DataTalksClub / DataTalksClub/podwiki
Content audit: individual podcast episode pages get very little traffic
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- Python
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Description
Finding (content audit, last ~3 months)
- The podcast landing page gets visits, but individual podcast episode pages get very few — tiny numbers compared to other pages.
- The pages pulling the most traffic are articles about the courses and the survey posts (AI tools, data engineering tools, MLOps — the tooling surveys whose results were posted to the blog).
"Why so few click-throughs to the podcasts? Partly because the landing page can be improved — a lot. Not just grouping by season and images, but filtering too — think about what's actually useful to a visitor. And second, the internal linking..."
Hypotheses / directions (umbrella issue)
This is a tracking issue for the why and the fixes:
- Landing page is weak → redesign with topic filtering + visual grid so people actually browse into episodes. → #10
- Podcast pages are crawl-orphans / linked to YouTube instead of the page → fix internal linking from main-site past-events. → #11
- On-page discovery is weak → surface related podcasts/episodes/books so one episode leads to another. → #7, #8
- Compare against what does work (course articles, survey posts) — what makes those discoverable/linkable that podcast pages lack?
Next step
Alexey has the full content-audit data — worth attaching the actual pageview numbers here and prioritizing 1–4 against them.
Source: content-audit feedback (translated from Russian voice note).
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by attaching Alexey's full content-audit data and comparing podcast pageviews with course articles and survey posts. Review the related work in #10, #11, #7, and #8, then document which hypotheses are supported and prioritize directions 1–4. Done means the evidence and next priorities are recorded.
Written by the indexing model from the issue text.
Assessment
- Domain
- content
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100