50% growth in MRR. ARPU growth from €15 to €21. Churn increase from 2% to just 3.3%. This case is just insane - and it all started with a company that knew their pricing was based on gut feeling, not data.
A Dutch TelCo Disruptor offers a virtual phone system (SIM-less) for mobile services aimed at businesses. They operate in a SaaS model and have over 10,000 clients who, as we’ve learned, consider the product more valuable than the price they are paying for it. Developing detailed buyer personas helped align product pricing with customer needs and value perception, providing a stronger foundation for pricing decisions and targeted marketing.
But let’s not get ahead of ourselves too quickly.
Initially, their product pricing was not optimized to attract potential customers or maximize acquisition of more customers, limiting their ability to grow the customer base and fully capitalize on market opportunities.
The results speak for themselves, but we can’t stress enough how extremely helpful the guidance and support were with the implementation. Despite the initial hesitation, the Valueships team reassured us about the process and made us feel comfortable with the level of changes in pricing.
The client’s decision to ask us for help was motivated by a few factors.
The most significant reason was the realization that the current pricing was far from perfect. It was based more on gut feeling than thorough analysis, and the company knew there was still a lot to improve in terms of prices and monetization. Like many SaaS companies, they initially relied on cost plus pricing - setting prices based on cost plus a fixed margin (cost plus pricing cost). While this approach is simple and commonly used, it often fails to capture customer perceived value or adapt to market dynamics. There was even an internal attempt to make some price adjustments a year before our partnership started, but the client’s team wasn’t satisfied with the results, and neither were the clients.
This is something we see constantly in SaaS pricing optimization engagements - companies know their pricing isn’t right, they’ve tried fixing it internally, but without the right methodology and data, the results fall short. That’s when bringing in an experienced SaaS pricing consultant makes the difference.
So, with the increasing desire to grow and the understanding that it was certainly within their grasp, the client’s CEO asked us for help.
The initial plan was simple. The client's goal was to achieve a 10% MRR (monthly recurring revenue) increase.
Of course, there were some additional expectations in terms of expansion revenue, churn, and customer satisfaction, but the main target was as clear as a cloudless day.
What happened next exceeded everyone's expectations. By a factor of five.
Before we move on, let us explain what’s going to happen here.
In some case studies, it’s completely fair to leave out some of the details and exact steps to make the story more compact and focus on what’s most important. In this one, it wouldn’t be fair to do so.
The final impact of our project is so immense that we feel it’s only fair if we explain exactly what happened there and how those numbers were even possible. We ran a full value-based pricing strategy engagement covering all four phases of our methodology.
As part of our process, we evaluated popular SaaS pricing models, since the choice of pricing model is a core driver of how SaaS companies generate revenue and grow their market share. Strategic adjustments to pricing models can directly influence a company's ability to increase market share and remain competitive in the industry.
The first phase of the project was very extensive. We knew that we needed to get deeper than in most cases due to the unusual nature of the product. We couldn’t take anything as certain.
The standard procedure included gathering and analyzing all the available client data to get a full understanding of client segmentation, churn patterns, MRR waterfall, discounting policy, revenue dependency and concentration, NDR/GDR, and more. Developing detailed buyer personas was essential for understanding different customer profiles and aligning pricing strategy with their specific needs and value perception.
However, in addition to data, we conducted detailed interviews with users, asking them about the key features, issues, and their opinion about the service. We wanted to know what exactly they value in the client’s service and what can possibly make them churn. When discussing the per-user pricing model, we found that the number of more team members directly influenced the pricing structure and adoption rates.
During the process, we discovered issues regarding legacy plans, add-ons, discounts, and more. The SaaS discounting policy alone was a significant revenue leak - something we’ve seen across multiple engagements, from Brand24 to Navifleet. We also noted the importance of avoiding complex pricing structures that could confuse potential customers, especially when analyzing legacy plans and add-ons.
After we finished the first phase, we came up with quite an interesting conclusion. We realized that just by sealing the discounting policy, we could achieve 15% growth in MRR. And it was just one element. We knew that there was still a lot more to improve in terms of aspects like legacy plans and packaging issues. So, it became clear that the potential to increase MRR went far beyond what anyone initially expected.
Competition analysis was a tricky challenge because the client really has only a couple of direct competitors, but indirect competition (big telco providers) in this case matters more than in classical cases. We compared their service to companies like T-Mobile and other mobile giants. While it wasn’t exactly an apples-to-apples comparison, it gave us a much better understanding of what exactly they provide that those companies can’t.
Reviewing competitors' pricing pages also helped us identify opportunities to present the client's value proposition more clearly and design a pricing page that attracts and converts potential customers. This is a critical step in any SaaS pricing strategy — understanding not just who your direct competitors are, but where your true differentiation lies relative to the alternatives your customers actually consider. At this point, we were ready to proceed to pricing research and the final solution.
The third step brings all gathered data into action. In the spirit of our value-based pricing methodology, we aimed to determine the accurate willingness to pay and create entirely new, data-based pricing that works both for our client and its customers. To execute value-based pricing well, we conducted customer pricing research—collecting feedback, analyzing usage data, and assessing willingness to pay across segments. This approach allows companies to charge a higher price point and can promote customer loyalty, but it also requires significant investment in data collection and may exclude some customers due to higher prices. Companies offering unique or highly valuable features or services are better positioned to leverage value-based pricing than those selling commoditized products. While value-based pricing can lead to advantages in sales, elevated price points, and customer loyalty, it is not a guarantee of sales success.
More specifically, during this process, we came up with the solutions, tweaks, and exact numbers for the monetization model, product packaging, customer segmentation, willingness to pay for the overall service, willingness to pay for each individual feature, and more. The new pricing structure introduced different price points to serve various customer segments, including options at a higher price point and even the highest price for customers seeking maximum value. We evaluated the per user model for its scalability and alignment with customer value, especially as more team members were added. New features were incorporated into the product packaging to deliver more value and justify price increases, ensuring our product pricing remained competitive and attractive.
Based on the pricing research, we co-created a new SaaS pricing strategy and operationalization model. We also estimated the impact for existing customers, as grandfathering was not an option. To ensure we were aligned on expectations, we used the Monte Carlo method to simulate different price increase and churn scenarios. Our initial impact calculation predicted up to a 30% increase in revenue after implementing all the changes and accounting for potential churn. When implementing the changes, we communicated the value delivered since the last pricing update to help justify price increases.
Communicating pricing changes effectively is essential to minimize customer churn and maximize monthly recurring revenue (MRR). We recommend providing advanced notice before implementing price increases, framing the increases in terms of the additional value delivered, and offering grandfathering options for existing customers where possible. Customers appreciate transparency and advanced notice, which helps maintain trust and reduce churn, especially since customers often view price increases through the lens of loss aversion.
Regularly reviewing and adjusting pricing based on market changes and customer feedback is essential for SaaS companies. Those that do so see up to 30% higher growth than companies with static pricing models.
As it turned out, we were being conservative. The actual result was nearly double our prediction.
The changes we proposed were quite significant, and there was slight hesitation on the client's side. Their team was afraid of the customer reaction and the possibility of major churn. But we were confident about the method and created a step-by-step strategy on how exactly the client should communicate and upgrade each customer to the new pricing.
On top of that, we conducted an additional analysis that factored in a possibility of a very high churn, and it still made the numbers work in the client's favor. This is how you raise SaaS prices without losing customers — you model even the worst case, confirm the math still works, and then move forward with confidence rather than fear.
But, as we'll learn in the next section, the fear was completely unnecessary.
Now, the madness begins. The growth surpassed even our most optimistic predictions.
The new pricing strategy not only boosted MRR and ARPU, but also helped the client grow their market share and acquire more customers. Effective strategies to increase MRR, such as upselling, cross-selling, and optimizing pricing tiers, were key parts of our approach.
To put this in perspective: a 1.3 percentage point churn increase traded for a 50% MRR increase. That’s the kind of trade every SaaS company should make - and the kind of outcome that’s only possible when pricing decisions are backed by rigorous research, not guesswork.
After the implementation, we also helped them create entirely new pricing for their business expansion in Spain - extending the engagement beyond the Dutch market and into new European territory. As a pricing consultancy operating across Europe, this kind of cross-market work is something we do regularly, and it confirms that a well-built pricing methodology travels across borders.
But let’s leave the Spain story for another day.
+ 50% MRR Growth
Average Revenue Per User (ARPU) grew from 15€ to 21€
Churn rate increased only by a tiny margin: from 2% to 3,3%
Certified expert in price, revenue and margin management in B2B companies and e-commerce. Member of the prestigious Professional Pricing Society. At Valueships, he is responsible for the implementation of consulting projects and taking care of the profitability of clients. Prior to joining Valueships, he worked at McKinsey & Company in the area of pricing and strategy.
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Certified expert in price, revenue and margin management in B2B companies and e-commerce. Member of the prestigious Professional Pricing Society. At Valueships, he is responsible for the implementation of consulting projects and taking care of the profitability of clients. Prior to joining Valueships, he worked at McKinsey & Company in the area of pricing and strategy.
