How to apply data analytics to call centers

The call center is perceived as the nervous system of a company. It can warn of risks and potential threats, gather information about the environment in which the company operates and last but not least – it is the direct link to the most important asset that a company can have – its customers. Like any nervous system, it processes a huge amount of information. If such information is quantified, then we have big data. The application of big data analytics within call centers is the focus of this blog post.

Before we share our understanding of the application of data modeling and analytics for call & contact centers, we want to dispel a common misconception.

Data analytics is expensive for call centers

Some experts are frightened by the huge investments in technology and computing power, by the “stratospheric” wages of the data scientists, and the misty optimization potential. We`ll be crystal clear on this – no, no and no. Access to the enormous computational power required for adequate mathematical modeling is not explicitly related to owned servers on premise. Long live the cloud! It gives us the opportunity to pay just as much as we use eliminating the huge initial investment into powerful machines. Data scientists’ salaries are far from low, but we must not forget that instead of maintaining our own analytical unit with a few people, we can also opt for analytical companies that have the necessary models or are ready to quickly create some. Their professional services could be more affordable than the owned analytical unit’s annual budget. Even though we agree with the conception that the use of analytical services is expensive, we should not forget that if such services are reducing the customer churn to a net worth of millions, this is a reasonable deal at least.

Speech analytics is hot and here is why

Each call to a call center operator contains information that can be machine-readable using Natural Language Processing (NLP) algorithms. Most of the call center conversations are recorded but few are used to retrieve useful insights. Speech analytics automates the process and dramatically reduces the time it takes to transform speech into actual data. Thus, extracting important information from such data pool can happen almost in real time. This is how the basis for proper business decisions is created. We love the story of a well-known payment processing company that used speech analytics to identify the reasons of the customer’s churn. Its operators handle up to 15,000 calls per day, each of which is recorded and transcribed. The company analyzes a chain of calls made by a customer to identify the words and phrases suggesting that the customer can churn. Because of speech analytics, the payment processing company establishes 2,000 calls per day indicating a possible customer drop out. Working proactively with these clients classified within a risk category reduced the churn with 600 accounts on a Q basis, with a value as of $ 1.7 million.

Call center staff analytics

contact center data analytics

The most valuable resource in each call center operations is the people who work in it. Staff analytics application in businesses such as call & contact centers is crucial because its capability to determine how many operators you will need in New Year’s Eve, for example. Drill down the data per individual operator can not only identify the most successful and effective among them all but also determine how they made it. This can be an excellent good practices basis, the application of which leads to more efficiency. Staff analytics can also be useful by identifying the most appropriate motivators for operators to be more effective. Because the fruit basket in the morning may be a nice perk, but it does not necessarily lead to more leads, faster-solving customer problems, or drastically reducing the churn rate.

Customer & journey optimization

The UX expert is the one who can create the most convenient design, but the call center operator is the one who can verify it and give an insight on its improvement. The truth is that the result (or lack of such) from UX’s work goes to the support and retention experts united in the contact center. These professionals can successfully identify a loophole or problem in UX and its logic. What’s more, call center operators talk to thousands of users and know the customer base of every business to the fullest extent. Their personal impressions, as well as big data unified in correlations, can offer the best instant snapshot of an actual buyer’s persona. This is priceless information for marketers, who will be provided with a tool for easy identification of the most effective triggers within a customer`s journey, as well as to target their campaigns even more accurately.

IVR analytics

Interactive voice response (IVR) is the technology that allows a person to communicate with a machine over the phone. Thus, IVR allows users to interact with a company via their phone keypad and/or voice recognition. Often annoying to users in real life, but effective for business and its customers. This technology is subject to analytics, which can be applied in the following couple of areas:

  • Opt-out analytics – How many people trust the IVR service and how much do they insist on talking to an operator? This approach will help increase the effectiveness of the IVR solution.
    Reason of calling – such an analysis will identify the most pressing issues for a company’s customers and will inform whether they have been resolved successfully after being addressed by the supplier’s actions.
  • Misroute analysis – we have already mentioned the customer’s journey design, this type of analysis is directly related to it. Retrieving data about the proportion of users who are lost during the journey and needs to be transferred is a key tool for optimizing it.

In conclusion

Big data can play a key role not only in the IT industry but also in contact and call centers. And that will continue to be so. Big data is the key to higher quality service, better KPIs. Big data can help operators to tell the right things at the right moment of the right user, and this is a skill we do not all have.

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