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Case Study

MVP Ventures

MVP Ventures leverages data to source, enrich, and score its investable universe of startups

About

MVP Ventures (MVP) backs elite, early-stage teams building differentiated and defensible technology. The firm is “on a mission to be the highest value-per-dollar investor on your cap table,” and its portfolio companies include Dataminr, Turing, Loft Orbital, and Gecko Robotics. The firm uses Harmonic to identify the universe of companies that align with their thesis, and score those companies based on a diverse set of company metrics. Harmonic is one of “our key data sources,” says Jason Mintz, Partner and CTO. “That data allows us to spend our time doing what matters most—talking with founders about the things we might not know, and can’t learn elsewhere.”

Firm size

12

Stage

Seed through Series B

CRM

Custom

Website

mvp-vc.com

Portfolio

Anduril, Dataminr, Odeko, Mighty Building, Turing, and more – see the full list at mvp-vc.com/portfolio

Features used

Console, Saved Search API, Enrichment API

MVP’s Workflow with Harmonic

  1. New thesis-aligned companies are synced from Harmonic into MVP’s data warehouse weekly
  2. Company data (e.g., industry information, growth, funding details, team backgrounds) in their CRM is regularly enriched through Harmonic
  3. Companies are passed into MVP’s scoring algorithm and ranked
  4. Investors review the highest-ranked companies to determine who to reach out to

MVP Ventures (MVP) backs elite, early-stage teams building differentiated and defensible technology. The firm is “on a mission to be the highest value-per-dollar investor on your cap table,” and its portfolio companies include Dataminr, Turing, Loft Orbital, and Gecko Robotics. The firm uses Harmonic to identify the universe of companies that align with their thesis, and score those companies based on a diverse set of company metrics. Harmonic is one of “our key data sources,” says Jason Mintz, Partner and CTO. “That data allows us to spend our time doing what matters most—talking with founders about the things we might not know, and can’t learn elsewhere.”

The Problem: “We need data to focus on the right founders at the right time”

There are roughly one million companies in MVP’s investable universe—but the team only has time to talk to around 3,000 companies each year. Filtering down to the highest potential 0.3% to make meaningful inroads in deep tech can be a painstaking process, in which a lot of remarkable startups are overlooked due to time constraints. What’s more, since MVP funds early-stage companies, there isn’t always much available data on background or traction. “For a full diligence process,” Jason explains, “we really need to talk to the founder.” MVP needs data “that allows us to focus on the right founders at the right time.” This means the ability to filter and score startups based on characteristics like growth, team experience, industry and sector data, and funding history. With this data, the firm could prioritize outreach to certain companies—all within their homegrown CRM solution.

MVP’s in-house CRM is part of what makes the firm unique in the space. The team couldn’t find an existing CRM solution that could house the full deal lifecycle, from sourcing through investment. The tools on the market were too rudimentary for the substantial amount of data the firm was looking to ingest, and too inflexible to meet a variety of data-driven use cases. So they set up their own data warehouse and built a homegrown solution. Then they just needed startup data to fill it. 

The Solution: “With Harmonic, we can identify 1M+ companies and use AI-powered scoring to rank them”

MVP uses Harmonic for “two core use cases,” which, combined, save the firm’s investment and data teams countless hours of manual work every month. In the first place, the firm uses the Harmonic console to build and save searches that identify companies within its investable universe. “One thing Harmonic does very well,” explains Jason, “is flag companies early on in their creation—frequently even before they have a website. That's huge for us, especially since we can now track founders. If a one-time founder starts another company, or a VP leaves Google and starts her own company, those are terrific signals.” After all, these are people MVP’s investors want to get in front of as early as possible.

The second use case is “depth search,” Jason explains: “diving deeper and learning more about the company. What do they do, who are their customers, what are some comparable companies in the space?” MVP has built its own algorithms and scoring models based on company data. It leverages Harmonic’s data to rank its database based on a range of factors. “So given two companies in a similar sector and at a similar stage, we can decide to back team A over team B because team A has characteristics we’ve seen historically lead to success.”

We have a backend that calls out to data sources like Harmonic, pulls that data together, and organizes it. On the front end, the investing team uses it to both identify companies and to manage and track our outreach workflow until we make an investment.”

Enrichment keeps company data fresh at scale, improving MVP’s research time and increasing its deal pipeline

Harmonic’s enrichment API allows MVP to continuously retrieve the most up-to-date information about the saved companies within its investable universe. Harmonic feeds the firm refreshed intelligence such as new hires, website changes, updated funding information, departures, employee count over time, headcount growth by department, and changes to social following. Jason describes his approach to Harmonic’s company enrichment data as “a pyramid.” MVP doesn’t care about the whole pyramid equally, but the enrichment data on companies “at the bottom of the pyramid”—startups the firm has yet to do deeper diligence on—plays a crucial role in enhancing MVP’s predictive analytics and helping them monitor the ecosystem. On the other hand, MVP pulls weekly or monthly data on the companies at the top of the pyramid—“the high-value targets we’re actually trying to invest in.” 

MVP continually passes this person-and-company-data into its sophisticated scoring model that, among other things, predicts when a founder is going to raise. After all, “if we talk to a founder when they’ve just closed a round,” Jason explains, “it's obviously too late. And if we talk to them two years before they start raising, that’s not the best use of our time—we’d be better-served having another conversation with a different founder.”

MVP’s Partnership with Harmonic (“Being there earlier, better, and faster”)

Now, Jason says, “we have a backend that calls out to data sources like Harmonic, pulls that data together, and organizes it. On the front end, the investing team uses it to both identify companies and to manage and track our outreach workflow until we make an investment.” 

For MVP, the name of the game is being there “earlier, better, and faster” than competing firms. Harmonic’s data allows the team to best focus their time and inform their investment decisions.

Lauren Shufran
Content, Harmonic.ai
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