In this report, we analyzed the 101 biggest brand events of the year using the world’s leading social listening platform, Brandwatch Analytics. Specifically, we used Iris, our AI-analyst, to look through one billion conversations about brands and determine the 101 biggest events of the year. Iris only looked at conversations where brand names were mentioned. In order to achieve this Iris analyzed a 10% sample of conversation from across the web. In total, Iris collected and analyzed conversations from 95 million sites.
Gender, Interests & Profession: Gender, Interests, & Profession are evaluated learning user account or profile information as well as machine-learning techniques.
Rules & Categories: Brandwatch’s rules, which rely on Boolean logic, allow users to separate specific conversations into specific categories. Rules can be understood as “queries within queries.”
Emoji Detection: Brandwatch can pick up usage of emoji published by any operating system.
Sampling: A statistically accurate 10% sample was used to collect the data. This sample was extrapolated by 10x in the report to give an accurate estimate of what the full coverage would be.
The report analyzed 500 brands in total. The 500 brands were selected through two processes. First, we examined revenue and output lists, industry literature, and social data to compile a list, then we cross-referenced with well-known brand ranks like the Fortune 500, the Interbrand Best Global Brands, and the Social Outlook Report.
Iris is a smarter and faster way to discover insights from Brandwatch. It automatically analyzes peaks in your dataset and explains what caused conversation to grow. Here’s how Iris works:
1. Detection Iris starts by analyzing your data for significant peaks. It does this by calculating the daily median and then checks if any peaks are significantly higher than that median.
2. Analysis After detecting the peaks, Iris automatically searches for drivers causing the peak. Iris looks for:
- Tweets with a significant volume of retweets
- Facebook threads with a high volume of comments
- Hashtags used significantly more than usual
- Link shared significantly more than usual in tweets
- Videos mentioned significantly more than usual
- News articles mentioned significantly more than usual
Once Iris detects a high volume tweet, thread, hashtag etc., it will instantly compare the peak to 20 days of historical data. This comparison allows Iris to determine if the mention is significant.
For example, if a news article receives 200 shares during the peak, but an average of only 5 shares historically, Iris will recognize it as significant.
3. Display After calculating the most significant drivers, Iris will present them in the sidebar of your chart.
Peaks are labeled A, B, C and so on, based on their significance. The peak labeled ‘A’ will be the peak with the largest deviation from the daily median. The sidebar will contain up to six different drivers.