Pricing is more than just a number on a contract — when used thoughtfully, it can become a strategic tool for your SaaS product that can drive product adoption, customer satisfaction, and business growth. At this year’s SaaStr Annual AI Summit, Akshay Sharma, Head of Pricing and Monetization at Miro, chats with a panel of experts, including Janie Lee, Head of Product at Loom, Alison Harmon, Head of Growth at OpenAI, and Carsten Holm, VP of Pricing and Monetization at Splunk, about their nuanced approach to pricing and monetization.
Pricing As Another Product Feature
“First off, I want to try and make you reset your mind about what pricing is,” Akshay Sharma, Head of Pricing and Monetization at Miro explains. “I call it another product feature and just like other product features, it changes what your customer’s behavior is.
So, you should think about it the same way and use it intentionally to drive growth, revenue, or whatever else, but think about it more than something you set at once and forget. It’s an active part of your product, and you have to evolve it just the same as you do with other products or product features.”
Pricing is also more than just the bottom-line price level. When evaluating or updating your SaaS product pricing, also weigh the following factors into your pricing model:
- Value metric (seats vs usage)
- Terms & conditions (payment terms, etc)
- Product packaging or bundling
- Offering types (extra freemiums or incentives)
- Bespoke / custom pricing vs ‘as requested’
- Flexible licensing programs
- Volume or multi-year discounts
The core principle in pricing comes down to this: customers evaluate a product by weighing the benefits (green side) against the costs (red side). Effective pricing ensures that the perceived value significantly outweighs the cost.
Akshay Sharma, Head of Pricing and Monetization at Miro explained: “As you change the green side, your product, your features, go to market, your marketing, budget discounts, you have to change the pricing side appropriately so that you can achieve the goals you want.”
So, How Should You Price?
Amongst Miro, OpenAI, Loom and Splunk, our panel of experts agreed that pricing decisions should always be driven by specific business outcomes. These outcomes might include:
- Driving user growth
- Maximizing revenue
- Ensuring user satisfaction
- Scaling adoption
A different outcome would lead to a different pricing choice. For example, if you’re trying to drive user growth, you want the least amount of pricing friction. But if you’re trying to maximize revenue, you have to find the revenue maximization point. There are a lot of tricks and trials that you do to figure out these things, but this is the mental model: ‘What’s the outcome? Am I doing the right things for that outcome?’
Here’s a real-world example from when Miro was an early product. The ideal outcome was to ensure users loved the product. They figured out that user love happens when a lot of people come together on the Miro board and collaborate.
Miro’s pricing strategy evolved with their business goals:
- Early stage: Minimum five-user paid plans to ensure collaborative value. Though it seems counterintuitive to maximize revenue, Miro intentionally set the threshold high enough that when someone bought Miro, they were getting the collaborative value because the users needed to use the product as a team vs an individual (which early data showed the highest churn in).
- Growth stage: Introduced business plans with 20-user minimums to provide enterprise-grade features. As the product matured, larger teams wanted more security and features. So, they introduced the business plan with a minimum threshold of 20 users to justify the more ‘enterprise’ value of added security layers and collaborative features.
- Scaling stage: Reduced to single-user plans to maximize accessibility. As Miro grew and we were scaling up, they wanted the product to be once again available to everyone. They experimented with pricing and packaging until they figured out how to reduce the limits back down to one user without hurting any of their key metrics.
This example shows how as goals changed, Miro adopted its pricing and strategy to achieve its desired outcome as it scaled.
There’s No Silver Bullet In Pricing
There’s no silver bullet in pricing. The key theme of how these successful SaaS companies have scaled and adapted their pricing is the value exchange of pricing and what your success metric is.
It depends on what your ultimate objectives are: user adoption, value extraction, you name it. You’ve got to adopt or change and customize your pricing model towards that. The other aspect is really zeroing in on capturing customer value. Pricing you can play with but the metrics, like customer value, are really hard to change after the fact. So be clear around how you want to capture value, and set a fair value exchange, meaning the customer agrees that is also the way to capture value and reflects how they derive value from a product as you scale.
For example, OpenAI’s success metric is the growth and adoption of the platform. Alison Harmon, Head of Growth at OpenAI shared, “the AI space is new and we want as many customers, and companies as possible to try it out. And so we, as many of you probably know, have set this track record of releasing something really intelligent and then even as we improve it, lowering the price.
The goal of that is as we get more efficient at serving that, it yields more adoption within a company and across companies because they can serve that to even more people which is a bit counterintuitive. After all, you think as we increase value, you should increase price, but really it’s while we’re trying to ignite the market and just get as many companies exposed to as AI as possible.”
Experimentation: When and How to Do It
How do you validate if your pricing is the right decision? How should you experiment or roll it out? The first question you need to ask yourself is, what are you trying to get out of it? Are you trying to validate if a completely new product or package you’re introducing has product market fit, or are you trying to de-risk a really big change to an existing popular package customers love?
“The reality is it’s really hard to have enough volume so far down the purchase funnel to actually run an experiment that gives you precise results,” Janie Lee, Head of Product at Loom explains. “And even if you do, it’ll take you a really long time to get enough data on things like churn that actually will give you the precision that, you need in real time. And if you’re enterprise or sales-led, that volume is not going to be there.”
So it becomes really helpful to think about what the risk profile looks like. The risk of rolling out a new product or net new package is very different from changing limits or pricing on a package that most of your customers are on.
Rolling Out a New Package vs. Changing Pricing of an Existing Product
When rolling out a completely new product, the worst case is that no one buys it, which is truly terrible but does not detract from the bottom line. Whereas, if you’re changing an existing package that a majority of customers are converting on, any little move can change the bottom line of the business instantly.
So, think through those questions and determine if experimenting is the right move. Depending on the risk tolerance or your goals, you may decide that the experiment is important for the business and that you can afford to wait 6-9 months for results. Companies like Loom usually reserve these types of experiments for the highest-impact products rather than running quarterly pricing experiments.
Be Clear On Guardrail Metrics
You need to be clear on what matters most when you’re experimenting, but you also need to be clear on guardrail metrics. With pricing and packaging, you might consider it a wild success if conversions or revenue goes up. But across the market, there are examples where, later down the line, you see it was a bad decision for your product or company long-term because it hurt engagement or user growth in an unintended way.
Key principles for pricing experiments include:
- Keep it simple
- Be customer-friendly
- Ensure cross-functional alignment
With pricing and packaging in particular, it’s easy to go down the rabbit hole of all the edge cases to control for. Instead, ask yourself where 80% of the impact you can get is. Is it geo-based or segment-based?
And finally, do right by your customers. Being on the receiving end of pricing and packaging experiments can be really frustrating if you find out. If someone does find out or is unhappy, find a way to give them the most generous version of your policy so there aren’t negative consequences or brand impact.
Partnerships between sales and support teams are important here to help have these conversations with customers.
Once You Have Pricing in the Market, It’s Hard to Roll It Back In
It’s usually a one-way door, but you have to be willing to roll something back in when you’re testing. In Enterprise sales, pricing isn’t super transparent, so in these cases, it’s easier to do pricing experiments in places where fewer people will hear about it, but you can still get the right insights out of it.
You also have to de-risk your own mindset. Experiments fail often, so assume the mindset that failure will happen.
Packaging and the Right Price Levels
With packaging, you want to segment your customer base. You do that because different customer segments have a different willingness to pay, which means different price points and value realization. That’s the real goal here: customer segmentation.
At Splunk, they were selling a bunch of products that included an Enterprise platform and add-on sales. You have to buy the platform and the add-ons, which means two line items and quite a bit of friction in the sales process.
Over time, they figured out that as people adopt these use cases, they end up buying more of the platform. Instead of selling them individually, they rolled them into one entire suite. By doing this:
- User adoption was much higher because people felt they could experiment without any real risk of commitment.
- The average sales price for the platform went up in those deals where it was positioned as a bundle.
That’s just one example of how packaging can change the game.
Sooner or Later, You have to Have a Number
Price level is another thing. You eventually have to put a number on the price list. It’s much easier to adjust pricing in Enterprise sales because it’s much less transparent, giving you flexibility and options. And across the board, it’s much easier to lower prices than raise prices.
So, start higher and give yourself the option to discount it away later. The general wisdom on price point is that you shouldn’t change it more than once or twice a year. At OpenAI, they’ve changed pricing many times.
They launch a model and, weeks later, launch another one and lower the price. That flies in the face of general wisdom because the AI market is moving fast. For the average company, you shouldn’t change pricing and packaging all the time in this way.
Demonstrate The Value Associated With Price Increases
Price increases go bad with messaging and a lack of value communicated. How do you let customers experience that increased value that justifies pricing? Some price increases were successful with time or usage-based trials that allowed customers to see how much the product improved. But the value needs to be clear in two seconds so there isn’t an immediate negative reaction from customers in the market.
From a product perspective, you’re always considering pricing, especially when building a new feature or product. Who are you building it for, and what package should it go in? Overall, you’re likely thinking about pricing and packaging all year, but that doesn’t mean changing it.
It all depends on the market you’re in and how quickly it’s changing. Small and medium tweaks and changes are likely fine as your business commands, and every time you make a change, there will likely be a little friction unless the change delights your customers.
Key Takeaways
