Spencer Davies - June 24, 2020
Database segmentation is far from a new concept. However, the implementation, both within sport and in other sectors, often takes a too simplistic and generalised approach by using just a limited number of demographic and behavioural attributes on which to categorise customers and fans.
Typically, in the sports world, a segmentation will split fans based upon factors such as attendance type (season ticket or match-by-match); ticket category (hospitality/premium or standard); age (junior, adult, senior); gender (possibly) and location.
Whilst this approach is useful when communicating to fans about season ticket renewals, pre- and post-match news and clubs signings, these segments are crude categorisations that don’t reflect the true range of engagement that fans have with their club, the diversity of their behaviour and how this changes over time.
One factor limiting the type of segmentation created is that often the only transactional data that is readily available to clubs is from ticketing, but this shows just a fraction of the engagement a fan has with the club. Only by adding in other behavioural data such as attendance, loyalty points and e-cash activity, retail spend, email communication engagement, website logins, e-sports activity, competition entry, player of the year participation, tours, non-sporting event attendance and club-TV (to name just a few) can the club really begin to understand the depth and breadth of their fans’ engagement.
Just having access to this data is not sufficient. A good database segmentation will use this data to ‘segment’ the database into a limited, useful number of segments that help to describe, and bring to life, the different groups of fans. However, it does not stop at just describing what the fans look like and what they have done, it must also provide clear signposts to the what the fan might do in the future. In this way, rather than just being a tool to be used tactically, the segmentation becomes the foundation of the marketing and communication strategy. Here are a few examples:
Season ticket holders, although often communicated as a single group, vary widely in their level of engagement and commitment to the club. Segmentation, for example, helps to easily identify a passive non-attending season ticket holder from a highly engaged one. With this knowledge comes the ability to target communications accordingly so that the non-attending or high-risk season ticket holders receives communication that encourages them back. This can be done by sharing the experience they missed at recent matches, reiterating how important they are to success of the club, and helping them come back by highlighting the benefits of their loyalty scheme.
Being able to differentiate segments of retail purchasers into those that are match attending and/or engaged in other fan activities (and therefore likely to be buying for themselves) and those that do not attend matches or engage outside retails purchases (and so are more likely to be buying for others) provide a simple but effective means of targeting the timing and types of promotions. Maximising engagement and sales.
A significant proportion of a club’s database will either never have purchased a match or season ticket or do so very infrequently. Segmentation helps to understand how these fans engage with the club and therefore target communications that are related to a more remote, but not necessarily detached, form of fan engagement. This could be by providing updates about e-sports games, competitions or promoting the clubs digital membership options.
It is this group of fans that may hold the key to club’s future financial security, particularly if the trend for remote engagement, exacerbated by Covid-19, continues.
Database segmentation provides simple means of tracking the movement of fans between segments over time. Analysis of migration rates, to higher or lower engaged segments, identifies where movement is most likely to occur and what activity (or lack of) leads to that migration.
Communication plans for each segment therefore primarily revolve around retaining fans in the segment and secondly, towards promoting migration to the most likely, higher value or more engagement, segment.
As a result, migration rates to lower value or less engaged segments are reduced over time, and ultimately this leads to a more engaged fan base and higher revenue generation.
Finally, a good segmentation should readily provide the ability to track the size, revenue and rate of change of each segment, over time, providing a range of key performance indicators on which to measure success of whichever strategy it taken.
Jonas Sports have been working with a number of clients to provide richer, more dynamic segmentations that are helping to evolve their communication strategies. This work has been underpinned by a fully integrated single fan view, that pulls together, in one place, all the interactions that a fan has with their club. We call it the Jonas Sports Data Hub.
If you want to better segment and target your data for improved loyalty and increased revenue, get in touch.