How to find the best pricing metric for your offering

tl;dr: Here's the most important question for identifying the right pricing metric for your offering:



What’s the metric that would allow your revenues to grow as your customers’ revenues grow, if you acquire no new customers?



Answering this question is not that easy, it turns out, because first and foremost there are many metrics to consider and evaluate. The criteria we laid out below might be helpful to vet the alternative options. And, as always, we’re here to help. Happy reading!


How to find the right pricing metric

A pricing metric is the unit by which buyers acquire your offering. It also represents the unit-of-measure that a buyer uses to estimate the benefits and value of your offering. Examples of a pricing metric includes per user, per usage, per month, per year, per quantity of consumption etc.



The right pricing metric aligns your revenues with how and by how much different types of buyers benefit from your offering.

Here's an example. We've recently worked on a solution that helps businesses manage their environmental data. The company had been charging a per user, per month price for their solution. At a first glance, this made sense, because one of primary benefits of such a solution is providing access to environmental data for various stakeholders, such as employees, A&E consultants, regulators, field data collectors, chemistry labs etc. However, the types and levels of activities that are needed for collecting, analyzing, managing, and governing environmental data do not necessarily scale by the number of users. Instead, they scale by the risk and compliance drivers that are specific to the size, type, and lifecycle of the site. Therefore, in this case, we transformed the company's pricing metric to per sampling location per month and added a secondary metric to adjust for the level and complexity of the data management activities.

Price metrics are powerful because product owners can pick from many metrics available for a specific type of offering. They can even structure their pricing using multiple metrics at a time - as we did in the example above. The most commonly found pricing metrics are

  • User-based: Per number of concurrent users, teams, viewers, members etc.

  • Usage-based: Per unit of output delivered, per GB, per contacts, per emails etc.

  • Adoption-based: Per types and levels of features and attributes included in a plan

  • Value-based: Against an agreed-upon performance level or outcome

An effective pricing metric inherits the following criteria:

It aligns your revenues with the metric by which buyers realize value


For example, consider endpoint security solutions that monitor and detect security gaps on a network. The benefits that these buyers accrue increase as the number of devices on their network increases. End-point solutions apply the most current security protocols to the devices on a network, therefore, mitigate the risk of a security breach one device at a time. A unit based price metric suits these types of solutions because it scales the revenues with the benefits.

However, an opportunity to consider might be the risk profiles of different types of buyers. For example, the security breach risk for an insulin pump at a hospital device is significantly different than that of a point-of-sale device at a retail location. This is where a secondary pricing mechanism might introduce additional monetization opportunities from different types of buyers.

It ensures that whoever benefits more pay more

Next, consider building automation solutions that optimize energy consumption in commercial buildings. At the highest level, energy efficiency outcomes scale by the size and mechanics of the building. Secondarily, the value delivered by such solutions vary by the building type, systems configuration, seasonality, location, peak demand costs etc. The number and types of equipment such as HVAC systems, production equipment, industrial equipment, PCs, etc. impact the quality of the indoor environment. Therefore, most building automation software providers price their solutions by the total energy savings that their solutions provide on an annual basis.


If building automation solutions utilized a single pricing metric, such as per system size per month or per sqft per month etc., they would leave money on the table. Because the outcomes vary at the individual building level rather than the use case level.

It is easy to explain and easy to budget

Your pricing must make it easy for prospects to estimate their total spend. This can be particularly difficult for usage-based metrics or for pay-as-you-go models. If prospects cannot estimate what their adoption or usage levels will be then, it makes it harder to implement usage-based pricing. In such cases, implementing tiers of usage-blocks rather than granular usage units might be more manageable for you and more flexible for your buyers.


This is an area where enterprise solution providers particularly struggle. It is difficult to estimate annualized usage levels for solutions that stand up across multiple business units and sub-accounts over 2-3 years of deployment schedules. Pricing gets even harder when enterprise buyers require large scale deployments with all-you-can-eat benefits, and economies of scale in their costs. The long and nuanced sales, procurement, and contracting processes don't help either as they take away the focus from ROI and value delivered to discounts, terms and conditions, and entitlements.

Estimating usage and adoption levels - especially at enterprise scale, require new metering, analytics, and tooling. We've developed quite a few of those in the recent years: Customer blueprints, usage estimators, and customer affinity maps, back-end metering algorithms etc. Ping us to learn more about those.


It enables paths to increase customer lifetime value

This is particularly important for revenue-benefits alignment between you and your customers. Consider the case of business-to-business integrations such as EDI protocols. Most providers in this space charge per connection between two B2B partners. Once customers deploy their configuration among B2B partners, then they get unlimited transactions through those connections. This is a good example of misalignment - especially for mature, large scale clients where most of the revenue likely come from.


In this example, charging by partner integrations is less than ideal for two reasons:


1. Once B2B partner connections are configured, customers' growth in establishing new and additional partners is limited. Therefore, the growth in customer account value will be limited.


2. Customers transact with partners more as either of their businesses grow. The value that your customers receive from each connection will increase indefinitely whereas your revenue will stay stagnant.


Solutions similar to this EDI example require two pricing metrics rather than one: First, a metric that scales by the size of the use case and second by the usage of it.


It is critical to implement fences, caps, and triggers into your tiers so as to move lower-value segments to higher paying tiers as their needs and usage grow.

It is identifiable by different types of buyers

Your pricing metric must make sense to buyers, particularly in how they make their revenue or how they fund their operations. Consider an education technology platform for conducting and managing complex research for evaluation companies. In this space, buyers do not necessarily self-identify their needs by the number of users but by the number of studies because that’s how social research is funded. While the comparable products such as Qualtrics and Survey Monkey may price on a per user basis, it actually doesn’t make sense for this research platform to price on a per user basis as it does not truly reflect how buyers budget for such a solution.



It is as dynamic as your market play


Your pricing must be as dynamic as your market, buyers, and the ways in which they benefit from your offering. Static pricing metrics miss the opportunity to monetize your solution as your buyers' needs and usage shift. As providers accumulate data in key feature adoption, engagement, and conversion metrics, they can better estimate their buyers’ future needs and units of purchase. This is valuable information when making product roadmap decisions. It's easier to make trade-offs when you know the estimated adoption levels for a feature and the right pricing metric by which to monetize it.


We can help with all that. As always, please reach out to explore. DM your questions to us and we’ll ping you when we post your answer.


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