What call centers can learn by understanding their data

“Top performing callers were rewarded with – and closed more frequently – the best leads, while newer and lower performing callers were given less qualified leads to practice on.” 

Alex Azoury, founder of CEO at Home Grounds.

In the past years, Data and analytics solutions have helped call centers to understand the here-and-now situation. By integrating advanced data science analytics, call centers can now predict what will happen in the future. 

Understanding data can help call centers gain better customer satisfaction, higher first call solutions (FCR), and improve sales. Companies with already applied data science solutions have reduced customer handle time by up to 40%, cut employee cost by 5$ million, and lift the conversation rate on sales calls to almost +50%. At the same time, increased customer satisfaction and employee engagement. [1]

Even though the idea about data science is old, is the way we can use data generated analysis to increase insight about your business relatively new. The analytic tools help call centers provide more comprehensive support, improve customer experience, and boost employee productivity and efficiency. 

A report from Business reports and analytics shows that 86% of all business leaders agreed that data and analysis play a big and important role in sales changes. But the most interesting point in the report is that 39% of the leaders only use a limited amount of data, and the data they use is easy to access, like revenue figures, sales figures, and feedback from employees.

The report indicates that only 2% of every sale gets a second look, and therefore 98% of valuable sales knowledge is getting ignore [2]

With data science, is it now possible to gain insight about call prospects, coach your employees to do better, or analyze how you can give a customer a better experience when they call. It is now possible to understand what your customers want when they want it – and maybe more importantly, you can know what they don’t want. 

Improve customer satisfaction

If you want your customer to be loyal to your brand, product, or service, you must keep them satisfied. Keeping existing customers is 10 times more profitable than establish new relations [3]. That is why almost every company rank customer satisfaction as a top priority. 

The main goal of using data science in a call center is to retain customers, grow revenue, and work more efficiently. The smartest way to do that is by identifying customers’ pain points and predicting customer’s future needs; Why are they calling, what product is the right match for them, or which result is the most valuable for the customer? 

Increase First Call Solutions (FCS )

An analysis shows a 15% drop in customer satisfaction every time a customer has to call more than once to have a problem or issue solved [4]. Call center leaders can use data science to track a specific employee or group’s recall amount.

The analysis highlights the missing information that resulted in the recall. The information gives the leaders actionable insight to coach the employee or the group to give more precise help. 

Personalize employee coaching

With data science, call center leaders can predict up to 100% of the individual employee’s customer evaluation scorer. 

That means the leaders can indicate when and why an employee doesn’t get a high evaluation scorer. Based on that knowledge, leaders can focus their time on the right employee and easily plan an individual coaching plan for the areas where they need improvements.

The analysis also indicates which employee gets the best results in a specific case. 

This insight helps the leader discover tactics that they can use to train other employees, recognize their top performers, and focus on keeping them motivated and planning their work to interact with the customer segment they are most likely to sell to.  

Data science can analyze conversation tone, hold time of the customer or employee, or if either one is taking over the conversation, how often and what there is being said. After each call, the tool gives the employee feedback and statics about the call there can be used to improve for the next call.  

 

Customer sentiment

Understand what your customer wants when they call and how they feel. Data science makes it possible to tag a call with a positive, negative, or neutral sentiment by analyzing each conversation element; pauses, intensity, words, conversation length, etc.

Understanding customer sentiment also includes identifying patterns for a customer who is about to churn. Analyzing Inbound calls can provide information on how to approach a specific customer according to their behavior.

The data science tool runs together with the call center workflow-solutions to correlate sentiment data with other data as call duration, hold time, silence, NPS, evaluator scores, etc. By finding patterns in the customer sentiment in real-time, call centers can quickly make the changes that will positively impact the customer’s engagement. The tools help the employee predict how to give the customer the best service possible.

By segmenting each call by negative scores by employee, team, or group, the leaders and executers can proactively coach the employee or group. The segmentation identifies what the paint point is for the customer and therefore is easy for leaders to improve the specific area for the group or the employee. 

Predict net promotor scorer (NPS)

Just as data science can predict how a customer will evaluate an employee, it can also predict the NPS. Based on the previous dataset, it is possible to find patterns that lead to a bad or good NPS.

By predicting the NPS, the executives can identify why some customers will not recommend the company to others. Therefore, the leaders can handle the elements there causes the bad NPS. 

Lead generating and improving Sales

Know what your customer wants, when they want it and how to approach them over the phone. By analyzing existing phone call recordings, mail correspondences, customer behavior, you can predict which customer will buy what product at what time. By analyzing previous datasets, data science can forecast when weather conditions or peak times to envisage the best moment at the best day to carry out a specific activity.

For a call center, that means you can segment your prospect, so you are focusing on calling the prospect there most likely will end up buying your product.

The predictions help you target your calls to the right customers and personalize up-sells and what kind of offer you are selling to each segment.

The main reason why many call centers aren’t working with the data science analyzes are simple: they don’t have the right foundation or a data science department.

Borbaki can help you get data science benefits without creating a whole new department. We will act as your data science department and help you use your data’s value.  

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