May 02, 2018

Why Outcome-Based Pricing is More Effective for Digital Chat Agents


Outcome-Based Pricing

Time is money, especially in the world of customer service. The longer an agent spends resolving a customer issue, the greater the cost to the organization, and the more frustrating the experience will be for the customer.

This is why so many companies are implementing self-serve chat solutions – they allow customers to find answers to a number of problems on their own, deflecting a large portion of inquiries away from costly call centers.

But what happens when customers can’t find answers on self-serve channels? They’ll likely get frustrated and track down the company’s 1-800 number to call for assistance – the exact scenario companies are trying to avoid. This is where digital chat agents come in. Digital chat agents combine the skill of high performing human agents with powerful predictive technology to provide a chat experience that meets the expectations of today’s digitally-savvy, time-constrained consumers. Able to see the full context of a customer’s journey, digital chat agents can jump in as needed to provide a quick resolution to problems, without requiring the customer to start over.


Pay for the Metrics that Matter

Many digital chat vendors charge enterprises hourly for their services, regardless of how chat agents are performing. This pricing model puts zero onus on agents to solve customer problems – they can simply pass a problem on to someone else if they find it too difficult to handle. This also means the enterprise, not the vendor, is shouldering the responsibility for performance, good or bad, and will have to micromanage the vendor if they want to hit their monthly metrics.

[24]7.ai Digital Chat Agents are offered on an outcome-based pricing (OBP) structure which means enterprises are paying for results, not technology. OBP models are essentially a pay-for-results agreement between an organization and vendor where the vendor is only paid if and when specific agreed-upon performance targets are met or exceeded.

With OBP structures, both the organization and vendor are focusing on the metrics that really matter – things like resolved chats, improved NPS and CSAT scores. This type of structure also allows enterprise leaders to take a step back from day-to-day operations and stop micromanaging vendors because they’re confident in the results they’ll be getting.  Additionally, because the vendor only gets paid if and when there is a resolved chat, their goals will be very closely aligned with the enterprise and they’ll be motivated to continually meet or exceed performance expectations.

Moving to an outcome-based pricing model typically saves an organization 20-30% and contributes to much better business results.

A Vendor You Can Trust

Choosing a vendor with years of experience eliminates many of the concerns associated with implementing digital chat agents. With [24]7.ai, you’ll benefit from:

  • An industry leader who has conducted more than 40 million digital chat transactions in the last decade
  • More than 7,000 digital chat agents in six locations globally
  • Industry-leading technology that consistently outperforms target metrics in every engagement

Let [24]7.ai design a custom pay-for-performance digital chat agent model aligned to your unique business objectives.  


Cable Provider Realizes the Benefits of OBP Structure

Background: A leading US cable company was interested in leveraging [24]7.ai digital chat agents, but were concerned about how costs compared to competitors who offered similar services. [24]7.ai digital chat agents cost $10/hour, while a competitor charged $7/hour.

Analysis: [24]7.ai requested to review and analyze one month’s worth of chat transcripts to determine the volume of chat interactions happening each hour and how many of these interactions were being resolved.

After review, we found that agents were averaging three chats each hour, but only two of these chat interactions were being resolved; 1 in 3 chats were ending in failure for the customer.

Results: [24]7.ai determined that if agents were being paid the lower $7/hour rate, but only resolving two interactions each hour, the organization was paying $3.50/chat. We proposed an outcome-based pricing structure where [24]7.ai digital chat agents would solve four chats per hour, converting to a cost of $2.50/chat, an obvious savings for the company. Additionally, by providing a guaranteed amount of resolved chats, the organization would enjoy the supplemental benefits gained from increased employee productivity and higher NPS and CSAT scores.

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