
Mega-Brands: Investing in Mega Trends & the Mega Brands Best Positioned to Add Value to Your Wallet
Eric Clark
5.0from 5 ratings
- 93
- Episodes
- 2
- Transcribed
- 5
- Ratings
- Daily
- Cadence
- 2019
- First episode
About Mega-Brands: Investing in Mega Trends & the Mega Brands Best Positioned to Add Value to Your Wallet
Mega-Brands is a podcast for investors, traders, and brand marketers; Financial Advisors & investors who want to keep track of the most powerful mega-trends happening around the globe. In these podcasts we discuss the $50 trillion/year global consumer spending theme as well as other trends like: Artificial Intelligence, Digitization, and the Experience Economy. Let's make some money investing in Mega-Brands! https://www.globalbrandsmatter.com
- Publisher
- Eric Clark
- Category
- business
- Language
- en
- Explicit
- No
- First episode
- 3 Dec 2019
- Latest episode
- 20 Nov 2025
Latest episodes
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[…] And we tracked 200 relevant brands every year, and we're getting next month in December. I'll update that 200. Actually, we're going to push it out to 300 just because there's been a lot more names that have gone public. So we have a lot more interesting choices across all the sectors. So, you know, it's a fun topic because as consumers, we are, we kind of have emotional connections to our favorite brands anyway. And, you know, it's nice to know what you want and to be able to put your money in the things that you, that you utilize on a daily basis. And what, I mean, what could be a better hedge than making money in the companies that you spend your money on in real life? […]
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- Spotify
- 5.0 / 5
- 5 ratings
Podcast Authority Score: 37 / 100
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- 42
- Social presence
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- YouTube
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Questions about Mega-Brands: Investing in Mega Trends & the Mega Brands Best Positioned to Add Value to Your Wallet
How often do new episodes come out?
The show publishes daily, based on its RSS feed.
Does Mega-Brands: Investing in Mega Trends & the Mega Brands Best Positioned to Add Value to Your Wallet take guests?
Yes. Guests have been identified in episode analyses.
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