Change is a new constant in twenty-first century advertising. For many businesses the internet is certainly changing how many companies do advertising today—and, as we will see, the internet changes how advertising creative can be tracked. Yet traditional advertising forms are not going away. For the Quick Service Restaurant business—fast food—television is likely to continue to be the main channel for driving customers through the door. Nothing beats television for its ability to create top-of-mind brand awareness and to romance the appetite appeal of food. As a result, according to the U.S. monitoring service Competitrack, each month over thirty new commercials for QSR restaurants debut on national television.

In fact, in this category spending on TV advertising is likely to increase. A decade ago in the QSR category there were 14 companies with over a billion dollars in annual sales. Today there are 24. Advertising is a mission critical function for every one of these franchise organizations competing for their share of the enormous amount of money Americans spend eating away from home. Many of the ads are for new product offerings, as these companies continually reinvent their menus to attract fickle customers.

These highly efficient operators can tell you to the second how long it takes to cook the perfect French fry; they can tell you the sales rates of new product offerings within a few days, and not weeks, let alone months. But the creative battlefield that their advertising has to compete in is shrouded in fog. For example, few QSR operators pre-test their commercials before airing them. A typical reason given for not doing this kind of research is that they are moving too quickly for this research step. As a corollary, it’s almost unheard of to test competitive ads.

Many QSR companies use tracking studies to evaluate the performance of their advertising in market. While traditional tracking studies are effective at monitoring certain advertising effects over time with brand metrics, such as awareness and preference—they are severely limited in their ability to pinpoint the particular advertising executions which are driving them. In a typical twenty to thirty-minute tracking interview there is simply not enough time to collect more than a few recall or recognition metrics about two or three commercial executions. In a cluttered marketplace where thirty new ads are coming out each month, this creates a considerable gap in knowledge about what is really going on from a creative standpoint.

Some of the more sophisticated firms also use marketing mix modeling in an attempt to get a handle on advertising ROI. But statistical modelers rarely include a variable in their models to explicitly represent the creative quality of the advertising, and instead focus on the amount and timing of media spend. The reason for this is, of course, the paucity of quantitative data, e.g. copytesting metrics, profiling the strength of individual creative executions. This has the potential to cause management to undervalue the contribution that brilliant creative can make in terms of leveraging the media investment.

Here’s a question: Would Man o’War have beaten Seabiscuit? The answer: It doesn’t matter because these horses never ran in the same race. One of the limitations of conventional copytesting, as it evolved in the pre-internet era, is that it is norm-based. Scoring an ad based on how it performs versus a database of commercials that have been tested at some time in the past, perhaps five, ten or more years ago, could give a false sense of security in the quickly changing world of QSR advertising.

Imagine that you are the ad manager for a major fast food brand. How should you feel if your research gives your commercial a grade of “B”? How do you feel if I tell you your major competitor’s latest commercial got a score of “C”? Now, how do you feel if your competitor’s commercial got an “A”? Businessmen intuitively understand that the job of advertising isn’t to beat some abstract historical norm—it’s to beat the other guy’s advertising, right now.

Until recently real time competitive intelligence about advertising performance in very dynamic categories like fast food has not been available, largely because of the prohibitive cost of such research. The internet provides a fast and inexpensive channel for data collection, and has begun to drive the development of automated tools for interviewing, analysis and reporting. As a result, a number of research companies have begun to develop new systems for producing a continuous stream of data on traditional media such as television and print advertising.

One of these new approaches to tracking creative performance across a highly cluttered category provides the data for the analyses reported in this paper. It is designed both to provide real time rankings of advertising performance, using standard metrics of creative quality, along with rich diagnostics into the reasons why the winners are winning and the losers are losing. So far, this new approach to tracking the creative landscape has been applied successfully in a variety of fast moving categories, from retail to financial services, and for print as well as television media. Most of these studies, however, are proprietary—but in the case of QSR our data source is syndicated and thus can be reported here.

Compared with older methods of tracking, creative tracking provides a more complete dataset on all the commercials currently airing in a highly competitive category. This greatly expands the scope of analyses that can be undertaken on the dynamics of advertising performance. Using the QSR category as an example, we will see how this kind of data can be used to validate management assumptions about how advertising is supposed to work to drive short term sales results, as well as to provide new insights into how consumers process fast food commercials into long term brand images.


The Ameritest Ad Appraiser system is a syndicated information service that provides in-depth information about the creative quality of television commercials currently airing in the QSR category. Every new television commercial in the category is tested while it is fresh, within a couple days of its appearance on national television, and results are uploaded to a web portal within two weeks of airing. As such the data that is reported is based on a census, not a sampling, of the television advertising in the QSR category, which allows users to analyze the entire body of creative work that competing brands are putting on air over any given time frame.

Each commercial is tested monadically, with a twenty-five minute interview based on a standardized Ameritest pre-test (which is described in more detail on our website). The performance metrics collected include attention-getting power (in a clutter of current QSR ads competing for the consumer’s attention), branding, motivation to visit the restaurant, motivation to buy the featured product and brand attribute ratings.

The primary measures of Attention, Branding and Motivation are combined into a single summary statistic called the Ameritest Performance Index (API), which is indexed to a rolling past 6 months database of QSR ads tested.

Diagnostic measures that are collected include open-end communication, verbal diagnostic ratings of the ad, three picture sorts (Flow of Attention, Flow of Emotion, Flow of Meaning) and two copy sorts (Flow of Attention and Relevance). In addition, from the screening interview, the fast-moving brand metrics from traditional tracking studies—such as top of mind brand awareness and brand preference—are also collected.

The sample for each commercial interview consists of 225 respondents for the clutter Attention score and 75 respondents for the remaining metrics. Respondents are recruited nationally to be past 30 day fast food consumers, with quotas for age, gender and brand usage. Respondents are also screened to live or work near a restaurant of the brand whose advertising is being tested. For this paper, approximately a year’s worth of QSR advertising was analyzed; therefore, the size of the total respondent database is slightly more than 30,000 interviews.

Validation for McDonald’s Sales


During the past few years McDonald’s has enjoyed one of the most positive stories of any QSR brand in terms of growth in same store sales versus one year ago in the U.S., which you can see in publicly available monthly sales data for the period January ’07 to Feb ’08, shown in Exhibit 1a. During this same period McDonald’s aired 42 new TV commercials nationally.

Media spend data for this period is not publicly available. To approximate a monthly share-of-voice, a surrogate measure can be easily constructed from the creative tracker data by dividing the number of new McDonald’s ads in a given month by the total number of new QSR ads for that month, as shown in Exhibit 1b. Measured in this way, we see that McDonald’s “share-of-creative-voice” varies considerably across the time period of this analysis, from a low of 11% to a high of 43%.

The quality of McDonald’s television creative also varies considerably. The percentage of McDonald’s commercials scoring above versus below average for each month is shown in Exhibit 1c. Again, the measure of creative quality used here is the Ameritest Performance Index, which is a weighted combination of branded attention and motivation, with each carrying roughly equal weight in the performance index.

Finally, the focus of McDonald’s messaging varies from ad to ad. Taking as a measure of the brand value being communicated an index score greater than 110 (category = 100), “convenience” and “for the whole family” are the most common messages conveyed by McDonald’s commercials, shown in Exhibit 1d.


Using these inputs we built a regression model to explain McDonald’s sales growth over this fourteen month time period. The most important predictive variable in the model was the Ameritest Performance Index, which by itself explained 43% of the variation in sales growth. Adding the variable share-of-creative-voice improved the R-square to 52%; and adding the communication of the “for the whole family” brand positioning further increased the R-square to 62%. In other words, using only three variables from the data self-contained in the creative tracking system, we can explain approximately two-thirds of growth in same store sales for McDonald’s over this time period. (See Exhibit 2)


As an additional point, we also note that mid-way through this time period a new item—“fresh ingredients”—was added to the set of brand value ratings collected by the creative tracker, reflecting emerging communication strategies in the category. Looking at the set of new commercials from August ’07 through February ’08, we see that the correlation with same store sales growth is highest for this new message of fresh ingredients, at 63%, which is significantly higher than the 40% correlation found for the whole family. (See Exhibit 3) This suggests that in adapting to the rapidly changing realities of fast food advertising, McDonald’s was also successful in finding a fresh, new communication strategy for its advertising.

Discussion: McDonald’s Model of Success

The simple regression model for McDonald’s recent sales success story over this time period is thus:

Sales Growth = Ad Quality + Share of Voice + Communication of Brand Positioning

This three variable model explaining advertising’s impact on sales should have face validity for advertising practitioners for the following reasons.

First, advertising professionals understand that the quality of the creative, and not just the amount of spend, is essential for generating a good return on the advertising investment. This belief drives the specialized research category of commercial pre-testing, represented by firms such as Ipsos-ASI, Millward Brown and Ameritest. All the major pre-testing firms—each with their own validation-to-sales story—provide some kind of measures of attention-getting power, branding, and motivation / persuasiveness as metrics of creative quality. The measure of ad quality used in this model, while it is extended by indexing scores to the performance of competitive advertising airing during a contemporary, and not a historical, time frame, fits within the standard paradigm of the industry.

Second, share-of-voice has long been known to be a predictor of sales, for example, from work done by Michael Moroney and, independently, James Peckham (reported in Jones, 1986). While the share-of-voice estimate used here, which is derived from a count of commercial executions, may not be as accurate as a figure calculated from actual spend data (or even better, GRPs), it’s a reasonable surrogate for a company as prolific in its creative as McDonald’s. One source of error in this estimate, however, is that it does not include commercials which are no longer new but which McDonald’s chose to air again or update with minor re-edits.

Third, this model shows that a consistent communication strategy over time is also important. While we shall shortly see that communication strategy is to a certain extent reflected in the API itself—the clear communication of a relevant message is an important driver of motivation—the cumulative effect of reinforcing the same message again and again across executions is important for maintaining a stable market positioning for the brand. For any given execution, however, tactical considerations may call for a different communication objective, as evidenced by the variety of messages that McDonald’s has been broadcasting and in particular, by their increasing use of the fresh ingredients message.

Diagnostic Insights into the Brand Building Process

Building a brand is an ongoing, long-term process. Two major functions of advertising, besides driving short term sales, are to position a brand clearly in the marketplace and to continuously add to the brand’s image with fresh, unique and engaging imagery. A brand’s positioning deals with the semantics of advertising communication. Brand imagery is largely visual, and carries much of the emotional content of advertising. Both of these aspects of the brand-building process are measured in the creative tracker with the extensive diagnostic parts of the interview.

To understand how this information can be used to provide diagnostic insights into creative effectiveness of QSR advertising, it is first helpful to start with a highly simplified conceptual model of what a fast food restaurant is.

Framing the QSR Restaurant Experience


To conceptualize a QSR brand, one might begin by deconstructing a restaurant in the way a sociologist might—into its three component places or domains of experience: the dining room or eating area, the kitchen or food preparation area, and the boundary between the two, the counter, where the menu is presented, orders are placed and food is served. (See Exhibit 4) In sociological terms (Goffman, 1986), these three places “frame” distinct regions of perception, providing three very different kinds of information—sensory, emotional and rational—which, taken together, define the total restaurant experience.

For a restaurant, the dining room is the place where the restaurateur must maximize social appeal of the brand experience. The dining room can be thought of as a “front-stage,” governed by well-defined rules of social interaction. The goal of a restaurant operator is to make the dining room an emotionally inviting place where a group of fast food consumers would want to spend time with family or friends. In this part of the restaurant, consumer perceptions are managed carefully to create the right social atmosphere. For example, perceptual boundaries and design elements such as artwork and human artifacts convey a sense of connectedness to society or tradition—a place for satisfying your social needs and interests.

In contrast, the kitchen is the place where a restaurateur must maximize sensory appeal. To elevate perceptions of food quality the goal should be to excite all five senses: sight, smell, sound, touch and taste. The kitchen is normally a “backstage” area where different rules of social behavior apply; but in the case of fast food restaurants, the work of the kitchen is, by design, highly visible to the customer. For a fast food restaurant the goal is to convince the customer looking at the food preparation that this is indeed a kitchen and not a food factory.

The boundary between these two places, the countertop in a fast food restaurant, is the third place that is important, in this case from a rational information processing standpoint. This is the place where the menu presents the customer with semantic descriptions of the variety of food choices that are available. This is the place where the food orders are communicated and received; this is the place where financial transactions take place; and it is the place where in-store promotions are most highly visible.

From a marketing standpoint, each of these three places in the restaurant contribute distinct experiential components—emotional, sensory, rational—to the total experience of the restaurant that a fast food marketer might choose to focus on in advertising. To see if this is true, let’s analyze all the different messages that were being communicated by all the brands in the QSR category during the year we’ve been studying. We’ll start with the semantic content of the ads.

A Semantic Network Analysis of the QSR Category

In terms of language, the associative relationships between words can be interpreted as their semantic meaning. In more general terms, it can be used as a representation of how our brains store knowledge. The semantic aspect of advertising communication is critically important for understanding strategic marketing concepts such as a brand’s positioning in the market-place and the unique selling propositions embedded in effective advertising campaigns.

The analytic tool we will use to perform a meta-analysis of the semantic content of an entire year’s worth of QSR advertising is the semantic network, first developed by Quillian (1967, 1969). Originally used by students of artificial intelligence in the design of computers to store word meanings, cognitive psychologists use semantic networks to model semantic memory (i.e. the part of the brain typically accessed by verbal probing and advertising recall questions). The particular method of constructing a semantic net used here follows a simple iterative procedure as described in Young (2004).

The raw material for our semantic network analysis of the QSR category is drawn from brand value ratings collected in the creative tracker. In particular, we are looking at correlations between the ten brand values as we look across all the QSR ads in the database for the year.

As in traditional factor analysis, correlations are interpreted as distance metrics, providing measures of the strength of association or linkage between brand values. Unlike factor analysis, which is usually used as a form of data reduction to collapse the differences between highly correlated items into a few underlying dimensions, with semantic network analysis the focus is on doing the opposite: the objective is to “explode” the differences between correlated items in order to explore the context of closely related—but not synonymous—ideas.


This simple semantic net in Exhibit 5 sketches out a verbal roadmap to the mind of the QSR customer. It identifies multiple “selling paths” or communication strategies which different advertisers have followed in their quest to find selling propositions that motivate visits to a restaurant. For example, one path McDonald’s followed to Good Value was to advertise the restaurant as a place to take the whole family (e.g. where you would find meals at an inexpensive price point). In contrast, Subway carved out a High Quality niche by advertising adults (e.g. the weight loss star Jared) eating healthy products.

Importantly, the semantic network of selling ideas derived above fits the sociological model of a restaurant experience. The three strongest correlates of commercial performance, the API, are the three pillars of restaurant branding: first, an appeal to the five senses in terms of products that make you hungry and taste good (the kitchen); second, an appeal to the social or emotional benefits in terms of the restaurant being an enjoyable place to eat with friends and family (the dining room); third, a rational appeal to choice and value (the counter-top).


We can also show how performance on these three core brand values correlates with overall ad performance by comparing results from the top quintile of ads to the bottom quintile of ads in this QSR ad database. (See Exhibit 6) On the overall ad performance index, the top ads outperform the bottom 143 to 72. On each of the three primary brand values—good taste, enjoyable place, and good value—the top performers also outperform the bottom performers by a significant margin. This is not to say that all top performers focus on each of these strategic ideas equally, but rather that across a large number of strong executions all three values are being strongly communicated by the top performers. This suggests that the communication of at least one of these three ideas may be “cost-of-entry” for an effective QSR commercial, while communication of less common ideas, such as healthy or fresh ingredients, may be important for modifying the central idea in order to differentiate the brand.

Importance of the Visual

In general, advertisers buy television to put pictures in front of the eyes of the consumer, not to talk to them. So, to a certain extent talking about the semantic content of commercials misses the main point. If the brand’s positioning is the part of the communication that an advertiser needs to hold constant across all the commercials in a campaign, the brand’s image is the part that needs to keep changing in order to grow its association with fresh, unique, and engaging visuals.

As a commercial diagnostic, a number of Picture Sorts® have been developed over the years by Ameritest to diagnose an individual ad’s performance on the three dimensions of the API. For example, the first of these, the Flow of Attention®, has been extensively validated as a diagnostic for Attention and Branding. (Kastenholz, Kerr and Young, 2004) The second picture sort, the Flow of Emotion®, has been broadly validated as a powerful diagnostic for Motivation. (Young, 2004) And the third sort, the Flow of Meaning®, has been found to be a strong visual diagnostic for identifying the specific visual cues in a commercial that powers communication. On one level, then, these tools provide direct insights into why some ads are winning and others losing in the QSR creative rankings.

On another level, when viewed across the entire QSR category of television creative, an entirely new kind of meta-analysis becomes possible. The picture sorts from several hundreds of commercial tests yield a database of thousands of photographic images, each of which is tagged with important consumer metrics of attention-getting power, emotional response and meaning. Sorting through all of this information to provide new insights into the brand dynamics of the QSR category is not particularly difficult—the consumer has already done the job for us. What the consumer sorts out tells us a great deal about what QSR advertising looks like through their eyes.

But before we look for additional insights into how television commercials add to a brand’s image in the QSR category, it is perhaps useful to review each of the three picture sorting techniques in a bit more detail.

1. The Flow of Attention


The first Picture Sorts® methodology uses the power of a photographic stimulus to evoke a recognition response in order to measure some of the pre-conscious visual filtering involved in watching film. The attention sort is a simple binary sort based on whether or not the respondent remembers seeing each particular image about fifteen minutes after a forced viewing of the ad. On average, respondents only report seeing only about two-thirds of the images in a typical commercial on the first viewing. The patterns of remembering are plotted as a time series in a Flow of Attention® graph. (See an example in Exhibit 7)

We interpret the peak images in these flow graphs—which are the most important of the predictors of commercial attention, recall and branding—as the “focal points” of attention. In the terminology of contemporary neuroscience, these peak moments can readily be identified with the concept of the “attentional blink” or attention “spotlighting” and are a consequence of the limited bandwidth or workspace of the conscious mind.

2. The Flow of Emotion

It is well established by modern brain researchers that emotions, both unconscious and conscious, play a critical role in long-term memory formation. The valence of emotional associations, positive or negative, is important for understanding our unconscious approach or avoidance behaviors. In short, our emotions more than our thoughts, are the drivers of our motivations.

Still photographs are one of the best inventions ever created to capture and store human emotions—that’s why we all keep albums of pictures of our friends and family. To measure the flow of audience emotions through a piece of film or video, therefore, the second picture sort uses the remembered set of photographic stills from the first sort. Respondents sort these pictures on a five point scale, from positive to negative, to report the feelings they had while watching the ad. To analyze the dramatic structure of an ad, the two extremes of the scale are plotted on a Flow of Emotion graph. (See Exhibit 7)

The volume of emotion pumping through a commercial is determined by first “partitioning” the audience’s emotional response curve into small units of emotional response with the discrete set of still pictures and then adding them together in a way that is analogous to how Newton’s integral calculus determines the area under a mathematical curve.

3. The Flow of Meaning

For imagery to make a contribution to long-term brand value, images need to mean something specific in terms of the marketer’s communication strategy. The words and concepts which one part of the brain stores as semantic memories must somehow link up with non-verbal imagery stored in other parts of the brain. The visual, emotional brand image must be anchored in the brand’s positioning.

To identify the semantic meaning of visual imagery we conduct a third picture sort called the Flow of Meaning. In general, with this sort respondents could place images into a variety of semantic categories of interest, such as the particular kind of emotion they experienced, or which of their five senses were being activated. In the creative tracker of the QSR advertising, the sorting is done based on the brand values the consumer associates with each image in the commercial.


In other words, the same ten brand values used to construct the semantic network in ehxibit 5 were used as ten categories into which each of the images from the commercials could be sorted. Examples of the kinds of visuals associated with each semantic brand value can be seen in Exhibit 8.

Across all the ads in the database, the Flow of Meaning produces thousands of images which form a kind of visual dictionary to the core values of the QSR category.

Identifying Branding Moments

Not all moments in our lives are equally important for remembering; some vanish quickly and others stay with us forever. Modern neuroscience researchers have found that human memory involves hierarchical encoding. (Levitin, 2006) Apparently, moving pictures bound together in time by narrative structure are encoded in our memories in a hierarchy of importance. The director Sergei Eisenstein wrote about what he considered the “privileged moments” of film. (Deleuze, 1986)

Experiments conducted by Unilever have found that the most salient, emotionally charged images in an ad—the peaks from the Flow of Attention that are also high on the Flow of Emotion—are the ones most likely to stay in long term consumer memory, for many years after an ad has gone off air. (Young, 2006) Other experiments have shown that these are also the images that are most useful to use in constructing a visual stimulus—a storyboard of three or four images representing the gist of an ad—to measure ad recognition in a conventional on-line tracking study. (Young, 2005)

Empirically, we have found that the Flow of Emotion is not highly correlated with the Flow of Attention. (Young, 2004) Consequently, if we plot the time series data produced by the Flow of Attention against the Flow of Emotion, we now have a tool for identifying the most salient, emotionally charged imagery in a commercial, as shown in Exhibit 7, Branding Moments Quadrant.

We conjecture that images falling in the upper right hand quadrant of one of these maps are those most likely to make a contribution to the long term memories associated with the advertised brand. We call these the “branding moments” of the commercial.


Exhibit 9 reports the median number of branding moments for commercials from the top quintile versus the bottom quintile of the QSR database. The top performers have four branding moments, versus three for the bottom performers. In analysis we find that these memorable, emotionally charged moments represent the content that is the most meaningful to the consumer. From this meta-analysis, therefore, we can say that the best QSR commercials are those that convey the most meaning to the consumer.

Branding Moments from Five Ads


To illustrate this point more concretely, we looked at five of the best performing commercials tested during this time period, with one ad each from five of the top QSR brands. The branding moments from these five ads, categorized according to their dominant semantic meaning, are shown in Exhibit 10.

The branding moment images rated high on “good value” tend to be high on rational, semantic content. As we can see in our example, these are visuals containing information such as the price point of a deal, or the image of a menu showing the variety of product choices available in the restaurant.

The branding moments rated high on “enjoyable place” show people in a social setting, having a good time. It is important to note that an enjoyable place is not necessarily literally in the restaurant—in one case, we see pizza delivery men eating in someone’s living room, another at a football stadium. In general, imagery rated high on enjoyment is social and emotional, full of smiles and conversation, with clear relationships between characters—which can obviously be translated into the kind of experiences consumers hope to have in restaurant dining rooms.

We also observe in the enjoyable place category some imagery from a famous Taco Bell ad that ran in the Superbowl. It shows two lions having a conversation, getting ready to have lunch at the expense of some travelers on safari. The lions are on the Serengeti, far from a Taco Bell. This reminds us that it is important not to interpret the role of branding moment images literally. As Zaltman (2003) has pointed out, the role of metaphor can be important for understanding how advertising works in the mind.

The branding moments rated high on “made me hungry” contain the most images. These are product shots that activate all of your senses, from a highly tactile ‘cheese pull’ to a pizza steaming hot from the oven, to a sweet, luscious milk shake smothered in whipped cream, to a classic bite-and-smile. Fast food marketers intuitively understand the importance of highly sensory product imagery in their advertising—though perhaps advertising researchers may not spend enough time studying them.

Finally, the fourth category of branding moments was of visuals that identified the brands being advertised. The role of this type of branding moment is clear. Previous research has shown that a commercial needs to have a peak moment of attention containing a brand identifier in order to generate a good day after recall score. (Young and Robinson, 1989) It is evident that for a commercial to create a long term brand image, the other three categories of visual imagery, need to be filed away in the mind under the right brand name.

Theoretical Discussion

One of the most important discoveries in recent years in cognitive neuroscience is the existence of mirror neurons in the brain. (Blakeslee and Blakeslee, 2007) Basically, a mirror neuron is a neuron which fires both when an animal performs an action and when the animal observes the action performed by another animal. Overwhelmingly visual in nature, these neurons “mirror” the behavior of another animal. Mirror neurons are the biological basis of how we learn by watching others through mimicry—and perhaps may be linked to why we have physical reactions to sensory-loaded images of food in a QSR commercial when a character eats a product on-screen, or when the camera “consumes” a product for us.

It seems likely that one of the chief functions of advertising is to create “false memories” of brand experiences that you never really had in real life. (Zaltman, 2003) When these imaginary experiences are mixed together in the mind with real experiences of the brand, the mind stores the false with the real in the same memory systems.

One of the mechanisms by which fast food advertising may do its work is through a form of “virtual consumption,” which is why advertisers have long been taught to sell the sizzle, not the steak. Virtual consumption events multiply the number of experiences you share with a brand beyond the real ones. This could be one of the reasons large advertisers enjoy such a strong business advantage over non-advertisers in terms of their ability to use advertising to strengthen brand relationships. Advertisers can create sensory memories of products which the consumer has not actually consumed.

One of the oldest theories of how advertising works is the hierarchy of effects model: learn-feel-do. This model was rightfully criticized by ad agencies for putting too much emphasis on the role of semantic learning in advertising, and the model was revised to account for emotional advertising: feel-do-think. In both of these cases it was assumed that the “doing” was the actual consumption behavior of the consumer, interrupted by the effects of advertising, either learning or feeling. What the findings of modern neuroscience suggest is that the “doing” can be inside the ad, too.

Our hypothesis is that mirror neurons and the sensory memory systems of the mind are involved in the mental rehearsal of eating behavior. Virtual behavior may be one way advertising works to actually drive consumer behavior. Indeed, it might be possible to confirm this hypothesis in short order given the current work being done on advertising with modern brain imaging techniques.


This paper demonstrates the importance of the quality of the advertising creative, and not just the amount of media spend, in driving sales growth in the QSR category. The quality of creative has two aspects. First, the semantic quality of television advertising relates to the clarity of the brand positioning being communicated over time. Second, the non-verbal quality of television is multi-dimensional, and is related to the multi-dimensional quality of the restaurant experience itself: sensory, emotional and informational. Most importantly, understanding the fast changing context of what kind of advertising competitors are doing should be of central concern to those managing the advertising for a fast food brand.


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