AI is playing an increasingly significant role in customer service.
It’s assisting live agents, surfacing new insights, and – of course – automating interactions.
By 2029, Gartner predicts that AI agents will resolve 80 percent of customer issues.
Contact center providers are pushing this trend forward, launching their own AI agents and beckoning an era of human-AI hybrid workforces.
That’s a tantalizing prospect. However, many contact centers are struggling to turn vision into reality.
Siloed data, integration challenges, and limited IT resources have them stuck in the mud.
Yet, there’s another roadblock that often goes undiscussed: per-seat pricing.
The Limitations of Seat-Based Pricing
Seat-based pricing, either concurrent or named, is the go-to model for most CCaaS providers.
As the name suggests, it ties a contact center’s tech costs to their agent seat count.
It’s a simple approach. However, seat-based pricing has become increasingly misaligned with the realities of modern customer service operations.
There are several reasons for this:
- Inflexibility: With per-seat pricing, companies must predict their exact staffing needs, often leading to over-provisioning or scrambling to add licenses during peak periods. That problem will only worsen as increased contact automation makes forecasting more tricky.
- Penalizes Efficiency: As organizations strive to improve self-service options and reduce the need for human agents, they're essentially penalized by having to maintain the same number of seats.
- Discourages Innovation: The focus on human agents discourages investment in AI and automation technologies that could improve customer experience and operational efficiency.
- Unpredictable Costs: Seasonal fluctuations and unexpected spikes in demand can lead to sudden cost increases or temporary lapses in service quality.
Given all these limitations, contact center providers have tried to find alternatives.
For example, some use subscription-based models. However, they’re also fixed in nature, meaning that contact centers can again pay for more than they use.
Some vendors also have different pricing models for AI functionality vs. core contact center functionality, using tokens or service credits on top of subscriptions. This makes forecasting even more difficult.
Outcome-based is another option. It ensures contact centers only pay for AI when it drives “success”. Yet, defining what that “success” looks like is a minefield.
There are also freemium models. These give away some beginner AI capabilities for free and others as part of a tiered pricing system. However, these tiers may not align with the contact center's needs.
Additionally, outcome-based and freemium models overlay seat-based pricing for live agents. As such, the limitations listed above are still present; they have just softened.
Thankfully, there’s a better way: consumption-based pricing.
The Rise of Consumption-Based Pricing
Consumption-based pricing aligns costs directly with usage. That brings several advantages.