Do you know what can be done with your telecom data? Who decides how it should be used?
Telecommunications isn’t going anywhere. In fact, your telecom data is becoming even more important than ever.
From the first smoke signals to current, cutting-edge smartphones, the objective of telecommunications has remained the same:
Telecom transmits data across distances farther than the human voice can carry.
Telecommunications (or telecom), as an industry with data ingrained into its very DNA, has benefited a great deal from the advent of modern data science. Here are 7 ways that telecommunications companies (otherwise known as telcos) are making the most of your telecom data, with machine learning purposes.
1: Aiding in infrastructure repair
Even as communication becomes more decentralized, signal towers remain an unfortunate remnant of an analog past in telecommunications. Companies can’t exactly send their in-house software engineers to climb up the towers and routinely check on the infrastructure. This task still requires field workers to carry out routine inspections, even if no problem visibly exists. AT&T is looking to change that through machine learning models that will analyze video footage captured by drones. The company can then passively detect potential risks, allowing human workers to fix structural issues before they affect customers. Read more about AT&T’s drones here.
2: Email management and lead identification
Mass email marketing is a vital asset of the modern corporation, but even as the sending process becomes more automated, someone is still required to sift through the responses and interpret the interests and questions from potential customers.
To make your life easier, you could instead offload that task to AI. In 2016, CenturyLink began using its automated assistant “Angie” to handle 30,000 monthly emails. Of these, 99% could be properly interpreted without handing them off to a human manager. Imagine how much time the human manager would save, without having to sift through that telecom data.
The company behind Angie, California-based tech developer Conversica, advertises machine learning models as a way to identify promising leads from the dense noise of email communication, which enables telcos to efficiently redirect their marketing follow-up efforts to the right representatives.
3: Rise of the chat bots
Dealing with chat bots can be a frustrating (or hilarious) experience. Despite the generally negative perception that precedes them, it hasn’t slowed down bot implementation into the customer service side of most telecom companies. Spectrum and AT&T are among the corporations that utilize chat bots at some level of their customer service pipeline, and others are quickly following suit. As the algorithms behind these programs grow more nuanced, human customer service, which brings its own set of frustrations, is beginning to be reduced or phased out.
4: Working with language
The advancement of natural language processing has made interacting with technology easier than ever. Telcos like DISH and Comcast have made use of this branch of artificial intelligence to improve the user interface of their products. One example of this is allowing customers to navigate channels and save shows as “favorites” using only their natural speech. Visually impaired customers can make use of vocal relay features to hear titles and time-slots read back to them in response to spoken commands, widening the user base of the company.
5: Content customization
If you’re a Netflix user, I’m sure you’ve seen the “Recommended for you” and “Because you watched (insert show title)” recommendations. They used to be embarrassingly bad, but these suggestions have noticeably improved over the years.
Netflix has succeeded partly on the back of its recommendation engine, which tailors displayed content based on user behavior (in other words, your telecom data). Comcast is making moves towards a similar system, utilizing machine vision algorithms and user metadata to craft a personalized experience for the customer.
As companies begin to create increasingly precise user profiles, we are approaching the point of your telco knowing more about your behavior than you do, solely from the telecom data you put out.This can have a lot of advantages, one of the more obvious ones include being introduced to a new favorite show.
6: Variable data caps
Nobody likes data caps that restrict them, but paying for data usage you’re not actually using is nearly as bad. Some telecom companies are moving towards a system that calculates data caps based on user behavior and adjusts the price accordingly, in an effort to be as fair as possible. Whether or not you think corporations will use tiered pricing in a reasonable way depends on your opinion of said corporations. On paper, big data may be able to determine what kind of data consumer you are and adjust your data restrictions to fit your specific needs. This could potentially save you hundreds of dollars a year.
For as long as data could be extracted from phone calls, the telecommunications industry has been collecting your telecom data. “Call detail records” (CDRs) are a treasure trove of user information.
CDRs are accompanied by metadata which includes parameters such as the numbers of both speakers on the call, the route the call took to connect, any faulty conditions the call experienced, and more. Machine learning models are already working to translate CDRs into valuable insights on improving call quality and customer interactions.
It’s important to note that phone companies aren’t the only ones making use of this specific data. Since this metadata contains limited personal information, the Supreme Court ruled that it does not fall under the 4th Amendment, and as such, CDRs are used by law enforcement almost as much as by telcos.
Contributors:
Sabrina Dominguez: Sabrina holds a B.S. in Business Administration with a specialization in Marketing Management from Central Washington University. She has a passion for Search engine optimization and marketing.
James Kennedy: James holds a B.A. in Biology with a Creative Writing minor from Whitman College. He is a lifelong writer with a curiosity for the sciences.
This is the first part in a series identifying the practical uses of data science in various industries. Stay tuned for the second part, which will cover data in the healthcare sector.
