Most advertisers expect research companies to provide norms as part of their advertising testing services. Those norms are usually averages or means of core advertising variables. Norms provide advertisers with a comfort zone, enabling them to compare new ads against their existing ads and competitive ads.
Norms, however, have two basic flaws, which are sometimes overlooked by research firms and their clients.
- Norms are averages, the middle ground. If a commercial comes up to the norm on a measurement, it only means that the commercial has hit dead center for that type of advertising. Ideally, advertisers should strive to be better then average.
- Norms are the accumulation of advertising measurements over a period of years. Often, they can include measurements for ten or more years. Advertising trends, the market, and consumer mind sets can change over that time period. This is especially true considering the current state of the economy and the rapidly changing communications technology.
Therefore, strict adherence to norms as the ‘gold standard’ to measure success in communications research can lead to a shortsighted view of advertising success. One well-known, advertising pundit referred to norms as: “the mattress on which lazy advertisers lie.”
HCD Research has developed an alternative method for traditional advertising norms in order to provide a more meaningful and up-to-date standard against which to measure advertising success. The methodology is comprised of two main elements.
As mentioned earlier, people, markets and advertising change over time. Therefore, a more current set of benchmarks must be employed. Rather than provide benchmark data (norms) that stretch back in time, HCD Research uses a 12-month rolling average of normative data for each communications study. In this way, the benchmark data (norms) will represent current responses to advertising rather an average derived from many years of previous communication research. Times and the environment change and so should benchmark data.
Presenting a Range of Responses
In most cases, responses to key advertising measurements are shown as individual scores; these scores are represented as averages. However, those responses can be scattered or conversely, very similar, very ‘tight.’ Currently, responses to advertising measurements are shown something like this:
By presenting the range of responses to questions about the measurement, the advertiser can obtain a much better idea of how well that range of responses compares with the benchmark.
In the example given above, the range of responses provides data that, in fact, has quite a wide range (or the converse) and presents how the responses relate to the benchmark norm.