Our site traffic looked the same. Our sales were down 50%. Over the summer we changed our sales page. We put small business right in the headline. Then sales dropped. Jon, my marketing director, had all the data saying the same thing: traffic was the same. So I asked him: do we really know that traffic was real? It wasn't. "We're experiencing the same problem that a lot of other websites and companies are experiencing right now, which is bots." We'd turned off a setting in Cloudflare that let a whole bunch of bots through. So now we have two problems. → Fewer people opening and clicking our emails → A sales page that isn't converting the way it needs to Jon put it simply: our list used to grow from Google traffic. People aren't going to search engines. They're going to AI. Less traffic means the page has to work harder. So I'm going back to the grindstone on conversion. I've got some crazy ideas up my sleeve. If your numbers look fine but your results don't, check whether your visitors are even human. Has this happened to you? Reply "Yes" if bots have ever fooled your numbers 👇 Press Start: The Road to 3,000. #SocialMediaExaminer# #SMMW27# #ConversionRateOptimization# #MarketingAnalytics#

Everyone says "fix your data before you do AI." Data is only the first of 3 levels of context your agents need. The third, "Sauce", is what adds the most value. 1. Data Systems of record and other raw data sources that give your agents an ontology to understand your business. Ex: CRM, marketing analytics, quickbooks, product usage 2. Context Your company brain that know's the current state of everything. Ex: Call recordings, emails, Slack, positioning docs, customer profiles, etc. 3. Sauce Maps of how decisions and deliverables get made. Ex: Expertise, playbooks, taste. Most Sauce is stuck in your founder or best peoples' heads. You have to get it out. Sauce takes you from AI handling grunt work with a human micromanaging it, to AI driving outcomes without you being the meat proxy between the real world and AI.

There are 10,000 AI agents. Nobody knows which ones they actually need. Build vs buy is just the first question. Run your use cases through this 3 step decision tree: 1. Is it generic or unique to your company? - Generic: a standard function every company does the same way. Support inbox, meeting notes, invoices. Buy a vertical agent. - Unique: it runs on your expertise and playbooks. The stuff no vendor has. Build, or configure a horizontal agent. 2. One-off or recurring? - One-off: analytics deep dive, some landing pages, a research report. Buy and configure a personal agent. No need to build from scratch with an agent framework or SDK. Grok Bot is getting crazy good. Integrations are easy. I spun up a half decent marketing analytics agent in an hour. The missing piece is evals. But if it's just for one project, and you're the human in the loop, you're fine. - Recurring: it runs every week, every deal, every client. Build it. 3. How much does a human touch it? The less human in the loop, and the more complex the work, the more engineering you need. Human babysits everything, internal, low stakes, handles basic tasks? Nontechnical person can vibe code it. E.x.: marketing dashboard, lead enrichment script, content repurposing workflow. Little or no human, your playbook and expertise at scale, customer facing, handling a large body of complex work? You need AI engineers. E.x.: healthcare customer support agent, background agents that work while you sleep, custom web apps non-technical teams use all day.