Detect the clubs already running a competitor, ready to switch
I turn scattered public data, the software every club already uses, into a list of targets ripe for a vendor change.
The client
A booking SaaS for sports. Its market, padel clubs, one of France's most dynamic but also most fragmented: hundreds of clubs, each with its own site, legal status and tool. It wanted to identify which clubs to sell its software to.
A database where every club arrives segmented by the competing software in place and by real ability to switch, with the right contact and the booking URL. The client's rep no longer prospects at random, they go after clubs already equipped with a specific rival, the ones with the most reasons to change.
The problem
The real question wasn't "which clubs exist", any directory lists them. It was "which already run on a competing software and might change", and above all "which can actually buy". A non-profit club with no online booking is not the same prospect as an incorporated club with online payment already live. Answering that by hand means opening each site, spotting the tool used, checking the status, noting the contacts. A few minutes per club, hundreds of clubs: unfeasible at scale and stale before you finish.
How it works
A system that reads each club's public site and outputs an already-qualified prospect: which booking software it uses today, whether padel is offered, whether online booking and payment exist, its legal status, and its contacts with the booking URL. The client doesn't get a list of clubs, it gets a map of who to equip among its competitors, segmented by software in place and by maturity.
The system crawls each club's public site, then a structured extraction pulls the fields that matter: the booking software already in place (Playtomic, Ten'Up, Anybuddy, BalleJaune, Eversports, MyRezApp, Doinsport and others), whether padel is present, whether you can book and pay online, members-only access or not, the legal status (non-profit or company such as SAS and SARL), the registration numbers, and the contact details. A second pass identifies the real booking link among all the links on the page. The whole thing is built to last: access rotation to absorb volume, automatic retries on failure, and a strict rule, a tool is only reported if it is actually visible on the site, never guessed. In output, each club carries its current software, its ability to buy, and its decision context.
Key decisions
- Start from the competing software actually present on the site, not a generic directory: it's the decisive field nobody bothers to map.
- Qualify real purchasing ability, not just the club's existence: legal status (non-profit or company), online booking and payment live, members access, registration numbers.
- A second pass to isolate the real booking link among all the links on the page, delivered ready to use to the rep.
- A strict anti-hallucination rule: a tool is only reported if it's visible on the site, never inferred. Access rotation and automatic retries to absorb the volume.
What it proves
I don't find prospects, I find the prospect ready to switch, by reading what's public on each site: the competing software nobody bothers to map. The same engine serves any vendor that wants to take share from a competitor: detect who is already equipped with the rival, and focus effort on those who can change.