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The recruitment of undergraduate students within English universities is of vital importance to both the academic success and the financial stability of the organisation. Despite the primacy of the task, there has been a dearth of research looking at related performance and how to ensure that the process is optimised. The purpose of this study was to investigate the degree of variation both within a university and between different universities. The reliance that individual programmes and/or universities place on the Clearing process is key; given its uncertainty, resource demands and timing shortly before students take up their places.

The Nomogramma di Gandy diagrammatical approach utilises readily available data to analyse universities’ performance in recruiting students to different programmes, and the degree to which they each rely of the Clearing process. Inter-university performance was investigated on a whole-student intake basis for a sample of English universities, representative of type and region.

The study found that there were disparate patterns for the many programmes within the pilot university and also disparate patterns between different types of universities across England. Accordingly, universities should internally benchmark their programmes to inform both strategic and tactical decision-making. Similarly, Universities and Colleges Admissions Service benchmarking inter-university patterns could inform the overall sector.

The approach and findings provide lessons for analysing student recruitment which could be critical to universities’ academic and financial health, in an increasingly competitive environment.

Institution-wide staff turnover in universities might be considered “satisfactory”, but can mask wide counterbalancing patterns between departments and different staff. This paper aims to explore the benefits of detailed turnover analysis in managing talent in the complex changing landscape of Higher Education in the UK.

Staff turnover was analysed for both new recruits and staff leaving, as well as net turnover. The inverted Nomogramma di Gandy highlighted overall patterns and outliers. Staff characteristics examined included age, gender, staff type and contractual status.

There were (wide) variations in staff turnover for age, gender and type of contract, with particularly high turnover for research staff (influenced by the use of fixed-term contracts). This disproportionately affected younger staff, who are more likely than their elders to seek employment elsewhere, but might stay if there are career opportunities and development. Practical processes are suggested to improve intelligence that enables the best talent to be identified and retained, support a life-span perspective and inform emerging issues such as gender pay differentials.

Given the increasing complexity of managing talent in universities, with their predominantly knowledge-type employees, the research serves to highlight that high localized staff turnover can adversely impact on a university’s research capacity, which in turn presents risks to the achievement of its strategic aims and objectives. Therefore, detailed scrutiny of staff turnover dynamics can pinpoint where recruitment and retention policies and practice require focus.

Scrutiny of staff turnover in large organisations is traditionally reactive, involving benchmarking against peers at institution level. Not being an outlier tempts the inference that turnover is “satisfactory”. However, individual departments exhibiting varied, counterbalancing patterns might be masked; meaning situations that present challenges and require action could be missed. The purpose of this paper is to investigate the degree to which headline staff turnover can mask internal variations in a large post-1992 English university with over 2,000 staff.

The methods scrutinised related mainstream benchmarking sources, and analysed turnover for both new recruits and staff leaving, as well as net turnover. The inverted Nomogramma di Gandy helped highlight overall patterns and identify outliers. Staff categories and characteristics examined included: age, gender, diversity, staff type and contractual status.

It was found that (wide) internal variations were masked between university departments and between different gender and age groups, with Generation Y presenting issues for future recruitment and retention. Localised high turnover rates were found, with particular issues involving research staff. A proactive approach is essential, analysing local data to reflect internal structures, and staff categories and characteristics. Understanding internal and external staff dynamics supports organisations to meet strategic aims and objectives, and target local action.

The approach and findings provided lessons for staff management relevant to universities, which are critical to many, if not most large organisations in the UK and internationally, particularly in times of uncertainty.