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Size of Company

<aside> 🪴 Company Size: 50 - 500 employees. Late Seed, Serie A or B Software Companies or Consulting Firms.

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Why mid-market now?

We have consciously chosen to target companies that typically fall between 50-500 and sometimes up to 1,000 employees (depending on the industry). Companies with fewer than 50 employees rarely have the maturity and volume of data necessary to extract critical insights for their business. However, if analytics is core to their business, such as in boutique consulting firms or fintech/health startups, they may be able to do so.

This is what we have learned trying to sell enterprise so far:

1. Complex Requirements of Larger Enterprises: Many larger organizations come equipped with security departments that frequently demand specialized security certifications, such as SOC 2. They also often demand deploying on their own cloud or on-premise. While we meet many of these security requirements and have executed on-premise installations for companies like Lilly and Vodafone, we've noticed a considerable delay. On average, these procedures extend the timeline by three months before potential clients can even begin testing our solution. Plus, it often requires us to offer additional supplementary services. To really serve these businesses well, a significant amount of resources would be necessary.
2. That’s why often is more effective to sell through a consulting company that will do all the heavy lifting for you. But we need to do more sales on our own first.
3. Alignment with Digital Native Companies: Startups in the Series A, B, and C funding stages usually consider advanced analytics fundamental to their business model. At this stage, C-level executives frequently take a hands-on approach, especially when incorporating tools like Graphext. This engagement often inspires greater motivation among the company's data scientists and analysts. Moreover, these startups are generally more receptive to collaborating with other startups, unlike larger enterprises that often relegate such tasks to innovation departments, that don’t really have any skin on the game.
4. High Impact on Smaller Data Teams: For compact data teams, ranging from 1 to 5 members, Graphext stands out as an invaluable addition to their tech stack. We've observed that our platform significantly boosts their productivity, effectively compensating for their limited manpower. This transformation makes Graphext indispensable to their operations.
5. Nobody ever gets fired for buying IBM. Too core use cases to trust a small startup that might to exits in a few years.

In conclusion, our focus on the mid-market allows us to deliver immediate value and learn faster from customers and get better engadgment metrics and CACs for the series A. At the same time, we are very aware that probably the only way to get to billions in revenue will be by selling to enterprise clients. So, we will not stop engaging with any enterprise client that we see as viable with this offering.

When and how Enterprise?

As I mentioned before, Enterprise deals will be instrumental for scaling revenue fast. At the end most cloud venders like Snowflake or Google Cloud relied on a few hundreds customers spending over 1M a year to hit their hundreds of millions and billions in ARR. At the same time the Cloud vendors should be the best partners as channel for sales, as Carto is doing now. They want their to customers to increase their spending on their cloud and they offer multiple ways of doing that: deploying on their customers private cloud, simply selling your own application and getting a feee (like with Tinybird)

<aside> 👨‍💻 User Persona Title: Data scientist, Data analysts, Data Engineer, Business Analyst

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<aside> 🌱 Department: Data Science | Business Intelligence | Analytics | Insights | Digital strategy. Between 1-3 data scientists in startups, 5-15 in Mid market.

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