Business intelligence app in QlikSense

Business Intelligence app built in Qlik Sense, based on the dataset from AirBnB. The goal is to explore and visualize key patterns in Airbnb listings across major European cities using different data visualization techniques.

📊 Enhanced Data Categorization for Deeper Insights

To uncover trends more effectively, I implemented custom bucketing logic across multiple variables using Qlik Sense's IntervalMatch feature. These buckets segment continuous metrics into intuitive categories, improving visual analysis and filtering capabilities.

• CityCenterRange: Classifies listings by proximity to the city center (e.g., Central, Inner Ring, Outer Ring), enhancing location-based pricing insights.

• MetroRange: Clusters listings by distance to the nearest metro station, helping assess accessibility-driven value.

• RestaurantRange & AttractionRange: Groups listings by nearby restaurant and attraction density, revealing patterns tied to amenity-rich areas.

• SatisfactionRange: Segments properties based on guest satisfaction scores, enabling quality-based comparisons.

  This structured binning approach makes it easier to visualize and analyze Airbnb listings in a user-friendly, non-technical way. Full Qlik Sense script can be read here: airbnb_disertatie.qvs

The Financials page displays Airbnb’s key financial indicators and their evolution over time (2017–2024). On the left, a container includes two charts comparing revenue with operational expenses first, and then with marketing expenses, to explore potential correlations. A filter in the top-left allows users to select the year (default is 2024). However, the timeline chart on the left and the table in the bottom-right are unaffected by this filter, as they use Set Analysis to preserve the full time-series view regardless of user selections.

The EU Overview page offers a quick snapshot of the most visited European cities, combining key metrics such as booking volume, average price, tourist attractiveness, and guest satisfaction. It features a map and pie chart highlighting top cities like Rome, Paris, and Lisbon, alongside bar charts comparing average prices (highest in Amsterdam), attractiveness (led by Rome and Barcelona), and satisfaction scores, which remain high and consistent across cities—suggesting location has little impact on guest satisfaction.

The Report Builder section provides an interactive feature for generating custom reports based on Airbnb data. Users can select cities and apply additional filters (such as distance from the city center, proximity to the metro, capacity, etc.) to analyze key indicators. The predefined table displays metrics such as minimum price, maximum price, number of bookings, and average price.

The data model follows a snowflake schema centered on the Listings fact table, which captures property-level metrics (price, satisfaction, location indices). It connects to dimension tables—Cities, DayType, RoomType, and FurnishingLevels—using surrogate keys for clean relational joins.

A separate Financials table stores annual company KPIs for high-level analysis. The schema is optimized for Qlik Sense with bucketed fields to support segmentation and drill-downs.