Content

Segmenting Content Funnel Stage

Catagorize site content into top of the funnel (TOFU), middle of the funnel (MOFU), and bottom of the funnel (BOFU) stages.

Mohammad Abdin

Founder

February 9, 2024

In the dynamic world of digital marketing, understanding how potential customers interact with content across different stages of their journey is crucial for crafting effective strategies. The "Content Funnel Segmenter App" is a tool designed to help in this regard, by categorising website content into top of the funnel (TOFU), middle of the funnel (MOFU), and bottom of the funnel (BOFU) stages.

This Moonlit app aids in pinpointing the role each piece of content plays in guiding the buyer from awareness to decision-making. It uses a combination of techniques to analyse content distribution, including parsing sitemaps for URL collection, scraping web pages for company summaries, and employing language models for content classification. This article explores how the app works, focusing on its approach to segmenting content and the insights it provides for businesses looking to refine their content strategy.

Try it out here
You can also clone this app to your project to extend its functionality and edit configurations and prompts.

App Breakdown

The Content Funnel Segmenter App simplifies the categorization of website content into the stages of the buyer's journey: TOFU (Top of the Funnel), MOFU (Middle of the Funnel), and BOFU (Bottom of the Funnel), using AI. Here’s a streamlined breakdown of its structure.

Inputs:

  • Sitemap XML: Utilised to retrieve all indexable URLs for a domain.
  • Company URL: Used to scrape a summary of the company, aiding the AI in understanding the content's context.
  • Slug Filter: Applies a filter to URLs based on a specified slug, such as '/blog', to target specific content types.

Logic:

1. URL and Company Data Retrieval:

  • Extracts URLs, titles, and descriptions from the sitemap.
  • Scrapes the company webpage to obtain a company summary.

2. Content Classification:    

  • The language model classifies each sampled post as TOFU, MOFU, or BOFU based on content analysis, with context provided by the company summary.

3. Data Organization:

  • Another Python function organises the classified content data for visualisation, counting the instances of each funnel stage.

Outputs:

  • Chart Output: Visualises the distribution of content across the funnel stages.
  • Table Output: Displays the classified URLs and their segments for detailed analysis.

Testing and Observations

Achieving accurate segmentation required addressing the subjective nature of content classification. Input from content strategy experts and iterative testing with diverse examples have refined the process, yielding reliable results. 

I reached out to Lee Densmer, an expert in the field of content strategy and she generously offered her help in refining the prompt and assessing the results. This validated the app's utility in enhancing content strategy through detailed funnel stage analysis.

Final Thoughts

Addressing the subjective aspects of content segmentation has been a central challenge in developing the Content Funnel Segmenter App. However, the journey has led to rewarding outcomes. The insights garnered from the app enable a more nuanced approach to content strategy. Key applications of this data include:

  • Analysing content distribution across the funnel stages to identify gaps. This analysis aids in determining which areas of the content strategy need bolstering, ensuring a balanced approach that caters to potential customers at every stage of their journey.
  • Integrating this segmentation data with traffic metrics from tools like Ahrefs, Semrush, or Google Search Console offers a comprehensive view. This combination allows for a deeper understanding of how different segments perform in terms of engagement and conversion, enabling more data-driven decisions.

Ultimately, the success of this project lies in its ability to bring clarity to the often complex and nuanced task of content categorization. By harnessing the power of AI to dissect and categorise content, the app offers a practical solution for businesses aiming to optimise their digital content and align it more closely with their audience's needs and their marketing objectives.

Written By

Mohammad Abdin

Mohammad is a full-stack developer, and the founder of Moonlit Platform. He holds a Bachelor's degree in Computer Science & Artificial Intelligence, and is committed to continuous learning and skill enhancement. His journey is marked by a steadfast dedication to developing and delivering exceptional product experiences.

5

Minute Read