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Data for subscriptions podcast - Episode #1

The Rise of Usage-Based Pricing and Which Companies Are Taking the Lead


Jonas Wallenius

Jonas Wallenius
Strategic Product Manager at DigitalRoute

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Episode description

The rise of usage-based pricing and strategies for adoption

Usage-based pricing enables new possibilities for communications service providers (CSPs), software companies, and manufacturers alike. From improving services, to launching entire new business models, better management of complex service suage data is the key to success.

In this episode, host Behdad Banian speaks with Jonas Wallenius, Strategic Product Manager at DigitalRoute, to discuss how companies can experience tremendous growth with usage-based pricing and everything that goes into this model.


What is usage-based pricing and why should companies consider this pricing model?

Usage-based pricing means that you price and charge for a product or a service based on the consumption of the service instead of a flat monthly fee, or just selling the product outright.

It’s fair, in that it helps a vendor provide quality service or product that has a long lifetime value. Economically, it reduces the barriers of entry by making it cheap for customers to get started with your product after which the classic expansion strategy is employed to increase revenue as customer adoption grows.

It also aligns sales incentives. Instead of incentivizing salespeople to chase the next customer, they pay more attention to customer retention and personalization. Measurements in the past show that the companies who get this right tend to grow both revenue and profit faster than those who don’t adopt usage-based pricing.


Are there some performance upsides that companies experience with usage-based pricing compared to those who don’t?

In traditional businesses, you spend a lot of time understanding your customers and trying to do surveys across different user groups. This inhibits the need for companies to be fast and agile, to introduce new types of products or services, and to maintain a steady relationship with customers.

Usage-based pricing constantly gives you feedback, minute by minute, day by day, based on consumption from customers. Which makes scaling quicker and more efficient for companies that use them.

Also, you don’t need to do contract negotiations to consume more services. All these ultimately reduce friction points for the companies involved.


What are some of the setbacks to consider when adopting usage-based pricing?

Less predictable financial flow. It is sometimes safer for CFOs to work with a predictable finance when they are working with flat subscriptions.

It can also be a bit technically complex to get right for most companies, because there’s more data processing and more systems that need to work properly. Most companies struggle to find the right metric that is fair for their customers.


What are some companies that have moved past predictability as an issue while adopting a consumption-based model and what are some of the best practices?

The predictability issue is particularly noticeable for a small customer base with visible variations. To mitigate this, spend enough time to research your metric and actually figure out what to price. As you grow to have a larger customer base, the law of large numbers eventually starts canceling the variability out. And then it becomes more predictable again.

One company to talk about is DataDog. It is a SaaS company that sells observability services. They started out a few years ago with usage-based pricing, actually from day one. So they had a very simple metric, which is to charge based on the number of hosts that you monitor. They grew from about $20 million in 2015, all the way up to 1 billion USD last year 2022.

Apart from having a nice product at first, they were able to adjacently scale their services which fetched them more revenue. From one or two services in the beginning, they grew to 17 services in six years.

This pricing model is a really good way to grow a SaaS company since in the industry, it’s very easy to run AB testing. You can change the pricing for a few customers to find what works for efficient adoption. So that’s a very powerful tool that you must use for revenue growth.

Companies that offer physical products such as Michelin are no exception. This well-established manufacturer launched usage-based pricing model by leveraging the data generated by sensors that are integrated in their tires.

Rather than selling tires as a commodity, with an outcome-based pricing model, Michelin is able to sell their product as service. Servitization is a huge trend across manufacturing, which also sees OEMs launching bundled services that include software and consultation services along with physical products.

In terms of agile product development, the IoT data gathered from the integrated sensors helped Michelin to launch new series of tires that last longer, serving their customers better while also increasing revenue.

That means less maintenance and less hassle for the customer. Michelin earns more revenue for each tire because it lives longer. And hopefully, they get to produce fewer tires which is a win for the planet.


Do I need purpose-built software? Can I just use any generic data integration data management tool to get this done? And how do I perfect execution for this model?

We see many companies that start out with Excel or homegrown solutions, generic data integration software, and so on. And this can work for a start, but they usually tend to hit a wall once their usage scales.

This leaves a lot of less-rewarding work for the data scientists. Other companies do their research from day one and understand how usage data can quickly turn into revenue. They use our software from the beginning, and they scale from there confidently.

Let’s take an extreme example, Reliance Jio, a telecom operator in India, used our software to scale from zero users to 400 million users in four years. All of their services are charged in real-time.

For a perfect execution, you need to get it right from the top. Your shareholders, and your management. You don’t need to go all in from day one. You can start small and once you get to that point, then you need to start measuring the usage to find your pricing metric.

When you have the data, you need to clean it which is even easier for your data scientist. Figure out what works for you that’s valuable, and then simulate it with your metric.

This is a great way to build confidence internally, you can shadow run your billing, show it to your CFO, and lastly, make sure you also test your automation to ensure you are not leaking revenue