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Insights and Findings

After performing the Hotel Revenue Analysis using SQL and Power BI, the following key insights and findings were discovered:

  1. Revenue Growth by Year:

    • The visual data story revealed that the hotel's revenue showed consistent growth over the years 2018, 2019, and 2020. This positive trend indicates that the hotel's business is thriving, with an increasing number of guests and higher average daily rates (ADR).

  2. Revenue Segmentation by Hotel Type:

    • By segmenting the revenue data based on hotel types, it was observed that the Resort Hotel generated higher revenue compared to the City Hotel. This finding suggests that the Resort Hotel may have unique features or amenities that attract more guests and contribute to higher revenue.

  3. Parking Space Demand Analysis:

    • The analysis of required car parking spaces using a matrix table showed that both hotel types experienced a steady demand for parking spaces over the years. However, the Resort Hotel consistently required more parking spaces, indicating a higher proportion of guests with personal cars.

  4. Impact of Discounts on Revenue:

    • The visualization of average discounts offered to different market segments demonstrated that discounts had a minimal impact on revenue. Despite varying discount rates, the overall revenue remained robust, implying that guests were willing to pay the standard rates for hotel services.

  5. Seasonality in Average Daily Rate (ADR):

    • The line chart showcasing ADR trends over the years revealed a cyclical pattern. ADR peaked during the peak tourist seasons and dipped during off-peak periods. This seasonality insight can help the hotel management strategize pricing and promotional activities.

  6. Guest Preference for Meal Plans:

    • The data showed that guests predominantly preferred the Bed and Breakfast (BB) and Half Board (HB) meal plans, while the number of guests opting for Full Board (FB) and All-Inclusive (AI) plans was relatively low. This suggests that guests prefer having the flexibility to explore dining options outside the hotel.

  7. Impact of Booking Changes on Cancellations:

    • By analyzing the booking changes and cancellation data, it was observed that guests who made frequent booking changes were more likely to cancel their reservations. This insight highlights the importance of providing clear and flexible booking policies to reduce cancellations.

  8. Customer Segmentation and Repeat Guests:

    • The data revealed that the majority of guests were transient customers, and repeat guests were relatively rare. To improve guest loyalty and encourage repeat visits, the hotel management could consider implementing personalized loyalty programs and tailoring guest experiences.

These insights and findings provide valuable information for hotel management to make data-driven decisions and enhance revenue growth, guest satisfaction, and operational efficiency. The end-to-end visual project in Power BI enables stakeholders to have a comprehensive understanding of the hotel's performance and potential areas for improvement.