Content Funnel Segmentor

Trying to grow traffic is one thing—turning that traffic into revenue is another. Most websites are bursting with TOFU blog posts while MOFU tutorials and BOFU case-studies are scattered (or missing altogether). Manually opening every URL, skimming the copy, and updating a spreadsheet is soul-crushing and often skipped. Yet the data is clear: teams that nurture leads with funnel-aligned content drive 73 % higher conversions and 50 % more sales-ready leads at lower cost. The “Segment Content Funnel Stage” app above removes that grunt work by auditing any sitemap, giving you an instant TOFU / MOFU / BOFU map you can act on. For instance, our content automation examples offer a closer look at similar workflows.

Walkthrough: how the app works in Moonlit

1. Collect the URLs

We start with Extract Sitemap URLs. Drop in a sitemap, add an optional path filter (e.g., “/blog/”), and the node returns up to 100 rows with the URL, the page title, and its meta description. No crawling limits, no regex headaches—just a clean table ready for analysis. If you’re interested in streamlining your processes even further, you might want to automate content optimization for improved website performance.

2. Give the model brand context

Classifying funnel stage without company context leads to errors (a “pricing” page might be MOFU for one brand, BOFU for another). A second branch scrapes the main landing page you provide (Scrape Webpage with “page-summary” output). The summary tells the model what the product does, typical customers, and positioning—crucial signals when judging whether a blog post leans educational or sales-driven. Learn how to maintain a consistent tone in your content with advanced AI in our post Maintain Brand Voice across Thousands of Pages with AI.

3. Classify each URL

The Chat Model (GPT-4o) receives:

• the sitemap table

• the landing-page summary

• clear definitions of TOFU, MOFU, and BOFU

It returns a strict JSON list such as:

{
  "data": [
    {"url": ".../what-is-xyz", "segment": "TOFU"},
    {"url": ".../xyz-vs-abc",  "segment": "MOFU"},
    {"url": ".../xyz-pricing", "segment": "BOFU"}
  ]

4. Turn the JSON into a snapshot

A short Python Function converts the list into a dataframe, groups by segment, and outputs a count per stage. The app surfaces both the raw table and an auto-generated pie or bar chart so you can spot gaps at a glance (e.g., “79 % TOFU, only 6 % BOFU”).

Customization ideas

• Custom segments: swap TOFU / MOFU / BOFU for your own lifecycle labels—Awareness, Consideration, Decision, Retention—by editing the prompt.

• Add prioritization logic: extend the Python step to calculate a “gap score” (e.g., traffic × segment scarcity) and export a task list for writers.

• Team notifications: pipe the JSON into Zapier or Slack via a Webhook node to alert the content team whenever the BOFU ratio drops below a threshold. You can also explore how content automation can further boost your team’s efficiency.