Welcome to the team! 👋
This page is your starting point. Skim the links in Where to start to get the lay of the land, then read Commercial context to understand what GoodFit actually does for customers.
Where to start​
- GoodFit overview — high-level introduction to our technology
- How we work — how projects, features, and backlog items flow end-to-end
- Code organisation and repos
- Development Lifecycle — where QA happens and how features ship
- How we design systems
- Engineering support — the weekly support rota you'll join
- How To guides — step-by-step instructions for common tasks
- Best practices and tips
- Common concepts and terms
Commercial context​
💡 Background reading: Why GoodFit exists.
GoodFit lets customers configure a dataset tailored to their requirements, powering their GTM strategy of choice.
- The three GTM motions we enable: GTM Motions GoodFit Enables.
- Sales-led — we make companies more productive and effective, moving them away from rep-based prospecting and unsophisticated data setups.
- Programmatic / hybrid — we're a requirement. Running these GTMs successfully needs large volumes of accurate data up front.
For a deep dive on customer types and common use cases (with examples), see Customer Types & Common Use Cases — worth reading carefully.
The end-to-end workflow​
The set of jobs GoodFit enables, in order:
Map market → Enrich → Score → Prioritise → Segment → Personas & Contacts → Distribute
- Map the market — source companies that meet the customer's criteria. Common concepts here: TAM, SAM, SOM. What does good "mapping" look like? How wide or narrow?
- Enrich companies — gather a consistent set of data across every company in the market.
- Prioritise (Scoring Analysis) — analyse the customer's recently closed-won deals (or a set of ideal customers) to determine what's uniquely true about them. This separates a qualified customer (one we can sell to) from an ideal customer (qualified and matching recent-win attributes). Deep dive: Scoring Analysis.
- Score — auto-score every company in the market by similarity to recent wins. High score = close match.
- Segment — once mapped and scored, customers build segments. The shape depends on their GTM motion: sales-led teams distribute account sets to reps (e.g. high-scoring accounts in a region); programmatic teams draw circles around groups with a shared need to deliver an automated message. Deep dives:
- Personas & Contacts — customers can optionally pre-enrich accounts with contacts. Sales-led teams do this to save reps manual research; programmatic teams need it to run automated campaigns end-to-end. Deep dive: Personas & Contacts.
- Distribution — feed accounts and contacts into the customer's reps or tooling. Most commonly a segment syncs to the CRM, and from there is either assigned to reps or pulled into GTM tools (Outreach, Lemlist, 11x, etc.).
E2E demo: Chili Piper (simplified dataset)​
Watch on Loom if the embed doesn't load.