The trend towards Usage-Based Pricing (UBP) is gaining momentum among SaaS companies. According to OpenView Venture Partner’s 2023 State of Usage-Based Pricing Report, 61% of providers were actively making the shift last year. UBP offers appealing flexibility and exact billing for customers, charging them only for what they use. This billing transparency is a major draw for consumers and companies alike.

However, with revenue directly tied to service usage, businesses using this model cannot afford to skimp on their customer support. Weak support can stall revenue growth by preventing customers from resolving issues and using their products. To address this risk, AI, and specifically post-sales CX observability, is becoming a must-have for support teams.

AI’s Proactive, not Reactive, Approach

Predictive AI technologies like natural language processing (NLP) are leveling up support centers and how support teams handle queries and customer issues through the language and (literal) voice of their customers. Monitoring all support data across channels in real-time, predictive AI can anticipate customer needs they’re likely to encounter throughout the customer journey. This foresight allows teams to get ahead of issues and potential escalations, promoting an experience with fewer interruptions and higher satisfaction.

Understanding Customer Sentiments

In a UBP model, accurately understanding and addressing customer issues and concerns is crucial as it directly impacts engagement with a business’ service. This engagement is key because it translates directly into revenue flow, making the quality and speed of customer interactions a fundamental driver of financial performance. AI can analyze vast amounts of customer interaction data to detect mood and satisfaction. By identifying and responding to these emotional cues—whether it’s frustration from a laggy interface or satisfaction from a quick resolution—support teams can tailor their immediate interactions to meet individual needs more appropriately. Quick resolution of negative experiences and reinforcement of positive ones not only prevent churn but can also encourage customers to explore and adopt additional features and upgrades, which are critical to revenue growth in a usage-based pricing model.

Augmenting Human Ability

AI is frequently lauded for its process automation abilities: features like automated case routing and intelligent response suggestions let service agents focus on solving more complex and sensitive cases, where real human empathy and understanding are irreplaceable. However, AI also gives agents highly detailed customer insights by summarizing previous support data while maintaining context and factoring in a customer’s history and prior cases. This “human-like” intuitive ability at scale not only speeds up time to resolution and helps customers return to using the services as quickly as possible, but it enables personalized experiences that improve long-term customer satisfaction and loyalty.

Scaling with Demand

As the customer base grows, so will the number of tickets that flow into the support center. In a consumption-based model, businesses must be able to scale and manage that influx. With AI observability, companies can manage an increasing volume of requests without a linear increase in support resources. This ability to scale helps guarantee that customer support stays consistent and responsive, regardless of any spikes in usage, which is a known pain point in traditional support setups.

Continuous Improvement

Both predictive and generative AI systems are designed to constantly learn as they ingest new data from interactions, improving their accuracy and helpfulness over time. The continuous learning and analysis of experience trends provide businesses with key insights into usage patterns and customer behaviors, informing product development and room for improvement.

By integrating AI into support, SaaS companies can go beyond meeting the basic demands of a UBP model – they can turn their support centers into powerful hubs that drive innovation and customer loyalty while protecting revenue growth.