Airbnb Data Analysis New York City

Goals and Business Needs :

1. To provide hosts with insights into their listings' performance to increase revenue and occupancy rates.

2. To help hosts make data-driven decisions and improve the quality of their listings to attract more guests.

3. To enable hosts to identify trends and patterns in guest behavior and preferences to enhance the guest experience.

4. To provide a competitive edge for hosts by comparing their performance with similar listings in the market.

5. To increase customer satisfaction and loyalty by improving the quality of listings and enhancing the overall guest experience.

To discover and present meaningful insights from Airbnb Dataset , I followed these steps:

1. Identified key performance indicators (KPIs): Determined which metrics are most important to business needs and focused on analyzing those. Some examples include occupancy rates, average nightly rates, or guest review scores.
2. Filter and drill down: Used filters and drill-down functionality to explore data in more detail. For example, filter by location, property type, or guest demographics to identify trends or patterns.
3. Create visualizations: Used different types of visualizations such as bar charts, line charts, and scatter plots to highlight patterns and trends in the data.
4. Analyze the data: Looked for correlations, anomalies, and trends in the data. Identified areas where Airbnb is performing well and areas where we can improve.
5. Present insights: Summarized the findings in a clear and concise manner using charts, graphs, and tables. Used storytelling techniques to explain the insights and highlight their significance.
6. Take action: Used the insights to make data-driven decisions and take action to improve the listings' performance.

By following these steps, effectively discovered and presented meaningful insights from the Airbnb Tableau dashboard, enabling us to make data-driven decisions and improve the quality of Airbnb listings.