https://github.com/aniruddhakhedkar/analysis-of-airbnb-data-to-understand-customer-satisfaction

Power_BI_Project_1
https://github.com/aniruddhakhedkar/analysis-of-airbnb-data-to-understand-customer-satisfaction

advanced-filtering dashboard dax

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Power_BI_Project_1

• Host: GitHub
• URL: https://github.com/aniruddhakhedkar/analysis-of-airbnb-data-to-understand-customer-satisfaction
• Owner: Aniruddhakhedkar
• Created: 2024-07-24T09:11:14.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-16T05:58:05.000Z (3 months ago)
• Last Synced: 2024-09-16T07:05:29.452Z (3 months ago)
• Topics: advanced-filtering, dashboard, dax
• Homepage:
• Size: 1.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project Title:- Analysis of Airbnb Data To Understand Customer Satisfaction

### Project Description:- 

The project deals with the Airbnb data to reveal insights into it's customer experiences and satisfaction levels with the numerous listed stays. The project also includes gaining a deeper understanding of Airbnb's operations and draw meaningful insights from the data. 

Furthermore, the insights were derived using the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework and developed a real-time monitoring dashboard to efficiently track & achieve all project objectives. This approach ensured a systematic & effective goal management throughout the project. The detailed analysis procedure, findings, and recommendations are documented in the PowerPoint presentation attached above for reference and review.

### Tools/Software:-Power BI and Microsoft PowerPoint

### Tasks/Objectives:-

1) Assessing District Location Scores: The aim is to pinpoint the location in the district with the least favorable location scores.

2) Examining Host Response Time Impact: The goal is to delve into the relationship between host response times and the overall ratings of Airbnb listings providing valuable insights.

3) Visualizing Airbnb Listing Prices: The objective is to create visual representations of Airbnb listing prices across different cities and summarize any noteworthy trends or variations.

4) Analyzing Composite Scores: The task involves creating a composite score that integrates check-in experience and host communication for various districts with subsequent analysis and insights.

5) Calculating Listing Age and Host Tenure: This objective entails computing the age of Airbnb listings and identifying hosts who have accumulated more than ten years of hosting expertise.

6) Property Type Price Analysis: The task involves the creation of a visual tree map that displays average prices for various room and property types with specific attention given to the property type associated with the highest prices for entire places.

7) Crafting a Comprehensive City Insights Report: This objective entails the creation of a comprehensive report that presents listing prices, guest ratings, and visitor trends for multiple cities, with a particular focus on assessing changes in visitor trends in 2020 in contrast to earlier years.

### Recommendations to the Airbnb:-

1) For least favorable locations, take more detailed review from customers and hosts.

2) Prompt response by hosts while accepting the guest request, impacted the listings’ overall rating. Hence, Airbnb should communicate this findings to hosts, as if their ratings improved they can charge premium price.

3) Customers can pay higher amount for stay if host identity, listings’ instant bookableness, listings’ overall rating are properly maintained. Hence, company should focus on improving visibility of these factors through its digital structure or platforms to potential customers.

4) Check-in level, cleanliness, and host communication are adequately being maintained, hence company can leverage these factors to uniquely position itself in the market.(Positioning)

5) 18.4% (51,330) properties are listed from more than 10 years, so a deep study should be carried out to further improve serviceability and profitability of these properties. 

6) As the customer onboarding had been increasing post 2018, company should crease new segment and tap the unmet needs of the new customers (Market development & penetration). 

### Data Description/Data Dictionary:- The data dictionary has been provided as an Excel file above.