First-time buyer return-rate report by channel
Problem
Whether a first-time buyer comes back for a second visit is answered today by a marketing manager building a one-off spreadsheet: export ticket sales from the ticketing system, export the CRM record for the same customers, match them by email address, and tag each row with the campaign or channel that produced the sale by memory or by checking the email platform's send list. The result is usually built once a quarter for a board update, goes stale within weeks, and rarely gets rebuilt when a new campaign is being planned. Nobody can currently say, at the point a channel decision is being made, whether last month's discounted first-ticket offer is producing repeat attendees or one-off bargain hunters. The spend keeps flowing on the assumption that it works.
Product idea
A scheduled report that joins ticket purchase history with the matching CRM record and the campaign or channel tag recorded at point of sale, and calculates what proportion of first-time buyers return for a second visit within a set window, segmented by channel, price tier and matchday type. It runs on a schedule (weekly or monthly) and produces a dashboard view plus a short digest a marketing manager can forward without editing. It deliberately stops at reporting: it does not build audiences, trigger campaigns or recommend an offer. Its job is to answer one question reliably: which channels convert first-timers into repeat attendees, so that spend and creative decisions are made against a number rather than an impression that a campaign felt like it worked.
Who it is for
Marketing managers and CRM managers who currently rebuild this analysis by hand each quarter, and the Head of fan engagement who would sponsor it to justify acquisition spend by channel.
Possible first version
A dashboard fed by CSV exports uploaded manually from the ticketing system and the CRM platform, matched by email address, with channel tags entered against each campaign at upload time rather than pulled automatically. It calculates first-to-second-visit conversion rate by channel, price tier and matchday type, with a fixed lookback window and a weekly refresh. Out of scope for version one: any live API connection to ticketing or CRM, automatic campaign tagging, and audience export back into the email platform. Those are worth building only once the report has shown which channel comparisons the team actually acts on.
- Build classification
- Workflow application
- Rough effort
- 3-5 week first release
- Roles involved
- Marketing manager, CRM manager, Head of fan engagement
- Relevant to
- Professional club, League office, Women's league, Venue & stadium operator
- Systems in play
- Ticketing systems, CRM platforms, Email and messaging platforms
- Product framing
- Analyse data
Questions we get asked
What data do we actually need on day one to make this useful?
Two exports at minimum: ticket purchase history with a buyer email address, and a CRM export showing acquisition channel or campaign against each customer. Without the channel tag anywhere in your systems, the report can still show an overall first to second visit conversion rate, but the channel breakdown, the part that actually changes where spend goes, depends on that tag existing somewhere before build starts. If channel is currently only in someone's head, that gets fixed first, not by this report.
Does this replace the reporting built into our CRM platform?
No. A CRM platform reports well on its own records, but acquisition channel data usually sits in the email platform's campaign logs and the actual purchase event sits in the ticketing system, and no single one of those three has all of it in one view. This report joins across them and stops there. Your CRM platform stays the system of record for the customer; this is a narrow analysis layer on top, not a replacement for it.
We already put this together by hand once a quarter. Why change it?
If quarterly is genuinely how often channel decisions get made, the spreadsheet is probably enough and this would not earn its keep. The case for automating it only holds if spend or campaign decisions actually happen monthly, or after each ticket window, while the manual version only gets rebuilt quarterly. In that gap, decisions are being made on a number that is already out of date, and nobody notices because there is nothing to compare it against.
Can it tell us which individual supporter to target with a renewal offer this week?
No, and that is deliberate. This answers a channel-level question, which acquisition routes turn first-time buyers into repeat attendees, not a person-level one. Building a per-supporter targeting list is a different job with different data protection and consent considerations, and conflating the two tends to produce a tool that does both badly. If the channel numbers show a pattern worth acting on, that action is a separate campaign or query tool, not this report.
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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