Dozens of studies have been shown that about 70% of advertising impacts cannot be reasoned by how much you spend nor which media mix you have chosen. Rather it is all about crafting an effective creative. But how do we craft an effective advertisement? It is only about 30% art but 70% hard science. This article highlights a proven Copy-Test-based method that ensures creatives that drive Advertising Effectiveness.
The Challenge with Copy Testing
Copy testing of advertisements is a common practice. Still the majority of campaigns don’t meet expectations. What’s wrong? Many interesting research methods have been added to the copy test toolbox in recent years. Eye tracking, skin resistance checks, Neuromarketing with EEG and fMRT measurement, facial emotion coding and Implicit Reaction Testing (IRT) are all powerful methods to detect to what target customer might think and feel about an ad.
But something is missing here
But something is missing: When trying to interpret one of those measurements you assume that you know how this measure relates to the outcomes you tries to influence. This is not the case. There is no single measure that will give success. Furthermore, how components interact with each other might be different per segment, product or situation.
Advertising can be stimulating but there are products where this characteristic is false. So far all surveys are built on the assumption how the measures are connected with the actual target.
Why a solution of the Copy Testing dilemma would be so valuable
If we could decode the hidden formula that enables us to predict outcomes like purchase intention or brand consideration using copy test data, we would be able to optimize the creative in a way that outcomes are maximized.
We would be able to write an individual manual of do’s and don’t’s for every brand on how to craft advertising. Such a manual would be our crash barrier on the creative road of advertising effectiveness.
The powerful solution: Copy Testing based on Causal Analysis
In a model-based copy testing approach, we need collect several information during a copy test:
- Data about the direct impression of a copy test – newer measurement approaches like IRT and facial coding can play a vital role here.
- Data about how the direct impression changes attitudes towards the brand and how it increases recall and recognition of the ad.
- Data about the outcomes of interest, such as purchase intention, willingness to inform, brand consideration
- Data about the respondent like demographics, their situation and history. Are they already customers? Are they a bargain seekers? Do they know prices well? All those items that might have impact on their desired outcomes.
The ultimate question is: how do we identify which changes would improve outcomes? It is a challenge to conventional methods as they are built to fit parameters of a known model.
What is needed is a method that builds a prior unknown model in the light a data, a model that incorporates indirect causal effects and do not neglect nonlinearity and interaction of those unknown relationships. This is exactly the strength of the Universal Structure Modeling approach that is performed by the NEUSRELTM Software.
The good news: Copy Testing based on Causal Analysis is proven, reliable, tried-and-tested
Results of model-based copy-testing has proven to be eye-opening of marketers and gives an unbiased rigorous guidance: A recent study has looked at a TV spot of a beverage. The spot made a great impression; everyone liked it, only it did not do anything with consumers. We found that the spot was evoking nonproductive emotions and needed to switch from balance to stimulating emotions.
We found that the spot missed brand anchoring during the advertisement and especially at its end to leverage recall. The optimized spot broke all records and has become the best performing advertisement in the brands in history.
Some say, similar recommendation could have been produced by alternative approached. This big and fundamental difference to any other approach is: We know and have statistically proven that our recommendations are the main levers to maximizing market outcomes. Are other approaches able of the same? We replace “hope” with “knowing” with our selflearning causal analysis.