How to conduct pricing experimentation?
Price testing and implementation for launch stage, post-PMF and high-growth stage.
Pricing is often overlooked as a monetization growth lever. Companies set their pricing and forget to innovate. Like other factors, this is a gap that, if left unchecked, can lead to competitive disruption in your category.
Pricing experimentation should always be an ongoing priority, and companies should continue to iterate on their pricing almost every quarter. Otherwise, you’re probably leaving money on the table. But how and where do you start price experimenting?
Like anything else, what and how you experiment depends on your context and WHY.
Goals or outcomes of a pricing test can range from increasing free to paid conversion, trial to paid conversion, conversion on higher tiers, scaling revenue faster, etc.
Depending on your goal, you might want to launch-test new pricing, optimize the existing pricing structure or innovate with a new model.
Unlike other product experiments, pricing experiments have a significant lift when you decide to move to a new variant. Therefore, implementation strategies have to be very well thought out, and you need the buy-in of multiple departments and stakeholders to make any pricing changes.
This is a framework of how I’d think about what pricing test to conduct.
Note: Many other factors, such as resources, data availability, monetization goal, etc., go into strategizing pricing tests, but this is a good starting point.
Let’s look at what pricing experimentation could entail at different stages of a company:
At Launch:
Creating your first price is difficult. There are equal chances that you might end up overcharging or undercharging for your product. It’s very difficult to get the price right in the initial launch. Therefore, it’s important not to overcomplicate it but think iteratively and be ready to adjust when you receive signals from the market.
A pricing model that aligns well with the customer value path and value metric is a good starting point.
Value Path is a series of actions a user takes that results in something of value. Value path completion can be connected to one or more value drivers and is often a good value metric.
Value Metric: The unit of consumption by which a user gets value. (Source: I like how Ibbaka describes these)
E.g., in the case of Miro, a typical customer value path is the ability to use different boards, collaborate with your team and share various boards. Therefore, Miro’s pricing is based on the hybrid value metric of the number of boards you can use on the free plan vs. paid and the type of team members/users you can collaborate with (guests vs. visitors).
Your starting price should be a function of the perceived value a customer gets from switching to your product against whatever they might be using. To arrive at the perceived value, there’s a likelihood you have to undergo multiple iterations. These pricing iterations should be based on the following:
Feedback from customers
Target Ideal Customer Profile and how they buy software
Competitive landscape and how your product is differentiated
Your costing model. E.g., if you use an external API to build your product, there will be some associated costs. If your price is too low or not scalable, you’ll lose money on the product until you iterate to recover those costs.
In some cases, it might also make sense to keep the end goal of total revenue to be achieved by, say, the end of the quarter or end of the year to estimate how many units you need to sell and at what price to make up that revenue. This is more common when you gear up to raise a new round or meet revenue targets in a VC-backed environment.
Offering discounts and coupons are widespread when you want to encourage product adoption or get customers in the door to get feedback. A discount or couponing strategy can drive quicker initial price discovery if implemented smartly.
Post Product-Market Fit / Scaling stages:
When you hear in popular media that you should always be testing pricing - this is the stage you’re expected to be in to conduct regular price optimization testing.
At this stage, you’ve found a product-market fit, aka you can retain customers, AND there is a solid monetization model that you’ve figured out. From here on, it becomes a task of expanding market share WHILE also aligning the pricing to be correct on various tiers targeted towards different sizes of the ICP and optimizing the price itself to find your sweet spot. (Read: how Growth and ICP are connected)
Again, your goals for price testing have to be very clearly outlined. What is the purpose of price optimization? Is it to increase trial conversion? Is it to drive more free-to-paid conversion?
Depending on your goal, price-testing strategies may fall under one of these categories:
Repackaging of plans:
This applies when your initial pricing model is too simple and you didn’t offer many tiers. You might be leaving out a section of the market that doesn’t need all the features available on your highest plan but cannot do much with a lower plan. If there are signals from the market or a lack of a trade-up plan that fits well for a specific type of customer, you might want to try repackaging or even repositioning specific plans. E.g., Miro has a plan only targeted towards consultants who work with external partners and not internal team members, which is its enterprise use-case.
The reverse is also true. In some cases, you might find yourself with one too many tiers. It might be important to consolidate a few plans to reduce the cognitive load for a customer and convert leads better.
Modulating seat limits or usage to find a new conversion sweet spot: If you started with a value metric and initially set it at a certain limit, you might be too afraid to modulate it. But your value metric is probably the single largest pricing lever you can play around with to arrive at a pricing that is not detrimental to your lead conversion and also helps you increase revenue. Use your product usage metrics from your existing customer base to identify areas of where you might be giving away too much value.
Bundling features and add-ons: Another tactic to try in pricing optimization is offering bundled pricing. Specific modules of the product make sense to be bundled and to give the customer an option to choose a bundle instead of having them upgrade to a plan. What makes for a good bundle or an add-on is a whole other topic of discussion. What matters here is how you think about testing different pricing structures that make sense to drive higher revenue without impacting conversion.
Price Elasticity: This is straightforward. A good tactic to experiment with when you want to understand the broader willingness to pay without hurting conversion - mostly tested top-of-the-funnel. Although, conducting willingness to pay surveys before running a price elasticity test might help derisk the time and resource investment in testing this top of the funnel.
Pricing Innovation: For startups looking to accelerate revenue growth
These are your classic Big Bets of experimentation. Once you are done tinkering with your pricing to optimize it and arrive at a healthy growth stage, there comes a time in a company’s maturity when you need to pull a larger pricing lever. It could result from the market or competitive pressures or just trying to identify a better way to scale your revenue as you acquire larger customers.
As you grow, you want a scalable pricing model that can capture the value delivered to a larger customer. Enter usage-based pricing, pay-as-you-go pricing, or one of the more popular ones - the hybrid pricing model - where you charge for a base subscription plus some usage-based price to capture value from those customers that are your power users. (This is an excellent article on the Usage-based pricing model by OpenView.)
Can you be usage-based from the beginning? Nothing stops you if you can figure out a model that works for you, but this is an important area to experiment with for mature companies who have continued with vanilla SaaS pricing.
Implementation
Testing:
Unlike other growth experiments, pricing experimentation is a pretty significant lift in terms of execution. A lot of cross-functional alignment is required to execute it well. Be it testing with existing customers or top-of-the-funnel - Sales, Support, CSMs, PMMs, and even leadership buy-in is crucial and for a good reason. After all, you are experimenting with something that can change how you sell your product and the resultant market feedback.
Therefore, building some validation to run a larger experiment is highly recommended.
Using techniques like a painted door approach, where you use messaging to effect a potential change but do things manually under the hood, is a good approach. Especially if you are looking to run an AB test top-of-the-funnel, it might be wise to build validation that a particular lever works through a painted-door approach on the existing user base.
If you are product-led with a free plan or trial structure, always test any pricing changes top-of-the-funnel to gauge the impact on trials and conversion metrics.
Another way to derisk pricing changes is by conducting willingness-to-pay surveys to build validation around pricing changes.
Roll Out:
It doesn't end at just testing a new pricing variant. Because pricing impacts almost every department in the company, the rollout of a new pricing plans needs a lot of training and enablement across customer-facing departments.
Over-communication internally and with customers is highly recommended with any new pricing changes.
While new customers wouldn’t require much hand-holding, there needs to be a well-thought-out plan for existing customers. Options such as the following can be used:
Grandfathering them into old plans
Offering incentives to upgrade
Offering extended trials of new plans to show value first
Communicating and migrating customers to new plans
Pricing is a vast topic, and we could likely get into many rabbit holes, but hopefully, this provides a potential list of options to experiment with.
Moreover, pricing is complex and contextual to your case. The fundamentals of setting and experimenting with pricing do not change. Outline the why, look at the potential opportunity or ROI of a change, think implementation and roll out to new and existing customers, clearly define success metrics and while you’ve done that, be ready to innovate again.
Additional resources:
Your guide to pricing transformations in 2023
Unspoken impact of pricing changes
Let me know in the comments below what else you would like me to unpack under pricing!