Fan drop-off root-cause assistant
Problem
When a renewal campaign underperforms or a matchday email sees a spike in opt-outs, the explanation usually comes from a meeting rather than the data. Someone remembers the send went out on a Friday evening, another blames the price rise mentioned halfway down the email, a third points at the fixture reschedule from the week before. The CRM manager exports the campaign report to a spreadsheet and cross-references it against the ticketing file by hand, and by the time any pattern might emerge the next campaign has already gone out under the same conditions. Nobody keeps a record of what was tried and what happened, so the same drop-off recurs season after season and gets explained from memory each time rather than evidence.
Product idea
The assistant keeps a running log of campaign sends, opt-outs, renewals, lapses and known conditions such as price changes and fixture reschedules, pulled from CRM, ticketing and email platform exports. When a CRM manager flags a drop, it checks the conditions logged against that drop and returns the past instances that shared the same conditions, not a single score. If a Friday evening send correlates with higher opt-outs three times out of four, it shows those four campaigns so the marketing manager can check them directly. It does not send campaigns, does not change pricing, and does not claim to have found the cause, only the pattern and the record behind it.
Who it is for
CRM managers investigating why a campaign or renewal cycle underperformed, marketing managers deciding what to change next time, and the Head of fan engagement who sponsors the tool and acts on what it finds.
Possible first version
Version one works from manually uploaded exports: campaign send logs, opt-out and unsubscribe events, renewal and lapse dates, and a short tagging sheet for known conditions such as price changes, fixture reschedules and send time. It shows past campaigns that share conditions with a flagged drop and links back to the exported rows behind the match. There is no live connection to the CRM, ticketing or email platform in version one, no automated tagging, and no prediction of future drops. It answers one question, what happened before, when asked, and nothing else.
- Build classification
- Workflow application
- Rough effort
- 4-6 week first release
- Roles involved
- Head of fan engagement, CRM manager, Marketing manager
- Relevant to
- Professional club, League office, Women's league, Venue & stadium operator
- Systems in play
- CRM platforms, Ticketing systems, Email and messaging platforms
- Product framing
- Diagnose
Questions we get asked
What do we need to have in place before this is any use?
A useful answer needs a season or two of history: past campaign sends, opt-out and unsubscribe events, renewal and lapse dates, and a simple tag on each campaign for known conditions such as a price change or a fixture reschedule. If that history is not being kept yet, the honest first outcome of this project is starting to keep it consistently. The correlation view only gets useful once there are enough past drop-offs to compare a new one against, which for most organisations means starting now rather than waiting for a perfect archive.
Correlation is not causation. How do we know it isn't pointing us at the wrong thing?
It does not claim to know the cause. It shows which conditions were present in past drop-offs that resemble the current one, and how many past instances share that pattern, so a thin pattern built on two campaigns is shown as thin rather than dressed up with false confidence. Whether the pattern is the actual reason is a judgement for the CRM manager or marketing manager reading it, with the underlying campaigns attached so they can check rather than take the assistant's word for it.
We already export campaign data to a spreadsheet and argue about it in a meeting. Does this replace that?
No, and it should not try to. The spreadsheet and the meeting are where the decision still gets made. What this adds is the step before that meeting: instead of everyone arriving with a different memory of what happened last time, the CRM manager arrives with the actual past campaigns that match the current pattern. It sits alongside the existing CRM and email platform rather than replacing either, and version one does not connect to them directly, it works from exports.
Who has to keep this fed once it exists, and what does that cost them?
In practice the CRM manager, since they are the one exporting sends and tagging conditions, a few minutes per campaign rather than a large task. If that tagging stops, the assistant has nothing new to compare against and simply stops producing anything useful, which is a fair way to find out whether it earned its place. The Head of fan engagement is the natural sponsor, since the findings feed decisions they would otherwise be making from memory.
Is this your workflow?
Tell us one sports workflow that still runs on paper, spreadsheets, WhatsApp or an outdated system. We will map it and show you what a simpler product looks like.
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