With over millions of users, the telecommunication industry deals with an enormous amount of data every second of every day. Data analytics is becoming a crucial part of the business, especially in the telecom industry. With the rising amount of competition and low cost of migration, the customers immediately change their telecom operators the moment they experience dissatisfaction in the service.
According to a survey conducted by IBM, about 66% of CSPs (Communications Service Providers) identified customer-centric objectives for adopting Big Data as their organization’s top priority.
The focus of CSPs has long back shifted from product-centric to customer-centric. With digitization, customers are well aware of their services in the market, forcing the telcos to invest in new technologies and advanced analytics to understand the needs of their individual customers. However, today’s customer wants more than just understanding- a valuable relationship, which may come through more timely, informed or relevant interactions.
According to an article published in Forbes, almost 53% of companies have already adopted Big Data, with Telecom services being the leading adopters.
What customers expect from their operators?
- A personalized experience which requires the company to gain an in-depth knowledge of each customer.
- Maximum benefits out of minimum costs, which is possible by taking advantage of actionable information available along with the insights from the market.
- Innovative offerings.
Challenges faced by Telecom Companies
Although Big Data Analytics provides actionable insights for decision making, telecom companies find it challenging to adapt to it and continue using legacy analytics. Some of the challenges that these companies face are-
- Determining a strategy to leverage the benefits of big data.
- Acquiring the resources to understand and analyze big data
- Cost and efforts associated with Big Data Analytics
- Identifying the best software and hardware solutions and determining the best overall solution
- Privacy issues related to direct and indirect use of big data sources.
How Big Data Analytics Improves Customer Experience
Even if the telecom industry faces multiple challenges in implementing the Big Data Analytics, once implemented it actually results in higher ROI since the telcos are able to provide customer-centric services. Big Data Analytics can help telecom companies in several ways to enhance the customer experience-
Lower Churn Rate
Retaining their customers is one of the major challenges faced by the telcos. Big Data analytics not only help the companies to reduce the churn rates but also help them understand why a customer leaves and how to stop them from leaving. With big data analytics, you can understand customer’s sentiment and identify value proposition of individual customer loss and create targeted strategies which enable to reduce acquisition costs and increase marketing efficiency.
Big Data Analytics helps the businesses get access to real-time data so that they can make the offerings more targeted. And when the customers get what they want, they tend to remain loyal and know that they are not just understood, but are also valued. When digital technology is combined with analytics and predictive intelligence, marketers will be able to adopt a one-to-one personalization approach which results in greater customer satisfaction.
There’s a reason why about 70% of telcos consider location-based services critical for their success. For a competitive industry like telecommunications, it is imperative to understand trends, and patterns in a quick and efficient way to keep up with the evolving market dynamics. The location information is not just assisting the businesses with their business challenges, but are also driving new opportunities for telecom operators and enterprises to easily utilize infrastructure to support intelligent positioning services.
With data that is available from multiple sources, (network, services, social media, customers and others), the telcos should aim at creating a 360-degree view of customers and extract actionable insight from the data to increase loyalty, create targeted marketing campaigns and develop new services.
mViva leverages HBase, a distributed, scalable big data store backed by class-leading Hadoop map-reduce framework with robust HDFS(Hadoop Distributed File System) as the data storage layer to enable analysis of large, diverse and ever-increasing customer data to reveal patterns, trends, associations and key behavioral traits and put them to effective use by channelizing the right offer to the right customer at the right time.