Workforce Analysis in Customer Loan Data: A HDFC Bank Use Case

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You've probably heard about HDFC Bank, right?

It's this giant in the Indian banking world that pretty much has a loan solution for anything you can think of - from home loans to personal buffers for those rainy days. But here's the scoop for 2024: they've rolled out these home loans linked to the Repo Rate.

What's cool about this?

Well, interest rates are playing in the range of 9.05% to 9.80%, tailored to what you need and how much you're borrowing.

In the financial year of 2023, HDFC was all about individuality, dedicating a whopping 83% of its loans to individuals. We're talking about home loans that have filled up the piggy banks with over 88.09 Billion U.S. dollars. That’s a lot of dreams being funded!

Now, let's talk Data for a sec. HDFC is not just about traditional banking; they're all in on digital loan applications. This move is a game-changer, making everything more accessible and flexible for folks like you and me. They play with lots of data every single day.

In this article, we'll understand how Banks like HDFC might uses Workforce Data Analysis to improve its loan operations.

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Try this business use case by yourself here👇

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About Dataset

Let’s Inspect our Customer Loan Data. This dataset offers an overview of loan agreements, showcasing essential details like customer information, loan specifics, and associated sales personnel. It reveals uniform loan conditions, such as interest rates and scheme codes, across various agreements. The involvement of multiple dealers and a consistent focus on the Hyderabad region highlight the localized nature of these financial transactions.

Additionally, the dataset illustrates the organization's structured approach to loan processing and sales management, providing insights into its operational focus and market strategy.

Each row in the dataset corresponds to a loan transaction . We have the following features:

Personal and Loan Information

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Loan Conditions

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Sales Information

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Geographical Information

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The Customer Loan Data contains over 16999 rows and 8 of our Columns have String Data type, 6 of our Columns have Integers Data Type, 1 column Float Data type has and 1 of the columns have Date Data Type.

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How HDFC Uses its Workforce Analytics to Improve Loan Operations?

To start with the analysis of workforce analysis, it is necessary to follow the 4 factors of Data Analysis and that are : 

1. Identify the users or stakeholders for the dashboard.

2. Design Empathy Map to define Users' Goals and Challenges or pain points.

3. Identify Metrics or KPIs Matter the Most.

4. Understand the Objectives and Goals.

5. Ask Business Questions

I know it looks a bit overwhelming, that's why in this article, we'll lay the foundation for a top-notch Workforce analysis‍.

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Step 1: Identify the users or stakeholders for the analysis

Define the User or stakeholder  who will use the Workforce analysis data story. In our case, We’ll use a Loan Manager. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story. 

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User Persona: Loan Manager

Responsibilities:

• Assessing loan applications to determine creditworthiness and compliance with lending criteria.
• Managing loan portfolios to ensure a healthy balance between risk and return.
• Monitoring loan disbursement and repayment activities to ensure they adhere to agreed terms.
• Collaborating with sales and finance teams to develop and refine loan products.
• Providing financial advice and support to clients throughout the loan process.

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Needs:

• Access to detailed financial data and credit reports for risk assessment.
• Tools for managing and analyzing loan portfolios.
• Up-to-date information on market conditions and regulatory changes affecting lending.
• Systems for tracking loan disbursements, repayments, and performance.

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Challenges:

• Balancing the need to grow the loan portfolio with the imperative to minimize bad debts.
• Quickly adapting to changes in financial regulations and compliance requirements.
• Managing relationships with borrowers, including handling late payments and renegotiating terms.
• Identifying and mitigating risks associated with loan defaults and market fluctuations.

Since, we have defined the User Persona & have mapped the needs, challenges and responsibilities. Our Next step will be to design an Empathy Map which will map the pain points of the user.

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Step 2: Design Empathy Map

To truly connect with the experiences and expectations of Loan Manager, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. 

By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.

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Step 3: Identify the Key Performance Indicators (KPI’s)

The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into workforce analysis and loan operations.

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Step 4: Understand the Goals & Objectives of User

Now, on the basis of Empathy Map and KPI’s, we need to define our goals and objectives of the Users. So, that it will align with the data story functionalities to ensure decision making. Here are the Key Objectives & Goals :

Objective : Enhance workforce efficiency and performance in the loan management department to optimize resource utilization and drive business growth. This objective aims to improve sales manager productivity, streamline loan processing, and recognize top performers. By implementing targeted training programs and performance evaluations, we seek to achieve measurable improvements in loan disbursal rates and customer satisfaction within the next quarter.

Goals : Efficiently improve sales manager performance, streamline loan processing workflows, and recognize top performers to enhance customer satisfaction and drive business growth. These objectives aim to optimize resource utilization and achieve measurable improvements in loan disbursal rates and customer conversion within the next quarter.

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Actionable Insight: Refrain from shipping machines via same-day delivery mode due to their profitability constraints. Allocate machines to more cost-effective shipping methods to mitigate losses and improve overall profitability within the same-day.

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Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives. 

Workforce Analysis:

1. How is our workforce distributed across all regions? Can we compare it to our total loan amount disbursed?

• With 40 Sales managers, 490 Sales Representatives and 691 Dealers across the country have reached 16998 customers.
• Metric: Total Number of Employees, Total Loan amount

2. What is the average loan amount and number of customers converted by sales managers? Are there any sales manager performance we need to investigate?

• There seems to be a difference between our top sales manager i.e. Rakesh Kumar and other remaining managers.
• Metric: Average Loan Amount, Customer Count

3. Who are our top 10 sales managers in terms of loan amount and sales rep and dealers under them?

• Metric: Count of Sales Rep, Dealer, Loan Amount

4. What is the reason behind the exceptional performance of our top sales manager?

• Since it seems Rakesh Kumar is operating in all the regions across the country, he has exceptional performance.
• Metric: Total Loan Amount, Customer Count

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Explore Workforce Analytics Data Story Here👇

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Outcomes

1. The workforce comprises 40 Sales Managers, 490 Sales Representatives, and 691 Dealers, reaching 16,998 customers, indicating a broad national coverage that should be analyzed against the total loan amount disbursed for productivity insights.

2. An analysis on the average loan amount and customer conversions per sales manager is needed, with a focus on Rakesh Kumar's exceptional performance compared to others for potential improvement areas.

3. Identifying the top 10 sales managers by loan amount and their team's composition (Sales Reps and Dealers) will highlight the key performers and their strategies.

4. Rakesh Kumar stands out for operating across all regions and achieving exceptional performance metrics, suggesting his methods could offer valuable insights for the entire sales team.
   
   

Conclusion:

In conclusion, understanding the factors behind Rakesh Kumar's outstanding performance, alongside the strategic distribution of the sales force and their effectiveness in loan disbursement, can provide a blueprint for replicating success across the organization. By aligning workforce distribution strategies with proven high-performance models, the company can optimize its sales operations to enhance productivity and increase the total loan amount disbursed.