Turning manual lead hunting into a automated scored pipeline
Formani's marketing team replaced manual lead sourcing with one scored lead database, giving them a clear, scalable view of the architecture and design market.
Formani designs and produces luxury door handles and door hardware. Its marketing works bottom-up: it sends samples to architects and interior designers, so they specify Formani in their projects. We worked with the marketing team of about three to four people.
Challenge
Formani found leads in three ways: purchased lists, sample requests through its website, and manual searches on Instagram and architecture platforms. Purchased lists were often low quality, and some suppliers proved unreliable. Many website requests came from irrelevant applicants. Team members spent hours each week searching, checking and typing leads into the CRM. There was no shared standard for judging a lead, so nobody could easily see why one architect got a sample and another did not. The result: time lost on weak prospects and a process that could only grow by hiring.
Solution
We built a platform that collects lead data automatically, stores it in one database and scores every lead. The team reviews leads in a dashboard and sends the best ones to ActiveCampaign, their existing CRM.
- Scraping engine: collects companies, professionals and projects from architecture platforms and websites.
- Data processing: links related records and removes duplicates.
- Enrichment: adds missing job titles and email addresses through external data services.
- Scoring: rates every lead on the same criteria, so the strongest prospects are enriched first.
- Dashboard and Instagram search: filter, review and push leads to the CRM, and find profiles by keyword.
Nothing is sent automatically: the team decides who receives a sample or an email.
From design sprint to test phase
Building in weekly sprints, with the marketing team checking every step.
- Design sprint We scored every requirement on business impact and technical effort, and set the first version's scope with the client.
- 6.5 weeks Development In five one-week sprints, we built the system component by component. The data volume added complexity, so we added one and a half sprints.
- Weekly feedback Each week, the team reviewed scoring logic, data structure and usability with us.
- 4 weeks Testing The team now uses the system in a four-week test period before final delivery.
Reimer KoopalAI Engineer
Zineddine GesselAssociate AI EngineerImpact
The marketing team now works from one database instead of three separate channels. Seven connected sources have already produced tens of thousands of companies, profiles and projects, and new platforms keep being added. Every lead is scored on the same criteria, so the team can see why a lead gets a sample. Selected leads go straight into the CRM, without manual data entry. Lead volume no longer depends on how many hours the team can spend searching.
Test The system is in a four-week test phase.
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