De la qualité à la quantité: Comment Conviva est devenu un leader incontesté de la mesure vidéo

Table of Contents:

1. Introduction
2. Challenges in Audience Measurement
3. The Evolution of Streaming Audience Measurement
4. The Role of the Stream Sensor
5. Data Collection and Normalization
6. The Importance of Clean and Normalized Data
7. Privacy Considerations
8. The Publishers' Ownership of Data
9. Session Level Data and Privacy
10. Multi-Variable Measurement and Data Privacy

Introduction

In this article, we will explore the challenges and evolution of audience measurement in the streaming industry. We will discuss the role of technology, specifically the stream sensor, and how it has revolutionized audience measurement. Additionally, we will delve into the importance of data collection, normalization, and privacy considerations in this context.

Challenges in Audience Measurement

🔍 Challenges in Traditional Audience Measurement Audience measurement has always been a challenge for content creators and broadcasters. Traditional methods, such as surveys and ratings, have limited accuracy and fail to capture real-time data.

🔍 The Unique Challenges of Streaming The rise of streaming platforms has presented new challenges in audience measurement. The ability to track viewership accurately and efficiently in a digital landscape has become a pressing concern for content providers and advertisers.

The Evolution of Streaming Audience Measurement

📺 From Quality of Experience to Audience Measurement In the early days of streaming, the focus was primarily on ensuring a quality viewing experience. Companies like Canada stepped in and deployed sensors to measure the quality of experience. This initial foray into audience measurement paved the way for future advancements.

📺 The Role of the Stream Sensor The stream sensor is a crucial technology in audience measurement. Acting as a funnel within the video player, it collects data on every aspect of the viewer's session. From buffering events to content genres and ad breaks, the stream sensor captures valuable information that enables accurate measurement.

Data Collection and Normalization

📊 The Fragmented Device Ecosystem One of the biggest challenges in data collection is the fragmented nature of devices. Every device and application reports data differently, making it necessary to normalize the collected data for accurate measurement.

📊 The Complexity of Data Normalization Normalizing data in a fragmented world involves collecting audience data from various devices and applications and organizing it in a standardized format. This ensures consistency in metrics, measurements, and timestamps, thereby enabling reliable analysis.

📊 Continuous Measurement Technology Continuous measurement technology plays a crucial role in ensuring accurate data normalization. Investing in the right technology is essential for reliably collecting, cleaning, and normalizing data across the entire supply side.

The Importance of Clean and Normalized Data

✨ Reliable Measurement and Analysis Clean and normalized data is the foundation of reliable audience measurement. It provides consistent and accurate insights into viewership patterns, enabling content creators and advertisers to make strategic decisions based on concrete data.

✨ Standardization of Measurement Standardized, clean, and normalized data allows for measurement across different publishers and devices. It establishes a measurement standard that eliminates disparities and provides a level playing field for all stakeholders in the industry.

Privacy Considerations

🔒 Safeguarding User Privacy Privacy is a crucial aspect of audience measurement. Sensors embedded in applications collect data with the user's consent. It gives viewers the option to opt-out if they decide not to share their data. Data privacy rules and regulations ensure that all data collected is handled in compliance with privacy guidelines.

🔒 The Role of Publishers in Data Ownership Publishers are the rightful owners of the collected data. The audience data belongs to them, and as a measurement provider, the company ensures that all data collected is within the publisher's ownership. This approach maintains data privacy and ownership rights.

Session Level Data and Privacy

🔐 Data Collection and Privacy Compliance As a session-level measurement provider, the company ensures privacy compliance while collecting data. By operating within the application environment and with the publisher's permission, all data collected remains privacy compliant, preserving the user's privacy.

🔐 Multi-Variable Measurement and Data Privacy The use of multi-variable measurement, collecting data such as IP addresses, device IDs, and app IDs, allows for better identity creation. Even if one variable becomes unavailable due to changing data privacy regulations, other variables can still provide valuable insights.

Conclusion

Audience measurement in the streaming industry has evolved significantly, thanks to advancements in technology and data collection methods. Companies like Canada have developed stream sensors that capture valuable data, ensuring accurate and reliable measurements. Data collection and normalization play a vital role in ensuring measurement standardization and providing clean, actionable insights. Privacy considerations are of utmost importance, with publishers retaining ownership of the collected data, and privacy compliance being a top priority.

【Highlights】

• Traditional audience measurement methods are insufficient in the digital streaming landscape, leading to the need for more accurate and efficient measurement techniques.
• The stream sensor is a crucial technology that captures data on every aspect of the viewer's session, providing valuable insights for audience measurement.
• Data collection and normalization are essential in a fragmented device ecosystem, ensuring standardization and consistency in measurements across publishers and devices.
• Clean and normalized data is the foundation of reliable audience measurement, allowing for accurate analysis and strategic decision-making.
• Privacy compliance is a top priority, with publishers retaining ownership of the collected data, and measures taken to safeguard user privacy.

【FAQ】

1. Q: What are the challenges in traditional audience measurement? A: Traditional methods, such as surveys and ratings, have limitations in capturing real-time data and may lack accuracy.

2. Q: How does the stream sensor work in audience measurement? A: The stream sensor collects data on every aspect of the viewer's session, including buffering events, content genres, and ad breaks, providing valuable insights for measurement.

3. Q: Why is data collection and normalization important? A: Data collection and normalization ensure consistent measurements across publishers and devices, enabling reliable analysis and strategic decision-making.

4. Q: Who owns the collected data in audience measurement? A: Publishers retain ownership of the collected data, and privacy compliance measures are in place to protect user privacy.

5. Q: How does multi-variable measurement address data privacy concerns? A: Multi-variable measurement allows for better identity creation, even if certain variables become unavailable due to changing data privacy regulations.