Case Studies

How to Select the Best Team for a Customer Project

Estimated reading time: 2 minutes

Client: World leading strategy consulting firm

Challenge: Staffing consulting projects is biased and leads to high turnover

Solution: An IDP solution designed to match new project specifications with employee skills and interests


Staffing projects are biased

For a world leader in consulting and advising, staffing assignments to customer projects are challenging. Some customer engagements can be lengthy and arduous, with hard deadlines and slim profit margins. To consistently ensure the best outcomes, our client needs to balance employee attributes (i.e. skill, experience, geography, cost, equity) to fairly staff new client engagement teams, along with their employees’ goals and ambitions. If these factors are not considered, our client risks high turnover numbers, which can be costly.

Data related to employee demographics, skills, and ambitions reside in structured (tabular) and unstructured (natural language) formats, requiring an internal staffing manager to review dozens of candidates and select the perceived best fit. This process can be biased due to unconscious bias, resulting in top candidates being overlooked.


Building a tool that matches projects with employees

Infinia ML used deep neural networks to build a matching tool that predicts the top staff arrangement for a given project. Staffers select factors (i.e. budget, timeline) to select the best roster. The output of the machine learning models showcases an array of staffing options, allowing the best decision based on each candidate. To continuously refine and improve the staffing model, we developed capabilities to leverage individuals’ future project performance.


Seamlessly staff projects with the best people

Infinia ML’s solution provides our client with a seamless way to staff customer projects with the best people while increasing margins and reducing bias. This solution also increases the likelihood that employees will be staffed on projects aligning with their individual interests and goals, boosting employee satisfaction and retention.

Applying these techniques elsewhere

  • Correlate internal company data sets
  • Select the strongest candidate for a promotion

Related Case Studies