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Case Study Overview
Welcome to our in-depth case study on the transformative impact of AI-Based Psychometric Assessments in the Financial Services Sector. Conducted with a leading NBFC in India, this study utilized PMaps’ innovative Predictive Sales assessments to evaluate the performance and potential of employees across four critical departments: Automotive Loans (AD Loan), Car Loans, Farmer Equipment and Services Loans (FES Loan), and Pre-Owned Car Loans (POCL Loan).
With participation from 300 employees nationwide, this comprehensive study sheds light on the key competencies driving success in these roles. By analyzing performance data and employee feedback, we uncovered valuable insights into the characteristics that distinguish high performers from their peers.
Key Insights
Critical Competencies for Success
While conducting the pilot study for a leading organization, we observed the essential skills and traits that predict success across different departments, including Automotive Loans, Car Loans, Farmer Equipment Services, and Pre-Owned Car Loans. Even though all these roles fall under the broader category of sales, we discovered that each department requires distinct competencies. By identifying these critical competencies, we helped the organization streamline their hiring process and ensure that they selected candidates who were primed to excel in their specific roles. This insight can help you refine your own hiring criteria to build a more effective team.
High vs. Low Performers
We discovered unique attributes that set high performers apart in each department. Our analysis provided a clear blueprint of the characteristics to look for in potential hires. This differentiation is crucial for avoiding costly hiring mistakes and building a team of top-tier professionals. Understanding these attributes can help you focus on what truly matters during recruitment, ensuring you hire candidates who are not only skilled but also a perfect fit for their specific sales roles.
Overcoming Challenges
During our study, we identified common obstacles faced by employees and observed how high performers navigate these challenges successfully. These insights were tailored to each department, enabling the organization to better support their team and foster a resilient, high-performing workforce. Knowing these strategies allows you to proactively address potential issues and cultivate a more productive work environment.
Motivational Drivers
We uncovered what keeps top performers engaged and motivated in their roles. These motivational drivers varied across departments, highlighting the importance of understanding what specifically motivates employees in different sales roles. Our findings provided the organization with the tools to keep their team inspired and productive. This knowledge is invaluable for creating an environment where your employees feel valued and driven to succeed.
Why You Should Read the Entire Case Study
This case study is packed with actionable data and strategies that can transform your hiring and talent management processes. Equip yourself with the knowledge to enhance your recruitment strategy, boost employee performance, and drive organizational growth. Download the full case study now and stay ahead in the competitive financial services sector.
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ABSTRACT
This pilot study evaluates the effectiveness of PMaps' Predictive Sales Assessment, a key component of PMaps' AI-based psychometric assessment suite, in enhancing the recruitment process for sales roles within a prominent banking financial services company. The study examines the reliability, validity, and overall impact of these assessments on hiring efficiency and employee performance.
The pilot involved 300 participants across four departments: AD Loan (Automotive Division), Car Loan, FES Loan (Farmer Equipment and Services, also known as Tractor Loan), and POCL Loan (Pre-Owned Car Loan). Data was collected from employees working for the past six months across four regions in India: North, East, West, and South. The sample included both high and low performers, providing a comprehensive overview of the assessment's predictive validity.
About the Organization
A prominent non-banking financial company (NBFC) in India, with an employee base of over 10,000, has established itself as a key player in the financial services sector. This company provides a wide range of financial products and services, including loans for vehicles, home finance, SME finance, personal loans, insurance, and rural banking services.
With a vast network of branches spread across the country, the company has a significant presence in both urban and rural areas, striving to meet the financial needs of diverse customer segments.
Objective
The pilot study aimed to:
- Assess the effectiveness of PMaps' Predictive Sales Assessment in predicting job performance.
- Evaluate the reliability and validity of the assessments compared to traditional hiring methods.
- Determine the impact of the assessments on hiring efficiency and employee performance.
Methodology
The PIVOT study employed a structured methodology to address the recruitment challenges faced by the NBFC. The process involved four key steps, utilizing advanced data collection and machine learning techniques to develop and refine predictive models for hiring.
Step 1: Define the Business Problem
PMaps Diagnostic began by identifying the business problem and setting the desired hypothesis. This step involved understanding the specific recruitment challenges faced by the company in its recruitment process for front-line sales roles:
Identifying the Right Talent: The company struggled to find candidates with the right combination of skills, personality, and cultural fit for their sales roles, which are crucial for driving business growth and customer satisfaction.
Inconsistent Hiring Outcomes: Traditional hiring methods were not consistently yielding high-performing and long-tenured employees, leading to variability in team performance and productivity.
High Turnover Rates: The company experienced high turnover rates, indicating a mismatch between hired candidates and job requirements, which further strained their recruitment resources and affected operational efficiency.
Time-Consuming Recruitment Process: The existing recruitment process was lengthy and inefficient, delaying the onboarding of new employees and impacting overall business operations.
Limited Assessment Tools: The lack of sophisticated assessment tools made it difficult to accurately evaluate candidates' competencies and potential for success in sales roles, resulting in suboptimal hiring decisions.
STEP 2: Test Design - Solution
To address the recruitment challenges faced by the NBFC, PMaps proposed a comprehensive pilot study utilizing its AI-based psychometric assessments. The solution involved designing a customized assessment framework tailored to evaluate the competencies critical for sales roles across four departments:
- AD Loan (Automotive Division)
- Car Loan
- FES Loan (Farmer Equipment and Services, also known as Tractor Loan)
- POCL Loan (Pre-Owned Car Loan)
Once the tests were created, they were shared with the HR department of the company. The HR team then distributed the assessment links to a selected sample of employees, ensuring an equal distribution between high performers and low performers.
Around 300 employees participated in this study, with their identities and performance unknown to PMaps to maintain objectivity.
Step 3: Collecting Relevant Data
Upon completion of the tests, PMaps consolidated the assessment results into a comprehensive report, which was subsequently shared with the company. The company then provided PMaps with the employees' performance data, enabling the analysis of the validity of the assessments and facilitating future test design enhancements.
The Data Collected Included:
- Job Description (JD) Analysis: The job descriptions were analyzed to ensure the assessments accurately reflected the skills and competencies required for the roles.
- Employee Demographic Information: Details such as age and overall experience were gathered to provide context for performance metrics.
- Performance Data: Performance metrics from the past quarter were analyzed to correlate assessment results with actual job performance.
- Focused Group Discussions: Both high performers and low performers participated in discussions to identify behaviors leading to successful performance, challenges faced, and key competencies distinguishing them. These insights validated the competencies assessed by the tests.
- Stakeholder Feedback: Feedback was collected from stakeholders to understand their requirements and expectations for new hires, ensuring alignment with organizational goals.
Collecting this data was crucial for accurately predicting job performance based on a comprehensive understanding of the existing workforce.
Step 4: Building the ML Model and Improving Accuracy
In this step, the collected data was used to build the machine learning model. Various ML models were utilized, including:
- Two-Class Decision Forest
- Random Forest
- XG Boost
- Two-Class Logistic Regression
Python was the primary analytical tool used for implementing these models. These tools and models helped create a robust predictive framework capable of accurately assessing candidate suitability for sales roles.
Improving Model Accuracy
The final step focused on enhancing the accuracy of the machine learning model. Techniques used included:
- Cross-Validation: ML model techniques were cross-validated using an 80-20 ratio for training and testing. This ensured the model was tested on unseen data to accurately gauge its performance.
- Reinforcement Learning Techniques: Applied iteratively to improve the model's predictions and adapt to new data patterns.
This iterative process ensured the model was refined and validated to provide the most reliable and accurate predictions possible.
Findings and Insights
Our comprehensive pilot study on the PMaps Predictive Sales Assessment revealed impressive results, demonstrating an accuracy of 79% in predicting job performance within the financial services sector. This groundbreaking accuracy underscores the effectiveness of AI-based psychometric assessments in identifying top talent and optimizing recruitment strategies.
By integrating these assessments, organizations can experience:
- Improved Hiring Accuracy: Select candidates who are more likely to excel in their roles, reducing turnover and enhancing overall team performance.
- Enhanced Employee Engagement: Align candidates with roles that match their skills and personality, leading to increased job satisfaction and retention.
- Data-Driven Insights: Gain actionable insights into candidate capabilities and potential, enabling more informed hiring decisions.
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