Telecom customer insights with big data
Cracking the Customer Experience Code: How Telecom Operators can Extract Actionable Insights with Big Data Analytics
Telecom operators today have their task cut out. Traditional cash cows such as voice and messaging businesses are increasingly getting commoditized, even as the demands and expectations of millennials and other “digital natives” evolve rapidly. Average revenue per user (ARPU), customer lifetime value (CLV) and retention rates are declining for many carriers, while subscriber acquisition costs continue to rise in a competitive, fragmented marketplace.
In order to sustain relevance in this era of instant gratification, telecom companies must reimagine their relationships with prospective and existing customers to personalize the engagement. Carriers must understand how each subscriber interacts with their products and services across different touch points during the end-to-end experience lifecycle, and accordingly deliver what customers want–whenever and wherever they want it.
For doing so, telecom companies require actionable insights from customer data–the huge volumes of structured and unstructured data that are being generated across multiple sources including contact centres, emails and social media. These insights should be precise enough for carriers to optimize core business processes around sales, marketing, assurance, etc. so that subscribers experience a superior level of engagement throughout their lifecycle.
Big Data Analytics Delivers Big Insights
This is where big data analytics can help telecom operators effectively understand and anticipate changing subscriber behavior and preferences, and suitably tailor their offerings for each customer.
It’s important to keep in mind here that insights won’t count for much unless they can facilitate specific action items. Hence, insights should be actionable. Simply put, they should be tangible enough for senior decision makers across various functions to take concrete steps for optimizing, integrating or revamping business processes, databases and applications.
For instance, an operator’s data analytics solution finds out that subscriber A is at risk of leaving the network due to frequent broadband outages, and recommends swift resolution of the issue by network engineering. Now, that’s merely an insight–a rather generic one. However, if the analytics setup highlights that the outages occur during specific time periods of the day, then the operator’s support staff can take targeted measures to address the problem. This is actionable insight!
Let us look at a few examples of how big data analytics can empower an operator to reimagine the customer experience:
• Provision Intuitive Self-Care: Diverse data points pertaining to network performance, billing, customer calls, quality of service (QoS) and social media sentiment can be aggregated. Thereafter, the same can be mined to deliver a compelling self-care platform. Such a platform can have embedded features for individual customers to tailor their tariff plans in line with their changing consumption patterns. Also, subscribers can use the self-care suite to raise service requests and track the status of the same in real time. Doing so will not only boost customer satisfaction scores, but also significantly reduce the volume of customer calls to contact centres, thereby bringing down the capex and opex required to run multiple service channels.
• Micro-Segment Customers: With big data analytics, changing customer sentiments, clickstream patterns and churn data can be evaluated to create various customer micro segments. Accordingly, relevant pricing plans, product offers, etc. for each group can be crafted. For instance, real-time feeds around parameters such as recharge count and average balance can be captured to define customer segments.
• Push Next-Best Offers: Today’s customers are used to dynamic pricing when they shop for goods and services online–be it flight tickets, hotel rooms, or that latest smartphone. Why should telecom offers be any different? Big data can be leveraged to demystify customer service usage patterns, as well as their interactions across different inbound and outbound channels, in real time. This will help to swiftly come up with “next-best offers”. Based on actionable insights around customer demographics, location, content preferences and purchasing behaviour, these personalized recommendations can go a long way in enhancing subscriber engagement.
• Deliver Contextual, Interactive Notifications: Big data analytics can be harnessed to push contextual, trigger-based promotions and offers in real time, at the end of each customer call or session. This eliminates the practice of sending bulk, non-relevant messages. Similarly, on-the-go purchases and offer fulfilments can be enabled for instant gratification. So, if a customer is searching for a particular tariff plan on a website or application, relevant, personalized plans can be showcased at that point of time for higher conversions.
Cracking the customer retention code won’t be easy for telecom operators, given the rapidly changing priorities and expectations of subscribers in the digital age. However, as the likes of Uber and Netflix have shown, brands can crack this code if they leverage data-driven insights to continuously refine and optimize offerings, and deliver a contextual customer experience. Telecom operators must reclaim the lucrative customer relationships they have assiduously built and nurtured all these years, and create tangible business value from the same–using big data.
Of course, challenges around the reliability, management and non-integration of diverse databases remain. However, that cannot be a justification for telecom operators to not adopt big data and glean actionable insights from the same. The bottom-line, of course, being reimagining the customer experience. Instead of approaching this imperative as one massive exercise, these players will do well to approach big data analytics as a series of targeted, controlled experiments. The outcomes of which, of course, would determine subsequent initiatives, of course.
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