It is crucial for you to create the right price model for your recurring revenue business regardless of whether you are launching a new subscription-based offering, transitioning from a perpetual to a subscription model, or wondering why you are not monetizing as well as you should be on your current subscription product.
Zuora partner Simon-Kucher & Partners has worked on hundreds of projects helping companies determine their subscription pricing, and there are two aspects of determining price that are often marginalized as effective monetization levers:
Price metric: Define how cost is measured for customers (e.g. per seat, per concurrent user, by a usage measure)
Price structure: Define how the price level changes over time / usage / etc. (e.g. flat fee, variable, tiered)
Price metric = customer’s unit-of-measure for price & value
Price metrics are a powerful lever for subscription products because there are many metrics possible for a single type of subscription business.
A good example of this is in online computer backup services. Customers in this space include both consumers, who often have no idea how much data needs to be backed up, and businesses, who often have a network of computers that need to be backed up. Some online backup service providers charge per computer while others charge by data storage limit, but the majority have only one price model.
Carbonite is a great example of a subscription business that aligns its metrics with customer value and needs. Carbonite offers “Personal Plans” that are charged per year per computer and “Pro Plans” that are charged per year per xgigabytes. Using multiple price metrics lets consumers use as much data as they need and lets businesses support multiple computers.
The first step in identifying the right price metric is to understand the possibilities – what metrics align with customer value? What are competitors using for their price metrics?
Price metrics for subscription products typically fall into the following categories:
Create a list of metrics in a workshop or through email threads to brainstorm possibilities. Discussing even seemingly odd metrics can spark good ideas.
Once you have a list of 10-20 possibilities, you should evaluate the performance of each metric on its benefits to customers:
The metric should also have a positive impact on your internal goals and be rated on its benefits to your business:
Create a single score for “benefit to customers” and another for “benefit to your business”, and plot the score for each metric on a matrix (as shown in the Metric Evaluation Matrix). The metrics you should use are the ones in the top right “Ideal” corner of the matrix.
It is likely you will find that a few metrics land in the ideal quadrant. As you are choosing the metrics to move forward with, keep in mind:
Why do the best price metrics and price models fail?
When building your price structure, you should evaluate whether the intended price structure aligns with the firm’s brand positioning. (See exhibit 1). For example, a company that operates with a brand positioning of sincerity should have fewer multi-dimensional prices/surcharges. Doing so may drive the perception of nickel-and-diming customers, which would contradict the projected image of Sincerity.
Exhibit 1: Price structure should align with brand positioning
When building a price structure, you should consider customer preferences when choosing among the various permutations of flat & variable components.
Flat components increase predictability for the customers as they can estimate spend and budget accordingly. Typically, predictability is more important for larger customers with formal budgeting cycles. Higher predictability also benefits the firm by offering a consistent revenue stream to continue operations (smaller firms would oftentimes link this component with fixed costs in their business).
Companies with greater revenue coming from subscription pricing, which affords customers with higher predictability than transactional pricing (e.g. software license and implementation), get a higher revenue/EBITDA multiple in the market compared to their peers.
Variable components increase value-sharing. Customers like such components since the fee scales with their usage. Smaller customers also prefer higher variable components as their fee commitment increases as their business scale increases. When using such components, the firm is willing to position itself to succeed when their clients use their product. This increases the variance in revenue from month to month but the upside is oftentimes higher.
In some cases, one can also link variable components to business outcomes. The biggest challenge to adoption usually is the ability to measure & attractiveness of such a component to the firm. Such metrics are often not tracked directly through the firm’s products and the burden of reporting usage is the prerogative of the customer. For example, a firm that makes enterprise management software for insurance agents may not be able to measure the value of the insurance policies written by the agent (directly through the software).
A second aspect is if the business outcomes are not attractive. In the example above even if the firm can track policy premiums it still may not wish to use it in their pricing model as the overall premiums in the market may have remained flat or even have declined year over year.
The firm can also use a hybrid model by using structural modifiers. Establishing afloor (minimum commitment) can get predictability in a variable structure for the firm. Caps (maximum possible commitment) can help large customers address concerns that their risk is not unconstrained with a transactional model.
Tiers and breakpoints can be used to charge a different fee at different levels of usage. These components are used by firms to provide volume discounts to the customers with higher usage in a variable-component based model.
Price structure in action
A great example of using the right price model is a recent Simon-Kucher customer that sells trading software in the financial market space. The customer had been using multiple different metrics but had not seriously thought how that was affecting their customer base. They decided to focus on a “Sophistication” positioning and created a price model that had an upfront fixed fee and a tiered transactional charge that was capped. The company has since implemented and has received feedback from its customers that the new model is elegant (simplifies the unnecessary pricing complexity), scales well with growth and offers predictability as well.
Next: Price levels
Once you have the right price structure and metric, how do you set price levels? Stay tuned for articles this process and how to utilize price testing and customer research to make data-driven decisions.