Monetizing Usage Model Must #3: Plan for Scale

Our first two articles covered the importance of piloting a usage model before launch and leading practices for driving a more customer-centric approach to sales and customer success.  Today, we’ll explore how companies can prepare themselves for scale after they have validated their usage-based offer’s product-market fit and growth potential.

Sense and Standardize

Even companies that have been serving the same customers for decades end up gaining a whole new level of intelligence once they introduce a usage model to their mix. As methods for metering and rating telemetry data improve, companies can build more intelligence around what, where, how, how much, and when customers are using a product and its features come to light. Taking usage data and overlaying it with segmentation or firmographic data can inform how to standardize pricing and packaging options inside quoting tools and ecommerce engines. Doing so reduces the time sales teams spend wrangling with their deal desk colleagues while at the same time, opens up new opportunities to create new pricing models based on patterns specific to a particular industry or geography. Cloud software juggernaut Snowflake routinely ingests all of its usage, customer, and contract data to help product, finance, and sales leaders make adjustments to its model and predict how those adjustments might impact its forecast. 

But where to start? In most cases, companies should begin their scaling efforts with regions or customer segments where they had early success during the pilot, using data to gradually expand while continuously refining the approach. 

Automate the Hard Stuff

Scaling usage models is incredibly tricky because it requires new capabilities to ingest, clean, and rate a tremendous amount of data that outweigh other revenue model types. Companies with technical expertise may opt to build their own metering (measuring how much usage was incurred) and rating (calculating the dollar value of the usage) engines, but often at the cost of taking resources away from the core business as these engines require a full team to maintain the system as the business changes and grows. Without supporting technology in place, mediating usage data, which involves the aggregation, deduplication, and transformation of data so that it can be rated, this work would fall to IT teams who aren’t able to run these processes as quickly or accurately. 

Preparing for Organizational Change 

Beyond having the right technology, a company that is new to usage also has to work hard to make sure that there is alignment at the functional leadership level. This means redefining roles, adjusting compensation models, and investing in training programs to equip teams with the necessary skills. Sales need their account executives to be enabled around usage use cases and how to help customers navigate new per unit, volume, and tiered commitment levels. Finance needs new ways to forecast and recognize revenue based on historical usage metering and rating data. Product teams need telemetry and voice of the customer data to understand usage patterns, fine tune charge models and determine which feature to build out next. While usage scaling initiatives aren’t on par with full on business model transformations, a healthy dose of coordination and communication is needed to improve the likelihood of success. 

Conclusion

As companies embark on the journey of scaling their usage-based models, success hinges on strategic preparation. From leveraging data intelligence to standardizing offerings and investing in the right infrastructure, organizations must align both their technology and teams to meet the demands of a usage-based approach. Scaling isn’t just about increasing capacity—it’s about ensuring the entire company is ready to support continuous growth, with agility and coordination. By taking deliberate steps to implement new processes and technologies, businesses can unlock the full potential of their usage models and drive long-term success.