"The speech analytics market is expected to reach a value of at least $4.24 billion by 2026 with a compounded annual growth rate of around 22.14% during from 2021-2026" - CX Today
Does your call center leverage the power of analytics? If the answer is no, you're missing out on a lot of valuable insights that can take your business to newer levels.
There are many tools in the market that can extract valuable data from customer interactions. And this data is used to enhance agent performance, and create an optimized customer experience delivery strategy.
Let’s dive into understanding one such tool, call center analytics.
What is Call Center Analytics?
Call center analytics refers to collecting and analyzing data to provide actionable insights. These insights can be leveraged to improve customer satisfaction and enhance both agent performance as well as overall call center performance.
There are many call center analytics software that helps you reach your goals. Some of them focus only on specific analytics, while there are a few that provide a variety of analyses.
Before we look at selecting the right analytics software for your call center, let's understand the various use cases of analytics.
How Can Call Center Analytics Software Help You?
Using software to analyze data isn't limited to particular analytics. Multiple kinds of analytical tools have use cases in call centers.
Let's dive right in, to understand the different kinds of analytics and their use cases.
1. Predictive Analytics
Call center predictive analytics is used to predict customer behavior by using historical data as a reference point. This is done by leveraging artificial intelligence (AI) and machine learning (ML) to find patterns in previous data sets.
Predictive analytics can identify how many employees are required on duty during the holiday season. For such a request, data like the number of customer requests during previous holiday seasons, call volumes, and customer purchases will be analyzed to reach a conclusion.
The biggest strength of predictive analytics is that the accuracy of these insights keeps getting better with time as reference data multiplies.
2. Interaction Analytics
Interaction analytics gathers data from agent-customer interactions that happen throughout the customer journey. These analytics gives call centers a complete idea of how customers prefer contacting and being contacted by an organization.
For example, customers prefer a variety of channels when contacting customer support. They don’t want to rely only on calls. In fact, organizations with omnichannel customer engagement strategies retain 89% of their customers.
These insights are collected from all platforms that facilitate customer interaction, including calls, emails, social media, and chatbots.
Interaction analytics can shed light on customer behavior, their relationship with the brand, and their expectations.
When used sufficiently, it can also identify upselling and cross-selling opportunities as well.
3. Speech Analytics
As the name suggests, call center speech analytics can analyze data from customer calls. Speech and voice analytics are often used interchangeably despite working in similar methods to analyze phone calls.
These tools use machine learning and artificial intelligence to identify key phrases used during the conversation.
These tools also analyze audio parameters such as changes in tone, stress, and rhythm of speaking. These post-call insights from speech analytics help agents to understand customers better.
These insights also flag compliance violations and help agents reduce such occurrences in the future.
4. Text Analytics
Text analytics uses natural language processing (NLP) to gain insights from text.
This is easier to implement than voice analytics as there's no need to transcribe anything to text. This works on all text-based communication channels like customer feedback and chats.
Text analytics highlights data like customer details, persistent issues, and customer sentiment analysis.
5. Omnichannel Analytics
"Statistics show that 62% of customers like to interact with brands via multiple channels" - Squaretalk
Customer service has moved beyond phone calls and now is in an omnichannel era.
Any customer that has called your contact center most probably would've used other channels like email, social media, or self-help channels initially. It can be difficult to track all communication channels a customer has used before reaching an agent.
Data from omnichannel analytics can help agents to provide customers with a personalized support experience. These insights give agents the required customer data to provide a seamless experience that isn't dependent on the channel the customer has chosen.
Now that we've seen the different types of call center analytics, let's look at the essential features to have in any call center analytics software.
Results first, payment later.
Essential Features in Analytics Software
While there are many different use cases for analytics in call centers, having any software that covers it isn't enough. Many more essential features are needed in such software.
Let's have a look at them.
1. Integrated Workspaces
Contact centers use multiple software that are separated from each other; adding another one to the mix can take up a lot of time. Most call center software available today has massive integration capabilities.
When investing in new software for your call center, you should ensure that it integrates with all your existing software. This can help you to find all necessary data in a single dashboard.
2. Agent Coaching Capabilities
A major focus of call center analytics is to optimize the agent coaching process. It is essential for all analytical software to have personalized agent coaching that complements the insights on agent performance.
A personalized agent coaching program helps agents work on their key performance indicators and improve performance quickly and efficiently.
Having insights on agent performance without a method to implement them is like Anakin being the best racer on Tatooine, but without a pod to race in.
Any call center software with analytical capabilities should be able to provide a personalized agent coaching schedule. Only then can the insights on each agent's performance be put into use.
3. Customer Behavior Analysis
All the different kinds of analytics we've seen give insights into customer sentiment and behavior. But all of this needs to be compiled into a single dashboard for agents to see and act on.
Apart from agents seeing insights post-call insights, there should be actionable business reports that agents can access and use to improve their performance.
4. Compliance Management
One of the biggest worries of any call center is compliance.
With call center analytics, it has become easier to flag violations and educate agents on updated compliance policies.
Primarily using speech and voice analysis, these tools are trained with different compliance regulations so they can flag any violation immediately.
Now that we've seen all the features of such software, let's look at the best practices to follow while purchasing one.
Call Center Analytics Software Checklist
There are a lot of call center software with detailed analytics capabilities available today. Choosing the right call center analytics tool for your call center requires detailed research and planning.
Let's see how to pick the right call center analytics software for your call center.
1. Define Your Goals
The first thing before considering different analytics tools is to realize what your contact center needs from analytical tools.
This depends on factors like what software your contact center currently uses, how operations are run, and your expectations, to name a few.
For instance, if your contact center already uses an automated call quality monitoring tool integrated with your cloud telephony, you don't need speech and voice analytic capabilities. Interaction and text analytics would be the right fit for you.
Once you have defined your goals and expectations from adopting a call center analysis software, it's time for the next step - research.
2. Do Your Research
Once you've understood your needs from a contact center analytics tool, you need to find out what software meets those expectations.
Apart from just ticking those boxes, it's better to check whether the tool can integrate with your current contact center applications and experience delivery strategy.
For example, here are a few things you could add as part of your checklist:
- Integration with current call center software
- Scalability and ease of deployment with your current call center size
- Agent coaching capabilities
3. Check Compatibility by Using a Trial
Once you find 1 or 2 analytical software, there’s a final step before investing in a new call center software - trying it out!
Most call center software provide a free trial period before you begin a paid subscription. This helps you understand if the tool fits in well with your workflow.
If the software that meets your expectations performs well without much hassles during the trial period, investing in it for your call center is a good option.
Using Call Center Software for Analytics
Call center analytics has become highly relevant in a time where all kinds of data are important. Ranging from speech to predictive analysis, there is software that can help contact centers improve agent performance and customer satisfaction.
Tools like Convin analyze each agent-customer interaction to give actionable insights such as customer sentiment and the agent's performance. These details can be used to improve the call center's performance.
Frequently Asked Questions
1. What is Call Center Analytics?
Analytics in a call center refers to extracting data to provide meaningful insights to improve performance. There is a trove of information in call data that used to go unnoticed before.
2. What are the Different Types of Analytics in a Call Center?
There are different kinds of analytics that can be used in a call center. Some of them include:
- Cross channel analytics to evaluate the effectiveness of different channels
- Speech, voice, and text analytics to assess each agent-customer interaction
- Predictive analytics to predict trends using past data
3. How is Analytics Helpful in a Call Center?
The various kinds of analytics can help call centers to see improvements in customer satisfaction and agent productivity. Using these call center data meaningfully can also help call centers to improve their experience delivery strategy in a huge manner.
4. What is Call Center Speech Analytics?
Such technology uses artificial intelligence to analyze customer interactions and find valuable insights. It can identify keywords, phrases, and sentiments, to track customer satisfaction and identify areas for improvement.
5. What are the Benefits of Speech Analytics in a Call Center?
Speech analytics can have many uses in a call center. Some of them include identifying customer pain points, tracking customer satisfaction, and analyzing cost-efficient methods.
6. What can Speech Analytics do?
Speech analytics uses artificial intelligence and machine learning to identify data points from agent-customer interactions. It can identify recurring patterns, recognize keywords and phrases, and track agent performance.
7. What is Nice CX Speech Analytics?
Nice CX speech analytics is a cloud-based platform that has advanced analytics capabilities. It also integrated with various call center software for a smooth experience.