Revenue analytics: How to use monetization data to drive value

The more your technology company can predict your business, the more you can predict your revenue. 

Product managers want to know how a new software release is going. The sales teams want to know when upsell opportunities exist. Pricing and packaging teams want to make effective decisions, aligning the price charged with the value perceived by customers. The customer success team wants to be certain that the customer relationship is strong - and ongoing. And the chief finance officer wants to see all of it resulting in reliable recurring revenue.

Usage data can support all of those workflows - and drive the revenue to meet targets. Revenue analytics, built upon robust software usage and monetization data, can provide the actionable insights that drive value for technology companies, including software producers and intelligent device manufacturers.

Data You Need; Data Your Customers Need

In the world of software monetization and pricing, revenue analytics provides value both for a software producer and for its customers. For software producers, particularly as AI functionality is incorporated into products and services, product usage data helps identify appropriate approaches for monetization of the products and services. 

For customers, the more they can understand what they're paying for and the value they're getting, the better. Better yet, customers will have a clear ability to predict how much they're spending. Monetization analytics provides insights into real-time usage data, helping to illustrate how the price charged aligns with the value customers receive. Yet, today, price and value are "totally aligned" for only about ⅓ (36%) of surveyed product leaders, with challenges around understanding customer value drivers, data availability and insights, and uncertainty around pricing AI features the top blockers.

Revenue Analytics Includes …

A robust revenue analytics program provides complete data about software entitlements. This includes a count of the number of entitlements (including fulfillment records and available quantity), device activations, accounts, users, passwords, software downloads, and the number of entities that have been onboarded. 

Real-time analytics based on complete software usage data and a centralized entitlement management system that enables a 360-degree view of customers (across applications, deployments models, and monetization models) allows technology companies to provide answers about essential transitions like:

• Adoption trends: Did my key accounts adopt my product/service? What entitlements are being created? Are my customer end users growing or declining? When are usage spikes happening? What is the distribution of adoption by end customers and channel partners? What does the transaction history look like? What are the trends over the past year or quarter? How has a product adoption performed since launch? What might future adoption look like, based on predictive analytics? 
• Downloads: Did that software update get out as planned? Who actually accessed it? How are we doing per region? 
• Denials: For what products/features are customers reaching capacity limits? What is the distribution of denials? Why are denials happening (feature expiration, feature unavailable for that user, rule rejection, unavailable due to access rules, etc.)?

Use Revenue Analytics to …

The ability to report on revenue analytics means that technology companies can build reports that support a range of business goals across the monetization cycle and the entire quote-to-cash (Q2C) process. With robust software monetization analytics processes in place, conditional notifications can be set, triggering workflows to appropriate teams. For example, these may include information that's essential for product managers and their peers:

• Upsell and cross-sell opportunities: If feature usage exceeds a set threshold of, say, 80%, a workflow can be created (triggered by the data stored in a data warehouse) to initiate an email workflow that notifies relevant stakeholders about the condition having been met. In this case, the sales team can be notified, then use that information for appropriate outreach to customers. This may support an upsell or an evaluation of the monetization models and options available to the customer to best meet their needs. As high demand AI features are used, a usage- or token-based model may be appropriate for the customer who is exceeding the availability of a particular feature or that wants to explore an entire software portfolio, rather than be limited to particular products or features.
• Renewal opportunities: When requests for features are triggering "feature expired" denials, this provides a chance to reach out to those customers about their entitlements and renewals. 

Analytics that Drive Revenue

The ability to predict revenue can certainly be strengthened by a robust revenue analytics program. Companies looking to improve their monetization cycles will use data to understand customer engagement and satisfaction, renewals and retention, pricing, predictability (of revenue and of expenses), and will be able to build and report on KPIs to support their initiatives. Now's the time to adapt processes as needed for revenue challenges in the AI era.