First - congratulations to you and your team on the raise.
Second - a question: your last post on the topic from June 28th announcing your 2M second seed funding said that after 27 consecutive months spent pursuing some kind of funding, it was time for you to take a break from fundraising mode.
Given that was less than 6 months ago, it doesn't seem like you took much of a break after all :) Would you be willing to provide more insight and details re: what exactly changed?
After we announced the 2nd seed round the interest in us increased and we ended up raising more, and then when Brad Feld (at Foundry Group) offered to do the FG Angels syndicate on AngelList I figured "why not?" and I was curious to see the internal mechanics of doing one of those. So basically we raised because we've always be fans of "take money when you can".
By this point we had a ton of momentum in sales, had clarified our vision a ton in our month-long company retreat in August, and were not in a position of desperation for once. I felt like I should take heed of the old advice that "the best time to raise is when you don't need the money" so we did some analysis, had some coffees, and picked 6 firms to pitch. We committed to testing the market again.
This time, it worked.
This stuff is just messy.
in a word... "war-chest"
i worked at a firm that used mattermark, datafox, etc. the amount of inaccurate, duplicate, and missing data was very high. all of these services are like a mashup of data sources that are not very good.
there's a reason bloomberg, thomson, etc. focus on public companies.
For example, the date the company was founded is often wrong in Crunchbase (it is reported as more recent than it actually was). Many companies raise money and don't disclose it for months (staying under the radar). Many acquisitions never publicly release the price (so the founders can claim it was a "success" regardless of whether it was).
It is just a tough space to be in to try to make something of the data that is available. Public companies are of course much easier.
From a hacker's perspective you can do curl/wget, awk, etc to aggregate and filter data from various sources yourself. But to make the same tooling available to a wider audience is what gets you to the market. Dropbox is a solid example, for instance, that made rsync super simple to use for anyone.
Discovery, advanced filtering and visualization are increasingly solved problems. Open source solutions like Elasticsearch Kibana make it incredibly easy to analyze and visualize large amounts of data, and to do it better than many paid services.
For high-value commercial use cases, like those in financial services, data accuracy and completeness is all-important because your ability to identify the best opportunities and make good decisions is almost always proportional to your knowledge of the market.
Take a seemingly simple venture capital use case: I want to identify pre-Series A fintech companies in California and New York founded in the past 3 years that have raised between $100,000 and $1 million and last raised funds 4-8 months ago. If funding data and corporate information is incomplete or inaccurate, and/or funding events have not been properly categorized, the list of companies surfaced will likely exclude companies that meet the criteria, and include companies that don't meet the criteria.
For many use cases, it doesn't take many false positives or false negatives to render a data set effectively useless.
Raising $11.5M in total commits them to generalising though, and there's no shortage of competent competition in the market-data aggregation space.
At what scale and how long would that take? The VC/angel money allows you to move faster, take more chances and perhaps hire better and faster. And lose money. Important point.
Look at their pricing:
"Today, Morrill’s curiosity and her 27-person team’s"
Imagine the payroll for those 27 people and how many subscriptions they have to sell with what is (for now) a niche product.
What are the figures on fully loaded labor costs for a 27 person team?
Probably somewhere around $2mn - $4mn/year. That's a pretty big team for the amount of money raised to this point, they must be doing ok subscription-wise.