Data quality (DQ) drives an organization’s well-being, operating costs and overall customer satisfaction.

Leadership in business requires constant evolution and adaptability to changing industry trends. Organizations must think ahead and treat data as a key business asset, one that can reduce costs, cater to customers on a digital platform and help execute multi-channel marketing.

A leading utility provider struggled to manage data as an asset. A business case drew attention to its poor data quality, its adverse impacts and the investment required for its improvement. EXL’s experience with data, and our understanding of customer journeys, enabled us to highlight core data quality issues within the utility provider’s profile. We measured the impact of issues on the customer’s contacts, complaints, net promoter score and ability to self-serve on intelligent voice recognition (IVR) to highlight why good data quality is essential to customer centricity, growth and profitability.

As a result, we helped the utility provider size, monitor, prioritize, fix and assess the data’s overall impact on their business.


EXL identified key data quality focus areas, including duplicate customers, misalignments across systems and multiple customers on one identifier. We helped monitor and control these issues and isolate their impact on metrics like the call and complaint pattern, net promoter score and self-serve rates.

  • Improve the Customer Golden Record for Unique Identification and Customized Service

    A Customer Golden Record (CGR) measures the completeness and validity of data essential to providing a seamless customer experience. CGR is necessary for an organization to achieve business objectives such as drive-to-digital, improved marketing effectiveness, reduced call centre failure-demand and reduced bad debt. CGR is driven by better capture quality by call centre agents, customer prompts to update information via digital channels and prioritized third-party data purchases and additions. EXL helped create an end-to-end diagnostic capability to monitor CGR and track all data changes across all customer paths. This highlighted the loss in data and lost opportunities to capture data. We also assessed the impact of low CGR on customer experiences and business revenue. Overall, our initiative improved CGR by 17% over the last five years.

  • Size, Monitor and Fix Data Quality Issues

    When there’s a change in a customer’s profile, agent negligence or technical challenges can lead to data quality issues. It’s essential to fix these issues to avoid negative customer impact and failure-demand.

    • Fix Duplicate Profiles

      A customer’s profile should be uniquely labelled by a single identifier. However, due to limited front-end search and match capabilities, agents often register the same customer multiple times, leading to multiple profiles. These customers are prone to problems with online accounts, loyalty programs, paperless billing, etc. EXL helped the organization understand how and where duplicate profiles are created, prioritized their merge and pin-pointed challenges to the process. We also analysed the cost impact these duplications have on the business.

    • Correct System Misalignments

      The organization deploys multiple systems for tasks, including customer relationship management, billing and work requests. It’s essential that all systems stay in sync. For instance, the information on an agent’s screen within the customer relationship management system might not match data sent out to the customer. Therefore, the agent can’t effectively solve a customer’s query. We identified misalignments across systems and prioritized their correction.

    • Reduce Ambiguous Profiles

      If a single customer identifier has a joint name like “Mr and Mrs Smith,” then it’s part of the data issue. These customers are a potential GDPR risk, and there’s no way to identify the primary account holder. All information linked to the profile, like marketing preferences and email addresses, can be attributed to either customer name. They can’t be identified for self-serve processes and online account creation. EXL monitored the creation of these kinds of profiles and assessed their impact on the organization.


  • Calls and Complaints

    Our analysis highlighted that customers with DQ issues call and complain 1.5 times more than customers without DQ issues. We monitored month-on-month call, complaint and average handling time for each DQ issue and estimated an annual benefit of £2.3M through call and complaint reduction.

  • Self-Serve Rates on IVR

    The self-serve process requires certain key data items to identify and verify customers when they call. The pre-post analysis on data capture showed a 3% improvement in the self-serve rate. Similarly, for customers with duplicate profiles, there was a 4% increase in self-serve once the issue was fixed. This was estimated for 2019 DQ issues.

  • Impact on NPS

    Per the analysis done on 2019 survey samples, a customer’s valid name, telephone number and date of birth helps an organization improve customer service and overall net promoter score (NPS) by 10%. These NPS improvements translate to customer retention and a lifetime customer value benefit of £1.26M annually. An NPS comparison was also done for customers with data issues. Potential improvement was up to 8pp across journeys. This deep dive helped us pinpoint paths that were more negatively impacted by data quality issues.

  • Operational Efficiencies

    Valid customer data enables the utility company to optimize operations and better connect with customers. Billing efficiency improves as a result of increased actual reads received from SMS/Email/IVR prompts sent to more customers. Renewal and tariff change journeys are smoother as customers are sent timely reminders across different channels of communication. Timely communication with customers in debt enables the organization to reduce potential bad debt. A high-quality Customer Golden Record and good data quality helps the organization establish a relationship with the customer. They can uniquely identify them and serve them based on their preferences, which improves their core operational efficiency.

Reducing calls and complaints, improving self-serve rates and enhancing customer satisfaction were key focus areas for the business. The analysis proved data would drive an improvement.. The impact of poor data quality on the customer experience highlighted the need for timely remediation. It also underscored the need to improve data capture.

This business case helped the organization create an improved, endto- end framework for data quality management. It included a proactive measurement of issues, a root-cause analysis and impact-assessment that enabled timely corrections.

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