Will Tracksuit data be different to my historic data?

Tracksuit collects in a very similar manner to traditional research agencies, however, everyone does things a little different. The most likely sources of differences in the data are likely to be due to:

• Difference in the qualifying criteria, is the biggest source and most likely culprit of differences in data. Differences in timeframe, or the behavioural, interest, or usage options mean you are looking at a different audience and therefore their views and experiences are likely to differ. Consistency is key to mitigating these differences, when we set up any new customer we take this into account to replicate or take you as close to this same audience as possible.

• Using a different respondent panel, almost all agencies obtain their respondents to their surveys via a panel, these panels recruit their panelists from different sources to maintain and deploy surveys in different ways. Deploying a survey to two different panels is likely to illicit differences in data.

• Weighting procedures, are pretty standard for reputable research agencies but there are definitely some that skip this step or hang loose with the weighting of data so you may notice differences when these biases are corrected in Tracksuit’s data.

• Data cleaning procedures, Tracksuit heavily scrutinises the data it collects and makes exclusions whenever it identifies dubious responses. This is something reviewed constantly as the survey technology processes and AI evolves. Less rigorous cleaning procedures may also lead to differences in data between Tracksuit and other providers.

• Time of collection, Tracksuit is an always-on solution collecting funnel data of brands all year round, other solutions collect data at a more intermittent cadence or even in a single dip, this is also likely to provide differences in data due to these dip solutions only representing a few points of the year, which in some categories with seasonality can heavily influence the stats produced.