Identify hidden success strategies for your brands

The challenge:

Brands rarely lack data, surveys and evaluations. What usually remains unclear, however, is the question of the causal success drivers, the causal background, the question why customers choose one brand and not another’s brand. This is because of one thing – the comparison of facts, e.g. between buyers and non-buyers, or the correlation of drivers and results provide mainly spurious correlations.

The solution:

We use self-learning driver analysis methods (universal structure modeling) to understand the causal relationship between drivers and between drivers and brand preferences. It is possible to work with your current brand tracking data. Alternatively, you can fall back on our proven brand tracking methodology. It ensures that all information central to an impact analysis is collected.

Your benefit:

You reveal the growth levers of your product/service categories. For example, T-Mobile USA through BRAND.AI understood that new customers tend to be guided by the right “story” rather than by factual comparisons. As a result, the company developed the “Uncarrier” strategy and doubled its market share in just 4 years.

Here is more: Presentation on BRAND.AI at the ESOMAR 2016 conference: