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How Machine Learning Can Improve WiFi Performance

WiFi networking is complex. As households adopt more smart home technology and other IoT devices, the dependency on the home network grows. Home networking is comprised of data. The amount of data produced by a network and the devices on it has become so vast that it requires advanced technology to collect and interpret that data into useful information. In order to manage the growing complexity of WiFi, service providers are now looking to machine learning applications that manage data and provide advanced tools that improve WiFi performance. Managed WiFi offers machine learning applications that directly improve subscriber’s WiFi experience while providing service providers with valuable WiFi network insights to improve service offerings.

What is Machine Learning?

Machine learning is the facet of AI that allows computers to learn by themselves and perform operations without the need for human intervention. Machine learning algorithms use statistics to find patterns in large amounts of data. From the data collected from subscriber networks, service providers can use machine learning to find patterns and apply those patterns to get new outcomes. Prior to artificial intelligence and machine learning, it was difficult for data scientists to deliver tangible value to service providers.

Deep learning is the next level of machine learning. Machine learning works through vast amounts of data and interprets that data as it is. Deep learning goes a step further, by translating data and delivering predictive results. Both machine and deep learning are essential tools for the future of WiFi networking.

Machine Learning Applied to WiFi Management

Machine learning and AI technology offer an array of benefits to service providers, but the challenge lies with how service providers adopt these technologies without taking on an expensive digital transformation project. Optim managed WiFi is a solution that both adds value to service offerings and implements advanced technology to help service providers prepare for the future of WiFi. When combined with WiFi management software, machine learning can offer the following tools to service providers and subscribers:

Key Data Insights – Machine learning provides service providers with data-driven insights that inform business decisions. With the vast amounts of data that telecoms get from their customers, they can gain valuable customer insights. This is especially helpful in identifying potential cord cutters through data usage patterns. When a potential cord cutter is identified, the service provider can quickly adapt and offer a tailored service package that can match their needs, reducing customer churn and improving customer loyalty. In addition, the data from customers can also inform pricing for certain services, ensuring customers are getting the services they need for a reasonable price. Once data is pulled from the network, Optim can quantify that data into a number of reports.  Optim’s pre-built reports include geographical, SSID type, and network-based reporting filters for fully customized data. When armed with key data insights, service providers are able to make informed decisions that help them remain relevant in a highly competitive market.

Network Health Management – On a small scale, subscribers with managed WiFi can easily manage their network’s general health from a dashboard. Subscribers without any network management tools are often unaware of their network settings and even the devices connected to their home network, and as a result, these subscribers are more likely to encounter WiFi performance issues. Through the self-service dashboard, subscribers can manage their home network by reviewing the devices connected, monitoring the performance of their WiFi and implementing security features to safeguard their WiFi network.

On a larger scale, with the use of machine learning, Optim collects and reviews data from the entire network and measures network performance periodically. With this data, service providers can see areas in which their network is underperforming and make scalable adjustments to improve performance. As a result, service providers can deliver an exceptional wireless experience to their subscribers with fewer outages and network faults.

Proactive Troubleshooting – With Optim, subscribers and service providers both have access to review the health of the network and resolve potential networking problems remotely, often before they become a problem. Machine learning makes proactive troubleshooting a reality, diagnosing networking issues through inconsistent data patterns.

WiFi Optimization – WiFi networking is a highly dynamic environment. There are an even greater number of future challenges as the demand for bandwidth grows. Service providers can future proof their WiFi services by utilizing managed WiFi optimization tools. TechSee found that 63% of service providers have invested in AI and machine learning tools to improve and optimize their infrastructure, creating top of the line computing processes to optimize the network and improve their customer’s experience. Machine learning driven tools like automatic band steering ensure that devices are getting the strongest signal and fastest connection possible. This means that videos are crystal clear without long buffering times and audio is seamless.  WiFi performance and optimization are the primary drivers of subscriber satisfaction. When subscriber WiFi is performing optimally, service providers can expect fewer support calls and reduced churn rates.

Maintaining an entire network is far from easy. Service providers face a great number of challenges as WiFi continues to rapidly develop. Managed WiFi solutions like Optim offer reliable tools to help improve current service offerings while laying a strong foundation for the future of WiFi.

Optim has been deployed to over 5 million homes as the first managed Wi-Fi and service level platform. Service Providers wanting to learn more about Optim’s home networking and data management tools, should contact us today.

 

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