Case Study


Bespoke psychometric assessments delivered through a powerful new sifting platform


Next is a multinational clothing, footwear and home products retailer with around 700 stores in both the UK and wider Europe, making them the largest clothing retailer in the country.

Every year (especially during the busy Christmas period) NEXT attracts a huge number of applicants, while they also see significant volumes of candidates applying for their entry-level retail positions, including Sales Consultants and Stockroom Assistants.

The busiest recruitment period for Next is between October to January, when hundreds of Sales Consultant and Stockroom Assistant positions become available for seasonal staff vacancies.


NEXT case study


Candidates sifted in a 3 month period


Reduction in recruiter workload


Reduction in average cost per hire


Aiming to refresh their previous recruitment process  and responding to the challenge of a significant increase in applications for their retail roles, they needed to identify assessment methods, which would improve sifting and accuracy of a large volumes of candidates, while maintaining a fair and positive candidate experience.

The main aims for Next were to find the best ways to integrate these assessment methods into their online application process and in turn:

  • Increase calibre of candidates assessed by stores

  • Increase success rate and quality of hire

  • Adopt an efficient value for money overall approach


Our Approach

Next chose to work with us based on our previous (and significant) experience building retail assessments, as well as our online volume recruitment and selection assessment expertise.

To provide a fit for purpose and elegant online recruitment process, we identified the need to introduce an objective, role-focused sifting of candidates at the application stage.

This approach would be achieved by:

  • Introducing a set of rigorous online assessments at the early application stage

  • Seamlessly integrating the¬†online assessments¬†into the applicant assessment process

  • Communicating a positive, engaging candidate journey and client brand

Clevry conducted extensive in-store job analysis (using various techniques including focus groups and interviews) with current Sales Consultants and Stockroom Assistants, as well as their Managers, across different UK regions.

This gained an insightful and thorough understanding of the demands and challenges of the role as well as associated qualities, motivation and culture fit attributes to identify high-quality candidates for Next.


The Results

Based on our research, we designed bespoke role-specific cognitive ability tests and personality questionnaires, alongside online scored application forms in time for the annual Next Christmas staff recruitment drive.

To further improve the candidate experience, we worked in partnership with Next to build a fully bespoke web skin that also provided a seamless ATS integration  with their   online assessment platform. 

The combined work resulted in an improved candidate experience and efficient up-front candidate sifting, characterised by:

  • A seamless candidate journey

  • An engaging, topical and efficient assessment suite

  • Confidence in the Clevry infrastructure to concurrently assess large numbers of candidates (over 150,000 candidate assessments in a 3 month period)

  • Cost effective & value for money assessments


Following up confirmed an increased quality of hire, shown through a positive relationship between candidate assessment scores at the application and sifting stage as well as performance in the role.


Other key improvements included:

  • Improved recruitment process for all candidates overall¬†

  • Custom assessments built to incorporate NEXT company values¬†

  • Fully branded NEXT online platform for seamless candidate journey

  • Built to concurrently sift at large volumes, sifting over 150,000 candidates in 3 months

  • Reduced the cost of up-front candidate sifting

  • Fully integrated with the Next Applicant Tracking System

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