how to use AI for market research (step by step breakdown):
step 1: analyze your content performance
grab a batch of your best performing content and a batch that flopped
split them into two sections in a doc and feed to an LLM
and have it analyze the differences between what performed vs what didn't
your content performance is valuable data
as an AI can identify patterns in what resonates with your audience vs what doesn't
step 2: use deep research to find direct audience quotes
give a description of your target market and have it search reddit, quora, any forums really
and have it pull exact quotes from people discussing their pain points, desires, objections, fears regarding your offer
step 3: analyze customer conversations
you likely have DMs, customer support tickets, or direct conversations with customers
feed these into an AI to summarize common trends and patterns
step 4: create one comprehensive market research report
combine everything:
- performance analysis from your content
- direct quotes from target market
- research patterns from customer conversations
now you have a semi data-driven report
now you can feed it feed it into AI to get better feedback on new content ideas
or generate content ideas that actually resonate with your audience
or even draft content with context on what works vs what doesn't
bc now it understands your audience because you fed it real context/data
to recap AI market research:
- analyze best vs worst performing content
- use deep research for direct audience quotes
- summarize patterns from customer conversations
- compile into one comprehensive report
- use report as context for AI-generated content
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how to build an AI automation (a step by step breakdown):
step 1: map out the manual task
before automating anything, document how you do it manually:
- what's the step-by-step process if a human were to do this?
- what data and information do you need at each step?
- what are the key decision points?
step 2: decide how much variance you want in outputs
this depends on the task type:
creative tasks (writing copy) = more variance allowed
predictable tasks (categorization, data entry) = less variance needed
as this determines what guardrails you'll need later on
how to automatically scrape data from the internet (like a data engineer):
this is setting up systems that save information from the internet into organized databases
example: collecting TikTok videos, captions, and engagement metrics every day
and this data becomes the foundation for AI systems you build later on
the process for the tiktok example:
- tools like ScrapeCreators and Apify visit websites and extract the specific info you want
- saves everything to spreadsheets or databases automatically
- runs on whatever schedule you set (daily, weekly, hourly)
how to reverse engineer any successful AI product:
step 1: understand the manual process
before diving into a technical analysis, figure out what human task this AI product is automating
> what would someone do manually to achieve the same result?
> what decisions need to be made?
> what data is required at each step?
> what is the most painful part of this task that people are paying to automate?
step 2: create your own technical hypothesis
based on your knowledge of AI fundamentals (embeddings, RAG, APIs, etc.)
sketch out how YOU would build this
don't overthink it - focus on the core workflow and data flow