# Dexibit predictive museum analytics

> Analyses of visitor behaviour regarding the permanent and temporary exhibitions, based on the collection of attendance data, wifi data, website frequentation data.

**Wikidata**: [Q123643945](https://www.wikidata.org/wiki/Q123643945)  
**Source**: https://4ort.xyz/entity/dexibit-predictive-museum-analytics

## Summary
Dexibit predictive museum analytics is a system used to analyze visitor behavior regarding permanent and temporary exhibitions. It utilizes data enrichment, artificial intelligence, and data mining to process attendance, Wi-Fi, and website frequentation data. The project is notably implemented at the National Gallery, where it began in 2017 to forecast attendance and optimize exhibition planning.

## Key Facts
- **Start Date:** 2017
- **Instance of:** Museum AI project, data enrichment
- **Part of:** National Gallery
- **Technologies Used:** Artificial intelligence, data mining
- **Data Sources:** Attendance data, Wi-Fi data, website frequentation data
- **Primary Function:** Analyzes visitor behavior regarding permanent and temporary exhibitions
- **Outcome:** Forecasts attendance and supplements visitor data with additional context
- **Documentation:** Described at `digitalmeetsculture.net` (English)
- **Project Maintenance:** Maintained by WikiProject Museum AI projects (MAp)

## FAQs
### Q: What specific data sources does Dexibit use to analyze visitor behavior?
A: The system collects and analyzes attendance data, Wi-Fi data, and website frequentation data to understand visitor movements and interests.

### Q: How does Dexibit distinguish between different types of exhibitions?
A: The analytics tool specifically differentiates between visitor behavior regarding permanent collections and temporary exhibitions to provide targeted insights.

### Q: What is the relationship between Dexibit and the National Gallery?
A: The National Gallery hosts the project as part of its operations, having initiated the use of these predictive analytics in 2017.

### Q: How does the system improve data quality?
A: It employs data enrichment to supplement missing or incomplete information, enhancing the completeness and value of the collected datasets.

## Why It Matters
Dexibit predictive museum analytics represents a significant application of artificial intelligence within the cultural heritage sector. By moving beyond simple attendance counting to complex behavioral analysis of both permanent and temporary exhibitions, it allows institutions like the National Gallery to forecast future trends. This capability is crucial for optimizing exhibition planning, improving visitor engagement strategies, and managing resources efficiently. The integration of website and Wi-Fi data with physical attendance provides a holistic view of the visitor journey, bridging the gap between digital interest and physical presence.

## Notable For
- Integrating multiple data streams (Wi-Fi, web, attendance) into a unified analytics view.
- Applying data enrichment principles to supplement missing visitor information.
- Utilizing artificial intelligence to predict museum attendance.
- Being a documented case of AI implementation in a major institution (National Gallery) starting in 2017.
- Analyzing the distinction between permanent and temporary exhibition traffic.

## Body
### Definition and Scope
Dexibit predictive museum analytics is defined as a museum AI project and a form of data enrichment. It is designed to perform detailed analyses of visitor behavior, specifically distinguishing between interactions with permanent and temporary exhibitions.

### Technical Methodology
The system operates through the collection and synthesis of three primary data categories:
- **Attendance Data:** Physical entry counts and patterns.
- **Wi-Fi Data:** Connectivity information used to track movement and dwell time within the gallery.
- **Website Frequentation Data:** Digital engagement metrics prior to visits.

By applying **artificial intelligence** and **data mining** techniques, the project enriches these datasets. This process involves supplementing missing or incomplete data to improve the quality and usability of the analytics.

### Implementation at the National Gallery
The project is a significant technological deployment within the **National Gallery**. Initiated in **2017**, it serves as a prime example of predictive analytics in the cultural sector. The project is documented externally by `digitalmeetsculture.net` in an article titled *"The National Gallery predicts the future with artificial intelligence."*

### Project Context
Dexibit is classified under the broader initiative of the **WikiProject Museum AI projects (MAp)**. It serves as a case study for how data enrichment—a process typically associated with enhancing data completeness—can be applied to visitor analytics to support decision-making in museum environments.