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Two Sisters. One TV. 📺 podcast artwork

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Two Sisters. One TV. 📺

Tv · EN

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About this Podcast:

A fond, fun, and personal look at classic TV through the years This podcast comes from a gal who spent some of her happiest moments in front of the TV with her sister. We remember and pay homage to the TV shows we loved then, and still love today.

Pitch Analysis

Required Pod Score for this podcast. PitchCentric checks your profile against host openness, topical fit, and audience signals before you generate a pitch.

Contact path

Public web form

Booking probability

43%

Guest openness

Selective

80/100
Required Score

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Questions about Two Sisters. One TV. 📺

What is Two Sisters. One TV. 📺 about?

A fond, fun, and personal look at classic TV through the years This podcast comes from a gal who spent some of her happiest moments in front of the TV with her sister. We remember and pay homage to the TV shows we loved then, and still love today.

What has Two Sisters. One TV. 📺 covered recently?

Recent episodes include "The 70th Anniversary of “I Love Lucy”" (27 October 2026, 29 minutes), "Eddie Haskell. And The Man Behind Him." (13 October 2026, 27 minutes), and "Let’s Talk About Jasmine Guy" (5 October 2026, 21 minutes).

How often does Two Sisters. One TV. 📺 publish?

Two Sisters. One TV. 📺 publishes roughly weekly, with episodes averaging 26 minutes across 311 episodes.

How hard is it to get booked on Two Sisters. One TV. 📺?

Booking probability for Two Sisters. One TV. 📺 is 43%, and the host is rated Selective on guests. Two Sisters. One TV. 📺 requires a Pod Score of 80 or higher. Two Sisters. One TV. 📺's contact route on record: public web form.

About the host

Two Sisters. One TV. 📺 is a tv podcast with 311 episodes published.

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Recent episodes

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Recent guests

Recent guests on Two Sisters. One TV. 📺. Study who booked and why before you pitch.

Best industries to pitch Two Sisters. One TV. 📺 for

Based on semantic analysis of episode topics and host coverage, this podcast is a strong guest fit for executives in:

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Industry fit is computed by PitchCentric using vector embeddings of the podcast's episode catalog.

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