The Key to Saas Pricing: Choosing the Right Pricing for Your Customers

Kyle-Poyar by

Editor’s Note: The following is an excerpt from Clearbit’s new book Data Driven Sales: How the Best B2B Companies are Using Data to Grow Sales Faster. You can read the full chapter here.

In September 2009, Andy Wilson learned that Inc. Magazine had named his company, Logik, #181 on their list of the fastest growing businesses in the U.S. He had grown the e-discovery services company 1,067% over the prior three years, to $4.4 million in revenue. It was profitable, too. Logik had only 8 employees and brought in $3 million in profit in 2008.

We saw the future and it was not humans managing hard drives and creating databases. That’s going to be done by software.

That same year, the recession hit and Andy made an unconventional decision. He decided to shut down Logik’s core business and transform the company from a services business model to Software-as-a-Service (SaaS). Even though his business had been highly successful, Andy realized it was ripe for disruption. “We saw the future and it was not humans managing hard drives and creating databases. That’s going to be done by software. Software can do a better job, and it’s going to do a better job,” he said.

Andy and his co-founder Sheng Yang set out to rebuild Logik from the bottom up, leveraging automation and software to streamline the process of managing digital information so companies could do it themselves without needing an expensive third-party vendor. Andy counted 2,500 steps that were required to fulfill a client request as a services business. His goal was to cut that down to three steps or less with their SaaS offering. This would make it fast and easy for corporate legal teams, law firms and investigators to organize, search through and review a lot of unstructured data, a process they refer to as e-discovery.

After several years of development, Andy was ready to release the SaaS offering to the market, now branded as Logikcull. There was only one hitch: pricing.

He knew he didn’t want to price like the services businesses of yesteryear, but beyond that he had no idea how to structure his pricing. The e-discovery market had been dominated by old school, legacy services companies that charged exorbitant fees. “There’s a data fee for everything. It’s really unpredictable and can explode on you in a number of ways, like if you had 100 GB of data you needed to sift through, if you went with a legacy e-discovery provider that would probably cost well over $100,000 just to get it indexed and posted for review. And it’s probably going to take a week or more of time,” he told me.

Compared to this old school process, Andy’s new technology saved time, kept data more secure and opened up new use cases beyond e-discovery. Customers would be able to speed up the e-discovery process so it takes minutes or hours rather than weeks, cutting manual labor and associated costs. In other words, the product did a lot more than legacy systems, and he wanted to capture the additional value.

Andy’s first step was to list the problems that he wanted his new pricing model to solve for his business; two were top of mind: lumpy revenue and the commoditization of data. “One of the biggest [problems] was the lumpy revenue problem that you see in episodic types of business models… You’re really in a weird state of unpredictable business growth. So, we wanted to solve that by creating a subscription [model]. We also wanted to move away from commodity types of pricing models and the biggest commodity being quantity of data.”

Andy had one other rule of thumb. He thought that in order to get lawyers to be willing to change their habits, the company would need to provide orders of magnitude more value than existing solutions.

We needed to be ten times better, roughly speaking, and add ten times more value.

To overcome the inertia of the market and lawyers’ reluctance to buy new products, in other words, Andy couldn’t just rest on having a far superior product. He also thought he needed to launch with a cut-rate initial price point.

He decided to start simple: he launched a straightforward subscription model similar to that of MailChimp. The Atlanta-based email marketing provider ties its subscription pricing to the number of marketing contacts a customer uploads and sends emails to, rather than the amount of storage taken up by those contacts. Andy decided to try a similar model and charge customers based on the number of documents they wanted to host inside Logikcull. He set an extremely low price of $0.13 per document per month, which worked out to a much more affordable rate compared to the hefty gigabyte (GB) storage fees customers were paying legacy providers. He liked that this pricing model created a steady subscription revenue stream and shifted away from the amorphous gigabytes of data stored towards a metric that he thought would be easier for lawyers to wrap their head around.

Unfortunately, customers hated it. Unlike MailChimp customers, Andy’s customers had no idea how many documents they were going to have, so document-based pricing wasn’t any easier for customers to predict than gigabytes of storage. Even worse, Andy couldn’t tell a customer how many documents they would need “because the job of Logikcull is to unpack all this information,” counting up and de-duplicating all of the files that a customer puts into the system. Only after a customer used Andy’s product would they truly know their document count and hence the price they would have to pay. Customers’ document counts fluctuated over time, too, which made it difficult for them to commit to a given subscription tier. Andy knew this would hurt his business ultimately since many finance teams won’t sign off on a deal unless they know the exact price they’ll have to pay at the end of the year.

Looking for more? You can read about the solution Andy and his team came up with here.

  • The question of how to predict use, and therefore cost, is a critical one in designing pricing metrics. I wonder if we can start using concepts like Predictive Engagement to give insights into this and then use it as part of pricing design.