How Can Analyzing Real-Time Call Center Data Help You Improve Efficiency?

In the late 90s, I used to sit through several meetings on software requirements and quality assurance.

The most important thing that I understood was that software testability is key to the success of the software. It defines how easily, effectively, and efficiently an application can be tested by QA teams.

Let us assume that you are talking about test tools used for certification, and some of the certification test cases failed.

How do you figure out why they have failed?

You will do that by going through test logs. While some modules may have detailed logs, others may not. Different testers work on various modules – some maintain detailed logs, while others only log serious malfunctions.

This makes it difficult for the developers to identify the bug, and this is what is defined as low testability in software.

The solution is to have precise, consistent logs for all modules, whether or not they trigger bugs. This consistency is what will make the software easier to test and, therefore, more testable.

The more testable the software, the more successful testers will be in identifying the bugs and not pushing a buggy product into the market.

What does this anecdote have to do with call center data and improving efficiency?

Just like testability is key to the success of software, the availability of data is key to improving call center efficiency.

Data in Call Centers

Call centers are the face of your organization. Call centers are probably the first line of conversation that customers likely have with a brand. The call center agents are the closest to customer problems and challenges.

Analyzing data from the call centers is key to ensuring the operational success of your customer service function.

It gives you insights into how you can improve your call center performance and what kind of training your call center agents may need.

There are two types of data that you collect and work on. One is the customer data, and the other is the performance data of your operations. The second is straightforward, and we will address it in the latter part of this article.

We will spend some energy in understanding the importance of customer data with some industry examples.

• Inspired by your recent shopping trends
• Sponsored products related to this item
• Frequently bought together
• Customers who viewed this item also viewed
• Customers who watched this also watched
• Recommended movies for you

How often have you seen these suggestions on Amazon and Netflix pages?

Would you agree that you have ended up doing impulse buying or binge-watching based on these suggestions?

Are they random suggestions?

I am afraid not.

They collect your data and run them through their AI, ML, and deep learning engines before these are distilled and shown to you.

Let me give you an example that will make you understand this better.

Assume that I am a fan of Al Pacino, and I type ‘Al Pacino Movies’ in the search. The next time The Godfather movie comes up in my suggested watches, Netflix will show Al Pacino in the thumbnail and not Marlon Brando.

This is how well Netflix uses your data to personalize your offerings.

Do you know that the success of Netflix originals is 93%?

They engineer content creation with the data gathered from the viewing habits of their subscribers. The success of Stranger Things and Bridgerton were predicted to succeed much before they hit the screen.

What Kind of Data Can You Collect About Your Customers in the Call Center?

• What issues are your customers reporting?
• What are the repetitive issues that many customers report?
• Are there any surprises in how your customers are using your product or service as opposed to how you intended them to use it?
• Is there any specific feature or functionality that more customers are asking for you to add to your offerings?
• How often does a customer call you?
• How often have we offered something that the customer asked of us?
• Has pricing ever been an issue in the customer trying to make a decision?
• Do we have access to all the customer interactions across various channels and interfaces?
• Do you measure Customer Satisfaction (CSAT) score, Net Promoter Score (NPS), and Customer Effort Score (CES)?
• Do you keep a tab on Customer Health Score (CHS)?

Except perhaps CSAT, NPS, and CES, the rest of them can be monitored and measured in real-time, allowing you to make data-driven decisions on the fly.

What do we do with all this data?

The irony is that most call centers do not have a process in place to collect this data. The inputs from this data can make or break your organization.

The reason why any business exists is to serve its customers. The happier the customers, the better it will be for the company.

What Real-Time Data Should We Measure on the Performance of Call Center Agents?

Let us look at what we need to measure and how we should address the issues associated with it. 

1. Average Handling Time (AHT)

How long does it take for the customer interaction to get over from start to finish?

A high AHT is a concern, and you have to figure out how to address it. A low AHT is good for business; however, you should understand if the customer issues are resolved.

You should look at a combination of AHT and customer resolution.

2. First Contact Resolution (FCR)

Are we resolving customer issues in their first contact with us? This is a sign of how equipped our agents are to address customer issues, and it is also a mark of maturity in our customer service operations.

Are our agents empowered to do things?

Are our agents equipped with the right infrastructure – knowledge base, access to a single view of all your customer interactions – mainly the tools, processes, and know-how to sort things out when things go south?

3. Service Level

Every call center will have some defined SLAs. Are we adhering to the SLAs?

For instance, let us say that you have an SLA that states that 80% of the calls will be answered within 20 seconds.

This is a great thing to measure. While you will ensure adherence to this SLA, we should also measure the remaining 20% of the calls.

In the remaining 20%, are we delaying by a few seconds, or are we making our customers wait a couple of minutes before answering? If this is the case, then it is a red flag.

While SLAs are only the signposts, they are not the be-all and end-all of your call center performance.

4. Call Abandonment Rate

I read a statistic that said, “9% of calls get ditched before we even get a chance to chat.”

What does this mean?

One-tenth of our customers who want to talk to us don’t even reach us. It is bad for the business.

It frustrates the customers, and most of them quickly leave for your competition, and you would never know.

You should figure out a way to offer more channels or have more agents addressing the needs of your customers and reduce this 9% to near zero.

5. Occupancy Rate

If an agent spends 80% of their workday helping customers, then their occupancy rate is 80%.

In an 8-hour shift, the ideal talk time for an agent should be anywhere between 4.5 to 5 hours.

This will allow them to be super productive while ensuring that the agents don’t burn out.

You have to balance this to meet your business needs.

6. Average Wait Time

It is the amount of time a customer has to wait before they get to talk to an agent.

The shorter the wait time, the better it is.

When they wait, you can provide them the option to request a call back while their queue status is maintained. As soon as their turn in the queue comes, an agent will give them a call and speak to them.

Besides, you can let your customers know their position in the queue and the time it would take for you to address their needs.

This helps customers tremendously as they are not waiting endlessly.

7. Customer Health Score (CHS)

CHS will help you understand how likely your customer is to grow, stay consistent, or churn.

This will help you identify power users, account growth opportunities, and potential churns – allowing you to act on them immediately.  

8. Agent Turnover Rate

What percentage of your agents leave within the first year or within three years of being with you?

It is not about replacing them but to figure a way to make their lives easier and retain them. The turnover rate in the sub-10 % is acceptable, and anything above that will be a red flag.

You have to make it interesting for your agents – provide them opportunities to grow, incentivize them, and equip them with the right tools, processes, and technologies to do their jobs better.

A happier agent means happier customers.

What Do You Do with All This Data You Measure?

The first step is to measure the data, and the next step is to analyze them. This will allow you to make informed decisions for the benefit of your customers and your organization.

You measure data in real time, and your analysis will have to be real-time as well. This includes running voice analytics on all your recorded calls.

Real-time analytics will throw light on the following:

• How many customer issues were resolved on the first call?
• How many resolutions are provided by an agent?
• What percentage of transactional queries are moved to the self-service channel or FAQs?
• How many reported problems are not resolvable and require intervention at a higher level?
• What is the average time taken to resolve queries?
• How easy do the customers find it to interact with your resources?
• How easily do the agents retrieve information across different systems?
• How often do agents misguide or missell to the customers?
• Are there any compliance and regulatory issues?
• How do you maintain customer data privacy?
• Are there any specific training needs identified?

How often have you heard your customers say these?

• Your mobile app doesn’t allow me to process transactions above 2000 bucks
• I couldn’t find the appointment section on your website
• The interest rates for consumer loans aren’t mentioned on your website or mobile app
• I was stuck in your IVR, and I couldn’t understand the next steps
• I am logged into your site, but I can’t find the option to report my complaints
• When does my product warranty expire? It isn’t very clear
• Do I need to fill out that section in the form?

All of these are your real-time customer data, and your analytics engine can make sense of these conversations and provide you with ways and means to improve the customer experience you offer.

Every bit of data you measure will allow you to improve the efficiency of your call center platform.

Data drives organizations.