Podcast on ZoomInfo

Since many of us on this forum are invested in ZoomInfo, I thought sharing this podcast could be useful.




Thanks so much BCS for taking the time and effort to copy and paste and edit those key points from the transcript. That must have been a lot of work but it was well worth it for ZoomInfo investors.


It’s the same podcast that BroadwayDan also posted some time ago. And I agree it’s required listening:


Here are my notes, which may have gotten lost in my Aug write-up; perhaps useful to others:

About how it all started off:
“And the business grew profitably - obviously because we had no other money”

→ this is why the company is still growing so profitably; it’s part of the culture. It’s quite a stark contrast to a company like Cloudflare imo, where that cultural focus on profitability is absent, whereas ZI have it in their bones.

How DiscoverOrg got to be Zoominfo
The acquisition of Zoominfo in 2019 gave Discoverorg a world class tech team, which Henry Schuck the CEO - who is for me the stereotypical sales professional - was not able to scale by himself. He actually confesses as much in the podcast. The acquisition was a gamechanger. Since then the key person driving their tech has been Nir Keren, the CTO who is based in Israel. So the 2019 acquisition by Discoverorg, Schuck’s company, of Zoominfo, was transformational. They literally became Zoominfo then. Not only sales DNA but now also Tech DNA.

Q:Where do you get your data?
A: Lots of different places:

  1. We buy data
  2. We gather data through public sources
  3. Two contributory networks:
  4. Free Zoominfo access in exchange for email details - freemium model
  5. Customer contributory network from a portion of customers who share data: cleanse validate & send back to them and we use that
  6. Literally a million other sources other sources linked with ML which

→ This puts to rest the notion that they just scrape stuff from the internet, akin to buying email addresses in order to spam. It’s nothing of the sort: they use a variety of data sources including voluntary contributory networks and then meticulously cross-reference and clean the data with people and ML to get to the best possible set of identity data that could be useful to a B2B sales professional.

Q: What information do you collect?
A: Business contact information which is non-sensitive information.

→ Again important. Business contact information is non-sensitive from a privacy perspective. He stresses that they do not collect privacy-sensitive information. They collect stuff like: who is the Head of Sales, reporting to the Enterprise Business Director of Verizon Wireless and what is her business email, twitter handle and contact number. Not what is her home address and where does her kids go to school.

Q: Competition?
A: There aren’t really that many people innovating for sales people. Last big one was Salesforce.

Q: Revenue model: seat-based / data based?
A: Combination:

  1. seat based - 100 sales people &
  2. records under management / enrichment based. you may have a 100 sales reps for which you pay on a per rep basis and also marketing wants to enrich 1m records & keep them all up to date which is a data-based fee.

→ I like the breadth of the monetisation model. Both seats and data-based.

Q: What would drive market cap doubling?
A: Continuing to dev & deliver solutions for sales people & playing into their existing market white-space.

→ I guess he is saying that the market cap will double if they just keep on doing what they’re doing. Which is music to my ears.

Q:What keeps him (CEO) up at night?
A: It’s not competitors keeping up at night; only thing is execution - did I get the right people?

Q: What would he want investors to understand:

  1. Understand the end user of the product
  2. Don’t underestimate the value of a great GTM motion. Investors do not adequately appreciate how big the end user market is, how underinvested it is, and how very few companies are investing in the sales person.

Some metrics that I’ve not seen previously:

  • Average sales cycle is less than 30 days.
  • ACV is $30k plus.
  • S&M efficiency: 1.5-2 return on spend IN THE FIRST YEAR
  • Unit economics: LTV/CAC is well north of 10x and closer to 15x !!

→ For anyone wondering about this company, go and see what the comparative numbers are for some of our other companies. I think probably only Datadog comes close to a 30 day sales cycle. And the other numbers are top class.

-WSM (Long ZI)