Now that Nudge is in Open BETA, we are starting to focus on our Nudge Team product, and are thinking about pricing. Pricing your product is really hard. There are so many variables to play with, you are going to get it wrong before you get it right. However, when you find the right way to align your price with customer value - the magic starts to happen.

Jason Lemkin recently posted a great article on why all pricing is relative. There is some really good insight in that post. I hope my article can add some real world approaches to pricing you can try.

Joe Payne (ex-Eloqua CEO) and myself blew apart our pricing model at Eloqua in 2007, and started from scratch. It needed to be simpler. It couldn’t be per user. It needed to scale from SMB to the global enterprise, and it had to be implemented quickly. We eventually landed on two key protocols that have now become the default pricing standard for marketing automation platforms:

1. Good, better, best (functionality based on customer use cases)
2. Contacts under management (usage)

There were many nuances we learned after we implemented this, but at the core these were the two prime protocols and still exist today in the Oracle Marketing Cloud. But how did we get there? Especially since there was a lot of resistance to the proposed change:

• “Doesn’t scale with customer value”
• “Doesn’t work for B2C”
• “Too simple, need more flexibility”

Well it wasn’t easy, we had to do a lot of work, and tried some different approaches.

Pragmatic Marketing’s Technology Assessment Model

source: Pragmatic Marketing

This is an interesting tool to evaluate Impact to Customer vs. Depth of Technology. It can be used to assess viability of new tech projects, but also for pricing. You ask your prospects and customers to put a dot on this grid where they think your product should be placed. The further to the right indicates a high value proposition, and further up indicates deeper tech differentiation.

Obviously, if you are in the top right, you can have a high price point that will be sustainable. Bottom right, high price point that may erode as competition increases, and both left boxes make pricing very difficult.

Our challenge was that we had a decision maker who thought we had a huge impact to the customer, but the technology wasn’t very deep. And we had a power user who appreciated the depth of technology, but didn’t see as big of an impact.

SaaS Data Driven Approach

With all of our customers in the cloud, we could analyze the current customer scaling variables and determine what protocols correlate best to value and current MRR. I spent many hours laboring over the data, and was really hopeful something would stand out as the clear winner.

The challenge is that in any fast growing startup, we had many different variations in our “standard” contract. So all analysis showed what we intuitively knew, that nothing in the current pricing model correlated well with customer value.

The One Thing to Check: Evaluate How Extremes Motivate You and Your Customer

Finally we decided that contacts was the right pricing protocol, some of this was just gut, some of this was because we knew that marketers would know the size of their database in the sales process, and also be able to reasonably forecast it.

But, after we chose contacts, we wanted to ensure the edge cases drove the right behaviour for us and the customer. In every pricing model, you end up with extremes that sometimes are bad for you, the customer, or both.

An example of one of the comparisons we did is website traffic vs. contacts. What became clear was that even if contacts were not a perfect proxy for value and effort it drove the right behavior for the customer AND for us. And that was the final factor to solidify the choice.

So if you are working on pricing your product for the first time, or thinking about revamping your pricing to get to that first $1,000,000 SaaS deal, I think the one thing you have to do, is evaluate the extremes and ensure your interests and motivations are aligned with the customer.