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2.7 Awareness, Attitudes, and Usage (AAU): Metrics of the Hierarchy of Effects

Studies of awareness, attitudes, and usage (AAU) enable marketers to quantify levels and trends in customer knowledge, perceptions, beliefs, intentions, and behaviors. In some companies, the results of these studies are called "tracking" data because they are used to track long-term changes in customer awareness, attitudes, and behaviors.

AAU studies are most useful when their results are set against a clear comparator. This benchmark may comprise the data from prior periods, different markets, or competitors.

Purpose: To track trends in customer attitudes and behaviors.

Awareness, attitudes, and usage (AAU) metrics relate closely to what has been called the Hierarchy of Effects, an assumption that customers progress through sequential stages from lack of awareness, through initial purchase of a product, to brand loyalty (see Figure 2.2). AAU metrics are generally designed to track these stages of knowledge, beliefs, and behaviors. AAU studies also may track "who" uses a brand or product––in which customers are defined by category usage (heavy/light), geography, demographics, psychographics, media usage, and whether they purchase other products.

Figure 2.2 Awareness, Attitudes, and Usage: Hierarchy of Effects

Information about attitudes and beliefs offers insight into the question of why specific users do, or do not, favor certain brands. Typically, marketers conduct surveys of large samples of households or business customers to gather these data.


Awareness, attitudes, and usage studies feature a range of questions that aim to shed light on customers' relationships with a product or brand (see Table 2.4). For example, who are the acceptors and rejecters of the product? How do customers respond to a replay of advertising content?

Table 2.4 Awareness, Attitudes, and Usage: Typical Questions



Typical Questions


Awareness and Knowledge

Have you heard of Brand X? What brand comes to mind when you think "luxury car?"


Beliefs and Intentions

Is Brand X for me? On a scale of 1 to 5, is Brand X for young people? What are the strengths and weaknesses of each brand?


Purchase Habits and Loyalty

Did you use Brand X this week? What brand did you last buy?

Marketers use answers to these questions to construct a number of metrics. Among these, certain "summary metrics" are considered important indicators of performance. In many studies, for example, customers' "willingness to recommend" and "intention to purchase" a brand are assigned high priority. Underlying these data, various diagnostic metrics help marketers understand why consumers may be willing––or unwilling––to recommend or purchase that brand. Consumers may not have been aware of the brand, for example. Alternatively, they may have been aware of it but did not subscribe to one of its key benefit claims.

Awareness and Knowledge

Marketers evaluate various levels of awareness, depending on whether the consumer in a given study is prompted by a product's category, brand, advertising, or usage situation.

    Awareness: The percentage of potential customers or consumers who recognize––or name––a given brand. Marketers may research brand recognition on an "aided" or "prompted" level, posing such questions as, "Have you heard of Mercedes?" Alternatively, they may measure "unaided" or "unprompted" awareness, posing such questions as, "Which makes of automobiles come to mind?"

    Top of Mind: The first brand that comes to mind when a customer is asked an unprompted question about a category. The percentage of customers for whom a given brand is top of mind can be measured.

    Ad Awareness: The percentage of target consumers or accounts who demonstrate awareness (aided or unaided) of a brand's advertising. This metric can be campaign- or media-specific, or it can cover all advertising.

    Brand/Product Knowledge: The percentage of surveyed customers who demonstrate specific knowledge or beliefs about a brand or product.


Measures of attitude concern consumer response to a brand or product. Attitude is a combination of what consumers believe and how strongly they feel about it. Although a detailed exploration of attitudinal research is beyond the scope of this book, the following summarizes certain key metrics in this field.

    Attitudes/Liking/Image: A rating assigned by consumers––often on a scale of 1–5 or 1–7––when survey respondents are asked their level of agreement with such propositions as, "This is a brand for people like me," or "This is a brand for young people." A metric based on such survey data can also be called relevance to customer.

    Perceived Value for Money: A rating assigned by consumers––often on a scale of 1–5 or 1–7––when survey respondents are asked their level of agreement with such propositions as, "This brand usually represents a good value for the money."

    Perceived Quality/Esteem: A consumer rating––often on a scale of 1–5 or 1–7––of a given brand's product when compared with others in its category or market.

    Relative Perceived Quality: A consumer rating (often from 1–5 or 1–7) of brand product compared to others in the category/market.

    Intentions: A measure of customers' stated willingness to behave in a certain way. Information on this subject is gathered through such survey questions as, "Would you be willing to switch brands if your favorite was not available?"

    Intention to Purchase: A specific measure or rating of consumers' stated purchase intentions. Information on this subject is gathered through survey respondents' reactions to such propositions as, "It is very likely that I will purchase this product."


Measures of usage concern such market dynamics as purchase frequency and units per purchase. They highlight not only what was purchased, but also when and where it was purchased. In studying usage, marketers also seek to determine how many people have tried a brand. Of those, they further seek to determine how many have "rejected" the brand, and how many have "adopted" it into their regular portfolio of brands.

    Usage: A measure of customers' self-reported behavior.

In measuring usage, marketers pose such questions as the following: What brand of toothpaste did you last purchase? How many times in the past year have you purchased toothpaste? How many tubes of toothpaste do you currently have in your home? Do you have any Crest toothpaste in your home at the current time?

In the aggregate, AAU metrics concern a vast range of information that can be tailored to specific companies and markets. They provide managers with insight into customers' overall relationships with a given brand or product.

Data Sources, Complications, and Cautions

Sources of AAU data include

  • Warranty cards and registrations, often using prizes and random drawings to encourage participation.
  • Regularly administered surveys, conducted by organizations that interview consumers via telephone, mail, Web, or other technologies, such as hand-held scanners.

Even with the best methodologies, however, variations observed in tracking data from one period to the next are not always reliable. Managers must rely on their experience to distinguish seasonality effects and "noise" (random movement) from "signal" (actual trends and patterns). Certain techniques in data collection and review can also help managers make this distinction.

  1. Adjust for periodic changes in how questions are framed or administered. Surveys can be conducted via mail or telephone, for example, among paid or unpaid respondents. Different data-gathering techniques may require adjustment in the norms used to evaluate a "good" or "bad" response. If sudden changes appear in the data from one period to the next, marketers are advised to determine whether methodological shifts might play a role in this result.
  2. Try to separate customer from non-customer responses; they may be very different. Causal links among awareness, attitudes, and usage are rarely clear-cut. Though the hierarchy of effects is often viewed as a one-way street, on which awareness leads to attitudes, which in turn determine usage, the true causal flow might also be reversed. When people own a brand, for example, they may be predisposed to like it.
  3. Triangulate customer survey data with sales revenue, shipments, or other data related to business performance. Consumer attitudes, distributor and retail sales, and company shipments may move in different directions. Analyzing these patterns can be a challenge but can reveal much about category dynamics. For example, toy shipments to retailers often occur well in advance of the advertising that drives consumer awareness and purchase intentions. These, in turn, must be established before retail sales. Adding further complexity, in the toy industry, the purchaser of a product might not be its ultimate consumer. In evaluating AAU data, marketers must understand not only the drivers of demand but also the logistics of purchase.
  4. Separate leading from lagging indicators whenever possible. In the auto industry, for example, individuals who have just purchased a new car show a heightened sensitivity to advertisements for its make and model. Conventional wisdom suggests that they're looking for confirmation that they made a good choice in a risky decision. By helping consumers justify their purchase at this time, auto manufacturers can strengthen long-term satisfaction and willingness to recommend.

Related Metrics and Concepts

    Likeability: Because AAU considerations are so important to marketers, and because there is no single "right" way to approach them, specialized and proprietary systems have been developed. Of these, one of the best known is the Q scores rating of "likeability." A Q Score is derived from a general survey of selected households, in which a large panel of consumers share their feelings about brands, celebrities, and television shows.4

Q Scores rely upon responses reported by consumers. Consequently, although the system used is sophisticated, it is dependent on consumers understanding and being willing to reveal their preferences.

    Segmentation by Geography, or Geo-clustering: Marketers can achieve insight into consumer attitudes by separating their data into smaller, more homogeneous groups of customers. One well-known example of this is Prizm. Prizm assigns U.S. households to clusters based on Zip Code,5 with the goal of creating small groups of similar households. The typical characteristics of each Prizm cluster are known, and these are used to assign a name to each group. "Golden Ponds" consumers, for example, comprise elderly singles and couples leading modest lifestyles in small towns. Rather than monitoring AAU statistics for the population as a whole, firms often find it useful to track these data by cluster.

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