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Consumer demands are changing. And telecommunication is no exception. Everyone wants a fast and seamless experience without any network issues or immediate resolutions in case of errors. 

The telecom operators are adapting and changing their operational strategies to meet the demands of customers. Many were even quick to embark on a journey to evolve and become digital providers. 

However, the industry also has to face several challenges:

• Operational
• Architectural
• Network
• Customer support

To deal with that, the sector is running both preventive and corrective maintenance models. As per this strategy, if you have the correct information at the right time, you can ensure lower operational costs with better network stability concerning electric systems and business intelligence systems. 

This system aims to determine a strategy that helps with corrective maintenance needs while reducing the chances of failure or decreased performance. 

In this article, we will learn more about this approach and how it is beneficial in shaping the future of the telecom industry. So, let's get started!

Getting The Timing and Planning Right

The predictive maintenance measures take into account historical and real-time data from the Network elements to predict failures in the upcoming days or hours. Thus, acting as a periodic inspection of the equipment and that too in an intuitive way. Hence, it increases equipment's availability time and reduces any chances of any unplanned emergency work.

However, to do that, it demands crisp and solid information platforms and network reading systems. So, how can one place an effective predictive strategy? 

For this, the operators need to collect data from operations and maintenance services. And process it in a way that will help the organizations learn about many parameters like average, mode, standard deviation, probability of recurrences, and so much more. 

Still, wondering what you can find with this process? Let's find out more!

• Real-time analysis: It is the ability to evaluate real-time data.
• Recurrent analysis: The system can learn about repetitive issues and their solutions.
• Pattern recognition: It is the ability to spot data/ patterns linked with specific events. 
• Optimization of operations: It is about creating a foundation that helps to optimize plans and strategies. 

Besides this, one needs to build an analytical engine that will help you work around different probabilities. The following image suggests the main stages of a predictive model and how it works.

Maintenance Strategy Continuum

But how does this model impact the telecom industry?

• Since the equipment stays for a longer time, the quality of services increases, resulting in higher revenue.
• System reliability improves because of higher equipment performance. 
• The chances of emergency repair services reduce since maintenance and predictive analysis helps to work upon diagnosis way before it happens.
• Reduces offline costs or downtime, thus enabling companies to maximize their and their equipment reach.

 Now that you know about the impact let's move to the benefits of Predictive maintenance and analytics. 

Predictive Maintenance and Analytics: Benefits

• Meeting SLAs is very crucial in the telecom industry. This directly lays a foundation for marking reliability and commitment. Did you know that poor customer service results in service costs of more than $70 billion? The key to this is managing the incident reports.  Using MLA algorithms and spotting breach patterns helps the operators determine the most prone situations of SLA violations. With this foresight, telecom organizations can significantly reduce losses, customer complaints. And this often results in building a business reputation. 

• Human error, malfunctions, or natural disasters often result in unplanned downtime. And let's not forget, that is not good news. It can cause a significant impact on customer service or operational efficiency. Did you know that more than 80% of telecom companies lose around $2 million on average if there are 4 hours of downtime? However, with predictive maintenance, you can anticipate problems well before time and maintain, upgrade, or replace the equipment when necessary. This is to improve your efficiency and productivity, which will turn you into reliable customer service or offerings. 

• There won't be bad calls anymore. With this system in place, one can easily customize and schedule decisions. Call center managers will figure out the time when they can expect inbound calls. Thus, enabling them to optimize their staffing and scheduling in a way that they can handle volume fluctuations. Therefore, improving brand reputation. Plus, the same system helps the companies reroute the agents' calls with the required skill and knowledge. 

• Companies face higher customer churn rates as well. While it is unavoidable, one can reduce the number of unhappy customers with predictive maintenance and analytics. The risk of churn rates can easily be predicted with previous interactions, thus reducing the chances of switching to the providers. 

By this, you have an idea that predictive maintenance is shaping the future of the telecom industry. However, many challenges might halt the progress initially. But once everyone gets the hang of this system, it will only improve operational efficiencies, resulting in happy customers.

Isn’t that what every organization looks for? So, if you are also looking to improve your brand’s recognition, it is high time that you transform your business digitally and use the latest innovations for better business success.

Final Takeaways: The Future of Telecom Industry

The predictive maintenance and analytics, along with ML/AI, are helping organizations boost their customer satisfaction rates. It’s pretty clear that integrating this system into a simple operational framework will only benefit the industry moving forward. 

After all, these analytics provide an actionable set of data that allows the companies to improve their overall service in terms of reliable products with extended life. Hence, delighted customers will always follow.

Plus, this is one of the industries that capitalize on the exchange of data. So, to deal with the growing volume of data, one needs advanced analytic tools which can help decipher the data for better predictive maintenance insights. 

One can see that the companies can spot inefficiencies, reducing any unexpected downtime that often results in labor-intensive solutions. Thus, it helps to decline the rate of losses due to inefficient network maintenance and improving customer experiences.