Advertising effectiveness. How is it measured?
Achieving maximum advertising effectiveness is one of the main objectives of marketing teams. Brands have limited budgets and need to get the maximum impact from them. And this is helped by research and media analytics with different solutions that cover complementary objectives:
Predicting the success of a campaign before it is broadcast. This is achieved with advertising pre-tests , market studies that predict the success or failure of a campaign by analyzing its creativity. In addition, advertising pre-tests evaluate the details of the creativity to find ways to improve the final execution.
Measuring the effectiveness of a campaign. To measure the impact of a stand-alone campaign, brands have all the media analytics tools at their disposal and advertising post-tests . The post-test measures how many people remember having seen the campaign, measures brand attribution and other important variables to build a brand funnel and know if the campaign has met its objectives.
Measuring the impact of advertising gambling data japan phone number on the brand. When a brand runs several campaigns over time, it makes less sense to measure the impact of each campaign individually, as the different actions overlap in the media and in the consumer's mind. In these cases, the brand often equips itself with brand tracking , which assesses the impact of all activity on the brand on an ongoing basis. In addition, a significant part of the impact of advertising occurs over the long term, so continuous measurement helps to better understand the cumulative effect of the campaigns carried out.
Measuring the impact of advertising on sales and business objectives. There is no doubt that sales impact is the advertising effectiveness measure of greatest interest to CEOs and CFOs. The most common ways to measure the impact of advertising on sales are attribution models and marketing mix modeling.
Attribution models
“Half the money I spend on advertising is wasted; the problem is, I don’t know which half .” John Wanamaker’s famous quote is still on the lips of many CMOs today. We have more data than ever before, yet it’s difficult to get a clear, complete view of the sales impact of different parts of the advertising budget.
One way to measure advertising effectiveness is through attribution models. These models assign credit to different channels or campaigns in a customer's conversion process. However, despite their usefulness, they have limitations in providing the full picture that CMOs need:
Media consumption has become fragmented. Before, television was everything, but now there are many media outlets that share the consumer's attention, and that makes the distribution of credit more complex and therefore less explanatory of reality.
They underestimate the long-term impact of advertising. Our purchasing decision process is influenced by all the advertising we have seen over the years, and no data source covers such long periods. This underestimates the role of brand value built up over the years.
It’s harder to measure the impact of offline media. Tracking where consumers clicked in the later stages of the funnel is easy, but it’s much harder to measure the impact of offline advertising. This means that offline media is often undervalued in attribution models.
Data from each medium is often collected in silos. Combining different data sources to get a complete picture requires effort and expert knowledge that takes into account the biases inherent in each data source.
Attribution models are useful, but it is important to recognize their limitations and use them with caution. Critics argue that attribution models have led today's marketing to measure its impact primarily by its immediate results rather than by the impact of long-term actions. This "short-termism" may have weakened the impact of marketing actions and the importance of the function in achieving long-term business objectives. That is why more and more brands are complementing their attribution models with marketing mix modeling.
What is Marketing Mix Modelling?
Marketing Mix Modelling analysis is a statistical technique used to assess the impact of different marketing investments on the progress of a brand or business. MMM does not seek, as attribution models do, to record the impact of each medium on consumers and distribute the credit for the sale among them. MMM looks for correlations between an explained variable – for example sales – with a set of explanatory variables – the different investments in marketing. And it does so through econometric models that analyse time series.
Combining attribution models with marketing mix modelling allows you to see a much more complete picture of the impact of your marketing investment. MMM integrates all media and other marketing variables into a single analysis, so it provides a much more complete and balanced picture. It is not biased towards online media, since it is based on marketing activity and not impact, making it easier to obtain data. Nor does it undervalue the contribution of brand value, since in any analysis there are always “base” sales that are not affected by marketing activity.
