Artificial Intelligence (AI) has been a buzzword in the technology community for quite some time. AI is about machines mimicking human intelligence to solve problems. AI is gaining momentum across many industries, but one industry stands apart.
According to Information Age, “Application of this technology extends to mobile and telecommunications too. Here, it has become an important next step in helping operators’ transition from Communications Service Providers into more advanced Digital Service Providers that can predict their customers’ wants and needs.”
There are a wide range of use cases for artificial intelligence, but within the telecommunications industry, predictive analytics is the one gaining the most momentum. Predictive analytics can help telecom providers predict what subscribers will want and when they will want it. This makes it possible for telecom providers to deliver timely, relevant, and attractive marketing materials to increase sales. So how exactly does predictive analytics work?
Artificial Intelligence: Predictive Analytics
Predictive analytics is meant to predict future customer behavior by analyzing past and current behavior. To do this effectively, telecom companies have to utilize a big data environment to store massive amounts of data on their subscribers. Then they can analyze all of this data and apply predictive analytics to determine when each subscriber is likely to churn, to upgrade, etc.
These are prized pieces of information to help design the right offers for each subscriber. Globally, the telecom subscriber base continues to grow and the power of predictive analytics is that it can be trained to automatically incorporate any changing circumstances. Now, telecom providers just need a predictive analytics platform to harness the insights in all their data.
Identifying the Right Predictive Platform
To successfully implement a predictive analytics program, telecom providers should identify a solution with an all-inclusive, end-to-end predictive analytics platform. The platform should be built to handle not one, but rather many steps from data ingestion and preparation to analysis and operationalization, in real-time. The platform should also be able to dissect and analyze massive amounts of data at lightening speed.
The platform should apply machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting. Any user within a telecom organization should then be able to easily create reports and dashboards to visualize the results and collaborate with others.
To learn more about predictive analytics as part of an all-inclusive data intelligence platform, download the data sheet below.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.