Debt collection in the Energy and Utilities (E&U) industry is the proverbial double-edged sword. Utility providers' attempts to recover bad debt pose significant challenges in terms of customer perception, engagement and loyalty.

To further complicate matters, very few customers choose to engage with their providers to resolve debt-related issues. In fact, they are more likely to feel that they are unfairly pressured to repay their debts by unsympathetic E&U companies. This adversely impacts not only customer experience and loyalty, but companies' financial performance as well.

Revenue and profits take a beating as utility providers routinely write off bad debts rather than be constrained by efforts of failed collections and an exacting regulatory environment.

Regulators across the globe are training their sights on multiple aspects of consumer collections — including first-party debt collectors, borrower communications, collection of disputed and time-barred debt, and audit trails.

In spite of using reputed and regulated debt collection agencies, debts in E&U companies have been on the rise. According to a 2017-18 PwC study, there has been a four percent compound growth in net debt levels relative to revenues.1

An integrated approach to debt collection is therefore the need of the hour. E&U companies must look to balance the needs for proactive identification and management of high-risk customers on the one hand, and enhanced customer experience on the other.

Analytics provides just the right support.

Driving Payment Behaviors Through Predictive Analytics

With regulators severely restricting E&U companies from turning away customers defaulting in bill payments, companies should devise innovative methods to eliminate negative fallouts arising from wrong actions or even inaction. Identifying and predicting payment conditions and ensuing customer behaviors (and developing supportive plans to keep their accounts current) thus becomes critical.

Data literally becomes the fuel for such an exercise. Internal data from companies' systems and external data from social media are rich inputs for risk-based evaluation of customer accounts. And predictive analytics can add muscle to optimize bad debt collections. Through the right segmentation of customers, predictive analytics can provide accurate insights into customers' intent and ability to pay. For example, which customers will pay without reminders? Who will respond to early intervention versus reactive efforts? Companies can effectively map their resources across the different customer segments to determine suspension or termination of services, or even repayment assistance programs. This is exactly what a U.S. gas and utility company has done. They leveraged a predictive modeling solution toward forecasting and optimization, integrating data management, and creating a more streamlined collections process.

An analytics-driven strategy enables efficient pre-delinquency management of debt without losing out on customer engagement. E&U companies can reach their customers ahead of payment time with individualized messages through the right channels to influence payment behaviors.

For example, a leading E&U company2 increased its debt collection by 50 percent using a propensity-to-pay predictive data model and decreased operating costs by 20 percent. Assigning a propensity-to-pay score to every customer, the company predicted the likelihood of customers being able to pay their dues after their accounts were finalized. Accordingly, the company designed focused delinquency management strategies and prioritized different customer segments for specific actions.

The next generation of smart meters is poised to flood E&U companies with sharper and better quality data that will improve capabilities toward optimizing collections. E&U companies can design creative customer payment plans and proactively address potential concerns and issues with real-time information on customers' changing usage patterns. Complaints around inflated bills and bad debts can be quickly resolved, and companies can positively interact with their customers.

Predictive analytics is really all about visualizing, segmenting and prioritizing customer conversations. It efficiently segments customers for differentiation and prioritization much before they become a risk. Additionally, they can measure engagement levels, usage patterns and the cost-effectiveness of channels. Credit counseling services may also be put in place to further raise levels of consumer satisfaction.

The ultimate objective is to have a robust database of customer expectations, usage and satisfaction. The good news is that every E&U company has billing and payment data to start this journey. A consumer's billing history, credit, payment and collection patterns are good starting points to design processes and models for efficient debt collection.

Data science allows E&U companies to proactively manage potential high-risk customers with confidence. On a reactive level, it allows for customer segmentation for debt recovery with customized corrective actions. Companies can engage better with their customers for effective early arrears management and rehabilitation.

Users' payment behaviors with other suppliers can be fed back into segmentation algorithms for better predictive models and more accurate personalization of collection strategies.

Influencing Customer Sentiments with Social Media Analytics

If predictive analytics leverages customer profiles, past approaches and trends to create successful models, social media analytics taps customers' likes, dislikes and sentiments to determine the kinds of communication and conversations that will be most effective. Will the consumer be more responsive to a phone call, e-mail or a letter? Who will pay larger amounts? Who will require payment assistance?

Intelligence from structured and unstructured social media data can be analyzed just as effectively for debt management as it is done to improve brand awareness or reputation management. These include positive, negative and even neutral conversations and comments posted on social media platforms and other blogging sites, customer care notes and customer survey responses.

Sophisticated analytic tools harness the power of social media data and integrate real-time insights [with Business Intelligence (BI) and Customer Relationship Management (CRM) platforms] to sift the customer voice from the noise and facilitate proactive decision-making.

Braintree Electric Light Department in Massachusetts, U.S., uses a social engagement platform to get customer awareness and sentiment feedback, real-time weather events and outage reports, logistics support and analytics.3

Social business analysts will need to coordinate with business decision-makers to create the right social pulse relevant to organizational goals. By integrating social media customer data with Enterprise Resource Planning (ERP) and CRM applications, business managers can get holistic BI for the right actions, initiatives and improvements.

Additionally, companies can develop social media apps that motivate consumers to use self-service channels. This promotes two-way communication and information flow from the social media page to the company's page and vice versa, besides providing valuable data to improve customer experience in billing and collections.

Framework for Social Media Analytics

  • Crawl social media sites and consumer forums
  • Capture relevant conversations and posts
  • Extract key information on products and services
  • Use analytical models and sentiment analysis to determine business context and insights
  • Consolidate and monitor for continuous insights

An Intelligent Path for Value-added Business Outcomes

Having the right data and systems to access, connect, understand and manage huge volumes are real challenges for E&U companies. This requires the right talent, tools, accurate insights and the ability to execute timely actions.

The value chain is complete only when insights are transformed into effective business processes for multi-channel customer engagement that influences payment behaviors.

Predictive and social media analytics are a formidable combination. Predictive analytics closely dissects data elements to extract patterns that predict the future behaviors of customers on an individual basis.

Social media analytics provides the understanding and inputs for executing messages that will drive customers toward expected behaviors. It enables the lessons from the predictive models to be 'right-channelized' for relevant, targeted and prioritized messages for multi-touch customer communication and engagement.

Converging the predictive and social analytics layers empowers businesses to ask the relevant questions, look for the right answers and take decisions. In doing so, they can strike the winning balance between revenue and customer goodwill.

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