The analysis for a single channel has been automated, and can be send to a potential customer with a reasonable response rate. However, converting the lead into a customer requires an analysis that delivers recurring product. Given a creator that outputs content on a regular cadence. The customer would like to know about the performance of the channel. First know what happened. Later, understand why it happened. The descriptive section would include information about the comments, and the engagement metrics (i.e. likes). The diagnostic section would map comments to engagement metrics. Then, compare audience insights to the channel's historical performance. The historical comparisson will provides insights to diagnose the trend trajectory of the channel, and how to influence it.
Once the content performance is set. Customers want to know about their niche. There are two core drivers: are they increasing their leadership position within their niche, and at what pace is the niche growing. Following the same structure: descriptive, and diagnositc. The niche analysis starts reviewing the top performing content, and channels within the niche. Later, based on mapping the comments to the engagement metrics. The resulting yield would be an audience, and genre description. Again, there would be comparissons to previous trends to understand the dynamics of the niche.
Growth suggestions is the final section of the analysis based on forecasting, and prescriptive analytics. A suggested approach would focus on: a) maximizing the channel's existing content, b) suggesting new content ideas based on niche, and audience insights, and c) explore long-term positioning within the niche, and genre of the channel. Including long-term opportunities. Maximizing suggesting titles, and even exploring short-form content to promote, and highlight the best segments. Regarding the suggestion, will be from the starting point of the channel's mission. Provide rich description, and backed by evidence. The vision of the channel will not be automated, but a design of metrics to standarize the communication of performance, and genre characteristcs will be designed, and written.
Solution Overview
There are three key performance indicators that creatores care about: popularity, growth, and positive engagement. The report starts by providing metrics on those for the given period (week, or month). Comparing previous periods, and global performance. Then, zoom in into the content attributes: explaining the content attributes, the change with respect to the historic and optimal content attributes. Next is the explainability of performance, starting from the expected output, insights from the audience if available, and compared to the genre. Once the performance of the content is reviewed the review of the niche starts following a similar structure: review the best (and worst) performing content, gather audience insights, and summarize what is new in the niche. After that the content suggestions are delivered.
Technically important changes are required to deliver the recurring report. Add time as a native attribute of texts, and set-up the DB infrastructure: including support for measuring the evolution of metrics (as arrays). To support the recurring analysis, the data layer also requires extending the embeddings model to 12-dimensions, a GenreCatalog, and a MetricsBank to benchmark engagement values to final outputs. Finally models to measure the comments sentiment, positivity, intent, and the content. Once the data is collected comment insights will be mapped to metrics. The final section of the recurring analysis identifies potential new leads.
On a technical level, this may be the last PR before moving to multiagent systems. As such, focus will continue to be placed in improving the predictions accuracy, hierarchical summarization, and the automation of the introduction report. The goal for improving the accuracy of predictions is to outperform a linear-regression embedding-based model with Mistral 7B based on Reflexion, and RAG. Given that the global analysis is at a descriptive level of maturity for now the goal is to be able to record a video summarizing the insights. Finally to measure the success of the report automation the goal is to invest less than 1 hour preparing the presentation. As a stepping stone, a written report will be the primary output. Supplemented by JSON maps of complementary information for each slide. Strategical preparations will continue to prepare a vision for how to handle represent, and visualize data, considering its high-dimensionality, the multi-view possibilities, and even outlining potential ideas for multimodal data, starting with images.
Developmenrt roadmap
[ ] Time dimension as native attribute.
[ ] Setup the DB Infrastructure.
[ ] Track evolution of metrics.
[ ] Support 12-dimensional embeddings.
[ ] GenreCatalog, and MetricsBank to support report
Problem Statement
The analysis for a single channel has been automated, and can be send to a potential customer with a reasonable response rate. However, converting the lead into a customer requires an analysis that delivers recurring product. Given a creator that outputs content on a regular cadence. The customer would like to know about the performance of the channel. First know what happened. Later, understand why it happened. The descriptive section would include information about the comments, and the engagement metrics (i.e. likes). The diagnostic section would map comments to engagement metrics. Then, compare audience insights to the channel's historical performance. The historical comparisson will provides insights to diagnose the trend trajectory of the channel, and how to influence it.
Once the content performance is set. Customers want to know about their niche. There are two core drivers: are they increasing their leadership position within their niche, and at what pace is the niche growing. Following the same structure: descriptive, and diagnositc. The niche analysis starts reviewing the top performing content, and channels within the niche. Later, based on mapping the comments to the engagement metrics. The resulting yield would be an audience, and genre description. Again, there would be comparissons to previous trends to understand the dynamics of the niche.
Growth suggestions is the final section of the analysis based on forecasting, and prescriptive analytics. A suggested approach would focus on: a) maximizing the channel's existing content, b) suggesting new content ideas based on niche, and audience insights, and c) explore long-term positioning within the niche, and genre of the channel. Including long-term opportunities. Maximizing suggesting titles, and even exploring short-form content to promote, and highlight the best segments. Regarding the suggestion, will be from the starting point of the channel's mission. Provide rich description, and backed by evidence. The vision of the channel will not be automated, but a design of metrics to standarize the communication of performance, and genre characteristcs will be designed, and written.
Solution Overview
There are three key performance indicators that creatores care about: popularity, growth, and positive engagement. The report starts by providing metrics on those for the given period (week, or month). Comparing previous periods, and global performance. Then, zoom in into the content attributes: explaining the content attributes, the change with respect to the historic and optimal content attributes. Next is the explainability of performance, starting from the expected output, insights from the audience if available, and compared to the genre. Once the performance of the content is reviewed the review of the niche starts following a similar structure: review the best (and worst) performing content, gather audience insights, and summarize what is new in the niche. After that the content suggestions are delivered.
Technically important changes are required to deliver the recurring report. Add time as a native attribute of texts, and set-up the DB infrastructure: including support for measuring the evolution of metrics (as arrays). To support the recurring analysis, the data layer also requires extending the embeddings model to 12-dimensions, a GenreCatalog, and a MetricsBank to benchmark engagement values to final outputs. Finally models to measure the comments sentiment, positivity, intent, and the content. Once the data is collected comment insights will be mapped to metrics. The final section of the recurring analysis identifies potential new leads.
On a technical level, this may be the last PR before moving to multiagent systems. As such, focus will continue to be placed in improving the predictions accuracy, hierarchical summarization, and the automation of the introduction report. The goal for improving the accuracy of predictions is to outperform a linear-regression embedding-based model with Mistral 7B based on Reflexion, and RAG. Given that the global analysis is at a descriptive level of maturity for now the goal is to be able to record a video summarizing the insights. Finally to measure the success of the report automation the goal is to invest less than 1 hour preparing the presentation. As a stepping stone, a written report will be the primary output. Supplemented by JSON maps of complementary information for each slide. Strategical preparations will continue to prepare a vision for how to handle represent, and visualize data, considering its high-dimensionality, the multi-view possibilities, and even outlining potential ideas for multimodal data, starting with images.
Developmenrt roadmap