amVizion
commented
1 month ago Growth Analysis
The building pieces to understand the present channel's content are set. Now, is about to think, and suggest the possible futures. One approach would be to enlist the different possibilities. A second one is to complement the content analysis with additional data. Including comments, related channels, short content, and metrics. The data would culminate on a powerful vision that paints an inspiring picture about the future. There are different possibilities on how to start on this second stage of the marketable analysis. There could be an investment on infrastructure, paired with the improvement connection with the YouTube API. A second alternative would be to dive deep on each enhanecement separately, as set a course of action once all data is collected.
Niche identification
Connection with the API is required, including handling pagination. A data architecture will delineate what is data is required for identifying the niche, and the subsequent analysis. Options include comments, subscriptions, topics, and engagement metrics. Also important is how deep to go into the graph, how to filter channels, or navigating. Also possible is the visuazations to complement the data. A possibility is to approach this task visually. Also relevant is whether to reuse the cluster capabilities to start with a niche analysis, and from there derive growth possibilities for the channel. Such analysis would include the different directions a channel could take, if prospected with growth data it would be extremely useful for decision making, and content itself. There would be interesting data related to the physics, and direction of language based on historical data. Once the different possibilities are elaborated, suggestions on best practices would be valuable. This suggestions could come from the additional data sources. A final decision is to establish how to present the niche results, with a formal report or only via data available for exploration on the app.
Comments insights
When retrieving comments there are important decisions, including the quantity of comments retrieve, and its representation. A first use case is to match given topics to attributes of comments. This creates a correlation table, even matrix. Where the verticals of the content, and the verticals of the comments correlate. Opening the door for prescriptive analytics. For example, optimizing a given topic to a type of comments. Conversely, if the comments received are in other direction the approach to the content is missing. Comments provide a third dimension for which to cluster channels. Next to content, and subscriptions. This creates a second correlation matrix. A possible deep dive to understand how to treat multimodal data could be helpful. Also enlist different use cases beyond the prescriptive metrics for creating content. Comments are only representative for large channels. Small channels could use it to navigate their growth, but their value may diminish if no direct, actionable insights. Verticals will be reused with the content attributes, but is possible that the number of attributes be expanded. For example, for emotions, or the scores retrained, if initial results are below expectations. It is possible that individual comments are not enough, to create meaning. Instead, comments could be aggregate by video, or channel.
Short video analysis
Metrics benchmark
Conclusion
Possible
- [ ] API Enhancement.
- [ ] Data Architecture.
- [ ] Visualize niche.
- [ ] Historic growth data.
- [ ] Niche clustering.
- [ ] Include comments.
- [ ] Correlation matrix.
- [ ] Comment aggregation.
- [ ] Comment verticals.
- [ ] Prescriptive analytics.
- [ ] Niche selection.
- [ ] Content optimization.
Problem Statement
Today, BI-LLM is a general purpose tool that lacks a specific use case. This makes it difficult to promote the tool, grow its adoption, prioritize features, and deliver value. It also risks the successful design of experiments to improve predictions accuracy. Lack of specificity delivers shallow results, does not allow to create a deep understanding of the domain, and wastes time collecting data that will not be reused. Without an audience for the analysis it becomes difficult to get human evaluations for the quality of reports, and without funding the opportunities for innovation are limited. For example, improvements to existing reports depend on manual prompt engineering, while it would be possible to automated prompt optimization, finetunning models, or even train brand new large language models focused on numeric, and time series predictions from semi-structured data.
Content creators face a multitude of challenges. Including deciding what topics to cover, how to present the content (selecting a title), and generate engagement from viewers. In particular, they want answers to the questions: what drives engagement, and how can they grow their channel. YouTube is a source of multimodal data that includes the title, and description of a video. The script, including the hook, chapters, climax, and call to action. The audio that uses entonations to capture attention, and communicate emotion. The video that includes scenary, performance, pace, and editing. And finally, the thumbnail that attracts viewers. Additionally, creators must consider the algorith, engagement metrics, and viewers comments to make a successful a video.
For creators, challenges don't stop there. The YouTube ecosystem is huge, with diverse audiences, and a plethora of niches. Each demographic has unique characteristics, interests, and shifting attention. Without guidance, creators must make decisions based on intuition. The rate of experimentation is slow, and stakes for success high as their livelihood depends on it. There is also little visbility on their competition, and niches causing them to miss growth opportunities, or make repeated errors from other creators. Finally, there is the issue of their industry or niche: tryining to keep pace on a constantly evolving landscape of news, trends, and preferences. For example, video is often the slowest format to communicate content. Often news start with a text, and only a handful of content gets translated into video, as it is the most expensive to produce, store, stream, and watch. Keeping pace with the development of their niche is more than a full-time job. It is not only the creator that looses, but also the viewer that does not get to see content that would otherwise have enjoyed.
Solution Overview
The product should have answers for two questions: what drives engagement, and how can I grow the channel? The section for the engagement is almost ready. It would start with an introduction of the channel's content. Then an engagement analysis section by vertical detailing what attributes are correlated to high engagement. Then a subsection, indicating what type of content to avoid. One possible enhancement, would be to include titles that encapsulate the attributes. A second distinction would be for correlated attributes to differentiate between the titles that correlate positively, to the ones that not. Also, for attributes that have a high variance indicate examples and that performed positively, versus the ones that not, and possibly explain the reason for the variance. An ideal explanation would include the correlation across verticals. For example, titles with a given emotion perform better if they are correlated with a specific topic.
The growth section would start by identifying the niche based on the titles. Next, it would explain what the comments appreciate from those channels. Clustering & summarization techniques could be used, or reuse, and train new verticals for the comments. Next, would be the topics that resonate the most with the audience, and even drew similarity with the ones from the creators library. Finally, metrics: how does engament compare to the competition, both historically (for previous content), and present content. This insights would give specific information to the creator about what the audience cares about, what content is already successful, and an idea of what is requried for the next level. It could even be possible to take the existing successul content, and improve it.
Development Roadmap