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Usage Pricing: Better Than Subscriptions?

During the past decade, the subscription pricing approach has been widely adopted. After all, many tech companies have built thriving businesses with this business model, such as Salesforce.com.

Then again, subscriptions provide simplicity and predictability for customers. As for the vendors, there is the benefit of recurring revenues.

But as for the past few years, there has been a change—that is, a growing number of fast-growing tech companies, like Twilio, Snowflake, Jfrog, Stripe and DigitalOcean, have been eschewing subscriptions. Instead, they have focused on usage-based models.

Why so? Well, first of all, there are some problems with subscriptions. Perhaps the biggest is that customers often purchase licenses that they never use. This has actually been made worse during the COVID-19 pandemic. 

“In general, we’re all users of a variety of subscription models and we’re super frustrated with them,” said Khozema Shipchandler, who is the Chief Financial Officer at Twilio. “You’re locked in from the start. There’s no room for movement and no way to account for the peaks and valleys that inevitably fill any business season such as the holidays, increase or decrease in demand, etc. In fact, the subscription model is increasingly becoming a relic of a bygone era. In my opinion, it will ultimately disappear.”

But with the usage-based pricing model, there is much more flexibility. It’s easy for a customer to try a new service and evaluate it for a negligible cost. This approach has proven quite effective with developers. 

“A lot of times, especially for us, developers are the ones who get started with it first,” said Shipchandler. “They see how easy it is to use our products, they get a use case up and running, and the customers start benefiting without a major decision maker having to weigh in.”

There is also an alignment of interests between the vendor and the customer. If the service does provide value and is used frequently, then the customer will definitely be willing to pay more, right? Definitely.

“Adopting a usage-based model is the ultimate realization of being a customer-obsessed company,” said Kyle Poyar, who is a Partner at OpenView. “There’s no room for shelfware or bad user experiences. The upside is that you directly share in the success of your customers, which pays dividends years into the future.”

He points out the following metrics for those companies using the usage model:

  • 38% faster revenue growth rate than their peers.
  • Stronger net dollar retention rates (seven of the nine most recent IPOs of cloud companies have shown this).
  • 50% higher revenue multiples versus the broader SaaS companies. 

Of course, the usage-pricing model will not suddenly transform a company. There still needs to be a strong product and a top-notch team. 

Yet it still a good idea to evaluate the usage model. If anything, as it becomes more common, customers may demand this approach.

“I strongly believe a consumption model is the future for software because it changes the fundamental nature of the relationship between the vendor and the customer,” said Bill Staples, who is the President and Chief Product Officer at New Relic.  “The vendor understands that if they don’t build great products that customers enjoy using, they won’t get paid. And consumption isn’t just a revenue model. It is the explicit understanding that if we aren’t focusing every function in the company around making our customers successful, we aren’t living up to our commitments or our ability.”

AI Startup: What You Need For Your Investor Pitch Deck

The funding environment for AI startups remains robust—and some of the rounds have been substantial. Just recently Dataiku, which operates a machine learning platform, announced a $100 million Series D investment. 

AI truly represents a transformation in the tech world.  “AI is making software much more dynamic and improves as it understands user behavior,” said Gordon Ritter, who is the founder and General Partner at Emergence

OK then, what are VCs looking for when evaluating an AI deal? What should be in your pitch deck?

Well, to answer these questions, I talked to a variety of VCs. Here’s what they said:

Sri Chandrasekar, partner at Point72 Ventures

A slide on Why Now. What technology has recently been developed that has made solving this customer problem now possible. It might be “speech-to-text technology has gotten good enough to use in 95% of a call center’s communications.” Or “recent deep learning focused processors have made it possible to do computer vision on the camera instead of in the cloud.” It’s rare for people to identify a customer problem that nobody has heard about before–usually, what creates the potential for a large new company is that technology is now available to solve that problem in a new or differentiated way.

I also want to see a slide about solving the “Cold Start” problem. This matters most for the earliest stage companies, but AI companies need access to data to start training their algorithms. I like to see that they’ve thought about this problem and have a clear way to get access to enough data to build their business. The answer can be anything from buying data to partnering with an ancillary business to “faking it until they make it,” where they deliver the product or service with humans until they have enough data to build the AI model.

Mark Rostick, who is a Vice President and Senior Managing Director of Intel Capital:

When looking at a presentation of a potential AI deal, we look closely at the specific problem in AI/ML that they are solving and why solving that problem is important enough to build a company—not just a feature or tool. We also take a look at why the team is uniquely positioned to understand the problem they are trying to solve and how they are equipped to execute on it. The team must have line-of-sight to an economic model they can create that is capable of driving growth at “venture scale”.

Jake Saper, a partner at Emergence :

When evaluating companies that use AI to augment workers, I like to see charts that show the percentage of AI-generated suggestions that are taken by the user over time. For strong companies, this portion may start relatively low as the model is training and the UI is being tweaked. As both improve, you want to see the “coaching acceptance rate” improve to >75% and stay consistent.

Kenn So, a venture capitalist at Shasta Ventures:

There are a couple of slides I like to see:

#1: A high level architecture/diagram of how the data flows from source to training to AI predictions to the product that the users interact with. This helps brush away some of the AI pixie dust.

#2: Quantifying the value of the product or model for the user. For example, radiologists save 1 hour per day because the AI automates report writing. Only 10% of radiologists make adjusts to what the AI writes

#3: Defensibility of the data now or in the future. There are different ways to achieve this from proprietary data rights agreements to data network effects. In the end, ML is all about data. One thing to note is that data defensibility is just a minimum and not a sufficient condition of defensibility for AI companies.

Jeremy Kaufmann, a principal at Scale Venture Partners:

One of the most valuable metrics to show investors when pitching an AI company is how the accuracy rate of the underlying algorithm is improving over time. It’s important that investors see this improvement over time, particularly if humans are in the loop, as this analysis points to the fundamental solvability of the underlying problem. Investors are scared of the potentially asymptotic nature of AI algorithms (that they will never get good “enough”), so it’s very important to define “good enough” in a business context (what a business user will accept in terms of error rate) and then overlay this underlying expectation with the quantitative measure of how an algorithm is performing over time.