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Here's 5 Reads I Keep Coming Back To

Here's 5 Reads I Keep Coming Back To

This is a short list of content I've found myself thinking a lot about this recently. These are some of the most useful and thought-provoking reads on marketing, productivity and strategy I've come across in a while, so I hope you get value from them.

How To Talk To Users (Y Combinator)

This was aimed at engineers and product people, but this is just as applicable to marketers. Marketers need to develop a strong intuition for what their target audience will respond to, and you can't really do that just by staring at GA data or analyzing LTV:CAC. You will have to talk to users, which is a skill with a lot of gotchas. Most people are bad at it.

The LNO Effectiveness framework (@shreyas)

This is like the Eisenhower matrix but much more prescriptive and with examples for people who work in tech. The lightbulb for me was that for some tasks, you shouldn't bother trying to do them well – just avoid doing a bad job.

Who's your audience? (Ana Andjelic)

A nice short read that gave me a lightbulb moment that's lasted for over a week. Why do some brands become embedded in the culture, while other brands – while successful – never become known outside of their niche? The answer is that one serves multiple audiences, while the latter only cares about serving one.

How to actually calculate CAC (Andrew Chen/Brian Balfour)

As a marketer, you probably throw around the term LTV:CAC around a lot. I've already done it once in this blog post. The problem is that a lot of marketers calculate CAC in a very naive way. If you're deriving your CAC by taking this month's expenses then dividing by the number of net new customers ... read this post.

8 Lessons from 20 Years of Hype Cycles (Michael Mullany)

I am personally caught up in the hype cycle of web3, NFTs, DAOs, etc. So it got me curious about previous innovations that captivated the public imagination, and the accuracy of the predictions about how those innovations would transform society. The author took a look at Gartner hype cycles then audited them for their predictive ability. Interesting results.