Market response models use statistical methods of analysis of historic market data, to estimate the impact of advertising and other elements of the marketing mix on sales. The topic is covered in some detail in the chapter Market Mix Modelling. This section is devoted specifically to Millward Brown’s Awareness Index (AI), a widely used metric for gauging advertising “efficiency”. It estimates the ability of an advertisement to generate awareness at a given level of media weight.
The origin of the Awareness Index dates back to 1986, when Gordon Brown introduced the notion in his paper, “Modelling Advertising Awareness.” In 1987, Brown published another paper “The Link between Sales Effects and Advertising Content”, and the Link pre-test was developed a year later.
The model measures the efficiency of advertising in generating awareness. It is based on the premise that advertising awareness depends on:
The measure used for advertising awareness is brand-led claimed advertising awareness: “Have you seen any advertising for [brand] recently?” The word “recently” curtails the time frame so that conclusions relate largely to current/recent campaigns.
Awareness Index, a measure of ad quality, represents the incremental level of claimed ad recall generated per 100 GRPs.
The model introduces the notion of “base level” advertising to reflect advertising heritage. Base level is the level to which recall will gradually decline when advertising stops. It represents the proportion of people who would claim to be aware of recent advertising, even if there is no advertising for some time.
Brands with historically high levels of memorable advertising tend to have higher base levels. Brands without memorable advertising or without much advertising history have low base levels.
The model states that awareness is a combination of residual awareness from previous advertising plus learning. As shown in Exhibits 15.18 and 15.19, awareness decays exponentially towards base level, and learning is the proportion of people who become aware of the brand’s advertising due to its presence on air in the current week.
The base line is assumed to be linear, increasing (or decreasing) gradually at a constant rate every period. Because the retention rate (0.9) is fixed, the pace at which advertising declines is determined by the base level. Only powerful, memorable, heavy advertisement campaigns are capable of raising base levels.
A key issue with this model is that base level and awareness index are interdependent. It follows that a number of combinations of base level and awareness index may fit the model equally well. The baseline is usually set by the modeller based on experience as well as norms sourced from a database of historical studies.
The strength of the model lies in its ability to distinguish the performance of different advertising campaigns, as can be seen from Exhibit 23.23, which pertains to the advertisements of Surf Ultra, a concentrated detergent powder, soon after its launch in India. According to the model, Campaign 2 (the memorable “daag dhondte reh jaonge” commercial) registered a high awareness index of 3.0 and succeeded in raising the base level. In contrast the initial campaign for Surf Ultra was relatively weak. Diagnostics for that campaign revealed the inability of the campaign to differentiate the concentrate variant, Surf Ultra, from Surf, the mother brand.
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Marketing has changed. More so in practical terms, and marketing education is lagging.
The fundamental change lies in the application of analytics and research. Every aspect of the marketing mix can be sensed, tracked and measured.
That does not mean that marketers need to become expert statisticians. We don't need to learn to develop marketing mix models or create perceptual maps. But we should be able to understand and interpret them.
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