What is Contact Center Analytics?

With customer experience being the driving factor for business success, it has become essential for them to offer quality experience at each touch point. This includes customer support as well. But to do that the support team requires accurate customer data and insights.

This is where contact center analytics comes into play.

Once you have finished with this content piece, you will learn –

• What is contact center analytics
• The types of contact center analytics
• It’s importance
• The KPIs you should track
• Best practices to follow

Let’s delve into the topic.

What is Contact (or Call) Center Analytics?

It’s the process of collecting, analyzing, and interpreting customer interactions in call or contact centers.

The data collected will include phone calls, email interactions, webchats, and social media interactions. The main aim of this approach is to improve customers altogether. Consequently, optimizing operations and enhancing overall business performance.

What are the Types of Contact Center Analytics?

With a lot of data (types) being collected and analyzed, there is a need for different types of analytics for each data type.

Let’s take a look at them.

#1. Interaction Analytics

There are two key players when it comes to contact or call centers – customers and support agents. Here, the analysis will be focused more on the support agents.

With the average cost per support call ranging from $2.50 to $5, it’s crucial for companies to reduce the number of calls and handle time. This is especially the case if you want to reduce the overall expense and save some.

Interactive analytics analyze data such as hold-up time, agent response time, issue resolving time, and more. In doing so, contact centers can understand whether the agents need more response or training or maybe create a self-service page for most common issues.

The aim of this analytics is to track agents’ performance and help them improve their productivity.

#2. Conversation Analytics

There will be both text and audio data to analyze. For audio, it has to be converted to a text transcript first. The data is then analyzed using the NLP technique. The interpretation can offer you the following customer insights –

• Customer sentiment
• Topics discussed
• Most used keywords
• Intend of the conversation

Follow the link to learn more about conversation analytics.

#3. Predictive Analytics

As the name indicates, this type of analytics predicts customer behavior.

The feature uses a blend of machine learning and artificial intelligence to mine customer data. They analyze the complete historical data to find the type of service disruption, the region, and so on. It will connect the dots between data to make sense of it and find hidden patterns, if any.

Based on the patterns and trends in data, it can predict customer behavior, preferences, and needs. As a result, companies can effectively manage resources and improve customer experience.

#4. Customer Surveys (Experience Analytics)

This is more of a direct approach to collecting valuable feedback from customers. You can send out customer satisfaction surveys to gauge the customer experience. The feedback can offer insights on how to improve the service.

For example, the feedback may shed light on how frustrated customers get when they can get hold support and are told to wait. Another one would be identifying concerns about how inappropriate the support agent was with the customers.

The types of surveys you can use to collect feedback are –

Post-call survey – This is where you can ask how attentive the agent has been, whether the issues were resolved or not, and more.

Interactive Voice Response (IVR) surveys – Surveys specifically designed for contact centers. Try reaching out to customers through the following channels –

• SMS
• Email
• WhatsApp

Here’s something to help you if you are looking for customer satisfaction survey tools.

#5. Customer Journey Analytics

Customers don’t just look up at a product and call contact centers for support. No. There will be solid reasons. So, by integrating customer journey data with others, companies (you) can get a complete picture of the customer experience.

For example, when customer data is analyzed, you find that the customers are usually contacted for support while using a specific feature. By understanding what went wrong (from customer feedback), you can make the necessary changes.

Similarly, customer journey analytics can provide insights into the areas that need improvements, points of friction, and more.

You can also ensure that the customers are routed to the right person for quick resolution. But most importantly, it can help reduce the cost of operating contact centers.

Why is Contact Center Analytics Essential?

There are many reasons why contact center analytics is crucial. A recent McKinsey report was able to quantify the results of businesses that have implemented call center data analysis. The following are some of the key findings.

• Reduce handle time by 40%
• Cuts employee cost up to $5 million
• Increase self-service containment rate by 20%
• Reduce repeat calls by 15%
• Boost conversion rate by 50%

With that being said, let’s have a look at the key importance of call center analytics.

1. Upselling and Cross-selling Opportunities

Historical data is a treasure trove waiting to be found. Through proper analysis of this data, you can identify how a customer has behaved. Like the products searched for, bought, and even shortlisted. Therefore, by using this information, you can further tailor your offering such that it meets customer’s needs.

Let’s take Amazon, for example. You have shortlisted a thriller book in it – the first part of a five-book series. In the past you had made purchases on books. In this case, Amazon can tailor the search, showing you better offers on the complete series.

PS – This is just an example of how you can upsell a product.

2. Train Agents in Areas of Lesser Competence

This is what we have discussed in interactive analytics. With contact center analytics, you can understand and measure the performance of your support agents. This is through identifying the time required for resolving issues, response time, and so on.

3. Extensive Customer Feedback

We have already discussed the different types of contact center analytics and the data it analyzes. That itself is an indicator of how extensive and deep the insights can be. It can fill the gaps in customer insights otherwise retrieved through other methods.

By analyzing these diverse data, it lets us understand not just the ‘WHAT’ but also the ‘WHY’ behind customer feedback. As a result, companies can make informed, data-driven decisions for growth opportunities.

4. Get a Competitive Edge With Top-Tier Support

Issues are something no business can avoid. It’s inevitable. That’s why having an attentive and responsive customer support team is crucial. If you are someone who has gone through platforms like G2 and Capterra, you might have seen examples of such comments.

• “Setting up the tool was a bit hard, but their tech support was responsive and made it easy.”
• “There’s a slight learning curve for the tool, but they compensate for it with their customer support team.”

At the end of the day, even if you are offering a unique product, it’s the support that makes the customers stay. And having this quality might be the last piece in getting the competitive edge.

What are the KPIs for Contact Center Analytics?

Like how roads are for navigating vehicles, KPIs are for meeting end goals. You have to define what your KPIs are and measure them throughout the campaign to keep track of the strategy. You can’t go wherever the wind takes you, no! This is the same when it comes to call center analytics.

The following are some of the key performance indicators you should focus on –

• Average Handle Time –

It’s the average time duration of an interaction with the customer. This includes the time the customer initiates the call (including hold time) to any further tasks that follow the interaction. The less time, the better it is.

• First Call Resolution –

It’s the percentage of calls where the issues were resolved in the first call itself. Here, the more the merrier.

• Average Abandonment Rate –

It’s the percentage of calls abandoned by the customer before connecting with the support agent. A higher percentage would indicate that the customers are dissatisfied with the holding time.

• Agent Retention Rate –

It’s the rate at which the contact center agent remains employed within an organization. A high retention rate indicates that the management practices, good work environment and more.

Other KPIs include –

• Average time to answer
• Average hold time
• Call transfer rate
• Cost per call (CPC)
• Contact center agent phone call etiquette

Apart from the above key performance indicators, the following are

• Customer Satisfaction (CSAT) – Gauge the satisfaction level of your customers. The more satisfied the customers the likelier they will stay with the business.
• Net Promoter Score^SM (NPS^®) – This is to check how likely are will the customer suggest the contact center to others. The score ranges between -100 to 100. Anything positive is good, but a good NPS score differs from industry. Benchmark it to know where you stand among the competitors.
• Customer Effort Score (CES) – This helps you understand the level of effort a customer has to make to contact the call center.

Keep track of these KPIs and stay on track with your contact center analytics software.

Contact Center Analytics Best Practices to Follow

It must be clear to you how effective contact center analytics can be and how it can improve your business success. However, if you are going to use this software – then try following the best practices to make the most out of it.

NOTE – Always use multiple data. This might be obvious to you by now. Therefore, we will be skipping this one.

1. Choose the right tool

With several options available, choosing the right one for you can be a daunting task. But what you need to remember is that the right tool can make your business, and the wrong – well, you know how it is.

While researching, shortlist those tools that offer all the key features essential for analysis. This includes speech and text analytics, omnichannel data collection, and so on. You can also make use of the free trial to see if it’s the right fit for you.

Furthermore, you must also check the quality and accuracy of the data (insights) the tool provides. After all, you are not looking for a tool to provide you with generalized insights. You want contact center analytics software that offers accurate, granular insights.

2. Use Text and Speech Analytics to the Fullest

In most cases, analysis of data is done in silos. The sales team has theirs, the marketing team has theirs, the support team has theirs, and so on. What happens here is that the data is split, and so are the insights you can expect. Not to mention the accuracy of the data.

So, what can you do here? It’s obvious – collect them all and analyze them under one platform. By following such an approach, you can expect more nuanced and accurate insights that you can use to make informed decisions.

3. Focus on the Overall Performance

Yes, it’s good to improve the support team’s efficiency and productivity. But try not to limit it. They interpret the insights to find opportunities for you to improve your (overall) performance.

Find ways to streamline the processes and improve customer satisfaction. And as we discussed earlier, satisfied customers are happy customers, which means better retention. This ultimately leads to improvement in the overall business performance and, thereby, growth.

4. Train staff on Data Usage

You have to ensure the analysts and front-line workers know how to use the insights. The training sections can include how to interpret insights (data) and how to make decisions based on them. This approach can enhance personalized customer interactions and improve customer service quality.

Contact Center Analytics For Enhanced Business Performance

The crucial role played by contact center analytics in enhancing customer service and streamlining operations is undeniable. And with the analytics software by your side, you can get deeper customer insights that will help you –

• Identify hidden patterns and trends
• Understand and predict customer behavior
• Refine the responses

Not to mention the improvement of agent performance. With well-trained and guided agents helmed to tackle customer issues, you can –

• Reduce handle time
• Lower cost per call
• Provide personalized experience to the customer
• Boost overall productivity and efficiency

As we discussed earlier, a great support team can be the one differentiating factor that can make or break your business. So, if you are still on the fence about whether to implement it or not – remember all we have discussed here today. The right call center analytics software and strive towards exceptional customer support.