Project Details

AtliQ Hardwares operates across multiple global markets with a wide product mix. Yet gaps in sales consistency, target achievement, and visibility into true profitability made it difficult for stakeholders to assess performance accurately.

This case study addresses those challenges through two focused analytical streams:

➡️ Sales Analytics
Analyzes three years of customer, product, and market performance (2019–2021) to identify growth drivers, underperforming segments, and regional trends.

➡️ Finance Analytics
Breaks down profitability using three years of P&L data to evaluate financial health, margin behavior, and division-level performance.

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This project builds a unified Power BI reporting system for AtliQ Hardwares by consolidating multi-year sales, finance, marketing, and supply chain data into one analytics suite. The goal was to replace siloed Excel reports with interactive, role-based dashboards that give leaders clear visibility across 27 global markets.

The solution enables:

🔸 P&L and profitability tracking (customer & product level)

🔸 Forecast accuracy and supply chain performance monitoring

🔸 Opex efficiency and marketing impact evaluation

🔸 Executive summaries for quick decision-making

Business impact:

⭐ Identifies high-risk regions, margin leaks, and promotional inefficiencies

⭐ Improves pricing, resource allocation, and market prioritization

⭐ Speeds up decision-making and strengthens AtliQ’s analytics maturity

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**Business Objective**
- Analyzed AtliQ Grands' hotel performance to identify revenue opportunities and improve business decisions in a competitive hospitality market.

**Tools & Techniques**
- Used Power Query for data cleaning and transformation.
- Built a star schema data model and created DAX measures in Power BI.

**Key Analysis**
- Tracked Revenue, RevPAR, ADR, Occupancy %, Realization %, and DSRN.
- Compared weekday vs. weekend performance and analyzed booking channels, hotel ratings, and property-level trends.

**Business Impact**
- Identified opportunities to optimize pricing strategies and improve occupancy.
- Highlighted low-performing hotels for further investigation.
- Delivered interactive insights to support data-driven revenue management decisions.

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This project analyzes sales, product, and customer data for AtliQ Hardwares to understand how portfolio expansion, sales performance, and pricing strategies influence business outcomes. Using SQL, the analysis focuses on transforming raw relational data into structured insights across products, channels, and customers.

Key outcomes:

➡️ Quantified product portfolio growth (+36% YoY) and identified segment concentration, with expansion heavily skewed toward Accessories and Notebook categories.

➡️ Analyzed sales performance across time, highlighting strong seasonality, quarterly demand imbalances, and event-driven revenue spikes.

➡️ Diagnosed channel and pricing dynamics by uncovering high dependency on retailer-driven revenue (~73%) and consistently high customer discounts (~30%), indicating limited pricing power.

➡️ Identified performance concentration across top products and significant cost variation, highlighting risks in revenue dependency and margin variability.

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This project analyzes booking, occupancy, revenue, and customer ratings data for AtliQ Hotels (May–July 2022) to understand how demand, utilization, and monetization differ across cities, hotel types, room categories, and booking platforms. Using Python and Pandas, the analysis focuses on data validation and exploratory diagnostics to surface structural performance patterns and risks.

Key outcomes:

➡️ Validated and cleaned booking and occupancy data by fixing invalid guest counts, revenue outliers, and capacity inconsistencies to establish a reliable analytical base.

➡️ Diagnosed utilization patterns across cities and room categories, highlighting strong weekend-driven demand, stable occupancy across room types, and persistent city-level performance differences.

➡️ Identified revenue drivers and structural risks by quantifying platform-led demand, exposing the gap between occupancy and revenue across cities, and using ratings data to flag experience-related constraints.

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🔍 **Business Problem:** Manual health insurance quote generation could take up to 24 hours, delaying customer decisions.

⚙️ **Tools & Methods:** Built an end-to-end ML pipeline using Python, XGBoost, Pandas, and scikit-learn. Implemented data preprocessing, medical risk scoring, and separate models for ages ≤25 and >25. Saved models using Joblib and deployed an interactive Streamlit app.

📊 **Key Insight:** Age-based model routing enables separate premium estimation for younger customers and older age groups.

🚀 **Recommendation:** Automate initial premium estimates to accelerate quoting, with human review where required.

🎯 **KPIs:** Quote turnaround time, prediction error, and application usage.

💼 **Business Impact:** Reduced reported quote turnaround from 24 hours to approximately 30 seconds (99.97% faster). Model performance should be reported using verified regression metrics such as MAE, RMSE, or R²; the 92% accuracy claim is omitted pending validation.

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🔍 Business Challenges

🔹 Limited visibility into revenue trends, city-wise performance, customer distribution, and settlement patterns.

📊 Key Insights

🟢 Analyzed ₹989M in revenue across 26,841 customers.
🟢 Offline-Agent contributed ₹551M (55.67%) of total revenue.
🟢 Evaluated 4 sales channels and 5 age groups to understand customer and policy preferences.

🚀 Actionable Recommendations

🟠 Strengthen agent-led sales while identifying opportunities to grow digital channels.
🟠 Tailor marketing campaigns and policy offerings to customer preferences across age groups.
🟠 Monitor monthly and city-wise performance to identify growth opportunities and underperforming segments.

Business Impact: Enabled data-driven sales planning, targeted customer engagement, and improved visibility into insurance business performance using an interactive Power BI dashboard.

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☕ **Business Problem:** Monday Coffee needed to identify the best Indian cities for its first physical stores.

📊 **Analysis:** Used PostgreSQL to analyze 10,388 orders across 14 cities, applying joins, CTEs, window functions, and data quality checks to compare revenue, customer base, market potential, and rental costs.

💡 **Key Insights:**
🔹 Pune generated the highest revenue at ₹12.6 lakh.
🔹 Delhi had the largest estimated coffee market, with 7.75 million potential consumers.
🔹 Jaipur had the lowest estimated rent per customer at ₹157.

🚀 **Recommendation:** Prioritize Pune, Delhi, and Jaipur for expansion, starting with four core products sold across all cities.

📈 **Measured Findings:** The three recommended cities contributed 46% of revenue from 38% of customers. Revenue-to-rent ratios were 82× in Pune, 74× in Jaipur, and 7.5× in Mumbai.

*Market size assumes 25% of residents drink coffee. Revenue-to-rent ratios are comparative indicators, not profit or ROI.*

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**1. Business Problem:** Analyze vendor performance, purchasing costs, profitability, and unsold inventory to support better procurement decisions.

**2. Tools & Methods:** SQL/SQLite for ingestion and ETL; Python (Pandas, SciPy, Matplotlib, Seaborn) for cleaning, EDA, correlation, and hypothesis testing; Power BI for dashboards. Excluded unprofitable and zero-sales records in selected analyses.

**3. Key Insights:** Top 10 vendors account for 65.69% of purchases; 198 brands have low sales but high margins; large orders show 72% lower unit costs; unsold inventory is valued at $2.71M.

**4. Recommendations:** Diversify suppliers, review pricing and promotions, optimize order quantities, and investigate slow-moving stock.

**5. KPIs:** Purchase concentration, unit cost, profit margin, inventory turnover, and unsold inventory value.

**6. Business Impact:** Identified cost and inventory optimization opportunities; benefits are potential, not measured after implementation.

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