Turning Signals into Sales: Unlocking Warm Leads & Pipeline Growth with Referly
DATE
2024-Present
Industry
B2B Sales Intelligence / Revenue-Operations
CLIENT
Referly
WEBSITE
https://www.referly.io/
18 months
Discovery team
1 Team Lead
Deliver value every day.
Context
Referly is a Paris-based SaaS company that helps go-to-market (GTM) teams transform relationship and intent signals into warm leads and pipeline. Their mission is to enable Sales, Marketing & RevOps teams to shift “from cold to warm” by automatically identifying which contacts in their networks and website traffic are most likely to convert.
Referly's mission is to unlock and monetize this data by helping teams prioritize leads. Their key areas of focus include: • Network-based signals (e.g., past clients who changed jobs, contacts in your network) • Website visitor tracking and de-anonymisation of B2B traffic • Workflow activation (pushing leads into CRM, Slack, engagement tools)
Referly reports that users achieved around +20% pipeline generation with warm leads, and -50% faster time to close when leveraging these high-value signals. They state reply-rates of >30% from past clients who changed jobs, >25% for product users who changed jobs, >20% for website visitors under certain conditions.
...I immediately saw that with Ludotech it wouldn’t be a problem; ... they establish routines to constantly communicate about their progress.
Objective
Understand how GTM teams currently source, prioritise and act on leads. Identify the friction points in turning signals into actionable leads. Define a scalable architecture to integrate multiple signal sources and support growth in volume and complexity.
Challenges
Referly aimed to help sales, marketing and RevOps teams overcome several common problems:
1.
Their lead-generation processes relied heavily on cold outreach with little visibility on who was actually warm.
2.
Data was scattered across calendars, email, website visits and network connections — making it hard to surface those high-value signals in real time.
3.
The conversion and close process lacked context: which contacts had network proximity, previous relationships or recent job changes that indicated increased buying intent.
Hence, the challenge was to build a system that could ingest, unify and intelligently prioritise these signals — then surface them in workflows where the sales team could act fast and with confidence.
1 - Stakeholder Interviews & Competitor Analysis
Engaged sales leadership, RevOps, marketing and growth teams to map current workflows, desired outcomes and gaps in insights.
2 - Data Audit
Mapped all existing signal sources — Gmail contacts & calendar events, website visitor tracking, CRM records, job-change feeds, network connections.
3 - Architecture Planning
Ensure the system would support future integrations and growth; Chose scalable cloud infrastructure.
4 - Build & Iterate
Developed backend pipelines for ingestion and enrichment; built the front-end dashboard; conducted usability tests with internal users (sales reps, RevOps) to refine prioritisation logic, alerts and workflows.
Solution & Deliverables
Signal Intelligence Engine
- A unified architecture ingesting multi-source data (website visitors, job changes, calendar & email connections) and identifying warm leads via AI/identity-resolution.
- Native integrations (HubSpot, Slack, Gmail/Google Calendar) so that signals automatically route into existing tool-stacks.
- Tech stack: React, Tailwind, NestJS, Postgres, GCP (Cloud Run, SQL, PubSub, Queue, Scheduler, Monitoring)
Dashboard & Workflow Interface
- Real-time signal prioritisation dashboard that highlights the highest-probability leads, with context.
- Finding warm leads and having the feature to send predefined or manual mails.
- AI Smart agents, where in case of a prospect we have a signal, then: A) Push to Hubspot; B) Push to Slack; C) Push to Lemlist; D) Generate recommended action.
- Export and reporting functionality: ability to generate reports on signal volume, pipeline impact, conversion metrics.
- Built with modern front-end frameworks (React) and visualisations.
Identify the warmest leads by combining multiple signals (relationship, intent, network proximity) and scoring them for prioritisation.
Qualify and enrich contacts (industry, size, persona) so that outreach is targeted.
The “signal → qualification → activation” workflow means it not only surfaces leads but plugs them into existing workflows (CRM, Slack) for faster action.
Strong compliance and privacy stance (built in Europe, GDPR-aware) to ensure data from contacts, calendars, networks, website tracking is handled appropriately.