P2:

Hotel Room Pricing and Demand Management

  • Key Objective: Maximize occupancy for a hotel with a focus on pricing strategies and market competition.

  • Room Packages:

    • Different types of rooms are offered:
    • Standard Rooms
    • King Rooms
    • Queen Rooms
    • Various packages available with different prices:
    • Package A: $100
    • Package B: $110
    • Package C: $90

  • Occupancy Goal: 95 rooms need to be occupied daily.

    • Importance of ensuring that all rooms are filled to avoid vacancies.
    • Maintain a buffer for cancellations.

  • Optimizing Price:

    • Setting the right price is critical to attract guests while considering competitors’ pricing.
    • Example: If competitors price a similar package lower (e.g., $105), it could affect occupancy.

  • Environmental Factors Impacting Pricing:

    • Analyze market demand and local events (e.g., festivals) that attract tourists.
    • Marketing campaigns aimed at specific demographics (e.g., tourists from Britain) may lead to discounts.
    • Example of a discount strategy:
    • Regular price: $110
    • Discounted price for targeted campaigns: $90.

  • Risks of Pricing Below Cost:

    • Price adjustments must consider minimum costs. E.g., if the cost to maintain a room is $85, pricing lower risks losses.
    • Bulk package sales might occur during low seasons, yet still better than incurring a total loss.

  • Data-Driven Decision Making:

    • Historical data analysis helps understand seasonal demand and customer preferences.
    • For instance, looking at past visits and popular room types (e.g., 40% of guests prefer a deluxe package).
    • Market observation (e.g., competitor actions, local events) supplies an additional 60% of features influencing pricing strategy.

Banking Environment: Default Prediction

  • Main Goal: Predict whether a customer will default on their loan payments.

  • Loan Details:

    • Customers are expected to make monthly payments on their loans on time to avoid default status.
    • Default defined as missing three consecutive payments.

  • Bank's Approach:

    • Analyze previous bank account and loan history, as well as demographic information (e.g., income, expenses).
    • Calculate default risk based on financial obligations:
    • E.g., monthly loan payment + additional expenses.

  • Calculating Affordability:

    • Example case for predicting loan default for a customer with a $300,000 salary:
    • Monthly obligations (loans, rent, living expenses).
    • Estimating if total monthly payment obligations exceed income capabilities.

  • Influencing Factors:

    • Historical default patterns (previous loan behavior) significantly influence predictions.
    • External environmental factors (e.g., economic inflation) matter less compared to internal bank data insights.

  • Comparison of Features:

    • In the hotel example, 80%-90% of features came from the environment, whereas in the banking example, 95%-90% derived from internal data sources.

Importance of Feature Identification

  • Feature Selection is critical across different domains:

    • No universal rule for identifying features; it varies based on specific problem context.
    • Exposure to various problems improves identification skills in machine learning tasks.

  • Internal vs. External Features:

    • Balance between internal data (historical customer data, past behaviors) and external market influences (general economic conditions, environmental factors).

  • Conclusion: Appropriate feature extraction leads to more accurate predictive models. No hard rules; experience plays a significant role in identifying relevant data points for problem-solving.