Businesses lose about $62 billion a year to poor customer experience (CX).
Why? Because they have an incomplete picture of the customer experience and the problems at hand.
The customer experience is immensely important to the success of any business.
A good CX can lead to upwards of 70% better customer loyalty and increase recommendations to friends by 65%.
But how do you improve the customer experience? First, you need data to lead your decision-making.
Data can be gathered through customer surveys at the end of a call or having managers drop in on calls to understand the interactions between customers and service representatives.
However, both methods can be time-consuming and have biased data.
Fortunately, technology has advanced to allow for data collection through speech analytics.
AI-powered techniques, like natural language processing (NLP), can record, transcribe, and analyze conversations to identify root causes and conduct sentiment analysis.
What is Speech Analytics?
Speech analytics is a way for businesses to gather data on customer experiences without requiring a survey and also avoids survey bias.
It helps companies extract unstructured data from interactions with customers and enables them to identify patterns to improve the quality of service to employees and improve training. It collects insights from consumer conversations – online and offline – so you can understand consumer needs and wants.
It can also be used to understand brand positioning in the market and identify consumer preferences or pain points.
Domain-specific AI can extract data from real-time and recorded conversations and automate things like speech recognition, transcription, and keyword searches.
It can also find actionable insights, report on trends in quality assurance and customer behavior, and provide root cause as well as sentiment analysis.
Businesses use to manually run these processes, but it was near impossible to do at scale before these advancements in technology came along.
Before AI-powered speech analytics, businesses were only able to capture about 5% to 10% of calls.
Speech analytics is commonly used at contact and call centers for customer service. It can also be used to report data back on sales calls, transcribe podcasts, etc.
How Does It Work?
Speech analytics mines audio – meaning it recognizes speech and converts it into data – using speech-to-text technology.
Speech-to-text has been available for a while, but including AI is what brings you to speech analytics. This is where the data is structured for valuable insights into customer behavior.
Speech-to-text only allowed for keyword searches, now speech analytics shows sentiment, brand awareness, buying habits, preferences, emotion, etc.
You can search for emotions and unearth interaction trends (quiet times, transfer times, wait times, agent speaking over caller).
Speech analytics has 3 main components:
- Data Processing
Data processing is the first component of speech analytics. The data is gathered from recorded audio, live speech to text audio, or automated speech recognition. The data is then transcribed and prepared for analysis.
During the analysis phase, the data is categorized, keywords and pauses in conversation are identified, and sentiment analysis is conducted.
Insights are provided once the analysis phase has been completed. Insights on call quality, agent performance, compliance monitoring, customer satisfaction, and common customer issues can be provided through the analysis of call conversations.
Why Is It Important?
Using speech analytics in your business in conjunction with surveys allows you to gather more information from calls and customers.
With the focus on data growing, any source of quality data is only becoming more important for businesses to find ways to gather.
The onslaught of data available can be overwhelming and understanding how to analyze it can be even more so.
That’s where speech analytics are important for businesses with call centers. 90% of US consumers prefer to resolve their customer service issues over the phone.
Implementing speech analytics can transform your business into a rich epicenter of insight.
Get the Most out of Speech Analytics
Speech analytics is driving big changes in how businesses approach the customer experience.
It’s providing businesses with more data and insights to be used to drive decision-making with marketing campaigns, paid advertising, business development, and CX training.
The customer experience can drastically improve with an average handle time reduction of 40%.
The data collected and analyzed by speech analytics allows businesses to better understand common customer problems and train employees on how to handle those issues effectively and efficiently.
Not only does it reduce handle time, but it can improve conversion rates up to 50%.
It can gather data from sales calls and show sales teams where they can improve their pitches, objection handling, demo scripts, and overall sales strategy on prospecting calls.
Although speech analytics is traditionally used in call centers, the use cases for speech analytics go beyond just that.
Tracking and analyzing sales calls, the customer experience, and brand recognition are just a few other ways you can get the most out of speech analytics.
Evalueserve Helped this Global Credit Issuer Improve Its Customer Experience Center Using Speech Analytics
A global credit issuer sought assistance from Evalueserve regarding data from its customer experience centers. Evalueserve has had a long-standing relationship with this credit issuer, assisting with survey and data analytics from customer experience (CX) centers.
One CX center was providing results that were starkly behind the results of other centers across the globe. The credit issuer wanted to understand how to better assist customers that were directed to this center and find the root cause of the problem.
The credit issuer realized that surveys have a leading bias due to the pre-written questions and responses available and wanted quality data that showed an overarching story as to why this call center was performing at a lower customer satisfaction rate than others.
Evalueserve stepped in and implemented a speech analytics program that used domain-specific AI to record, analyze, and provide insight into the conversations representatives of the company were having with customers.
The analysis of the CX data showed that calls were being transferred too many times in one conversation and that drastically drove customer satisfaction down. In addition to that finding, Evalueserve was able to dig deeper into the data to discover the number of transfers happening and the reasoning behind each transfer.