In many organisations, one department tracks how quickly customers are served while another tracks how employees perform. The two sets of figures rarely meet. Chioma Ann Udeh, a human resources researcher, argues that bringing them together can help leaders ask sharper questions about both efficiency and service.

Her paper, “Leveraging Data Analytics to Enhance Workforce Efficiency and Customer Service in HR-Driven Organisations,” published in 2024 in the International Journal of Research and Scientific Innovation, was written with Olufunke Anne Alabi, Funmilayo Aribidesi Ajayi and Christianah Pelumi Efunniyi. It examines how analytics can inform staffing, training and retention decisions in organisations where HR choices bear directly on the speed and quality of service.

The authors take a clear position on what efficiency means. In their framing it is a question of how work is organised and measured, not a demand that employees move faster regardless of the conditions around them. They weigh employee performance, satisfaction and retention together, and they treat privacy, the integration of existing records and the skills needed to interpret them as part of the task rather than an afterthought.

When the dashboard misleads

A telecommunications help desk illustrates the risk. A company may count the calls each agent answers in an hour, while customers care about whether their problem was solved. If agents face a target that rewards short calls, one dashboard can show rising efficiency while repeat calls and customer frustration climb elsewhere. Reading both measures gives managers the fuller picture, and shows why the choice of metric matters.

Individual performance scores carry the same trap. An agent may receive a poor satisfaction rating after dealing with an outage outside their control, while a colleague scores highly because others take the difficult requests. Before a score shapes a staffing or promotion decision, an organisation has to understand how work is assigned. In the approach Udeh and her colleagues set out, a number is an invitation to investigate, not a final verdict on a person.

Cade Massey, a Wharton professor who studies people analytics, made a related point in a 2014 interview with Knowledge at Wharton: “They are never going to be perfect. These are noisy processes.” He was discussing predictions about people, but the caution extends to service metrics. A forecast can tell a manager where to look while still missing what matters to workers and customers.

Forecasts and their assumptions

The paper’s treatment of predictive analytics opens a further set of choices. A business can estimate when demand will peak and arrange extra cover, or identify teams with recurring vacancies and recruit earlier. Every forecast, however, rests on its data and its assumptions. Public holidays, a new product or a sudden shift in demand can break a familiar pattern. The authors encourage leaders to revisit those assumptions as conditions change, and to check whether a forecast actually improved a decision.

Practical barriers follow. HR holds staff records, operations tracks workload and customer teams log complaints. Joining them requires agreement on dates, categories and who may see what. A mismatch between records can produce a convincing but false relationship. The authors treat data integration and privacy as central concerns because both determine whether an analytics programme deserves confidence. Responsible use, in their account, means limiting access, checking quality and explaining why a measure is relevant to the decision at hand.

Start small, measure carefully

For Nigerian businesses working with limited budgets, a focused pilot may reveal more than a large technology purchase. Managers could choose one service measure, such as the time taken to resolve a request, and one workforce issue, such as cover on busy shifts. They would set a baseline, make one defined change and track the results over a period long enough to capture normal variation, while asking staff whether the change made their work more manageable.

The paper offers an evidence-informed framework for that kind of decision. Its value lies in the discipline it asks of managers: define what better service means, identify the workforce decision that might affect it, and settle in advance what evidence would show improvement. A team that states those terms before acting gives employees and customers a clearer account of what leaders intend to change.

Efficient work and good service are related, but one does not guarantee the other. Their relationship has to be measured in the setting where an employer hopes to improve, and Udeh’s research gives managers a sounder basis for deciding what to test before the next training programme or software contract is signed.