StellarAlgo Helps Leagues and Teams Collaboratively Scale Fan Growth and Revenue with Season Fan Engagement. Learn more
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Plays
Sep 21, 2023
StellarAlgo
A single ticketing integration is all that’s required, though this Play scales very well with additional integrations.
For added value, include:
The First-Known Attendee Play can encompass part of your season, but is ideally run as a central pillar throughout your whole season.
This Play is highly flexible. First-Known Attendee upsell efforts can be run as a series of activations within a broader campaign, or as a series of campaigns with activations planned throughout the season.
A single campaign will typically run for less than 1 month.
First-Known Attendees generally require multiple activations to see a return on the investment of time and resources. First-Known Attendee lists are individually small, but add up over the course of a season to a significant number of new fans.
Opportunity identification is where you will identify the number of new fans that could potentially be activated during this Play. This will help you to assess whether undertaking this strategy is a worthwhile investment.
Using the First-Known Attendance Dashboard, identify how many First-Known Attendees in the current season have yet to purchase single game tickets for a second game or more for the current season.
Using the same report, identify what proportion of your single game ticket buyers were First-Known Attendees in the previous season and returned to purchase one (1) or more additional events in the same season. Identify how much revenue was generated from this segment of fans. This can be hugely valuable to convince your team to allocate resources to First-Known Attendee efforts.
Capture a baseline to compare season-to-season. This will also help you to answer the question “How did we do?” after running the Play. Capturing historical baselines is made easy thanks to the First-Known Attendance Dashboard in the StellarAlgo Platform.
Using the dashboard, historical First-Known Attendee counts and conversion rates can be captured to establish baselines. Baselines should also be set for revenue, ticket volume, average ticket price, and attendance to one or more additional games; all of these metrics can conveniently be pulled directly from the dashboard.
First-Known Attendee execution is best done in an ideal window of 24-72 hours after registering their first attendance. For some teams where multiple home games are played in quick succession, a First-Known Attendee campaign will be executed directly after each homestand.
A standard template is recommended for the approach, starting with boilerplate copy and creative with tailored messaging. This content should incorporate strategies you would use to introduce new fans, such as:
With boilerplate marketing collateral established, you can now broadly execute against First-Known Attendee segments with timing nailed.
Executing campaigns against First-Known Attendees is possible directly from the First Known Attendance Dashboard using the “Retarget Fans” button.
Additionally, it is possible to run campaigns targeting those who have attended two (2) or more games, in an effort to determine which new fans may be good candidates for small package products. These types of campaigns are best conducted when marketing and sales and service teams coordinate their efforts.
StellarAlgo’s Machine Learning based Event Propensity scoring can be leveraged to further segment First-Known Attendees. In this case, we would add one or a few Event Propensity lead filters for the games of focus.
First-Known Attendee value is primarily measured across converted fans who purchase after attending their first game, and the additional revenue they drive. This is compared to the baseline you developed from your last complete season.
The overall effectiveness of First-Known Attendee nurturing efforts can be easily tracked through the season using the First-Known Attendee Dashboard. The dashboard also enables you to quickly evaluate how the current season compares to previous seasons’ YTD as the difference between seasons is built directly into the main view of the dashboard.
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