Welcome to the team! 👋
Where to start?​
- Overview of GoodFit technology - introduction and older, longer list of technical points
- How we work - describes how projects / features / backlog items are structured and the end to end process that they follow
- Code organisation and repos
- Weekly meeting cadence
- Development Lifecycle
- How we design systems
- Best practices and tips
- Common concepts and terms
Commercial context​
See Why GoodFit Exists. GoodFit allows you to configre a data-set unique to your requirements to power your GTM strategy of choice
- The three main GTM strategies we enable are here: GTM Motions GoodFit Enables
- In Sales-Led
- We want to make companies more productive/more effective
- Moving them away from rep based prospecting, and/or unsophisticated data setups.
- For programmatic / hybrid GTMs…
- We're a requirement.
- In order to run these GTM motions succesfully, large volumes of accurate data are required in advance.
You can find a comprehensive document on the types of customer we serve, and their common use cases with examples in this doc. Customer Types & Common Use Cases . We'd recommend spending a good deal of time digesting this.
The end-to-end set of jobs we enable at GoodFit can be seen below.
Map Market (i.e source companies) > Enrich > Score > Prioritise > Segment > Personas & Contacts > Distribute to reps / Execution workflow
A brief description of each can be seen below:
- Map the market
- i.e source companies meeting the company crieria as outlined above
- Discuss: TAM, SAM, SOM - concepts you may come across during this process.
- Discuss: What does good "Mapping" look like? How wide/narrow?
- Enrich companies
- Gather a consistent set of data across all companies in the market
- Prioritise (Scoring Analysis)
- We next run an analysis on the companies recent closed won customers (or set of ideal customers…) to determine what is uniquely true about them.
- This determines the distinction between a qualified customer (i.e someone the company can sell to) and an ideal customer (a customer that is qualified, but also matches the attributes of recent wins).
- The goal is to prioritise the customers that match the profile of those the company has recently won.
- A deep dive into scoring analysis can be found here: https://docs.goodfit.io/goodfit-docs/product/scoring-analysis
- Score
- We auto create a score against every company in their market.
- Companies are scored based on their similarity to those the company has recently won. i.e high score = close match to recent wins.
- Segment
- Having mapped & scored their market a customer will build their segments.
- How they segment up their market is influenced by their GTM Motion.
- Sales-Led companies will want to distribute sets of accounts to reps eg. high scoring accounts in the region the reps work
- Those running Programmatic GTM will draw circles around sets of customers who have a common need and can be delivered a common, automated message.
- Deep-dive into segments, and the different ways it can be used in or docs:
- Personas & Contacts
- Customers can optionally pre-enrich their accounts with contacts.
- A sales-led companies does this to save reps manually researching contacts to reach at a target account - increasing rep productivity.
- A programmatic company needs contacts pre-enriched in order to execute on their GTM campaigns with no humans in the loop i.e companies & contacts need to pre-determined to run the automation.
- A deep-dive into personas & contacts is available here.
- Distribution
- At this stage we need to feed the accounts (and contacts) to reps or the tooling/workflows the customer is using to GTM.
- Most commonly a segment is sync'd to the CRM, and from their either assigned to reps to work or pulled into the GTM tools of choice for the custoer (eg. Outreach, Lemlist, 11x, etc).