The topics getting the most views in your industry right now are visible in your competitors' public video data. View count is a market vote — every view represents a member of your target audience choosing to spend time on that topic. Aggregating view counts by topic across multiple competitor channels tells you, with more precision than any survey or focus group, what your audience actually wants to watch.
The Aggregation Problem
Looking at one competitor channel's top videos tells you what works for that specific channel with that specific audience. Looking at view count data across six competitor channels gives you a category-level picture — topics that perform well across multiple channels are topics the whole market cares about, not just one channel's particular audience segment. That cross-channel signal is significantly more reliable than single-channel analysis.
How to Pull and Aggregate the Data
The YouTube Data API returns view counts with every video metadata request. Pulling the past 90 days of videos from 6 competitor channels, extracting the primary topic from each video's title and transcript, and then grouping videos by topic and summing view counts produces a ranked topic list. Topics at the top of that list by aggregate views are the topics your market is consuming most heavily. The YouTube Competitor Intelligence Monitor does this weekly — the AI analysis step extracts primary topics from transcripts and the CSV output makes aggregation straightforward.
View Count vs. View Count Velocity
Total view count aggregated over 90 days is useful for identifying established high-interest topics. View count velocity — views per day over the past 30 days — is more useful for identifying topics that are currently gaining audience attention. A topic with 200,000 aggregate views over 90 days but most of those views from videos published 60+ days ago is declining. A topic with 80,000 views but concentrated in the past 30 days is rising. Both signals are in the data — the question is what you are optimizing for.
Translating View Data Into Publishing Decisions
High aggregate view counts on a topic confirm audience interest. They do not automatically confirm that your channel should publish on that topic — that decision also depends on whether you have something new to say, whether your positioning in that topic is differentiated, and whether the topic fits your brand. View data answers the demand question. Your editorial judgment answers the differentiation question. The combination of both produces publishing decisions that are both market-relevant and strategically sound.