Contact reason analytics for supporter services
Problem
Call volume sits in the telephony platform's own dashboard. Case notes sit in the CRM. Complaint threads sit in a shared inbox with subject lines nobody categorises. Chat transcripts, where they exist, sit somewhere else again. None of it is tagged against a shared reason list, so when volume spikes on a fixture evening or after an email announcement, nobody can say in the moment whether it was refund questions, a broken sign-up link or accessible seating requests without asking agents to remember or reading a sample of tickets by hand. The supporter services manager typically pulls twenty or so recent cases into a spreadsheet once a week and reads them individually to get a rough sense of what is driving contact. By the time a pattern is confirmed this way, the fixture that caused it is long over and the upstream team that could have fixed it has moved on.
Product idea
A reporting layer that sits over the existing contact channels rather than replacing any of them. Calls, chat sessions and email cases are classified against a fixed reason taxonomy, agreed once with supporter services rather than invented per record, and the classification runs automatically rather than depending on an agent remembering to tag anything. The output is a dashboard showing contact volume by reason, channel and day, viewable against the fixture and on-sale calendar so a spike can be read against what caused it. A plain-language query box lets a non-technical user ask something like 'how many accessible seating requests last month' and get a filtered answer without writing a query. It does not resolve cases, assign complaints or reply to anyone. It reads what already happened and shows the pattern.
Who it is for
Supporter services managers and membership services leads who need to explain contact spikes to the rest of the organisation, complaints officers checking for recurring issues, and the ticketing or communications team that would sponsor the fix upstream.
Possible first version
A dashboard built on a daily batch export from the CRM and the telephony platform's call log, rather than a live feed. A fixed set of around fifteen reason categories, agreed with supporter services before build, with each record classified automatically on import. Filters by date, channel and reason, a view against the fixture and on-sale calendar, and a plain-language query box over the classified dataset. Out of scope for version one: live or webhook integration with the CRM or telephony platform, classification of email threads written in a language other than the primary one, and any automatic routing or reply.
- Build classification
- Platform module
- Rough effort
- 8-12 week build
- Roles involved
- Supporter services manager, Complaints officer, Membership services lead, Contact centre agent
- Relevant to
- Professional club, League office, Women's league, Federation / governing body
- Systems in play
- Telephony and contact centre platforms, CRM and case management, Email and shared inboxes
- Product framing
- Analyse data
Questions we get asked
What do you need from us to get the first dashboard working?
A representative history export of CRM cases and call logs, covering enough recent months to show patterns across more than one fixture cycle, and a short workshop with supporter services to agree the reason taxonomy those records get classified against. Without that taxonomy agreed up front, the classifier has nothing consistent to sort into and the dashboard will not mean the same thing to two different readers. Chat transcripts and email threads can be added once the CRM and telephony data are working, not before.
Does this replace our CRM or the contact centre platform?
No. It reads from both and adds nothing new for an agent to work in. The CRM remains where a case is worked and closed, and the telephony platform remains where a call is handled. This layer only aggregates what already happened across both and adds a reason to each record, so the two systems keep doing what they already do well and nobody has to log in anywhere new to close a case.
Our agents already tag cases in the CRM when they close them. Isn't that enough?
It would be, if every agent tagged every case the same way under pressure during a spike, but that is rarely true in practice. Tags get skipped when the queue is long, categories drift as different agents interpret them differently, and nobody goes back to retag historical cases when the list changes. Automatic classification against one fixed taxonomy, applied consistently to every record including the backlog, is what makes the resulting numbers comparable across weeks and worth reporting upward.
What does it deliberately not do?
It does not reply to a supporter, resolve a case, assign a complaint or contact anyone. It has no write access to the CRM or the telephony platform beyond reading records for classification. The output is a report, not an action, and any change it points to, a broken sign-up link, a confusing refund policy, a recurring accessible seating problem, still needs a person on the relevant team to pick it up and fix it.
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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