Jill was the one who initially championed the idea shortly after taking on the CEO job. "We need to align our pricing to our customer's success."
She had read about other SaaS companies successfully deploying usage-based pricing (UBP) and felt her company was a good fit for the model. It was a big bet, but Jill believed in the product and knew the more customers used it, the more value they would get and the more they would pay.
Jill was right, and the gamble paid off. UBP drove organic revenue growth from the installed base and pushed the company's overall growth rate and net revenue retention to new heights. Customers loved paying only for what they used. The board was pleased.
But a few months later cracks started to show. "I have three of our best customers yelling at me," said Terry, who runs customer success. "Their bills almost doubled from last month, and they want to know why. And frankly, so do I.” John the CFO chimed in, "I’ve got a whole team tied up dealing with the billing operations for this, they are doing their best to pull it all together but it’s a manual process - it all ends up in spreadsheets. When I double-checked the bills they are ‘mostly’ right, but I found errors on those spreadsheets, so we will need to re-issue the bills.” The VP of Sales added, "I can't even see any of the usage data in Salesforce; my folks are in the dark. "If Jill does not fix this ASAP, she will need to shut down the new pricing scheme and book flights for a "customer apology tour."
Jill's problem is not with the company’s CRM, CDP, CSM or billing systems - they all function fine; but they lack clean, granular, and timely usage and spend data. Having the right data infrastructure in place to properly maintain and distribute usage and spend data is essential to support UBP scalably, eliminate bill shock and remove errors. It also gives all parts of the organization, from Sales, Customer Success, to Finance a clear window into customer engagement.
Historically, as software shifted from one-time billing to subscription billing, things stayed relatively simple. Limited data concerning the subscription start date, end date, and amount of seats needed passed between systems and most applications covering the Subscription Management and Billing stack handled the nuances of subscriptions.
Usage-based pricing, however, is very different. The volume, diversity, and frequency of pricing vectors makes sharing between specific systems complicated. The more systems you have - the worse this 'many-to-many' problem gets. Companies want their existing CRM, CDP, CSM and Billing ecosystems to work for them by being interoperable and allowing them to freely move their usage and spend data about as a valuable strategic asset. But from a data infrastructure perspective, their stacks are built on shaky, gappy, foundations.
More often than not, the gap has to be filled either by using expensive and distracting in-house solutions to connect systems (often with manual processes) or not-fit-for-purpose adaptations or augmentations of other technologies trying to shoe-horn usage and cost data into other systems.
The need is a new category of data infrastructure tooling built with cloud-native, cloud-scale, developer-first capabilities that elegantly captures and rates customer data while allowing it to be federated across systems and users to make the most of it.
The critical insight is this: although billing may be the initial driver of customer usage tracking, several other systems require usage and spend data to provide insights into customer engagement and profitability.
Existing systems are not designed to capture large amounts of data, correct its errors (duplication, omission, overwrites, formatting), transform and store it, and then distribute it to multiple other systems. They are not designed to apply pricing rules beyond a few simple calculations and cannot handle near real-time pricing and billing updates as requested by customers. Handling and rating usage data requires fundamentally different data models, both to record key dimensions (who, when, where, what) and to orient around the right data objects to support pricing flexibility (accounts, products, pricing, and usage). Subscription data models can’t accommodate this complexity.
Regardless of industry, data type, and workflow, m3ter creates a level playing field for all players by providing the data infrastructure to deploy a standardized data infrastructure for usage-based operations that all of the systems in the wider ecosystem can leverage.
m3ter cleans and transforms the data for consumption by other systems. It also rates the data according to the customer's pricing plan and provides on-demand answers to "How much is my bill so far this month?". m3ter’s designed to be easily integrated with any other systems and is configurable, so only the data that needs to be distributed is shared.
Below are specific examples of how m3ter integrations can serve the entire organization by working in harmony with your existing systems.
Billing systems need aggregated usage and pricing data to generate accurate bills. m3ter can deliver spend data in whatever form the billing system needs, from high-level line items ready to go into invoices, to granular detail. Finance systems that handle invoicing, collections, revenue recognition, tax, and forward integration to downstream systems are typically great at their core function, but they just need to know what the spend is before they can do their work.
m3ter also automatically enables usage-based pricing best practice by empowering product teams to build customer billing portals that allow customers to see usage in near real-time and drill down by department, individual, or API.
CS systems are another essential integration. CS professionals need direct access to the most timely and detailed usage information to answer any customer inquiries and, more importantly, proactively engage with customers before any "surprise bills" or any potential churn events. Likely already receiving some usage data, CS systems benefit tremendously from the additional insights and predictive capabilities made possible with more frequent and granular customer usage data.
Sales management and marketing applications need access to timely usage data to optimize cross-selling and upselling opportunities. For example, as customers reach specific usage thresholds, individualized offers are triggered, or for larger accounts, account managers are notified and provided with usage reporting to better understand the customer’s engagement patterns before initiating contact. m3ter can even automatically populate contract pricing and terms. Additionally, for businesses engaging in direct outreach to prospects not currently using their system, usage trends and patterns of current customers can help build playbooks and tailor proposals for opportunities of similar size and use cases.
Adopting UBP does not require a “rip and replace” of any existing CRM, CPQ, CSM and Billing systems, but it does require careful management of the usage and spend data. A failure to recognize the complexity and need for federated access to the data will lead to acute operational problems with a UBP roll-out itself and will significantly sub-optimize the advantages the data can deliver across the organization.
Had Jill's UBP roll-out leveraged the m3ter platform and utilized simple integrations into her Billing & Finance, Customer Success, and Sales & Marketing systems, not only would the deployment have been easier, she would have happier customers and upgraded capabilities across sales, marketing, customer success, and customer profitability.
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