Gener8’s App Usage dataset helps to better understand people’s interests and behaviours
As 2025 kicks off, gyms are packed with individuals eager to tackle their New Year's resolutions, making early January the busiest time of the year for gym floors.
But while it's relatively easy to track search intent and web browsing gym-related spikes, how can we confirm whether people are actually stepping into gyms?
With gym apps increasingly serving as gateways to access gym facilities and book classes, analysing user app usage presents a valuable opportunity to infer gym attendance. This article explores the advantages of leveraging app usage as a passive data source to gain deeper insights into gym-goer behaviour and preferences.
Consistent gym app usage serves as a highly reliable indicator that the target user actively attends a gym, rather than merely claims to do so in a survey response.
By filtering our App Usage dataset for those who have used the most popular UK gym apps, we were able to create and then analyse the gym-goer cohort to gain a deeper understanding of their behaviour.
As anticipated, significantly fewer users accessed gym apps on Christmas Day, likely influenced by the closure or limited hours of most gyms. In contrast, a notable peak in activity occurred on 2nd January, as people returned to their routines and embraced the new year with renewed fitness goals.
What's more, an analysis of gym app opens per user by day of the week revealed that the most active days for the gym are Monday through Wednesday, with the weekend days being the quietest.
Clearly, using the app usage passive dataset can provide valuable behavioural insights for target activities.
By filtering other passive datasets and survey responses by the gym-goer cohort, we can gain deeper insights into their preferences. This is enabled by Gener8’s datasets being interconnected by a single user ID.
For instance, by connecting to Gener8 Snapshot survey data, we can observe that individuals who go to the gym index highly with following a specific diet or nutritional plan most of the time.
Additionally, in comparison to the general population, gym-goers place a higher importance on the nutritional content of their food.
By connecting your target app usage cohort to multiple data sources, you are able to quickly gain a holistic understanding of that audience’s demographics, interests, purchase intent and web browsing habits, simplifying the creation of pen portraits and audience insights.
Using a specific gym app not only implies gym activity, but also confirms a paid membership to that gym. With gyms differentiating themselves based on their offerings by USPs such as monthly membership price and types of classes, we can better understand the impact that this can have on user demographics.
For instance, we can observe notable differences between PureGym and Everyone Active.
Comparatively, Everyone Active indexes with those with a higher income. This makes sense, as Everyone Active offers a broader range of services, including swimming pools and group fitness classes. Meanwhile, Puregym focuses on affordability, whilst maintaining 24 hour gym access and good equipment.
It is thus perhaps not too surprising that Puregym indexes strongly with students, who are especially price sensitive.
These examples highlight how app usage can be a great tool in validating both user activity and membership to specific services to aid competitor comparison and planning projects. This is why app usage is utilised in conjunction with search, web browsing data and eReceipts to build a highly accurate view of user behaviour under our psychographics framework.
We utilised Gener8's App Usage dataset alongside Gener8 Snapshot survey responses and Demographic datasets to uncover insights from gym-goers.
Gener8 Labs’ complete data and insights solution empowers media and marketing businesses to find actionable consumer and market insights, using our unique, consented, first party panel data sets that are all connected around one user ID.
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