How telcos can leverage big data to increase revenues
Namit Sinha, senior sales director for technology at Oracle India, is responsible for growing Oracle’s Technology business in the telecom sector in India. He speaks to CXOtoday on the role Big data has to play in the field of telecommunications.
Excerpts from the interview:
How telecom operators can benefit from Big Data
Telecom operators capture trillions of bytes of unstructured information about their customers, suppliers, and operations. Millions of networked sensors are also being embedded in the physical world in devices such as mobile phones and automobiles; sensing, creating, and communicating data at very high speeds. Multimedia and individuals with smartphones are active on social network sites and are further fuelling the exponential growth in data. This large pool of information can be captured, aggregated, stored, and analyzed as part of every sector and function of the global economy. Here’s where the spectrum of Big Data comes into play.
For telecom operators, analyzing this diverse and unformatted digital data streams can reveal new sources of economic value and provide fresh insights into customer behaviour. They can track conversations on social media to understand what customers are saying about their products and services and take pro-active measures to defend their brand image and reputation. They can also look at a possibility of business hypothesis testing by digging deeper into this unstructured pool of information and analyzing it against existing business warehouse data with accuracy.
A recent study done by a market research firm concluded that profitability potential of an organization increases with a unit reduction in forecasting errors. One of the major reasons of forecasting error is due to omissions of valuable information while performing analysis. Big Data solutions provide effective levers to avoid such costly omissions and analyze data holistically.
Big Data also offers opportunities to gain cross channel insights. By analyzing transactional data at each level of engagement with the customer—from the first click on the operator’s website to an interaction with a customer care executive, leading to a final purchase or non-purchase – telecom operators can gauge if the customer’s experience was seamless or not.
Leveraging Big Data tools to improve customer experience
Big Data & Analytics can and already is playing an important role in improving customer experience in the telecom sector as described below.
Sentiment Analysis & Social Marketing: Operators can tap into volumes of data generated during network usage to understand customer demographics and psychographics and tailor-make their marketing strategies to target a stream of valuable customers. They can also leverage sentiment analysis data from social media feeds to improve as well as defend their brand image and reputation. They can gauge social media sentiment on newly released products, offers, and campaigns in a cost effective manner and proactively create service requests to improve brand perception.
Cross Channel Insights: A customer’s perception of a service provider’s performance and value is increasingly defined by how well the provider manages the interactions across channels. Customers often start the search for a particular product or service on the website, then talk with a call-center agent for more information, and finally complete the purchase in a retail store. The provider today has to ensure that each of these interactions is a seamless, friction-less experience for the customer. By analyzing customer data at each level of interaction (including mobile, web, call-centers, vendors, dealers, and retail outlets) the service provider can determine if the brand promise was fulfilled and whether the customer became a promoter or a detractor.
Location-Based Marketing: Telecom operators can capture a customer’s location when entering a certain area (“geo-fencing”) and correlate that demographic usage to create targeted offers and promotions for communications service providers (CSP’s) and other industry partners. CSPs can also analyze a subscriber’s mobile network location data over a certain period to look for patterns or relationships that would be valuable to advertisers and partners.
Network Optimization & Monetization
Using Big Data, it is possible to create personalized network usage policies by combining network data with unstructured data and analyzing it to detect customer behavioral patterns. These subscriber-specific usage policies would increase customer satisfaction for the vast majority of users (they’re not subsidizing the heavy consumers) and enable service providers to maximize data consumption revenue streams.
The ability to perform real-time network analysis (including service outages, interruptions, or slow-downs) using structured and unstructured data can also be used to impact operations performance in areas outside of core network engineering like sales, support, and call-center agent operations to help determine product marketing, customer management, customer care and revenue and churn forecasting.
Challenges faced by telecom operators while managing data especially in light of increased 3G and expected 4G services adoption in India.
India has the world’s second largest mobile phone users and third largest Internet users base. The growth in 3G and the recent launch of 4G services in India is set to further inundate the telecom networks with thousands of terabytes of data. Telecom operators in the country realize that they need to put in place sophisticated technologies like Big data and analytics to decode and analyze this deluge of data to maintain a competitive edge.
But while operators realize that there is competitive advantage in the information, they also need to recognize that they can’t drive big data with the same cost economics as they do for the regular business data. They need the right tools to handle various technical hurdles around scalability, complexity of the data, the rate at which the data is coming from the sensors and managing data latency.
There are different technology components to be considered as well, in the value chain enabling Big data. The generic process starts from building a big data platform. In considering all the components of a big data platform, it is important to remember that the end goal is to easily integrate big data with enterprise data to allow telcos conduct deep analytics on the combined data set.
In terms of infrastructure requirements, the system should address processes related to data acquisition, data organization and data analysis. When compared to traditional infrastructure for analytics, the biggest change in a Big Data ecosystem is in the data acquisition phase from multiple sources. The infrastructure required to support the acquisition of big data must deliver low, predictable latency in both capturing data and in executing short, simple queries. It should be able to handle very high transaction volumes, often in a distributed environment; and support flexible, dynamic data structures. Telecom operators also face challenge around some of the softer issues such as the quality, privacy and security of the data.
Adoption of Big Data tools by Indian telcos
In this ever-changing, highly competitive and complex environment, the telcos in India are realizing that Business Intelligence and Analytics can play a pivotal role in helping them to increase Average revenue per user (ARPU), reduce customer churn, drive growth and increase revenue. They are using telecom analytic tools that go beyond business intelligence technologies to help them increase sales, reduce fraud, improve risk management and decrease operational cost.
Big Data growth drivers in telecom
Some important factors that are driving the growth of big data in telecom are:
The need for third party data for decision support: The operators are looking at ways to tap third party data for decision support. A wealth of information is available in data residing “outside” the telecom operators information landscape, i.e. competitor activity logs, competitor messaging, competitor campaigns and customer and competitor interactions. Third party data also encompasses information arising out of political, economic, social, technological, legal and environmental engagements. Acquiring this data and combining it with the structured data set already available in the telecom operators data ware house (DWH), pose a significant challenge.
Explosion of volume of data closer to the origin: Data gets filtered along the value chain – i.e. from network elements to the DWH a large number of data points get filtered and missed. Telco’s have now understood that data closer to the origin such as handover statistics, congestion details bring in valuable dimensions for analytics. The challenge is storing and processing these levels of volumes in traditional databases and technologies.
The need to tap into activities on social networks: People are choosing social networking websites to discuss benefits or disadvantage of services or complain about poor customer experience instead of getting in touch with telecom operators directly. For telecom companies, tapping into this space is becoming almost mandatory because if they don’t, an opportunity to understand what customers (and competitor’s customers) “feel” about their service offerings in a timely manner will be lost. A telecom player can also analyze sentiments around its brand and design their integrated marketing programs for optimum efficacy. Many big brands have launched their Facebook pages and twitter handles to engage with their customer base and create brand resonance.
Enhanced fraud detection / revenue assurance: Bringing in the data closer to the origin enhances the timeliness and accuracy of fraud detection and revenue assurance. A few percentage points of leakage savings will improve the bottom lines significantly. Big Data poses challenges in processing large volumes in almost near-real-time latency.
Actionable outcomes of analyzing Big data
By deploying Big data and analytics, companies will be able to see a visible increase in ARPU, enhance profits, reduce churn and gain competitive advantage through cross channel insights, real-time context sensitive advertising, location-based marketing and network optimization and monetization. The sales and marketing teams in a company can gain better insights from customer feedback, preference and viewpoints through sentiment and social media analysis. Call center management can use the information to ensure better customer experience. Big data will help operators benefit from better strategized business plans, reduce operational costs and improved risk management.
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