6 Big Data Transformation Strategies in Telecommunications

Avatar photo Team Zaloni December 3rd, 2017

People are constantly connected to their networks through voice, text, and other smartphone interactions. This means that telecommunications companies (telecoms) have access to huge quantities of data and are metaphorically “sitting on a gold mine.” These companies require proper digging and analysis of both structured and unstructured data to get deeper insights into customer behavior, including service usage patterns, preferences, and interests in real-time. To address these requirements, here are some of the savviest big data solutions in telecom that traditional storage and analytics approaches cannot provide.

1. Customer Experience Management or Churn Analysis

One of the highest priorities for telecoms is to retain its customers; therefore, it is critical for communication service providers (CSPs) to monitor customer experience and churn around people who exhibit patterns that indicate they might leave the network. Customer retention costs are only 50% of the cost required to acquire a new customer, which means that CSPs can retain their existing subscribers at half the cost of acquiring a new subscriber.

CSPs are effectively using big data analytics to bring together various data points including – historical records, quality of service, network performance, billing information, call-centers call details and social media sentiment analysis to predict and prevent churn. Churn prediction models through big data allow telco’s to launch retention campaigns that identify and then address “at risk” customers via outbound channels.

2. Clickstream Analysis

Communications service providers can generate more revenue and create better customer experiences by tracking and analyzing customer clickstreams to understand their preferences and propensity to buy. They can optimize their website and social streams to search for specifying keywords or search terms and if clickstreams show a customer searching specific network plan, CSPs can promote targeted plans to that customer and introduce new tariff schemes for up-selling.

Big data analytics can be used to create tailor-made marketing campaigns that target customers by using click-stream data, location-based insight, and social networking data. Telecom service providers can better understand customer preferences and the likelihood of purchase, by tracking and analyzing customer click-stream data. They can then initiate targeted promotions or offers to that individual customer or target group. This can also help increase conversion and cross-sell opportunities.

3. Product Development

Telecom product managers can glean valuable insights by analyzing the rich data generated from their customer’s mobile devices. This enables them to proactively present the right offer at the right time, in the right context to the right customer to improve conversion rates. Examples include personalized data top-up plans or up-sell recommendations based on data usage.

The big data ecosystem can be used to store huge volumes of historical data over a time span and correlate that with customer likes and dislikes through advanced analytics. The marketing team can now have a 360-degree view of customers usage patterns, bill details, call frequencies etc, then create favorable tariff plans which fit the subscriber‘s wallet as well as beat the competition in both price & service quality.

4. Optimize Network Traffic Routing

By understanding how, when and where customers use networks by monitoring and analyzing network traffic data in real-time, telecoms can decrease outings, improve the average network quality and coverage area. A revision in network service of 3G-capable smartphone users running their devices on 2G could be an area where big data can help with various advanced analysis tools.   

Big data helps network operators take advantage of the available information within their networks in order to make them more optimized and robust. Real-time deep packet inspection can be done to optimize traffic routing and steer network quality of service to boost customer satisfaction.

5. Subscriber Communication and Upsell

Communication service providers now can send a more relevant communication for helping customers choose the correct offer or usage option by analyzing customer log reports, usage or non-usage patterns, customer satisfaction data on social and surveys. Upselling can be a part of this correspondence including matching pricing plans or target offers for sports and music enthusiasts.

A database management platform in the big data ecosystem can help in real-time communications that enable recommendations to be delivered at the intended time-frame. Technology platforms can quickly provide access to large amounts of relevant data from multiple sources with powerful data visualization, enabling telco managers to communicate better with target groups at the right place and time.

6. Proactive Care

Telco operators today are hyper-focused on proactive customer care to fix issues and reach out to subscribers to help resolve their pain-points before they negatively impact the experience. Identifying the gaps in communication and information from CSPs to the subscribers is a prerequisite to an improved client care.

With big data, telecoms can use advanced analytics tools to proactively identify issues and offer a solution before it impacts the customer. Not only does it provide a compelling customer experience but it also prevents calls to the customer care centers thereby lowering support costs.

Big data has been gaining ground in nearly every telecom business and they are finding ways to exploit it. To leverage the big data ecosystem, telecom operators need to redefine their business strategies as the benefits of it are many as mentioned above. Thus, whoever wins this battle of creating new revenue streams, analyzing preferences and effectively deriving value from data will be a true game-changer in the industry worldwide.

about the author

This team of authors from Team Zaloni provide their expertise, best practices, tips and tricks and use cases across varied topics incuding: data governance, data catalog, dataops, observability, and so much more.