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.
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.
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.
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.
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.
In India, our research led to an astonishing discovery—77% of people had made a purchase after watching Reels. This powerful statistic revealed in a Meta India video, caught the attention of local press and was featured in several publications along with Meta's launch of #MadeonReels. The pioneering #MadeonReels program empowers brands across diverse verticals in India to harness the advertising power of Reels by partnering with creators, driving impactful connections with their audiences.
Similarly, in Australia, 74% of people purchased a product or service after watching Instagram Reels whereas 60% messaged a business. Many findings likes these from our research in the Australian market have been compiled in Shift to Short: The Report. Launched by Instagram Australia, this guide aims to educate brands on the advertising potential of SFVs. and what makes Reels across Meta’s platforms unique in this domain. Furthermore, the report delves into the distinctive attributes that make Reels across Meta's platforms especially impactful in this dynamic domain.
We also identified the kinds of content people enjoy seeing in SFVs across markets, including which content types are associated with each SFV platform. This categorization helped Meta discover areas where Reels can be improved or leveraged to increase engagement among different consumer groups in each market. Additionally, it provided Meta with evidence to show businesses how they can create memorable and enjoyable content for their customers with targeted advertising on Reels.