Communication Service Providers (CSPs) today operate in a highly disruptive marketplace, characterized by rapid technological innovation and intense price wars. Subscriber churn remains a major challenge as customer demands continue to evolve in an increasingly complex digital landscape. Traditional retention strategies will no longer suffice.
How can telecommunication, cable and broadband companies then respond effectively? By embracing data analytics to enhance engagement with prospective and existing customers. Analytics can help you not only understand, but also anticipate and predict, shifting subscriber behavior and preferences, and thereby, personalize products and services for reduced churn.
Dynamic business environment
Many CSPs are grappling with declining Average Revenue Per User (ARPU) and Customer Lifetime Value (CLV), not to mention rising attrition rates and customer acquisition costs. Monthly churn levels among wireline telephony providers, for example, tend to hover around 2% to 2.5%, according to a Bain study.
When it comes to customer satisfaction (CSAT), Internet service providers (ISPs) and Pay-TV remain the weakest among the 43 industries covered by the American Customer Satisfaction Index (ACSI), despite improving their ASCI scores to 64 and 65, respectively, in 2016.
Fierce competition from new market entrants, who have been leveraging IP-based technologies and over-the-top (OTT) platforms, threatens to commoditize CSPs’ core cable, voice and messaging businesses.
Apart from confronting growing supply-side headwinds to revenue and profit growth, you also have to contend with increasingly unpredictable behavior and expectations of millennials and other ‘digital natives’. A case in point is the increasing popularity of alternative distribution channels for video, voice and data services among Internet-savvy customers, who seek personalized content on any device, at any time.
Data-driven insights for higher retention
Telcos, ISPs, Pay-TV and cable operators must overhaul their conventional customer engagement programs in order to sustain market share and margins. However, many of them continue to exclusively focus on agent-based sales and customer service channels that entail high operating costs. Furthermore, such channels are typically oriented around products and services, instead of consumer requirements.
This is where data analytics can play a major role in driving higher customer centricity and operational efficiency. CSPs can mine subscriber-related transactional and social data to generate actionable insights on customers’ unique demands, and revamp outreach and retention initiatives accordingly.
You can aggregate structured and unstructured data concerning existing and prospective customers from multiple sources, including social media, connected devices, contact centers, emails Customer Relationship Management (CRM) systems.
Merely collecting data will not help, though. The real value lies in using predictive analytics applications to anticipate customers’ future actions, based on past trends and social ‘signals’. This, in turn, can help you reduce attrition, and maximize CLV for each subscriber. Moreover, you can leverage data-based insights for targeted marketing campaigns, focusing more on the most valuable customers.
Personalizing customer experience
Advanced data analytics tools can empower you to harness relevant customer data for tailoring promotions, campaigns and services across the end-to-end customer journey–from acquisition through usage to exit.
Here are a few ways in which you can effectively engage with clients across the relationship lifecycle:
- Prospecting and acquisition
Build a 360-degree view of potential and existing subscribers by integrating related data from diverse internal and external sources, and gleaning granular insights around consumer sentiment. The key here is to segment customers based on comprehensive ‘buyer personas’, around which you can create service offerings that are specifically tailored to the interests and needs of each group.
Such an approach could also help you penetrate untapped markets, improve response rates, and boost up-selling and cross-selling.
- Customer engagement
Use analytics to better understand customer experiences in relation to products, networks and promotions, and respond appropriately. For instance, telecom companies can mine data pertaining to dropped calls to optimize their network infrastructure, and minimize such outages.
Similarly, telecom carriers can deploy advanced algorithms to analyze network usage, and accordingly ‘cluster’ their customers to optimize service and support. This could go a long way in enhancing customer satisfaction and brand loyalty, given the critical role of network performance in determining subscriber experience.
You could also look at redesigning the underlying workflows at self-care portals to streamline the various virtual services, thus reducing the volume of customer calls to contact centers.
Data-driven customer insights can enable CSPs to reduce charge-offs and maximize recoveries. By studying late payment patterns for individual subscribers, you can predict their propensity to default on bills, and ‘cluster’ such delinquent customers based on risk factors. This could pave the way for a tailored collections strategy, resulting in reduced credit losses and bad debt.
Alternatively, you could consider ‘right sizing’ the tariff plan for a high CLV subscriber–based on the latter’s actual service usage–in case he has been delaying his bill payments. Implementing such a strategy can help CSPs convert a customer satisfaction issue into a positive customer experience outcome.
Similarly, rising monthly data charges for a customer signal an opening for offering a more appropriate package, in order to reduce the risk of attrition.
In short, in-depth understanding of customer profile and behavior–based on accurate data–can foster informed, timely decision making for higher profitability and market share.
Advanced data modeling techniques such as neural networks, decision trees and logistic regression can help CSPs score customers according to their ‘churn propensity’, and thus, segregate subscribers for targeted engagement initiatives. You can define business rules underpinning your automated workflows to trigger an alarm in case of increased risk of churn or fraud.
In conjunction, companies can use algorithms to anticipate subscribers’ needs, and offer relevant, customized solutions to curb attrition. For instance, XO Communications, a business-to-business (B2B) telecom service provider boosted customer retention by 60% through churn modeling. In the inevitable event of some customers leaving, you can analyze their exit patterns to address any lacunae in network and service quality.