).

– Secondarily, do the same around feeling HAPPY.– Look for ways to decrease feelings of being BORED.

• Following up on this research…

– Consider replicating this with desired targets, emotions, and brands.

– Qualitatively explore experiences and cognitions that activate these compelling implicit emotions.

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HOW TO CONDUCT AN EMOTIONAL PROFILING STUDY

1. Determine your target market specifications.

2. Think about or decide what specific emotions you want to profile (up to 10).

3. Think about/decide how you want your brand to be represented (e.g., just words, logo, etc.).

4. Communicate this information to Paul Conner at experiEmotive® analytics (pconner@experiemotive.com or 314-752-0564).

5. Receive a detailed proposal from experiEmotive® analytics.

6. Agree on proposal and conduct the study.

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NOTES AND REFERENCES

1. Support for the fact that emotions drive behavior comes from neuroscience and psychology, and is being widely accepted in marketing. The following quotes and references provide support:

“The essential difference between emotion and reason is that emotion leads to action while reason leads to conclusions.” (Calne, D. (2000). Within Reason: Rationality and Human Behavior. Vintage Books.) “Although beliefs may guide our actions, they are not sufficient to initiate action. No matter how rational your thoughts about helping the needy may be, you need an emotional impulse before you actually volunteer to help. Emotions are prime candidates for turning a thinking being into an actor.” (Frijda, N.H., Manstead, S.R., & Bem, S. (2000). The Influence of Emotions on Beliefs. In N.H. Frijda, A.S.R Manstead, and S. Bem (Ed.), Emotions and Beliefs: How Feelings Influence Thoughts (pp. 1-9), Maisson des Sciences de l’Homme and Cambridge University Press.)

“Customers are always emotional. That is, they always have feelings, sometimes intense, other times barely perceptible, when they make purchases or engage in commercial transactions. One thing is certain: no one is entirely neutral about consuming.” (Barlow, J. & Maul, D. (2000). Emotional Value: Creating Strong Bonds with Your Customers. Berrett-Koehler Publishers, Inc.) “Emotion is not an aberrant element when making buying decisions, but a necessary condition if decisions are not to be continually postponed.” (O’Shaughnessy, J. and O’Shaughnessy, N.J. (2003). The Marketing Power of Emotion. Oxford Press.)

“We now accept that human beings are powered by emotion, not by reason. Emotion and reason are intertwined, but when they are in conflict, emotion wins every time. [Emotion] controls our rationality, our decision making.” (Roberts, K. (2002). Annual ESOMAR Conference, Barcelona.)

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NOTES AND REFERENCES

2. That people often can’t or won’t convey their true emotions happens for two primary reasons, which we call “unconscious” and “unwilling”. People often can’t reveal their emotions because emotions operate in large part unconsciously — i.e., below one’s level of awareness. Also, people often won’t reveal their emotions (i.e., they are “unwilling”) because it is often socially or culturally unacceptable to do so. Collectively, these two emotional dynamics — unconscious and unwilling — are called “implicit”.

Support for the “unconscious” problem is as follows:

“We are not aware of most of our emotional reactions: we cannot ‘feel’ the emotion. Most of our emotional life takes place in the unconscious. Emotions under the consciousness threshold do have a large influence on what we perceive and how we react to it.” (Franzen, G. and Bouwman, M. (2001). The Mental World of Brands: Mind, Memory and Brand Success. WARC.)

“The real causes of human action are unconscious, so it is not surprising that behavior could often arise — as in automaticity experiments — without the person’s having conscious insight into its causation.” (Wegner, D.M. (2002). The Illusion of Conscious Will. Bradford Books, The MIT Press.)

“According to most estimates, about 95 percent of thought, emotion, and learning occurs in the unconscious mind — that is, without our awareness.” (Zaltman, G. (2003). How Customers Think: Essential Insights Into the Mind of the Market. Harvard Business School Press.)

Support for the “unwilling” problem comes from Rapaille (Rapaille, C. (2006). Marketing to the Reptilian Brain. Forbes.):“It's not that people intentionally lie during surveys and focus groups; it's that they try too hard to please. When asked about their interests and preferences, they tend to give answers they believe the questioner wants to hear. This is because people respond with their cerebral cortexes, the part of the brain that controls intelligence, rather than emotion or instinct. Their answers are the product of deliberation. In most cases, however, they aren't saying what they feel.”

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NOTES AND REFERENCES

3. The implicit assessment feature of our approach been adapted from implicit techniques widely used in cognitive and social psychology. These techniques involve activating implicit emotional associations with targeted objects (in this case brands) and then misdirecting respondents to an unrelated task. Implicit emotional associations surface as “leakages” onto responses to the unrelated task.

Generally speaking, there are two types of implicit measurement approaches: 1) priming techniques, adapted largely from approaches credited to Fazio (Fazio, R.H. & Olson, M.A. (2003). Implicit Measures in Social Cognition Research: Their Meaning and Use. Annual Review of Psychology, 54: 297-327.); and 2) IAT (Implicit Association Test) techniques, adapted largely from approaches credited to Greenwald (Greenwald, A.G., McGhee, D.E., & Schwartz, J.L.K. (1998). Measuring Individual Differences in Implicit Cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480.). Both reveal “inner content” (e.g., attitudes, emotions, etc.) that are either unconscious (therefore, explicitly unavailable) or altered in explicit expression.

We adapted a particular priming technique for this research. The technique is called the Affective Misattribution Procedure (AMP) developed by Keith Payne (Payne, B.K., Cheng, C.M., Govorun, O., & Stewart, B.D. (2005). An Inkblot for Attitudes: Affect Misattribution as Implicit Measurement. Journal of Personality and Social Psychology, 89(3), 277-293.). It is beyond the scope of this report to comprehensively discuss the genesis and effectiveness of this technique. However, studies using the AMP have shown that implicit emotions can increase prediction of evaluations and behavior beyond their explicit expression (see Payne, B.K., Govorun, O., & Arbuckle, N. (2008). Automatic Attitudes and Alcohol: Does Implicit Liking Predict Drinking? Cognition and Emotion, 22 (2), 238-271.).

A particular feature of our technique is increased robustness by assessing the effects of multiple discrete emotions as opposed to global positive or negative emotions (which has been the standard in most priming and IAT studies). In other words, we adapted the AMP to identify more specific emotions that impact behavior (e.g., excited vs. impressed vs. confident vs. secure), not just “feeling good” or “feeling bad” in general. We felt accomplishing this would provide brand managers more powerful direction for marketing their products. To support the possibility of effectively assessing discrete emotions, Arbuckle found the AMP to distinguish implicit feelings of fear, anger, and disgust and showed that those differences related to different stereotypical images of African Americans (see Arbuckle, N. (2006). Developing an Implicit Measure of Emotion. Unpublished Master’s Thesis.).

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NOTES AND REFERENCES

4. Multiple regressions provide a well-founded statistical procedure for assessing the impact of our explicit and implicit emotions on brand preference and ownership. Multiple regressions work to examine the impact of individual predictor (independent) variables on an outcome (dependent) variable after accounting for the impact of all of the other predictor variables in the model.

To examine the impact of the explicit and implicit emotions on various measures of brand preference and ownership, we conducted three multiple regressions using three sets of predictor variables for each of the outcome variables (the brand preference and ownership variables listed). The three sets of predictor variables were the 10 explicit emotions by themselves, the 10 implicit emotions by themselves, and the 20 explicit and implicit emotions together.

The tables present the results of these multiple regressions. The following comments help to explain the information in these tables.

• R2 is a measure of the variance of the outcome variables explained by the predictor variables in the model.

• p indicates the significance level of the model. All models indicate statistically significant relationships between the predictor and outcome variables, assuming .05 is accepted as statistical significance.

• Significant betas indicate the standardized impact of emotions that had significant impacts on the outcome variable after accounting for the impact of all of the other predictor variables in the

model. Specifically, a standardized beta represents the number of standardized units of change in the outcome variable given one standardized unit of change in the predictor variable.

Since preferences were ranked measures, negative betas indicate that as an emotion “got stronger” (i.e., moved to a higher emotion effect), preference increased (i.e., moved to a lower rank, closer to 1, which indicated stronger preference); positive betas indicate that as an emotion “got stronger”, preference decreased.