Exploring How to AI-Enable IVR for the Contact Center: Page 2 of 2

When researching a vendor company that’s new to me, I often try to speak with customers and/or resellers that are implementing the solution. One of these was Jeff Coomans, director of vertical solutions for BluIP, a BroadSoft (now part of Cisco) partner specializing in solutions for the hospitality, healthcare, and enterprise verticals. BluIP deployed an Inference IVA in the hospitality industry.

 

The challenge the hotel chain asked BluIP to solve centered around calls into the reservation system. Because every call answered by a live agent incurs cost, the hotel was looking for a way to eliminate the need for reservation sales agents to respond to non-sales queries yet remain available for sales-related opportunities, all while optimizing the experience for the guest and the agent.

 

BluIP chose to approach this challenge using an Inference IVA that supports conversational speech and NLP. This IVA would interface with guests, responding autonomously to as many non-sales queries as it could while sending sales-related queries directly to live agents. BluIP used the Inference Studio interface to design the IVA, which uses the integrated artificial intelligence/NLP engine from Google’s Dialogflow.

 

As with many projects, there are complicating factors, and this project had several of them. For example, the hotel chain outsources its contact center operations to multiple technology providers. Furthermore, it has different contact centers for each brand in the hotel’s portfolio. In order for this solution to become operational, all the players -- the carriers, the outsourced contact centers, BluIP, and the hotel’s contact center specialists -- had to work together.

 

To equip the IVA with the information needed to respond to non-sales queries, the hotel’s property data, which was stored in a data warehouse, needed to be made accessible to the IVA. This data warehouse was designed for service queries from the hotel’s internal systems, but it hadn’t been built or structured to handle external queries. So, the hotel had to make some adjustments on the data side so that the cloud-based IVA could fetch the right information once the intent of a caller’s query was discovered via the NLP.

 

An additional constraint was that if the entire process of interacting with the IVA was too complicated or took too long, the customer would simply transfer to an agent anyway. To keep the customer engaged, BluIP programmed the IVA to speak to the customer using natural language, and through a sequence of prompts and example phrases, help the customer rapidly identify the right hotel property and the specific information about the property in which the customer was interested.

 

Following a proof of concept, the hotel chain has funded the project and is rolling out the IVAs this year.

 

As the BluIP project leader, Coomans offered up a few lessons learned from this experience:

 

Coomans’ experience has led him to believe that virtual agents are the way of the future. He commented, “It’s hard to beat a well-designed virtual agent that can interact with people 24/7.” His belief is that virtual agents will replace or augment many of the existing contact center agents over the next five to 10 years.

 

Inference and BluIP were a bit coy about IVA pricing. Like live agents, IVA cost is skill set dependent. They told me the best way to think of pricing is to imagine how much a real agent with the necessary skills would cost: The IVA would then cost some fraction of the live agent’s cost. For example, if a human agent cost $28,000 annually and the IVA cost $4,800 per year, there would be some significant savings if a virtual agent was able to replace some live agents. Furthermore, by using IVAs, the live agents could become more productive because they would avoid mundane queries, giving them more time to focus on selling and upselling.

 

As with live agents, an IVA can only be on one call at a time, so organizations would need to use their workforce optimization software to plan accordingly so that enough IVAs were concurrently available to handle the call volume.

 

Inference sells virtual agents to its channel partners for a fixed price based on the skills configured within a particular IVA. The partner then adds markup as it sees fit. There is no charge for the tasks. BluIP charges the end-user customer using two pricing models: One is usage-based, and the other is transaction-based. Usage-based implies that the monthly IVA cost is computed based on how much time the IVA agent is interacting with customers. In transaction-based pricing, a single customer interaction from the start of a call or text session until that session ends is considered a single transaction.

Inference sells through approximately 30 carrier channel partners. Because of its strong relationship with BroadSoft, Cisco’s acquisition of BroadSoft allows the company to also sell through Cisco’s traditional channels.

 

We will have other real examples of AI in the contact center at Enterprise Connect, taking place March 18 to 21 in Orlando, Fla., in the session titled, “What It Really Takes to Put AI to Work in Your Contact Center.” In this session, we’ll discuss chatbots, next best action, and behavioral pairing. I look forward to seeing you there. (Register now, if you haven’t already, using the code NJPOSTS to save $200 off the cost of attendance.)