Verint Speech and Text Analytics

Score8.4 out of 10

113 Reviews and Ratings

What is Verint Speech and Text Analytics?

Verint Speech Analytics is a tool used to transcribe and analyze millions of calls to discover customer insights and improve contact center performance in the cloud.

Categories & Use Cases

#1 most frequent

Finance and Insurance

42.9%

42 installations of 98

“I primarily use Verint Speech and Text Analytics to identify patterns in contact trends to see if there are shifts driven by changes in our processes or initiatives that have been implemented. Verint Speech and Text Analytics assists in pinpointing the volumes and the sentiment around the efforts.”
— Analytics Manager, Allstate Insurance (5001-10,000 employees)

#2 most frequent

Information

9.2%

9 installations of 98

#3 most frequent

Utilities

8.2%

8 installations of 98

Victor Hugo Delgado Morales

Presales manager in Engineering at Victor delgado (11-50 employees employees)

Use Cases and Deployment Scope

In our organizatio we use speech analytics to find resultas for collections, how to improve the way we close agreements, the way how the agents manage the calls, if make compliance questions, if close a deal and the way they managed special customers, also Review the feelings and emotions during specific parts of the calle, also to get statictics like silencie, holds, trasnfers or prohibited actions during the contact with the customer.

Pros

Find root causes
Find emotions
Increase nps
Obtain Quality scopes of the call
Obtain Bad words during the call

Cons

Better reports
Speed of the transcription
How to export the transcriptions

Return on Investment

Emotions
Fast results
Best practices
Easy to use

Usability

Alternatives Considered

Eleveo

Other Software Used

WFM, Adobe Product Analytics

Kelly Josey

Director Contact Center Operations in Customer Service at Pilot Co (10,001+ employees employees)

Use Cases and Deployment Scope

We use Verint Speech and Text Analytics to identify areas of concern and volume increases that help us spot trouble areas within our ecosystem that directly impact our guests and create higher call volume.

Pros

categories
speech "key word" identifiers
Customer and agent interaction

Cons

Easier set up for new topics you want to identify

Return on Investment

Once we could prove the amount of calls coming in to our mobile app team and developers, we were able to get the issue resolved and vastly improve our guest experience.

Usability

Eric Kirchhoff

Manager of Continuous Improvement in Quality Assurance at HealthSpring (5001-10,000 employees employees)

Use Cases and Deployment Scope

We utilize speech to determine call drivers and customer experience across all calls versus just a small subset or random samples. This helps us know where to spend time training and what areas to look at for improvement. In the end, resolving these items quickly and efficiently saves us money and reduces workflow.

Pros

Speech Categorization
Combining speech with call meta data
Dictating customer emotion

Cons

Some times categories go offline without explanation and they have to be re triggered
Root cause be be easier to manage
Setting up flags on specific categories could also be easier

Return on Investment

Saves us money
Saved us customers
Saved us time

Usability

Kim Kirchhoff

Program Manager in Quality Assurance at First American Home Warranty (201-500 employees employees)

Use Cases and Deployment Scope

I am a program manager for our quality and speech analytics program at First American Home Warranty. While we have been using Verint for quality for many years, we recently activated Verint Speech and Text Analytics. I'm in the process of scoping out our speech program that we will begin to develop in the next 4-6 weeks.

Pros

I love the use of the NEAR operator.
I'm a big fan of using transcripts for Speech Analytics vs a phonetics based program.

Cons

In my prior role, I used NICE (Nexidia), and while no platform is perfect, I really enjoyed having the ability to auto-bypass the non-talk time to work more efficiently. More embedded efficiency tools would be amazing.
I find the operator options with Verint Speech and Text Analytics to be a bit limited. I'd love the ability to leverage more complicated operators, like a subset, so I can target specific conversations without exceeding the 30 phrase limitation.
The ability to really customize views would be welcomed. Often I have to piece together the data that I need which can be time consuming.

Usability

Alternatives Considered

NiCE CXone, Gong and CallMiner Eureka

Kristina Kathman

Quality Senior Manager in Quality Assurance at Cigna (10,001+ employees employees)

Use Cases and Deployment Scope

Today we use speech and text analytics to monitor for key themes and trends that help us make actionable and easy decisions on real-time events and disruptions.

Pros

Identifying key phrases that are critical to the patient experience, like compliance statements.
Supporting Genie in helping provide insights that are quick and easy to locate.
Buckets similar calls for easy review/access.

Cons

Transcription of difficult words, like medication names and certain conditions.
Assisting with validation of the intended words and phrases identified.

Return on Investment

It has provided us valuable insights that help us make business decisions quickly.

Usability

NiCE CXone

Clarabridge (discontinued)

Nuance Dragon Speech Recognition

Verint Speech and Text Analytics

What is Verint Speech and Text Analytics?

Categories & Use Cases

Most Frequent Users

Finance and Insurance

Information

Utilities