Adventure Works Sales Performance Analysis
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📂 Table of Content
📋 Project Overview
This project focuses on transforming raw transactional data into meaningful insights using Microsoft Excel. Power Query is used to clean and shape the data, dynamic relationships are built with Power Pivot, and DAX formulas are applied to create powerful, custom calculations. Advanced Excel functions enhance the analysis with logical depth. All these elements are brought together through clear, engaging visuals that tell a compelling data-driven story.
📌Workflow:
- Power Query Magic — Import, clean, and shape data effortlessly for analysis readiness.
- Power Pivot Relationships — Build data model connections for dynamic, integrated reporting.
- DAX Formulas — Create powerful calculations and time-based measures for deeper insights.
- Excel Functions — Apply advanced functions to add logic and flexibility to your reports.
- Visual Storytelling — Design clear, interactive visuals that convey data-driven narratives.
- Microsoft Excel
- Power Query
- Power Pivot
📊 Summary of the Dataset
Hotel Bookings Dataset (hotel_bookings)
- Total Records: 119,390
- Total Columns: 32
Key Columns:
- hotel — Type of hotel (Resort or City)
- is_canceled — Booking cancellation indicator
- lead_time — Days between booking and arrival
- arrival_date_year — Arrival year
- arrival_date_month — Arrival month
- arrival_date_week_number — Week number
- arrival_date_day_of_month — Day of arrival
- stays_in_weekend_nights — Weekend nights stayed
- stays_in_week_nights — Weekday nights stayed
- adults — Number of adults
- children — Number of children
- babies — Number of babies
- meal — Meal plan
- country — Guest country of origin
- market_segment — Booking source category
- distribution_channel — Booking channel
- is_repeated_guest — Repeated guest flag
- previous_cancellations — Past cancellations
- previous_bookings_not_canceled — Completed past bookings
- reserved_room_type — Reserved room category
- assigned_room_type — Assigned room category
- booking_changes — Number of booking changes
- deposit_type — Deposit type
- agent — Travel agent ID
- company — Company ID
- days_in_waiting_list — Waiting list days
- customer_type — Customer classification
- adr — Average daily rate
- required_car_parking_spaces — Car parking spaces requested
- total_of_special_requests — Special requests count
- reservation_status — Final reservation status
- reservation_status_date — Status date
🔍 Scope
This analysis focuses on the following key objectives:
- Compare year-over-year KPI performance across Revenue, Profit, COGS, and Transaction metrics.
- Identify above-average yearly performance and highlight standout periods.
- Analyze monthly profit trends to detect seasonal patterns and business peaks.
- Assess profit variations by different week types (weekday vs. weekend).
- Conduct quarterly profit analysis for strategic long-term assessment.
- Examine profit distribution by weekdays to uncover daily operational patterns.