Driving operational transformation through the power of data and insights

Rising competition from new entrants, demanding customers, increasing cost pressure and stringent regulatory & compliance requirements are putting intense pressure on utilities companies to disrupt their legacy business models and traditional operations. In order to stay relevant in the market, companies need to revamp their operational strategy and move from ideation to execution faster than ever before. EXL’s Data and Insight Command Center (CC) is an off-the-shelf, bolt-on solution that bridges the gap between customer journey drop-offs and the operational interventions required to address these failures. It provides front line executives with a real time view of journey performance metrics, with automated business rules and robust algorithms driving dynamic planning and execution.

One of the biggest challenge with traditional operations is siloed teams working with a focus on individual KPIs, rather than collectively focusing on organizational KPIs. This results in customer journey failures at multiple touchpoints, resulting in significant rework to correct these failures. 

At its core, CC solves this problem by using advanced analytics to combine human and artificial intelligence. It constantly orchestrates customer journeys in real time by prioritizing and assigning tasks to various operational teams based on predefined annual management objectives for specific KPIs. 

In essence, CC functions as the brain of overall operations by integrating and synthesizing multiple data sources. CC deploys out-of-the-box analytics solutions, customizable alerts, dynamic resource planning and scheduling, and real-time interactive dashboards. This provides executives and other leaders with the tools and understandable, actionable data they need to make better, faster decisions, track improvements in customer experience, and eliminate inefficiencies by implementing transformation initiatives.

The CC has four key focus areas: 

  • Data strategy and management 
  • Market intelligence (MI) and reporting 
  • Modeling and insights 
  • Work prioritization and benefit quantification 

Data Strategy and Management

Utilities companies often struggle with creating a single consolidated view of their customers due to complex acquisition, billing and leave processes. Using the CC, organizations can gather all relevant customer journey data from across disparate sources, and map it back to operations within a single data repository. This data is mapped to specific events preceding a customer journey failure using a rule- or model-based algorithm to accurately forecast future potential problems. 

The CC is built on a robust data management framework for individual journeys. This ensures that accountability is fixed, data ownership is assigned, and improvements are focused and data-driven. This requires an organization-wide approach to data strategy, ensuring the right data is captured at the beginning, the right set of tools and technologies are deployed, and that data is kept consistent across the systems by working on the required structure, process and enablers. Assessing the current level of data management maturity can help organizations plan a data strategy framework that supports organizational objectives. 

A critical aspect of data management is prioritizing data to focus initial efforts for collecting, cleaning, and utilizing the data that is most valuable to an organization, both financially and for supporting the data strategy. For example, variables critical to get bills out of the door need to be prioritized over those used for marketing communication or segmentation. 

After integrating disparate data sources and ensuring data consistency, organizations need to make sure they have strong data governance policies that ensure accountability and that the data can be trusted. This responsibility falls to data stewards, the key links in any data governance structure.

MI and Reporting 

The value of a data management framework is measured by its ability to deliver meaningful, accurate insights. Market intelligence and reporting is responsible for measuring, tracking, and providing invaluable insights to the business. 

The Command Center’s MI and reporting capability provides high-quality journey dashboards based on real-time tracking for key journey metrics. The platform helps operations teams improve by using data to measure performance and drive changes through controlled A/B testing. 

These dashboards also support the detailed and interactive analysis of customer journeys through segmentation and drilldowns, allowing executives to review reasons for customer journey failures, as well as set thresholds, configure alerts, and investigate the root cause analysis of these issues. For example, instead of showing high overall customer churn alone, the CC enables users to narrow down to specific fuel type, tariff, region, product type, customer life stage, billing frequency, or profile class with the highest churn levels to facilitate accountability and action. Similarly, for the billing journey, observations like high billed-on-time with low bill-on-actual and read utilization for a specific group would raise an immediate call to action. 

The CC also has a built-in feature that automatically alters these reports based on pre-set rules and algorithms. Early warnings with calls to action can reduces the risk of breakdowns and enable timely interventions. A well-structured escalation matrix ensures these actions are worked upon appropriately, and receive manager intervention if required. 

Modeling and Insight 

While MI and reporting facilitate descriptive and diagnostic analysis after the fact, the power of the CC comes to life through its automation and predictive and prescriptive decision support.

As an example, consider bill cancellation and adjustments. For the majority of utilities players, cyclical bill cancellation accounts for 5-10% of the overall bill. There are multiple challenges utilities companies face related to these customer bill cancellations. Reasons for cancellations and adjustments are oft not accurately captured by agents, as most of the agents tend to select from first couple of dropdown options when seeking this information. Even when this information is captured correctly, the data alone cannot help prevent future cancellations. 

However, a precise recommendation stating “Agent X needs to reach out to COT team and update details for a customer based on their input before generating the bill to avoid cancellation due to a potential business move,” can enable avoiding a bill cancellation and the corresponding impact on cost-to-serve and customer experience. This is how the CC’s modeling and insight section works. 

At the back end, a rule-based model maps historic events preceding a cancellation or adjustment to tag the cancellation driver. Based on our experience with multiple clients, these rule-based classifications can correctly categorize reasons for cancellations for 95%+ cases. 

Subsequently, the CC uses this historic data and classification to forecast future events and drivers that can lead to potential cancellations. It then assigns a corresponding probability to these accounts. Next, the CC looks at various historic resolutions, or defining resolutions based on experience, to identify which one worked best for a particular cancellation type, and provides a recommended action minimize the chances for a particular cancellation type. In specific cases where actions can be automated, a program automatically trigged these actions. For tasks requiring manual interventions, either a corresponding task is created in client’s workforce management tool where applicable, or emails are automatically sent to the task owner at a pre-defined frequency of real-time, daily, or weekly.

Work Prioritization and Benefit Quantification 

The conventional approach of using a dedicated task-based workforce comes with its own set of challenges around understaffing and overstaffing, limited differential treatment for critical cases, not optimally using subject matter experts due to random work allocation, miscommunication due to multiple touchpoints.

The CC helps prioritize each activity based on pre-defined parameters, and reprioritizes these tasks in real-time as they are completed. With the help of a prioritization mechanism, organizations can move from static to dynamic, optimal resource allocation. For example, algorithms for assigning priority within the customer acquisition journey can assess situational parameters for each account and change weights dynamically.

The CC helps with assigning priorities to all the operational actions along with a priority band ranging from critical to low or no priority, and change their statuses in real time. Staff can be assigned work dynamically based on the task’s priority and the worker’s expertise.

Another benefit of the CC is its ability to provide clear visibility into performance improvements over a period. Performance is tracked end-to-end for all processes or journeys. For example, the tracking for an acquisition journey captures parameters from the day a sale is made to the day the account is setup in the system and payment is received against first bill. 

While the CC quantifies benefits to demonstrate the operational savings through elimination of failure demand and understanding the bottom-line impact, its core value comes out by looking at the real-time upfront improvements in process metrics. Returning to the customer acquisition journey example, improvements are tracked based on the increase in number of accounts set up successfully within the stipulated SLA by looking at the reduction in failure demand (contacts, complaints, exceptions) for a successful outcome. The CC measures performance improvement through three set of KPIs focused on customer experience and retention, first bill received, and first payment. The CC tracks this performance month-on-month and year-to-date, with an annual projection available at any given point in time. 

Conclusion

EXL’s CC is a step change from conventional analytical and automation solutions that work in silos in a bespoke fashion. This solution provides a comprehensive suite of advanced analytics and real-time dashboards to measure journey performance, identify and fix customer journey failures through root cause analysis, dynamic work prioritization and allocation, and detailed benefit quantification, all through a single platform.

This technology has proven itself in the field. EXL has worked with a large UK energy and home services provider to deploy this platform into customer operations. This has led to significant improvements in customer experience and reduction in cost to serve.

Some key highlights of these outcomes include: 

  • ~£2.6M cost avoidance by avoiding account setups delays 
  • ~£2M cost saving by mitigating repeat contacts 
  • 13% improvement in debt conversion rate 
  • 39 proactive controls implemented to reduce regulatory exposure and improve customer experience 

We appreciate that each business is unlike any other, and that customer needs, perceptions and experiences can change by the minute. The EXL CC is a proven customizable solution that monitors data and is designed to react quickly and effectively to these changes. While its backend engine performs complex modeling and analysis essential to make smart decisions, its front-end is designed to present insights in a format that stakeholders and decision makers can easily consume.

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