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At Aloware, we’ve always believed in empowering businesses with cutting-edge communication tools. Our AI Agents, capable of handling both voice and text interactions, have been instrumental in streamlining customer engagement. However, as we engaged with our clients, a recurring theme emerged: the need for a more predictable and results-driven pricing model.
The Challenge with Traditional Pricing Models
One of our clients in the equipment finance sector highlighted a common concern. They had optimized their processes to bring the cost of acquiring a signed application down to $88. When considering the integration of our AI Agents, the question arose: How does this fit into our existing cost structure?
Traditional usage-based pricing—charging per text or per minute—introduced unpredictability. It’s challenging to forecast how many interactions are needed to convert a lead, making budgeting a complex task.
Introducing Results-as-a-Service (RaaS)
This led us to rethink our approach. Instead of billing based on usage, why not align our pricing with the results we deliver? Enter Results-as-a-Service (RaaS).
Under this model, clients provide us with their leads—it’s important to note that we don’t buy or sell data. Our AI Agents then engage with these leads, employing voice calls and text messages, to guide them through the application process. We handle the entire interaction, from initial contact to securing a signed application.
If our AI successfully converts a lead into a signed application, we charge the client $88, matching their existing cost per acquisition. This model ensures clients only pay for tangible results, eliminating the uncertainties of traditional pricing.
De-Risking Client Engagements
By adopting RaaS, we assume the operational risks. If our technology doesn’t deliver, the client doesn’t pay. This approach not only builds trust but also emphasizes our commitment to delivering value.
As one client aptly put it, “You don’t want to be in the AI bot business. That’s our business. You want to be in the signed application business.”
Expanding the Model: Lead Qualification
Recognizing that not all leads are ready to convert immediately, we’ve extended our RaaS model to include lead qualification. Our AI Agents can sift through large volumes of leads, identifying those who are genuinely interested and ready to engage. By filtering out unqualified leads, we ensure that sales teams focus their efforts where it matters most.
Clients can opt to pay based on the number of pre-qualified leads we deliver, aligning costs directly with sales opportunities.
Navigating Data Quality Challenges
While RaaS offers numerous advantages, it’s not without challenges. The quality of input data plays a crucial role in the success of our AI Agents. We’re actively working on solutions to assess and enhance data quality, ensuring optimal outcomes for our clients.
Conclusion
Transitioning from a Software-as-a-Service (SaaS) to a Results-as-a-Service (RaaS) model represents a significant shift in how we deliver value. By aligning our success with that of our clients, we foster stronger partnerships and drive better outcomes.
At Aloware, we’re excited about this evolution and remain committed to helping our clients achieve their goals through innovative, results-driven solutions.
