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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Instead of raising an equity round, a startup might choose to borrow money- and for good reason. Venture debt dilutes founders much less than equity rounds. Low interest rates have increased the attractiveness of venture debt, because the cost to borrow is low. Millions of dollars for little dilution at little cost? Venture debt is an attractive financial product. No wonder it has grown in popularity by 16x in the in the last six years.
My algorithm is better than yours. My algorithm performs better on the precision/recall tradeoffs. It surfaces fewer false positives. It converges to an answer faster. Perhaps it requires a bit less data. Those statements might all be true. But none of these advantages confer a competitive sales advantage in the market. They aren't technology innovations leading to a go-to-market advantage.
There approximately 22 million trucks in the US. Many of these trucks run software to track the location of the vehicle, manage inventory, and comply with regulation. There are two SaaS companies operating at greater than $100M million in ARR in the space and they illustrate one of the mantras on this blog. There are many different ways to build a SaaS company.
A founder asked me recently if a dead zone in ACVs exist around the $10k price point. Yesterday, I listened to a podcast in which an executive asserted that infrastructure software priced lower than $250k in ACV threatens the viability of the company. What does the data show?
At SaaStr earlier this year, I spoke about the huge potential of machine learning in SaaS. In that talk, I broke down some of the advances in ML that might be useful for software companies. In the discussion that ensued, I stressed the importance of not letting the technology obfuscate the value proposition of the software. Yes, ML is a huge step forward, but it's not enough by itself. In fact, it likely isn't the most challenging part of building a disruptive product.
After a startup establishes product market fit, scaling demand generation becomes the the next major challenge. Doubling or tripling ARR each year for several consecutive years is not easy. The best marketers create a demand generation portfolio. There are four axes to measure this portfolio - scale of investment, sophistication, breadth and potential.
A friend recently asked, "Which path is better for SaaS startups? SMB to mid-market to enterprise or straight to enterprise?" It's a key strategic question for many founders building software companies.
A CEO uttered a brilliant statement in a board meeting recently. He said, "This experiment will cost $250,000 to run. After three months, we will know whether our new go-to-market strategy is viable." What's so clever about this statement? The CEO's frame of mind when marshaling the company's assets - time and money.
Every once in a while, I receive a FedEx from an entrepreneur I haven't met. Inevitably, this mail contains a modern rarity - a business plan. Ten to twenty pages describing the idea, the genesis, the business model, the team and its structure, customer acquisition strategy, sales model and other key details of the business. A plan for how to start a company, and a defense of the idea.
There's no quicker way to lose a user or buyer of your software than to lose their trust. The software didn't save my data. The database suffered corruption. The website is down frequently. Data integrity is a challenge every company storing data faces. Machine learning SaaS startups face another trust risk – one introduced by probability.