100M Offers Made Easy by Ben Preston 📚

A good overview of the $100 Million Offers approach with some “how to use with AI chat” tips. A good place to start if you’re looking for an entry point to mess around with LLMs for marketing task acceleration. Otherwise, you might get an idea or two on how to tweak your prompting approach.

Here are the non-prompt bits I highlighted:

An irresistible offer is more than just a sales pitch or a marketing gimmick. It’s a carefully crafted proposition that captivates your audience, solves their deepest problems, and compels them to take action.

The flow of your offer should mirror the natural progression of your prospect’s thoughts. It should start by addressing a problem they relate to, present your solution as the answer, and provide evidence to support your claims. Finally, it should guide them to take action, whether it’s making a purchase or subscribing to your newsletter.

An irresistible offer isn’t just about what you sell; it’s about how you package and frame the value you provide to your customers.

I won’t recreate the prompt verbatim here (if you’re interested, just check out the book), but the general flow is:

  • Classic role playing prompting opening: “Act as Alex Hormozi…”
  • Ask the LLM to rate your offer using Alex’s 4-part value equation framework (from his book $100 Million Offers) and outline the framework (TBD if you can skip the outlining, give it a shot)
  • After rating, take the scores generated from each step and calculate the offer equation
  • Ask it to provide advice on increasing each of the 4 scores as well as two alternate offer ideas

Follow up prompt:

  • Tweak the offer, however necessary, to reach the optimal offer score of 1,000,000

Other recommendations for prompting include:

  • List Unique Selling Points
  • Craft a Unique Value Proposition
  • Generate headlines, hooks, & stories
  • Map the offer structure as a sales letter / landing page
  • Add a dash of scarcity / FOMO to the offer
  • A/B testing ideas & analysis
  • Additional data analysis
  • Customer segmentation