The objective is to gather and analyze numerous pertinent data points to gain insight into podcast performance and forecast future success. This task can be accomplished through web scraping (or by integrating various APIs)
On-platform metadata pertains to information directly associated with the platform itself. For instance, in the context of Spotify, this may include:
| Data | Type |
| --- | --- |
| Title Podcast Show | Meta |
| Podcast Producer | Meta |
| Description Podcast Show | Meta |
| Description Podcast Show Language | Meta |
| Release frequency | Meta |
| Episode count per Show | Meta |
| Time stamps for releasing individual Shows | Meta |
| Amount of Episodes | Meta |
| Categories per Podcast Show | Meta |
| Availability on other podcast platforms (e.g Apple) | Meta |
| Weekly Podcast rankings (as alternative for amount of downloads / listens) | Predictive model data |
| Podcast Show Rating | Predictive model data |
| Podcast Show amount of users rating | Predictive model data |
| Change in Podcast Show Ratings and user rating | Predictive model data |
| Social media links in description / overview page / Bit.ly | Predictive model data |
| Instagram Data (Followers, Following, Amount of posts) | Predictive model data |
| YouTube Data (Followers, Following, Amount of posts) | Predictive model data |
| Twitter Data (Followers, Following, Amount of posts) | Predictive model data |
| Other links (e.g. branded) in the description | Predictive model data |
Spotify provides creators with open pages like the [one mentioned](https://podcasters.spotify.com/pod/show/pbdpodcast/support), which could be scraped for data. Ideally, we would continuously supplement our data sources to ensure a robust database, especially if additional data becomes available. However, for the minimum viable product (MVP), simplicity is key to determine whether further exploration is warranted. Alternatively, [RSS feeds](https://anchor.fm/s/2fa50a94/podcast/rss) could be considered to capture comprehensive data.
Limitations primarily revolve around the quality and quantity of data; relying solely on Spotify may not suffice in the long-term. Integrating Listen Notes could potentially enhance robustness, such as incorporating their LN score. [Rankings](https://podcastcharts.byspotify.com/de), ratings, and social media influence are presumed to be the most crucial indicators for the MVP model. Additionally, scraping data from Spotify may present challenges in terms of ease of access.
For later to explore and to add (or as substitute to Spotify MVP):
- Apple podcast ([Example](https://podcasts.apple.com/us/podcast/pbd-podcast/id1526697745))
Data should be ideally captured in a simple Google spreadsheet (or any alternative) that could function as the basis for the modelling. If there’s a need for limiting data points, one could just focus for the MVP for Europe or Germany - depending on budget size.
So task:
- Deliverable: Google spreadsheet / Airtable with as many possible meta and predicative data associated to Podcast Shows
- Geo: Europe
- Platform: For now, Spotify and others that might be easier to integrate
Budget: $200
Posted On: March 13, 2024 13:50 UTC Category: Data Extraction Skills:Web Scraping, Data Extraction, Data Scraping, Lead Generation
Skills: Web Scraping, Data Extraction, Data Scraping, Lead Generation Country: Netherlands
click to apply
Project ID:
3375651
Project category:
Web Scraping, Data Extraction, Data Scraping, Lead Generation
