How Big Data and Analytics can Drive Profitability for Telecom Operators
In today’s data intensive world of communications, it is challenging for telecom operators to deal with data in big volumes. However this challenge can be transformed into an opportunity with efficiently utilising big data and big data analytics techniques.
In today’s data intensive world of communications, the social media networks, connected devices, customer behaviours, government portals, call data records and billing information etc. produce a huge amount of data. It is challenging for telecom operators to deal with this surge in data volumes. However this challenge can be transformed into an opportunity with efficiently utilising big data and big data analytics techniques.
The massive amount of data when captured wisely and analysed professionally can reveal powerful insights. Big data and advanced analytics provide telcos with the tools and techniques to harness and integrate new sources and new types of data in larger volumes and in real-time.
Data analytics can help operators enhance the overall value of their business in regards to service optimization, customer satisfaction and revenues. Let’s take a look at which cases telcos benefit big data analytics the most:
Enhanced Customer Insights
With the help of big data analytics capabilities, telcos can turn an enormous structured and unstructured data into actionable customer insights. The big data that customers generate, with the right analytics, enables telcos to develop enriched 360 customer profiles, establish customer-centric KPIs and develop more targeted offers.
Thanks to advanced data architectures, operators can also store new types of data, retain that data longer, and join diverse datasets together to gain new insights. Let’s see which components of customer data are taken into consideration in order for telcos to reach meaningful customer insights:
Customer Information Data: Customer ID, MSISDN, demographic, services used, spending pattern, usage plan
Device Data: Brand, model, series, applications, technology used, device history
Usage Data: CDR, average revenue, VAS (value added services), mobile Internet usage information (URLs, time spent, content type, downloads)
Location Data: current location, most visited location, roaming data, location services usage etc.
Since telecom operators serve a large number of customers; by analysing customer data correctly (with sentiment analysis, customer churn analysis and clickstream analysis), they can also build micro-segmentations, which enable them to segment customers into similar groups.
This will allow telecom operators to personalise their approach to meet every customer group`s needs, to determine the most valuable customers – very critical component in order to make strategic decisions- and create campaigns that suit them. For example;
- They can identify loyal customers who have a high potential lifetime value with customer value segmentation; build targeted campaigns and reduce churn rates
- Determine high value customers who are more likely to repeat purchase patterns with predictive analysis
- Identify potential customers where they can adjust the target reach and cutting costs on non-associative client base.
- Offer tailored products for each segment depending on customer needs, behavior, demographics, device data etc.
Customer retention is one of the most critical challenges which telecom operators face (Industry trends show that there’s over 20-40% churn annually, especially in the Telecommunication industry) and also one of the biggest cost items, since they spend a lot of effort and resources.
Acquiring a new customer is more expensive than retaining the old one, so churn prediction is one of the most important priorities for operators. Predictive (customer churn) analysis and machine learning algorithms use the collected data(customer usage, transactions, complaints, social media etc.) make it possible to have better customer insights; therefore operators are able to accurately identify customers who are likely to leave.
Techniques such as data mining, which determines unseen patterns; or decision trees which enable long-term forecasting and early detection of customer’s value loss, allow operators to determine factors that influence customer decisions and use variables to easily identify potential churners.
Leveraged Customer Experience
Insights gained from big data analytics, improve customer experience at every touch point through high performance services, fast feedback and customised offerings. Today’s advanced big data analytics allows telcos to unlock new insights in real-time, enabling them to proactively offer services/products to their customers at the exact time they are most likely to subscribe, buy, or respond. Simply, customers can get exactly what they want, when they want it. In addition to leveraging customer experience, real-time personalised offers also significantly improve telcos’ ability to up-sell and cross-sell their offerings and enhance revenues.
With the help of predictive analytics, telecom operators can also accurately predict, when their customers might be moving towards facing a bad customer experience, or identify the scenarios that leads customers to experience issues with services; hence they can turn all the potential bad experiences into good ones, or contact customers before they contact the call center. Operators can also inform call center employees in real-time, so when a customer calls, employees will be informed if the customer has experienced problems at a particular location or while using a particular service; so that they can provide user-specific solutions.
Needles to say, with the help of accurate predictions and tailor made offers & pricing, the result is an improved customer service, higher levels of customer satisfaction and thus loyal customers who are likely to stay put.
Big data analytics bring a considerable value to decision-making and provide more meaningful insights, which help to build competitive advantages and a more efficient cost structure.
a.New Business Areas
Driven by analytics information on their customer’s behaviour patterns, operators can initiate new business models andventure towards new niche segments, which they haven’t tried before. According to their new segmentations they can launch innovative products and services such as location–based and event-based campaigns, directing customers to cross-sell (related feature/product) and up-sell (upgrade, new feature/product) offers.
Moreover telecom operators can sell (anonymous) customer insights on crowd movement, behaviour and interestsdata that are valuable to agencies, enterprises and government. They can also provide big data-related professional services such as infrastructure, connectivity and cloud to enterprise customers.
Big data analytics provide operators business optimisation capabilities, which help them to increase revenue through more-targeted marketing activities and to reduce costs by identifying expense and revenue leakages. For instance, they can analyse effectiveness of their marketing investments and optimise their marketing spends across channels to drive maximum ROI. As mentioned above, big data analytics also help operators to achieve cost, time and effort savings through churn prediction.
The large amount of information within operator networks help telcos to achieve better business decisions based on data analytics and move towards digital transformation of their businesses.
Improved Service Quality
Big data analytics help telecom operators to take advantage of the customer data insights within their networks in order to make them enduring, optimised, and scalable. This is reflected on the service quality in many cases.
a.Network Performance and Capacity Optimisation
Big data analytics allow operators to optimise call routing and quality of service by analysing their network traffic with Real- Time CDR Analysis. For example examination of 4G- capable smartphone users with location-based analysis can help operators to identify which locations they should improve 4G services or to better deliver media content.
Telcos, especially when operating in various markets, utilises demand forecasts to justify the considerable investment needed to ensure capacity availability at the right time. Demand forecasts are used for understanding customer dynamics and capacity optimisation. Customer dynamics are typically generated from new consumers using a product/service for the first time, established users changing their usage patterns, users of competing services shifting to the alternative service or those exiting from this segment of the market altogether.
b.Network Infrastructure Management
Telecom companies can derive optimal network management through real-time cellular network performance measurement, and data traffic measurement in addition to deep packet inspection to optimise traffic routing and network quality of service. For example call drops are one of the most important challenges for telecom operators. Real-time call drop analysis allows operators to monitor simultaneously and resolve root causes at the very early stages. Moreover, network failures can be predicted by the help of anomaly detection.
Big data analytics can also efficiently adjust maintenance schedules and enables proactive care by comparing real-time information with historical data with the help of big data analytics. Machine learning algorithms can reduce both maintenance costs and service disruptions by fixing equipment before they break.
Since telcos face high network and maintenance costs, advanced analytics will help in improving their profitability and gain a competitive advantage by optimising network usage thus, enhancing customer experience.
Telecom companies use big data analytics to identify and investigate anomalous and fraudulent activities simultaneously, which can be easily bypassed by humans.
With the help of anomaly detection, machine learning algorithms can monitor huge volumes of data such as customer demographics, sentimental data, customer usage patterns, geographical usage trends, calling-circle data, behaviour data from clickstream logs or support call centre statistic to name a few. In this way, the detection and prevention of unwanted threats/ fraud with analytically driven surveillance helps Telcos predict the likelihood of unexpected behaviour. For instance, with big data analytics, Telcos can help build models that can flag anomalous phone calls, which might indicate theft or hacking or analyse call data records in real-time to identify false answers or fraudulent long calls immediately.
Big data analytics are also used for detecting fraudulent claims by claims processing teams; identifying unauthorised devices, tracking payment processing and customer data protection.
Moreover, when security is enhanced with big data analytics, Telco companies can manage risk from cyber attacks through cloud and mobile environments.
Powerful big data analytics solutions are fundamentally changing the way that telecom operators manage their daily operations. The advantages gained from big data and analytics are substantial as mentioned above and will be increasing over time. Therefore, operators that are aggressively pursuing big data analytics and information strategies will be definitely differentiating themselves from their competition.