Last year, Genesys launched a tokenization model to reimagine contact center pricing.
In doing so, the CCaaS Magic Quadrant leader confronted a critical issue that many industry competitors are struggling to come to terms with: the demise of license-based pricing models.
These models tied a contact center’s tech costs to their agent seat count.
However, as AI promises to reduce contact center headcounts, CCaaS vendors have created a rod for their own backs.
As such, many are starting to rethink their pricing models to safeguard their businesses and ensure fairness for their customers.
That’s leading them to several possible alternatives. Yet, each has its drawbacks.
The Contact Center AI Pricing Dilemma
Consumption-based AI models are a highly touted alternative to seat-based pricing.
After all, they enable pay-per-use, offer freedom to experiment, and have many benefits for a contact center with fluctuating contact volumes.
However, the consumption route requires a flexible financial planning approach that many businesses aren’t set up for.
"People don’t like volatility in their bills," Liz Miller, VP & Principal Analyst at Constellation Research, recently told CX Today.
As AI-driven interactions increase, and seasonal interactions start to scale up, companies are seeing unpredictable price spikes - something the CFO particularly dislikes.
A much more predictable alternative is subscription-based pricing. This model simplifies financial planning and serves contact centers with steady, predictable traffic.
Nevertheless, it’s got downsides. For instance, its fixed nature means that some businesses may pay for much more than they use.
Given these flaws, more innovative AI models have come to the fore.
For starters, there’s outcome-based pricing, which Zendesk has recently experimented with. It’s an extremely attractive option for many customers, as they only pay after achieving success. Yet, defining what “success” looks like – on a customer-by-customer basis – requires a lot of negotiation.
A similar model has the vendor and customer analyze the revenue driven by contact center AI to split the earnings. While that may inspire close end-user relationships, it’s again tricky to define and adds complexity.