Hi, I'm a partner at Redpoint
. I invest in Series A and B SaaS companies. I write daily, data-driven blog posts about key questions facing startups. I co-authored the
book, Winning with Data
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There are three ways to create negative churn that I have observed in the market. First, usage expansion. Second, feature expansion. Third, product expansion.
Founded in 2006, Mulesoft is an 850 person company based in San Francisco that builds data integration tools. The company started originally as an open-source product and then focused on its paid offering. Today, the business generates nearly $200 million annually in revenue, and is growing at 70%. The business [filed to go public last week](https://www.sec.gov/Archives/edgar/data/1374684/000119312517047884/d287291ds1.htm), and the documents reveal a very impressive business operating at scale.
We've taught computers to do many things. We've researched how to teach them to identify cats, spot fraudulent charges, even categorize cucumbers. But what can we apply in our daily lives that computers have taught us? That is the premise of the book called Algorithms to Live By. Which of the advances in computer science can be applied to laundry, choosing an executive assistant, picking the best strategic plan and optimizing your schedule?
From the millions of Amazon Alexas to the self-driving car, new products are coming to market infused with machine learning. The innovation offered by machine learning techniques are real, and they will changed the SaaS world. But how? How can startups use machine learning to their advantage?
One of the hardest thing to do in sales, especially for early stage SaaS companies, is to disqualify customers. When a startup disqualifies a customer, they turn away a revenue opportunity, a chance to add $1k of MRR or $3k of MRR, and meaningfully grow the top line. But if the customer isn't the right customer, that incremental revenue bears a hidden cost.
Advances in machine learning are transforming the software world. Two of the most exciting applications of machine learning are speech recognition and natural language processing. After researching the space for more than a year, we are thrilled to announce our investment in and partnership with Chorus, a pioneer in speech analysis for sales.
Win probability charts like the one above have become the icons of popular predictive data analysis. I love data, but let me whisper a heresy to you. I detest these charts. I Instead of provoking thought, insight and questions, they close minds. They support the ideas of inevitability, of odds too great to overcome.
I met a physicist this week who told me all the Nobel laureates he had met in his studies have been the most modest of physicists. "They realize how small they are in the world, after discovering something incredibly special and new." Separately, I referenced an executive this week. A former colleague of this person told me," this is not a person who sees a model work once or twice, and instantly subscribes to the notion that it will work every time for every business."
Which is the more important priority? Growth or churn? Churn or growth? Early-stage companies have limited resources to focus their efforts. On one hand, growth is important in order to raise a venture capital round. Growth shows demand for a product. On the other hand, churn is a huge source of friction and raises questions of product market fit.
How much should a founder raise for their startup? I imagine almost every founder contemplating a fundraising round ponders this question. There are many different paths to developing an answer. The right answer that every startup founder has told me is as much capital as possible at the highest possible price. But what strategies exist to justify increasing the round size and consequently price? These are the three most common I've observed.