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Intermix

by Paula LaPasseLaunched 2016via Nathan Latka Podcast
See all SaaS companies using cold email
MRR$70k/mo
Growthcold email
Time to PMF6 months
Pricingsubscription
Built in6 months to first revenue
The Spark

Paula LaPasse had spent a couple of years consulting after selling GoGrid (which he co-founded in 2007) to Datapipe in 2011 for an undisclosed amount. In 2013, he joined a crash reporting company called Crashlytics (later acquired by Google and renamed Fabric) where he met co-founder Lars, the head of business development. While working with massive datasets—their library ran on over one billion devices across Apple and Android—Paula realized something powerful: they were sitting on goldmines of data they weren't monetizing. Lars approached him with an idea: what if they could package and sell this industry data to private equity firms and consultancies hungry for mobile market insights? "I said, sure, you know, how hard could that possibly be?" Paula recalls.

Building the First Version

When Paula hired a data scientist to extract and organize the data, the reality hit immediately. The data scientist said: "Okay, great, where's the data?" Paula replied, "It's in all of these databases." The data scientist's response was blunt: "I need it all in one place. It needs to be clean and complete and correct." Three months of intensive work later, Paula had organized the data into a usable format. Talking to peers in the industry, he realized almost every company faced this same challenge—making data scientists productive with fragmented, messy data. That insight led him and Lars to leave Crashlytics and start Intermix. They launched at the end of 2016 and remarkably achieved their first dollar of revenue just six months after writing the first line of code for the business.

Finding the First Customers

Intermix's initial customers came from Paula and Lars's deep network in Silicon Valley accumulated over 20 years. They signed up early alpha users to a "not fully baked product super early on." The pricing model was straightforward: enterprise SaaS with annual subscriptions ranging from $50,000 to $100,000 ACV. As Paula explains, "What you get for that is a single dashboard whereby your data teams...get a single view into all the apps that are connected, all the users that are running queries, the way that data flows through that system."

What Worked (and What Didn't)

By the time of this interview, Intermix had scaled to 25 customers and discovered their most effective growth channels: content marketing and intelligent cold outreach. Paula developed a surprisingly effective customer acquisition strategy—scraping job boards like Indeed for the word "Redshift" (Amazon's data warehouse product), which appears rarely enough that it reliably indicates which companies use the platform. They'd then identify employees with relevant titles and conduct phone outreach. "That's a really good question," Paula noted when asked how they built these lists. "The word redshift is pretty not common in job descriptions. And so if you scrape indeed, for example, you can kind of figure out which companies are using it."

This systematic approach, combined with content about data lakes and performance monitoring, created a low-cost customer acquisition engine. By the time they raised a $3 million seed round from Uncork Capital in May, they were spending approximately $10,000 to acquire customers with $50,000+ ACV—yielding roughly four-month payback periods.

Where They Are Now

Intermix had recently discovered an even more valuable niche: Fortune 500 enterprises undergoing digital transformation. As large companies migrate from traditional data warehouses like Oracle and Teradata to the cloud, they experience significant pain points—and Intermix is there to solve them. The company was growing at over 12% per month, with 30% of monthly growth coming from customer expansion (customers doubling down on their usage and paying more). With nine employees distributed across San Francisco, New York, and Europe, they were on track to pass $1 million ARR within Q2, up from approximately $30,000 per month a year prior—more than doubling year-over-year. Most impressively, they maintained less than 5% annual revenue churn while expanding existing customers by more than 5%, achieving over 100% net revenue retention. Paula was openly exploring venture debt options like Lighter Capital to fuel growth without further equity dilution, with plans to raise another round about a year out.

Why It Worked
  • The founders identified a universal pain point (data fragmentation) they experienced firsthand, which meant they deeply understood the problem and could articulate its urgency to prospects.
  • Their 20-year Valley network provided warm entry points to early adopters willing to use an incomplete product, which accelerated PMF validation and gave them credible reference customers for cold outreach.
  • They reverse-engineered their ICP by identifying rare job posting signals (Redshift mentions) that reliably indicated target companies, enabling them to focus cold outreach on high-intent prospects rather than broad lists.
  • They launched with enterprise-grade pricing ($50K-$100K ACV) from day one, which meant each customer provided substantial validation and revenue, allowing them to reach $70K MRR with only 25 customers.
How to Replicate
  • 1.Spend time working deeply in your target industry to identify a painful, recurring problem that multiple companies face, then validate this insight by talking to peers in that space before committing to the idea.
  • 2.Leverage any existing professional network to recruit 5-10 early alpha users willing to adopt your unfinished product in exchange for access and influence, prioritizing those who can provide detailed feedback on the core problem.
  • 3.Mine public job board data (Indeed, LinkedIn, etc.) for industry-specific keywords or tool names that appear infrequently and reliably indicate ICP fit, then use these lists to systematically run phone outreach campaigns to identified decision-makers.
  • 4.Price your product at the enterprise level from launch, even with limited features, to ensure each customer acquisition validates strong product-market demand and provides sustainable unit economics to fund growth.

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