A decade and a half ago, Clive Humby, a British mathematician and data scientist, coined the phrase “data is the new oil”, and it’s been used countless times since to underscore the value of data in today’s economy. Gartner’s Peter Sondergaard updated the concept ten years ago by saying, “Information is the oil of the 21st century, and analytics is the combustion engine.”
It’s hard to argue the veracity of these positions. Data has become the great unequalizer in business, as companies that harness the power of their data can quickly separate themselves. One need not look much further than Amazon or Costco to get the message.
We see this playing out in the software industry, where data applied with growing sophistication is being deployed to optimize pricing strategies—creating a disparity in revenue, profitability and customer growth between those that do it well and those that do not.
Three Scenarios Where You Need Pricing Data
As more and more software companies jump on the data bandwagon, some are paralyzed at the specter of identifying, gathering and organizing the right data. They fear it will be difficult or that they may not even possess the right data to generate reliable conclusions. To many, “data” is simply too big and unwieldy a concept and they don’t know where to start.
But sitting on the data sidelines is not an option. Not in a world where a company’s value will be measured as much by the data they have as the products they make and the customers they serve. Let’s point out three common stages that should trigger even reluctant software companies to prioritize their need for a pricing data initiative.
1. The move from on-prem to SaaS:
Although thousands of software companies have moved from a legacy on-premises product to a cloud-based SaaS solution, many have not. Those still contemplating this transition face a critical decision-point concerning how to license their software. This presents an ideal opportunity for data to drive the best decision.
In particular, historical usage data is valuable here. Any data from the company’s on-prem product that can color the picture of what the end customer is doing with the software—and how that changes based on situations, individual roles or customer types—will be instructive. This could include data such as frequency, transaction detail and specific feature utilization, and be augmented by institutional knowledge gleaned from your sales and customer service teams. This is often a missed opportunity, as we’ve seen few companies effectively plumb this data.
2. An existing SaaS platform with a deteriorating pricing model:
It’s not uncommon—even at successful software companies—for pricing models to deteriorate over time. There can be many causes for this: maybe their competitive space is a lot noisier than it used to be, or circumstances like COVID have changed how their solution is used. Regardless, the broken model is leading to sub-optimal performance and needs to evolve.
In this scenario, data can give clear guidance for improvement. Usage data, as in the scenario above, is still needed, but other information will also be available to help. For example, invoice data can reveal customer clusters and gaps, increasing or decreasing utilization, and customer churn detail, while data from CPQ and CRM systems can yield discounting behavior. Again, institutional knowledge can help round out the nuances in the numbers, so engaging sales and customer service teams is also valuable.
3. The new product launch:
A new product launch presents an obvious data challenge: how do you gather data when there is no data to gather?
New companies, or those launching new products, may not have historical customer data, but they do have data in the form of assumptions and projections. The key step in this scenario is to anchor this “data” in a structured monetization model that can illustrate how the assumptions and projections will hold up – a “monetization time-machine” if you will. Done correctly, this will reveal unforeseen gaps (e.g., how would things change in an enterprise deal vs. a standard deal), expose the ripple effects should assumptions not hold and yield a more valid set of qualitative perspectives. The discussions involved in this process—the deep digging and challenging questions—are themselves a great value to the new product team.
Armed with this foundational structure, real customer and sales data—as it comes in post-launch—can be applied to the model. For new ventures, this provides a reliable way to know exactly what is and isn’t working and adjust accordingly.
Real-Time Data Can Dramatically Improve Your Pricing Strategy
Until recently, software investors who wanted to analyze alternative pricing scenarios would rely on variable assumptions and market research to fuel their models. But now, with some profound leaps in technology and pricing-specific data expertise, real-time data feeds are revealing much greater decision clarity.