Case Study

Screening >100k applications for a single positions is a tedious task and takes up too much of HR man hours.

PROBLEM

Industury: Recruitment & Staffing
Client Persona: BPO, Call Centers, Insurance Companies

Screening >100k applications for a single positions is a tedious task and takes up too much of HR man hours.

  • 45,000 employees globally with a 2% turnover rate.
  • Over 120,000 candidates requiring screening.
  • 25 executives responsible for managing roles.
  • Lack of automation, Applicant Tracking System (ATS), or Artificial Intelligence (AI) tools.
  • Utilization of tools like ADP, TalkPush, SHL, and Emmersion.
  • Absence of a single source of truth or command center for data.
  • Heavy reliance on manual workflows with minimal automation.
  • No AI-enabled learning from data.
  • Lack of predictive analytics for making informed decisions.
  • Bottlenecks in processes due to manual workflows.
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Analysis and Discovery

At this stage, we have access to the data granted by the client. Since we have already validated the data, we can proceed to cleaning and processing. Processing entails deciding on the best course of action as it pertains to the kind of data analysis model or machine learning algorithm that fits the data.

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Visualisation and Final Report

Immediately following the processing and analysis stage, we verify the findings and pass on the results to our vis team. In the case of complex problems, we can use Tableau to visually represent data to support the recommendations made by our model in the last step.

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