Insurance IVR Case Study

Callers Get Faster Answers At Lower Cost, Thanks To Streamlined Ivr

CUSTOMER:A major US-based insurance provider.

THE SITUATION: An expensive “natural-language” IVR had been neglected and was unable to get callers the help they needed. Customers were left feeling frustrated and confused, an experience that was undermining the insurer’s “helpful and people-friendly” brand image.

WHAT WATERFIELD TECH DELIVERED:A simpler IVR that uses customer data and friendly directed dialogue to get callers to the right agent, fast. Ongoing monitoring and maintenance prevents “IVR rot” from setting in again.

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The Challenge

An Expensive Natural- Language Ivr Was Not Getting Callers The Help They Needed

An expensive but neglected natural-language IVR was damaging a major insurer’s business and undermining its brand image. Waterfield Tech delivered a simpler, smarter and more customer-centric IVR that saw customer satisfaction levels jump by 8 percentage points in just 3 months.

Just because a technology is advanced, it doesn’t mean it’s the best choice for your business. That was the lesson a major US property & casualty (P&C) insurer learned the hard way. Its investment in a sophisticated natural- language Interactive Voice Response (IVR) system ended up costing the company in abandoned calls, missed opportunities, and poor customer satisfaction.

The IVR operated on a natural language recognition basis, letting callers explain in their own words what they were calling about.

That’s a great idea in theory – but only if the system has been taught to recognize and understand the words that callers use.

In this case, the system had been given a vocabulary when it was implemented and hadn’t been taught anything new since. So as the terminology used by customers started to change, the IVR didn’t have a hope of keeping up. It simply didn’t understand what callers were talking about.

The situation came to a head when the insurer started receiving calls in response to a TV advertising campaign.

Callers naturally repeated words they’d seen in the campaign, but the IVR didn’t know those words, so it couldn’t route callers to the appropriate agent. Two major investments – the IVR and the ad campaign – had effectively cancelled each other out.

THE SEARCH FOR A MORE EFFECTIVE IVR SOLUTION

Something had to be done, so the insurer evaluated its options. It could get the original vendor back in to overhaul the existing IVR. Or it could have a look at what else was out there in the marketplace. In the end, it did both, inviting the original vendor to present a solution along with others, including Waterfield Tech.

One thing became apparent to the insurer during that pitch process: most of the vendors (including the incumbent) intended to sell the capabilities of their IVR software but were less focused on what the insurer’s customers needed from the system.

At Waterfield Tech, we take the opposite view. We talked to the insurer about the primacy of the customer experience, and how any solution must be designed with customers’ needs in mind. The actual technology – whether it’s based on natural language speech recognition, directed dialogue speech or touchtone (also known as dual tone multi-frequency, or DTMF) – should never be the primary consideration.

The insurer realized that Waterfield Tech’s customer – first approach chimed perfectly with its own brand positioning. Waterfield Tech was awarded the job of designing a new IVR and working alongside the customer to deliver it.

UNDERSTANDING WHAT CUSTOMERS NEED – AND HOW THEY ASK FOR IT

Our first job was to understand what callers were calling about, and what kind of language they were using when they picked up the phone. We conducted a usability evaluation of the current experience. In this discovery exercise, we spent time with the insurer’s agents and listened to incoming calls to determine customers’ main reasons for calling. In doing so, we confirmed some key factors that had made the existing IVR so unsuitable for the organization.

Natural language IVRs are fantastic for organizations where callers have many possible reasons for calling, and where a menu of options would be too long and impractical. But because they give callers free rein, they’re expensive to design and develop, and they must be maintained to ensure they keep pace with caller vocabulary and vocabulary tuning wasn’t the only issue making life difficult for this insurer’s customers.

Callers also ended up in the wrong place for other reasons. For example, they were often uncertain about how to respond to the system prompts – sometimes repeating an example phrase presented by the system, even if it didn’t accurately describe their reason for calling.

These issues meant that callers had to spend longer than necessary in the IVR; they got routed to the wrong agent; or they abandoned the call without getting the help they needed.
Our discovery exercise revealed a crucial fact: there were very few reasons for someone to call. Mostly, people wanted to file a claim, get a quote, make a payment, or inquire why their premium had increased. Beyond that, we identified two more frequent reasons for calling: to check on the status of a claim or (for auto insurance) to add a driver to their policy.

Our discovery exercise revealed a crucial fact: there were very few reasons for someone to call. Mostly, people wanted to file a claim, get a quote, make a payment, or inquire why their premium had increased. Beyond that, we identified two more frequent reasons for calling: to check on the status of a claim or (for auto insurance) to add a driver to their policy.

The Solution

A Smart, Directed, Customer-friendly Ivr

Based on that insight, we proposed a directed dialogue system to replace the natural-language IVR. It would be much easier to maintain, and would get callers the right help, fast.

With our system, callers would be presented with a manageable set of options, delivered as prompts recorded in a friendly voice, and using the kind of conversational language that callers themselves naturally use. This way, calling the IVR would still feel like a conversation, but one where the IVR understands what the caller is saying, and responds swiftly and appropriately.

EARLY TESTING AND FEEDBACK WERE CRITICAL TO THE DESIGN PROCESS

These issues meant that callers had to spend longer than necessary in the IVR; they got routed to the wrong agent; or they abandoned the call without getting the help they needed.

Our discovery exercise revealed a crucial fact: there were very few reasons for someone to call. Mostly, people wanted to file a claim, get a quote, make a payment, or inquire why their premium had increased. Beyond that, we identified two more frequent reasons for calling: to check on the status of a claim or (for auto insurance) to add a driver to their policy

The Results

Using Data To Improve The Caller Experience

With that in mind, we saw an opportunity to make smart use of basic data to improve the caller experience. Using Automatic Number Identification (ANI) to identify where a caller was calling from, the IVR could route them directly to a local agent if their responses to the voice prompts suggested this would be appropriate. We estimated this could reduce calls to the main contact center by 6%.

Using the same ANI data, the IVR could also attempt to match the caller’s number against a policy number, and check for any recent activity on that policy. That would enable the insurer to include personalized prompts like “I see you recently filed a claim – is that what you’re calling about?”

KEY LESSONS LEARNED

Companies looking to improve or upgrade their IVR experience can learn the following key lessons:

Get Started with a Free IVR Assessment

We’ll put the IVR through its paces and report back to you on:

  • Your IVR “persona”: does it accurately reflect your brand?
  • Your IVR dialog design: does it get callers what they need, fast?
  • Your on-hold experience: is it helpful, or excruciating?
  • Cross-channel integrations: can callers’ complete inquiries in other channels?
  • Areas that require immediate and longer-term attention
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