Startups have never been under more pressure to grow quickly. To do that, many of them must land large enterprises, the deep-pockets of the IT world, as customers. But as any company that sells to enterprises will tell you, it’s not easy to win such contracts.
One critical mistake most younger startups make is believing that their tech will practically sell itself, says Arsalan Tavakoli-Shiraji, co-founder and senior vice president of field engineering at Databricks, which helps businesses store and analyze huge amounts of data. But that’s not enough, he warns. Databricks today is an AI and big data storage giant, valued at $190 billion, with more than 70% of the Fortune 500 as customers.
But it was once a tiny startup, and Tavakoli-Shiraji was responsible for helping bring in its first big deals. But, he learned the hard way that “people and processes matter the most.” He remembers securing a meeting with a prospect in the early days, only to discover that it was the same person that signed a mega deal with his major competitor. He also remembers early deals fizzling because the startup wasn’t prepared for legal, contractual, or security questions.
He offered his stop tips for startups to win big customers. Sell under a bigger tech company’s umbrella When Databricks was young, it tried and failed to score a meeting with UBS, the large Swiss bank. Then it did “a partnership with Microsoft,” becoming an Azure cloud partner.
That meant sales agreements would be covered under Microsoft’s terms. But it also meant that Microsoft’s sales folks actually booked the meetings and advocated for the startup. Today, Databricks is the big company that works with startups, selling their products under Databricks’ own terms and cybersecurity setup.
Examples include Hightouch, which provides AI-powered marketing and customer-data tools (Databricks has also invested in it); Addepar, which offers data tools for investment professionals; and Tecton, a machine learning startup that Databricks later acquired. Tavakoli-Shiraji groups enterprises’ main concerns and objections into four categories he calls the four Cs: context, choice, cost, and control. Context means an AI that can be trained “like bringing on a new employee,” he says, learning the company’s terminology, processes, priorities, and so on.
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