Email and push opt-out spike diagnosis assistant
Problem
When an email or push send gets an unusually high unsubscribe rate, the marketing manager checks the send report, sees the number is high, and raises it in the team chat. Someone remembers a campaign from a few weeks back that also spiked, or thinks they do. Nobody checks the campaign performance spreadsheet in a structured way, because that means scrolling back through months of rows and reading them by eye. The explanation that wins the discussion, too frequent, wrong segment, bad subject line, is whichever the loudest person argues, not the one the data actually supports. The same mistake, sending to an overlapping segment twice in one week, gets repeated because the finding from last time was never written down anywhere searchable.
Product idea
The assistant reads the campaign performance export the email and push platform already produces: campaign id, date, list segment, sent count, opt-out count, a content tag and a promotional flag the marketing manager adds on upload. It flags any send with an opt-out rate outside the normal band for that list, then checks the flagged send against every past campaign along a fixed set of dimensions: segment overlap with recent sends, days since that list was last contacted, content category, day and time sent, promotional versus informational framing. It shows the two or three past campaigns that share the most with the flagged one, and which dimension they share, rather than declaring a single cause. It does not draft, schedule or send campaigns, and it does not read subject lines or copy itself, only the tags a person assigns.
Who it is for
CRM managers and marketing managers who run email and push campaigns, and the head of fan engagement who would sponsor it and use the evidence to settle send-frequency arguments.
Possible first version
A single-page tool that takes a manually uploaded CSV of send-level campaign data (id, date, segment, sent count, opt-out count, content tag, promotional flag), flags any row above a configurable opt-out threshold, and shows a ranked list of past campaigns sharing the most dimensions with the flagged one. Out of scope for version one: any live connection to the email or push platform, automatic tagging of content from subject lines or copy, and any statistical model beyond straightforward dimension matching. The CSV export and the threshold are both things a CRM manager can produce and set without engineering help.
- Build classification
- Micro-tool
- Rough effort
- 10 day prototype
- Roles involved
- CRM manager, Marketing manager, Head of fan engagement
- Relevant to
- Professional club, League office, Women's league
- Systems in play
- Email and messaging platforms, CRM platforms, Spreadsheets
- Product framing
- Diagnose
Questions we get asked
What data do we need to have ready before this is useful?
At minimum a send-level export from your email or push platform with opt-out counts per send, and a way to tag content category and promotional flag manually if the platform does not already capture those. Ideally a season's worth of past sends, because pattern matching against three campaigns tells you nothing reliable. If your history is thin, the tool would say so rather than force a verdict out of too little data.
Does this replace our email or push platform's own analytics?
No. Those tools report the opt-out rate for one send. What is missing is the comparison across sends over time, which currently lives in someone's memory or an inconsistently updated spreadsheet. This sits alongside your existing analytics and reads from the export it already produces. It does not touch sending, scheduling or list management.
We already argue this out in the team meeting. Why formalise it?
For a small list with infrequent sends, a five-minute argument in a meeting might genuinely be enough, and this would be overkill. It earns its place once a team is sending several campaigns a week across overlapping segments, because at that volume nobody holds the pattern accurately in their head, and the same avoidable mistake, messaging an overlapping segment twice, keeps recurring unnoticed.
Does it tell us how to fix the problem?
No, it shows evidence rather than a fix. It surfaces which past campaigns most resemble the flagged one and on what dimension, segment overlap, timing, content type, so the marketing manager can judge whether that dimension is the actual cause. Deciding what to change, and recording the finding for next time, stays a manual step in version one.
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.
Tell us about itMore in Fan engagement & personalisation
- After-hours membership cancellation save lineA phone line that answers membership cancellation calls after hours, offers a pause or downgrade, and hands anyone who still wants to leave to a retention specialist with the reason already recorded.
- Fan account chat concierge for loyalty and membership questionsA chat interface embedded in the app that answers a supporter's own account questions, loyalty points, renewal date, seat details, straight from their record, and escalates anything else to supporter services.
- Fan drop-off root-cause assistantAn assistant that checks a recurring campaign or renewal drop-off against the conditions present in similar past drops, and shows the specific instances behind the pattern instead of a guess.