How Two Failures Led To A $3.2 Billion Deal With Twilio

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This week Twilio announced a $3.2 acquisition of Segment. On the news, Twilio shares jumped by about 7% to $328–bringing the market cap to a hefty $41 billion. 

Segment is one of the top players in the CDP (customer data platform) market. It’s technology makes it easy and secure to collect, unify and route customer data. By doing this, a company can build apps to create compelling user experiences. Consider that Segment has over 20,000 customers, which include Intuit, Google, VMware and Levi’s. 

“For years now, we’ve been seeing the proliferation of channels and applications, and more broadly, the deluge of customer data,” said Rajeev Dham, who is a Managing Director at Sapphire Ventures and investor in Segment. “What Segment brought to the table was that unifying layer that companies needed. In my opinion, Segment is one of those rare, elegant, yet powerful solutions that can collect, transform and transport very valuable customer event data into different channels and marketing applications in order to drive better customer experiences.”

But interestingly enough, Segment may have never happened. How so? Well, according to an interview with CEO and co-founder Peter Reinhardt (in a blog from Alejandro Cremades), the company had two pivots and almost ran out of money in the process!

The story begins when Reinhardt was a junior at MIT in 2011. He and his roommate wanted to start their own company. So they dropped out and joined Y Combinator, which led to a seed round of $600,000. 

At first, the company’s focus was on building software for classroom lectures. It would allow students to alert their professors where they were having trouble with a lecture. 

Reinhardt worked tirelessly to code it. But there was a big problem: no one was interested in it! Students mostly wanted to spend time on apps like Facebook and Twitter.

Undaunted, Reinhardt would try again. This time he developed software for user analytics. Why? The reason is that Reinhardt realized that such a tool would have saved him much time with his failed classroom app. Hey, there are probably many developers who would want something like this, right?

Again, Reinhardt coded up the tool, which took about a year. During this time, there was little discussion with potential customers.

Then when the app was launched, it got tepid interest. While the software was solving a real problem, there were already a myriad analytics systems on the market. The fact was that Reinhardt’s software just did not provide enough reason for users to consider it.

With about $100,000 left in the bank, the situation was certainly dire.  Reinhardt had only one chance left.

This time around, though, the focus was different. It wasn’t about building an elaborate app. Instead, Reinhardt created a very simple piece of technology that solved a clear-cut problem. 

You see, when he was creating the lecture tool, he wanted to put analytics in it. But it was tough to send the data. To solve this problem, Reinhardt wrote a simple 50-line program.  He would then open source it and then eventually create a landing page for the software, which was posted on Hacker News. And yes, there was lots of traction. Reinhardt hit the magical market-product fit. And the more he worked with customers, the more he saw that there were huge opportunities for the tool.

Along this journey, it would have been easy for Reinhardt to throw in the towel. He even went back to his investors after he failed with the classroom app and offered to return their money. But most of them did not want to.  They knew that the early-stages of a venture are often about failed experiments. Moreover, in the end, it’s also about understanding the customer—and that’s what made the difference for Reinhardt.

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the COBOL and Python programming languages.





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