⚠️ Warning: Long ass post!
I've been seeing a lot of posts here lately about the struggle with cold outreach, especially in the context of AI automation solutions. It seems like many of us are sending out tons of emails and getting nothing back. I've also noticed people sharing templates from so-called "gurus" that are all about spamming as many people as possible.
This got me thinking about the whole "templates vs. frameworks" debate. Templates are easy, but they don't make you think. Frameworks, on the other hand, force you to think strategically. That extra effort is what can make your AI automation solutions stand out and truly resonate with potential clients.
I'm not claiming this is the "best" way to do things, but I wanted to share a framework that's been working for me. For the past few years, I've been handling sales prospecting for my own businesses. While they aren't directly in the AI automation space, I've found the core principles of this approach hold up remarkably well. The framework is built around Jordan Crawford's idea of a "Pain-Qualified Segment." (Zero affiliation)
My main advice is to find sales experts you resonate with, "steal" their frameworks, and see what works for you. This is just one idea to get the ball rolling, especially for those looking to apply AI automation to real business problems.
How to Actually Find a Pain-Qualified Segment for AI Automation
This is the hard part, but it's also the most important for successful outreach and solution selling. Here's a step-by-step guide to finding a PQS using public data:
Step 1: Start with a Broad Hypothesis
Start with a general assumption about a problem a certain type of business might have (or just Ask AI to come up with them!). Think about where AI automation could provide significant relief.
- Hypothesis A: "Marketing agencies that serve multiple clients probably struggle with creating and sending custom reports, a perfect candidate for automated reporting solutions."
- Hypothesis B: "Companies that rely heavily on paid ads are likely wasting money on campaigns that don't convert, indicating a need for AI-driven ad optimization."
- Hypothesis C: "Fast-growing e-commerce companies probably have a messy and manual order fulfillment process, ripe for AI-powered workflow automation."
Job Postings are a Goldmine:
- Keywords: Search for jobs on LinkedIn or Indeed with keywords like "manual data entry," "manual reporting," "coordinate between departments," "data cleansing." These are direct signals of a manual, repetitive process that AI can often automate.
- Role Combinations: A company hiring for a "Marketing Manager" who also needs to be an expert in "Salesforce administration" is a sign that their marketing and sales data is not well-integrated, presenting an opportunity for AI-driven data synchronization.
Analyze Their Tech Stack:
- Use BuiltWith or Wappalyzer: These tools can show you what software a company uses. Look for a combination of tools that don't integrate well. For example, a company using a modern marketing automation tool but an old, legacy CRM is a huge red flag for a data integration problem that AI automation can bridge.
Read G2/Capterra Reviews of Their Software:
- Look for Complaints: Read the 1, 2, and 3-star reviews of the software a company uses. People will complain about specific things, like "the reporting is not customizable" or "it's not integrate with our accounting software." This is direct feedback from users inside the company, highlighting pain points that AI automation can often alleviate.
Step 3: Synthesize Your Findings into a PQS (Pain Qualified Segment)
Now, combine your findings to create a very specific PQS. This precision allows for highly targeted AI automation solutions.
- Bad PQS: "Companies that need marketing automation."
- Good PQS: "B2B SaaS companies with 50-200 employees that use HubSpot for marketing and Salesforce for sales, and have recently posted job openings for 'Sales Operations' roles that mention 'manual reporting' and 'data cleansing' – indicating a clear need for AI-powered data integration and reporting automation."
Step 4: Building Your Outreach from the PQS
Once you have your PQS, the outreach becomes much easier and more effective, especially when positioning your AI automation services.
- Data-Driven Narrative: "I saw on BuiltWith that you're using both HubSpot and Salesforce. I've worked with several B2B companies with this exact stack, and a common challenge is keeping the data between the two systems in sync. This often leads to leads not being routed correctly and sales reps working with outdated information – a problem AI automation can solve."
- Permissionless Value Prop (PVP): "I noticed you're hiring a 'Salesforce Administrator' and the job description mentions 'data cleansing' and 'managing integration errors'. I recorded a short Loom video outlining a custom workflow using n8n that can automate the data syncing process and eliminate 90% of these errors, leveraging AI for intelligent data handling. Hope it's helpful."
🖊️ The Takeaway
Again, I'm not saying this the best or only way. The main point I want to leave you with is to start thinking in terms of frameworks, not templates, especially when developing and selling AI automation solutions.
Frameworks force you to understand your customer, their pains, and how your AI automation can genuinely help them. It's more work, but it's the kind of work that leads to real results and truly impactful AI implementations.
So, go out there, find some frameworks you like, test them, and make them your own :)