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Why Contact Centers Of The Future Need Machine Learning

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Why Contact Centers Of The Future Need Machine Learning


The involvement of machine learning in CRM makes customer grievance handling more streamlined and, above everything else, quicker.

Businesses must leverage the technology to make the experience of dealing with contact centers less tedious for already aggrieved customers.

The success of contact centers is measured on the basis of, more or less, two key performance indicators — the average call handling time (AHT) and customer satisfaction ratings. This means that each call made by a customer not only has to be completed quickly but also with the caller’s grievance resolved for good (preferably with no need for callbacks or escalation). That is a tall task during the best of times but becomes especially hard to accomplish during a difficult phase such as a pandemic. Machine learning and cognitive automation can be useful to resolve such problems and make contact centers faster and more effective in terms of customer grievance handling and query resolution. The implementation of AI in CRM can positively transform contact centers of the future. Here’s how:

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Reduction of Call Handling Time

If there’s one thing customers hate doing when they call a contact center, it is waiting on the line for an extended period. Long waiting times are incredibly frustrating for callers, and several customers may feel that organizations are simply disrespecting their valuable time for the sake of it. To return the favor, customers may cease using the products or services of an organization just because they’ve had to wait a long time before getting to hear a customer service executive’s voice.  According to a study, long waiting times are the reason why Americans consistently incur collective losses of about US$100 billion annually. From a business perspective, that translates into productivity losses of about US$900 per employee for organizations. 

The deployment of voice chatbots and text chatbots helps businesses resolve this problem to a great extent. For example, voice chatbots can immediately engage with customers, cutting the waiting time of a given call right from the onset. Voice chatbots use NLP to “understand” customer problems. In contact centers of the future, such applications will also be able to resolve calls involving simple customer grievances or demands—such as adding a hold bag or correcting a duplication error in one of the travelers’ names in a booked flight reservation. For complex queries, grievances or requirements, the system can simply redirect calls to appropriate Subject Matter Experts (SME) for resolution with minimal delay. This represents a massive upgrade over the same situation playing out in an AI-less environment wherein callers will end up wasting several hours trying to explain their situation to a customer service agent before even getting to speak to an SME.

Improvement of Customer Experience

Several organizations have their contact centers located in offshore regions. Customer service agents in those countries may find it challenging to understand international customers’ accents and other linguistic intricacies during a conversation, making it impossible to have a call completed and query resolved quickly. NLP enables voice chatbots to comprehend what a customer is saying, regardless of their language or accent.

Customers generally find it highly irritating when they’re made to repeat themselves over and over again during a call. The involvement of AI in CRM enables callers to have their queries understood and resolved in double-quick time, thereby raising the overall customer satisfaction index.

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It is safe to say that contact centers of the future can add several layers of effectiveness and speed by including AI in CRM-related communication.



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TECHNOLOGY

How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

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How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning


In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.

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Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

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Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.

Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.

 

No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.



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