How to use contact center analytics to grow your business

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Want to know why contact center analytics is so important?

Imagine having the superpower to decode customer sentiment or predict what they want! 

Today’s customer-oriented companies rely on advanced contact center data analytics to make the best business decisions. 

How?

Contact centers generate tons of customer-related information every day. By analyzing them through charts and graphs, you can gain meaningful insights into enhancing customer experience.

In this article, we’ll highlight the benefits of contact center analytics for your business. We’ll also highlight four things to keep in mind while using these analytics for contact center management.

This article contains

(click on the link to jump to that topic)

• What is Contact Center Analytics?
• 5 Benefits of Contact Center Analytics
• What are the Types of Contact Center Analytics?
• What Metrics can You Measure for Analytics?
• 4 Important Aspects to Consider While Utilizing Analytics
• What are the Differences Between a Contact Center vs Call Center?

Let’s get started.

What is contact center analytics?

(If you’re confused between contact centers and call centers, you can skip ahead to see their differences.)

The contact center analytics solutions involve examining various contact center metrics such as customer satisfaction, response time, etc. 

These analytics play a significant role in business intelligence software systems as they help discover the trends, impacts, causes, and results of your company’s products or services.

You can use these analytics to manage your quality monitoring (QM) processes, such as call monitoring and analysis.

Some of these analytics can also help you understand the customer journey from the beginning to the end and across different platforms.

5 benefits of contact center analytics

Let’s look at how the analytics solution can benefit your contact center:

1. Improves service quality

Speech analytics, which is the analysis of recorded calls to gather customer information and improve telecommunication, forms a large part of contact center analytics. 

Also known as interaction analytics, it provides a valuable insight into customer interaction, such as frequency of call transfers, unnecessary repeat calls, and other bottlenecks in the system. 

The analytics help understand which factors cause redundant calls. 

Some of these factors include:

• Specific types of customers such as senior citizens, new parents, etc.
• Convoluted IVR systems and processes.
• Different work procedures, etc.

The contact center manager can use this information to streamline operations, policies, etc. This type of optimization can significantly improve customer service levels, business outcomes and also prevent follow-up calls.

2. Identifies training needs

Some data analytics systems use artificial intelligence to understand employee behavior from their speech. 

You can quickly identify the soft skills lacking in a particular employee and modify your training programs accordingly. 

For example — if a particular call center agent seems nervous while addressing complex customer issues, the call center manager will know that they need more in-depth knowledge and training. 

Depending on the agent’s knowledge levels, the managers can decide whether they need hands-on training or a webinar would suffice. This type of workforce optimization will go a long way in enhancing the contact center’s functionality. 

3. Encourages self-improvement

The data analytics tool can generate scorecards that are useful for evaluating agent performance.

It provides actionable insights into improving employee productivity and providing a better experience to the customers.

You can also choose a specific insight such as etiquette, script compliance, etc., and compare an individual’s performance with that of their colleagues (anonymously, if needed.)

4. Provides better insights into customer satisfaction

A majority of businesses still use surveys for customer feedback. 

However, the speech analytics solution is turning out to be a much better method of determining customer satisfaction. 

Team members can use these customer analytics to recognize specific conversations and behaviors that result in successful or unsuccessful customer interaction.

They can pinpoint a particular tone or phrase that makes or breaks the deal for the customers. This insight can be helpful while training new agents during the onboarding process.

5. Provides cross-channel insights

Efficient analytics helps organizations identify which of their contact channels are preferred by their customers.

You can see whether your clients use your call center, social media page, or your chatbot. 

This helps you understand which channels are lagging and need extra focus. 

You can also implement the successful strategies used in preferred contact platforms to other platforms – strengthening the entire contact center system. 

What are the types of contact center analytics?

Advanced contact center analytics provides insights about customer experience and also about agent performance.

Different customer analytics can help you see the big picture. By knowing the details of each one, you’ll be able to figure out the best analytics for your business needs.

Here are the six different types of contact center analytics:

1. Speech analytics

Speech analytics analyzes the recorded calls to gain insight into customer experience and agent performance.

The call center software automatically recognizes and tags emotions. This data primarily focuses on understanding customer grievances from the tone and intonations of their voice. 

These metrics highlight the shortcomings in the current call center scripts and provide actionable insights for creating more efficient ones. 

You can also create a new system for a better customer experience and make your brand stand out from the competition.

2. Text analytics

Social media platforms are getting increasingly popular, not just for individuals but also for businesses and brands. 

Additionally, many businesses also depend on email marketing to reach a wider audience.

Since these platforms rely heavily on text, it has become extremely crucial that businesses employ text analytics to make better sense of the words used in these communication channels.

Text analytics can be used to monitor the messages sent by the contact center agents and the customers. 

The data generated by these advanced analytics help you understand whether your customers are happy with your team’s services or not.

3. Predictive analytics

Predictive analytics is a valuable tool for a contact or call center. 

It uses an in-depth review of various metrics to predict which problems may reoccur so that you can be prepared with their solutions. 

Some metrics measured by predictive analysis include: call volume, call handle time., customer satisfaction, etc.

Using these metrics, you can efficiently address concerns, such as:

• How many additional agents will you require during the sale season?
• What will be the call volume after the launch of your new product?
• How will the revision of your prices affect your customers?

4. Self-service analytics

Various companies are now opting for self-service facilities for specific tasks. 

You can track shipments, update bank details, track your food delivery, etc., all with the help of a self-service tool or chatbot. 

These tools require only a little human intervention after integrating them with the company’s websites or systems. 

An analysis of these chatbots can ensure that there aren’t any technical issues associated with them. It also gives an insight into how customers feel about doing these tasks themselves.

5. Desktop analytics

Desktop analytics captures and monitors all activities on the agent’s computer dashboard. 

If you combine this with real-time call monitoring, you can easily:

• Determine where the frontline or phone agents can improve.
• Improve call center security by tracking agent’s computer activities through screenshots.
• Measure the duration of any call.

Using these actionable analytics, you can ensure that all your systems are functioning efficiently and that the employees are using them well.

You can also use these advanced analytics to identify and remove redundant tasks to prevent agent and customer frustration.

6. Cross channel analytics

Your contact center can have a variety of customers using different platforms. 

Some may prefer a chatbot; a few would tweet about their concerns, while others may opt for a more personal connection through calls. 

A detailed analysis of all these platforms through the analytics software can help you tailor the customer experience accordingly. 

For example, if a customer prefers paying through PayPal, the phone agent can receive real-time call center script updates about this preference. They can then encourage the customer to pay their overdue payment using the same platform. 

These minor customizations can ensure a pleasant customer journey.

What metrics can you measure for analytics?

You can use analytics software to measure almost every metric and Key Performance Indicator (KPI.) 

Let’s take a look at some of them: 

1. Average hold time

The average hold time is the time customers spend on the IVR menu or on hold.

If the average time hold time is high, then your contact center might be understaffed, or that your IVR menu doesn’t redirect the customer efficiently.

2. First-contact resolution

First-contact resolution refers to resolving the customer query or concern in the first call they make. 

A high FCR means that customers don’t require many follow-up calls. This makes your contact center and brand seem efficient – which can consequently boost customer loyalty.

3. Average handle time

The average handle time is the average duration of the customer call. 

It measures the total time of the call, including the initial hold time. It can also track the time taken by agents to answer and resolve questions. 

The shorter the handle time, the faster you can serve your customers.

4. Abandoned call rate

The abandoned call rate reflects the number of calls abandoned by the customers. 

The reasons for abandoning a call may include:

• Prolonged hold time.
• Inadequate staffing in the contact center.
• Improperly trained agents. 
• Poor workflow, etc.

A high abandoned call rate will almost always indicate customer frustration and dissatisfaction. 

5. Average transfer rate

Nobody likes constantly being transferred from one department to the other without any resolution.

The average transfer rate measures the number of calls routed to other agents, the contact center manager, or even different departments. 

Call transfers play a significant role in the customer experience, and hence, are an essential measurable metric.

6. Agent attrition rate

Agent attrition is an important concern for any customer-centric business. There may be various reasons for a high employee turnover rate.  

Some of these include:

• Poor working conditions.
• Improper work distribution.
• Fewer opportunities for career growth. 
• Dissatisfaction regarding salary or increments, or others.

An analysis of these causes can help you identify the areas that result in attrition. 

You can then implement solutions to resolve these issues and ultimately enhance workforce management and increase the agent retention rate.

7. Customer satisfaction

The ultimate goal of a customer-oriented business and contact center is to enhance satisfaction throughout the customer journey.

An analysis of the customer experience will go a long way in helping you make intelligent business decisions.

For example, if you have an omnichannel approach for customer support, you can easily compare the phone support with other channels (such as the average duration of a web chat.) 

You can see which channel is more effective, so you can prompt customers to use that channel more often.

Check out the other call center statistics and metrics that you can track.

4 important aspects to consider while utilizing analytics

Here are a few vital factors that you should consider while implementing these actionable analytics:

1. Prioritize your needs

Instead of getting tons of analytics (even those you don’t need), evaluate what’s really important to you.

You can make use of a prioritization framework to understand your requirements. 

Here are a few questions you can ask yourself:

• What types of insights do you wish to gain? 
• What are your major contact platforms?
• Which metrics do you need to track right now, and which ones would be useful later?

Getting answers to these questions will help you develop a clear and focused analytics strategy so that you can measure the right metrics.

2. Have a single point of access to all data

Even if you have an omnichannel contact system, you need to ensure that the analytics are detailed. This will help you look at the big picture and improve the customer journey.

You can achieve this by analyzing data from multiple channels in a single system so that you can access all customer data from a single point.

Even if a customer uses multiple platforms to resolve their queries, having all the customer data in one place will enable you to give that customer a better service experience.

3. Ensure that your employees are ready to act on the insights

You may have a brilliant analytics system in place.

But how useful will it be if your employees aren’t willing to act on the insights?

Educate your agents about the importance of these data-driven insights and how they can help them and the company enhance the customer experience. 

Once they’re convinced about the value of these analytics, they’ll be more open and accepting of them.

4. Don’t fully replace human interpretation

Certain types of advanced analytics, such as speech analytics, can undoubtedly help analyze data that would have been otherwise difficult to interpret.

However, sometimes a human interpretation can be superior to an AI one. 

For example, a customer sentiment like sarcasm may be challenging to interpret through the software but can be easily understood by the agent.

Don’t completely ignore the human instinct while utilizing analytics.

What are the differences between a contact center vs. call center?

Contact centers are often confused with call centers. 

The main difference between the two is that contact centers comprise multiple in-person or cloud customer contact channels such as social media, email, chatbots, etc.

While call centers only offer customer support through phone calls. 

If you think about it, call centers can be a part of the contact center.

The primary functions of a contact center include:

• Providing customers with efficient technical support.
• Boosting customer service through trained agents. 
• Increasing sales through cold calling.

You can also integrate your contact center operation with your company’s Customer Relationship Management (CRM) software.

However, what call centers and contact centers in common are that they both use Interactive Voice Response (IVR) systems.

An IVR system is an Artificial Intelligence (AI) based digital assistant that operates over the phone through speech prompts.

Contact centers and call centers use IVR to predict the customer intent and rout them to the best-suited agents. A contact center IVR may even sort out customer issues without needing to involve a human agent.

Here are some more tools and trends that are used in a call center.

Wrap up

Intelligent data analytics is definitely a vital addition to a client-centric business plan. These actionable analytics will enrich the customer journey and build your brand’s identity. 

Ultimately, brand loyalty comes from a happy and satisfied customer!

So why not invest in a good analytics tool and see your brand flourish?