Airbnb Senior Data Analyst Interview

1. How to Identify the Top 3% Revenue-Generating Hosts in Each Country Using SQL?

ANS-

To find the top 3% of hosts by revenue in each country, first filter hosts with at least 50 stays in the last 6 months. Then use PERCENT_RANK() or NTILE() over a PARTITION BY country to isolate the top performers.

WITH filtered_hosts AS (
  SELECT host_id, country, SUM(revenue) AS total_revenue
  FROM stays
  WHERE stay_date >= CURRENT_DATE - INTERVAL '6 months'
  GROUP BY host_id, country
  HAVING COUNT(*) >= 50
),
ranked_hosts AS (
  SELECT *,
         PERCENT_RANK() OVER (PARTITION BY country ORDER BY total_revenue DESC) AS revenue_rank
  FROM filtered_hosts
)
SELECT * FROM ranked_hosts
WHERE revenue_rank <= 0.03;

2. SQL Trick to Find Users Who Rebook Within 7 Days After Canceling — Behavioral Retargeting Analysis?

ANS- 

To detect users who cancel and rebook within 7 days, self-join the bookings table on user ID and compare booking dates using DATEDIFF() or booking_date - cancel_date.

WITH canceled AS (
  SELECT user_id, booking_date AS cancel_date
  FROM bookings
  WHERE status = 'cancelled'
),
rebooked AS (
  SELECT user_id, booking_date AS new_booking
  FROM bookings
  WHERE status = 'confirmed'
)
SELECT c.user_id, c.cancel_date, r.new_booking
FROM canceled c
JOIN rebooked r ON c.user_id = r.user_id
WHERE r.new_booking > c.cancel_date
  AND r.new_booking <= c.cancel_date + INTERVAL '7 days';

3. Calculating Marketing Channel Conversion Rates in SQL Using CTEs and Window Functions?

ANS-

To compute conversion rates (bookings/searches) per marketing channel, use CTEs to join searches and bookings, then apply COUNT() with PARTITION BY.

WITH search_cte AS (
  SELECT user_id, channel
  FROM searches
),
booking_cte AS (
  SELECT user_id
  FROM bookings
),
combined AS (
  SELECT s.channel,
         COUNT(*) OVER (PARTITION BY s.channel) AS total_searches,
         COUNT(b.user_id) OVER (PARTITION BY s.channel) AS total_bookings
  FROM search_cte s
  LEFT JOIN booking_cte b ON s.user_id = b.user_id
)
SELECT DISTINCT channel,
       total_bookings,
       total_searches,
       ROUND(total_bookings::decimal / total_searches, 4) AS conversion_rate
FROM combined;

4. Detecting Duplicate Property Listings in Airbnb Data Using Fuzzy Matching and Geolocation Overlap in SQL?

ANS-

To catch duplicate listings, compare property names using LEVENSHTEIN() or cosine similarity, and filter by nearby lat/lon coordinates.

SELECT a.property_id AS prop1, b.property_id AS prop2,
       a.name AS name1, b.name AS name2,
       LEVENSHTEIN(a.name, b.name) AS name_distance
FROM properties a
JOIN properties b ON a.property_id < b.property_id
WHERE LEVENSHTEIN(a.name, b.name) < 5
  AND ABS(a.latitude - b.latitude) < 0.01
  AND ABS(a.longitude - b.longitude) < 0.01;

5. How to Optimize SQL Queries on Billion-Row Datasets: Indexing, Partitioning, and Materialized Views Explained?

ANS-

When your SQL query joins 3+ tables on a billion-row dataset, follow this step-by-step optimization strategy:

 Optimization Steps:

1. Indexing: Add indexes on join keys and filter columns (e.g., user_id, date, status).
2. Query Restructuring: Use CTEs to pre-aggregate or filter early. Avoid SELECT *.
3. Partitioning: Partition large fact tables by date or region to reduce scan size.
4. Materialized Views: Precompute heavy joins or aggregations and refresh periodically.
5. Execution Plan Review: Use EXPLAIN ANALYZE to identify bottlenecks.
6. Avoid Cartesian Joins: Always use proper ON conditions.
7. 
   

8. 6. Why Are Long-Term Airbnb Stays Declining in Europe? A Data-Driven Investigation Framework?

9. 
   
10.  Introduction
11. 
    

1. Long-term stays (28+ nights) are a key growth segment for Airbnb, especially among remote workers, digital nomads, and families. If Europe sees a decline, it’s critical to investigate using structured data analysis.
2. 
   
3.  Key Metrics to Track
4. • % of long-term stays: Share of bookings ≥28 nights by country and city
5. • Booking lead time: Average days between search and check-in
6. • Cancellation rate: Especially for long-term bookings
7. • Host availability: % of listings offering long-term options
8. • Average nightly rate: Compare long-term vs short-term pricing
9. 
   
10. User Segmentation Strategy
11. Segment users to uncover behavioral patterns:
12. • By traveler type: Solo, couple, family, remote worker
13. • By booking source: Mobile app, desktop, referral, partner sites
14. • By geography: Country, city tier (e.g., capital vs regional)
15. • By intent: Search keywords like “monthly stay,” “remote work,” etc.
16. 
    
17.  Hypotheses to Test
18. • Seasonality shift: Are users opting for shorter vacations post-pandemic?
19. • Pricing mismatch: Are long-term rates less competitive than hotels or rentals?
20. • Policy changes: Are local regulations discouraging long-term hosting?
21. • Host fatigue: Are hosts removing long-term options due to maintenance or risk?
22. • Competitor impact: Are platforms like Booking.com or local rental sites gaining share?
23. 
    
24. Conclusion
25. A decline in long-term stays isn’t just a booking trend — it’s a signal. Use data to validate hypotheses, guide product decisions, and support hosts in adapting to evolving traveler needs.

7. How to Measure the Impact of AI-Based Price Suggestions for Airbnb Hosts?

* Introduction

Airbnb’s AI-powered pricing aims to help hosts optimize earnings and stay competitive. But how do we measure its real impact?

* Success Metrics Framework

Track both adoption and performance:

• Adoption rate: % of hosts using AI suggestions

• Override frequency: How often hosts reject or modify AI prices

• Booking rate uplift: Change in bookings vs manual pricing

• Revenue per available night (RevPAN): Total revenue / nights available

• Conversion rate: Searches → bookings for AI-priced listings

• Host satisfaction: Survey feedback or support ticket trends

* Experiment Design

• A/B testing: Compare AI vs manual pricing cohorts

• Time-series analysis: Pre/post adoption trends

• Cohort segmentation: By host type, region, property size

* Bonus Metrics

• Price elasticity: How sensitive bookings are to price changes

• Competitive benchmarking: Compare AI-priced listings to similar ones nearby

* Conclusion

AI pricing isn’t just about automation — it’s about trust, performance, and host empowerment. A robust framework ensures the feature delivers real value.

6️. Why Are Long-Term Airbnb Stays Declining in Europe? A Data-Driven Investigation Framework

Introduction

Long-term stays (28+ nights) are a key growth segment for Airbnb, especially among remote workers, digital nomads, and families. If Europe sees a decline, it’s critical to investigate using structured data analysis.

Key Metrics to Track

• % of long-term stays: Share of bookings ≥28 nights by country and city
• Booking lead time: Average days between search and check-in
• Cancellation rate: Especially for long-term bookings
• Host availability: % of listings offering long-term options
• Average nightly rate: Compare long-term vs short-term pricing

User Segmentation Strategy

Segment users to uncover behavioral patterns:

• By traveler type: Solo, couple, family, remote worker
• By booking source: Mobile app, desktop, referral, partner sites
• By geography: Country, city tier (e.g., capital vs regional)
• By intent: Search keywords like “monthly stay,” “remote work,” etc.

Hypotheses to Test

• Seasonality shift: Are users opting for shorter vacations post-pandemic?
• Pricing mismatch: Are long-term rates less competitive than hotels or rentals?
• Policy changes: Are local regulations discouraging long-term hosting?
• Host fatigue: Are hosts removing long-term options due to maintenance or risk?
• Competitor impact: Are platforms like Booking.com or local rental sites gaining share?

 Conclusion

A decline in long-term stays isn’t just a booking trend — it’s a signal. Use data to validate hypotheses, guide product decisions, and support hosts in adapting to evolving traveler needs.

7️. How to Measure the Impact of AI-Based Price Suggestions for Airbnb Hosts

Introduction

Airbnb’s AI-powered pricing aims to help hosts optimize earnings and stay competitive. But how do we measure its real impact?

Success Metrics Framework

Track both adoption and performance:

• Adoption rate: % of hosts using AI suggestions
• Override frequency: How often hosts reject or modify AI prices
• Booking rate uplift: Change in bookings vs manual pricing
• Revenue per available night (RevPAN): Total revenue / nights available
• Conversion rate: Searches → bookings for AI-priced listings
• Host satisfaction: Survey feedback or support ticket trends

Experiment Design

• A/B testing: Compare AI vs manual pricing cohorts
• Time-series analysis: Pre/post adoption trends
• Cohort segmentation: By host type, region, property size

Bonus Metrics

• Price elasticity: How sensitive bookings are to price changes
• Competitive benchmarking: Compare AI-priced listings to similar ones nearby

 Conclusion

AI pricing isn’t just about automation — it’s about trust, performance, and host empowerment. A robust framework ensures the feature delivers real value.

8️.Tracking Adoption and Drop-Offs for Airbnb’s New Group Booking Feature

Introduction

Airbnb’s “Group Booking” feature lets users coordinate stays with friends or family. To ensure success, you need a funnel-based tracking model.

Funnel Metrics to Track

Break down the user journey:

1. Feature impressions: Who sees the group booking option?
2. Group creation: Initiation of a group booking flow
3. Invite sent: Number of invites shared
4. Invite accepted: % of invitees who join
5. Booking initiated: Group selects dates and listings
6. Booking completed: Final payment and confirmation
7. Post-stay feedback: Ratings and reviews from group members

Data Model Design

• 
  
• Entities:
• User, Group, Invite, Booking, Feedback
• 
  
• Relationships:
• One-to-many: User → Group
• Many-to-many: Group ↔ Booking
• 
  
• Events:
• Timestamped actions for each funnel stage

Drop-Off Analysis

• Where users abandon: Invite not accepted, booking not completed
• Device/channel friction: Mobile vs desktop drop-off rates
• Coordination delays: Time between invite and booking
• UX blockers: Confusing flows or lack of reminders

 Conclusion

Group bookings are complex but powerful. A well-designed funnel and data model help Airbnb refine the experience and boost adoption.