With increased competition in the telecom market, it has become even more vital for telecom operators to be proactive in providing services to their customers and to position themselves accurately in the complex market. Indeed, operators have generally shifted their focus from customer acquisition to customer retention, because with the near saturated market, it is simply deemed more cost-effective.
Once big data enters the scene, operators get a leg up in understanding their customers and in making accurate business decisions. Using the right technology, telecoms can have access to the information they seek in almost real time.
Why should we analyze?
We would be hard-pressed to find another industry with as much easy access to a wealth of user data as telecom. Traffic, user behavior, location, and more are easily attainable for the provider. With this information, providers can analyze which services their users use, for how long they use them and when. The can offer precise upgrades, accurately price their services, and provide a superior experience that can win loyalty in this extremely fickle market.
By deeply understanding consumers, companies like ClearStory Data or Informatica can create targeted advertising campaigns, understand when and where customers need certain services, craft data packages that better fit usage patterns, and save their clients money. They can even fix problems of service before clients become aware that they exist.
This information helps telecom operators manage their resources as well; data enables them to review network incidents, discover misuse of systems, prevent cyber attacks, and more. Maintaining flexible access to data is an absolute necessity.
What are some obstacles?
Telecom’s biggest opportunity – the sheer magnitude of data to which they have access – is also the most difficult obstacle when seeking to analyze the figures: there is just so much data! An enormous volume of information, from many different points, must be stored, analyzed, and accessed in near real time. Working with massive amounts of data on customer behavior can lead to numerous problems, including the “correlation problem” (just too many statistically significant answers), figuring out how to analyze the wealth of data, and finally, determining how to transform the data on customer behavior into practical, usable information. How do you get started analyzing customer behavior?
First, ask the right questions.
You know who speaks to whom, for how long and when; you know where they are; you know how your network is operating; you know how long and when your customers access data; you know how customers interact with your company; you know a lot of things. But this can only be useful if you know how to analyze this information. Ask the right questions by figuring out what you want to accomplish.
Trying to create happy and loyal customers? You need to know what they like, what services would delight them, their pain points. Do you want to increase ROI? Find out what clients need so that you can up-sell additional services. Asking the right questions enables you to organize the data in such a way that you will be able to reach your goals.
Finding the right software
The gathering of data (collection), the storage, and the analysis of data are three separate things. Companies have been collecting and storing data for a very long time now; there’s nothing new about that. They’ve even been analyzing data for a long time, just not the massive amounts of data that companies are now working with. The data is scaling, and technologies are underdeveloped to handle it or extremely costly.
Next gen GPU-powered analytics technologies were designed to tackle challenges such as the aforementioned, delivering flexibility and ability to handle huge amounts of data, at record speeds.
Applications such as geolocation analysis, customer profiling based on a behavior metrics (simple ones like where one spends their time, what their interests are, which networks they use and upgrade, what motivates them, etc.), help companies personalize the experience for their customers. Telcos can learn what their customers need and want, making this a fantastic tool for sales and for retention.
When looking for a powerful analysis software, look for a GPU database that can ingest data from multiple sources such as tweets, texts, CDRs, points of interest, IoT sensors, customer information, network events, and WiFi/3G/4G traffic into a single point of knowledge, and to analyze any aspects of interest in near real time. This sort of database, like SQream DB – a GPU database (SQL) designed for the tens of terabytes to petabytes range – will provide analytics capabilities such as customer mobility, device detection, location analysis, customer behavior, analytics for advertising optimization, network monitoring, and security. SQream’s software assists telecom operators in optimizing their infrastructure, analyzing deep historical data, identifying opportunities, automating marketing activities, and discovering security and abuse issues, among other things.
By selecting technology that is extremely infrastructure efficient, data that was previously thought impossible to parse is now accessible in almost real time. That’s an edge telecom operators can’t afford to pass up.
This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.