This is part five in our series designed to help businesses gain efficiencies, cut unnecessary costs, and protect their bottom line in today’s uncertain landscape. The Kepler platform addresses a vast array of Machine Learning (ML) use cases in the telecommunications industry, and its easy-to-use Automated Data Science Workflows ensure all leaders in telecommunications — and their teams — can leverage the host of benefits of ML-powered insights quickly and effectively (think days, not weeks). Below, we outline how to leverage AI for telecom with Kepler’s advanced capabilities to overcome some of the complex, multifaceted challenges that are putting businesses like yours at risk today.
The telecommunications industry has experienced unprecedented attention over the past few weeks. As people everywhere adjust to social distancing measures, they’re relying on networks to get work done, keep in touch with loved ones, and honour key milestones. The boom of video call app Zoom, which saw a surge of 10M users since December 2019, gives insight into how people are transitioning their lives online, now more than ever.
According to the World Health Organization, 29% of Americans have been able to work from home during the pandemic. That figure, however, doesn’t include the population of American’s who are unable to work. This is resulting in an unforeseen surge of network activities, adding significant strain to telecommunications organizations everywhere.
While some businesses are dealing with massive decreases in sales due to forced closures (think brick-and-mortar retail and hospitality), the state of the telecom industry is relatively positive. However, a recent report in The Economist notes that not all mobile or landline networks can seamlessly flip the switch to become “stay-at-home networks” highlighting the fact telecoms are having trouble delivering on demands.
Organizations across the globe have had various responses to the COVID-19 pandemic, ranging from eliminating data overages to donating millions of masks to hospitals and front-line workers. Leaders are finding ways to service customers and communities with speed and efficiency, all while keeping their workers safe. Mirko Bibic, The CEO of Bell, a Canadian telecom, told Bloomberg that his organization is undergoing massive shifts to meet demands. For example, augmenting digital self-serve options, and ensuring their technicians on the road can effectively direct customers through installations from their work vans parked outside. And, staff who can work from home do so, all while usage is up by 60% on average, maintaining organizational efficiencies has never been more crucial.
Prior to the measures implemented by governments and businesses to ease the spread of COVID-19, telecom organizations were already focused on leveraging AI to automate specific processes to save time and cut unnecessary costs. However, as economic contexts shift with the ebb and flow of working and living conditions, telecom leaders need to be ready to adapt to changing tides and consumer demands while networks are under strain and usage is at an all-time high.
Below, we outline three ways leaders can leverage Kepler’s Automated Data Science Workflows to ensure influx of usage translates to happy, loyal customers, efficient sales and marketing processes, and proactive device and asset maintenance.
Kepler’s Tabular Classification Automated Data Science Workflow empowers analysts in telecom organizations with the ability to predict categories of data based off of key pieces of information. For example, you can conduct sentiment analysis of your customer reviews and emails and look at purchasing patterns to identify trends and predict when a customer might churn. By using this data to proactively address points of concern, you can ensure your at-risk customer base is getting the support and service they need today. Shakier networks – and the possibility of your competitors incentivizing individuals to change packages based off of new usage realities – may increase churn for these specific customer groups, so planning ahead is critical. Note that our Text Classification workflow can be combined with Tabular Classification workflow to solve this problem as well.
Want to dive deeper into customer churn reduction? We have a demo video for that – get it here.
Kepler’s Clustering Automated Data Science Workflow allows telecom marketing, sales and customer success leaders to leverage powerful tools that can effectively drive engagement, help upsell, and convert potential customers. Clustering allows Kepler users to make sense of vast amounts of unlabelled data from various sources by grouping them into categories according to patterns and similarities. Analysts in telecom can leverage Kepler to segment customers according to behavioral and demographic information, and use these insights to create relevant communications and offerings.
Kepler’s Anomaly Detection Automated Data Science Workflow workflow empowers leaders to leverage advanced ML to tease out irregularities in datasets and proactively resolve issues. This is especially useful in scenarios where your teams are looking to detect where dated or faulty devices are leading to possible speed or usage issues. Outside the home, predictive maintenance can help identify outliers of other operational enterprise systems — which is especially crucial at a time where people around the world are relying on mobile networks more than ever. Predictive Maintenance through anomaly detection ensures your organization can maintain solid levels of asset performance and ensure networks stay up-and-running smoothly.
Want to learn more about Kepler’s AI for Telecommunication capabilities? Download our infosheet.
