Copy of Data Analytics for Testing Purpose
This is the Headline used for the Challenge
A multi-national client from the financial service industry came to us looking for a way to better predict brand drivers that were worth investing in. After we successfully set up a brand equity model for this client before, they wanted to go one step further - not only identifying promising areas for action, but predicting the success of future marketing campaigns. This should be achieved with an easy-to-use simulator that's based on invididual-level survey data and can be used across departments.
Our Approach to achieve goals is to have an approach
We decided to go with predictive analytics in order to create forecasts based on solid statistics models. To gain input for the prediction, we used respondent data collected in a brand tracking survey that we conducted in over 20 countries. Here we settled for brand preference as a strategic metric as it has strong influence on user behavior and therefore on company profit. To ensure accurate predictions, we compared several statistical models and chose the best predicting one for our simulator. The simulator was build in Excel since it's widely adopted and thus includes a wide range of possible users.
We help you to get an impact without injuries
The simulator provided our client with an easy-to-use tool with a self-explanatory front-end. It enables its users to simulate changes to several brand drivers’ performances based on real respondent data and see how it affects overall brand preference. Thus, it shows areas of action where marketing budgets will most likely be well spent. The excel tool is straightforward and can be used across departments by various stakeholders.
After delivering the tool, our client decided to expand the underlying brand driver study to even more countries. The simulator has been presented at several conferences as a showcase of how brand driver simulations can be directly utilized for marketing practitioners.
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