AI4CI 2 3 Don't Believe Everything AI Says About Your Customers

Take a look at this chart for a second. Between 2000 and 2009, margarine consumption in the United States dropped at nearly the exact same rate as divorce in Maine. Both trends moved in almost perfect lockstep, creating a correlation of 99%. That's an incredibly strong statistical relationship. It's actually stronger than most business insights you'll ever find. And if you didn't know better, you'd think margarine consumption and divorces in Maine were directly connected. Well, obviously margarine doesn't cause divorce and divorce doesn't make people eat more margarine. But this is what statisticians call a spurious correlation. Two things that move together by pure coincidence. So why would I bring up margarine and divorce rates? Well, AI, it can make these same kinds of mistakes with customer data. It might find patterns that look convincing but mean absolutely nothing. The difference is AI won't tell you when a correlation is meaningless. It presents every finding with the same confidence, whether it's discovering genuine customer behavior or just statistical anomalies. And this is really important here. Unlike humans, AI doesn't express doubt.

There's not a doubt in my mind, not one single doubt.

It doesn't sort of say, I'm not sure about this part. That's why you need to learn how to evaluate AI inputs before you act on them. In fact, last month I heard about a team. They made a mistake because they didn't stop to question their AI output as they were building a weekend only mobile campaign. The AI told them that customers who used their mobile app on weekends were more likely to upgrade to premium. So they launched this weekend only promotion and they targeted mobile users. Well, the campaign, it flopped completely. Turns out the AI had confused correlation with causation. Weekend mobile users, they weren't more likely to upgrade because they used the app on weekends. No, they used the app on weekends because they were already power users who had already upgraded. This is why critiquing AI generated customer behavior. Well, it isn't sort of optional, it's essential. So I have a simple four step framework for you to keep handy. Every time AI gives you insights about customer behavior, run them through these four critical questions. Does this make logical sense? If AI tells you that Taskflow users who log in at 3am are your most engaged customers. Pause. Does that actually make sense for a project management tool used by marketing agencies? Maybe those 3am users are in different time zones. Or maybe they're working under deadline pressure. Well, that could actually indicate stress, not engagement. Here's a Pro tip. Always ask yourself, if a human analyst presented this finding, would I ask for more explanation? Question 2, what's the sample size and time frame? AI might discover that customers who use Taskflow's calendar integration have higher retention. But if that insight is based on maybe 10 users over two weeks, it might not be reliable enough to make decisions on. Look for patterns that hold true across meaningful sample sizes and reasonable time periods. A behavior pattern that only shows up in the last month, well, that might be a temporary anomaly, not some lasting insight. Question three, could there be hidden variables? Now this is where most AI analysis falls short. AI is excellent at finding correlations, but it's really not that great at understanding context. So when AI identifies a behavior pattern, ask what else might be influencing that pattern? For example, if Taskflow users at larger companies have higher engagement, is it because the product works better for big teams? Or could it be that larger companies have dedicated project managers who are helping to ensure adoption of the product and the distinctions? They matter. They help you understand how you should act on the insights. Okay, and finally, question four, does this align with what you already know about your customers? AI might tell you that Taskflow customers who invite more than 10 team members in the first week, they have higher retention. Well, that's great. If you know from customer calls that successful agencies emphasize getting everyone on the same platform, then this makes total sense. And it suggests that you should encourage broader team invitations during onboarding. But if you also know that most of your target customers are small five person teams, well, then this pattern might just reflect that larger companies are naturally stickier customers, not that inviting more people causes success. This distinction gives you clearer actions. You could either promote team invitations or focus on better serving smaller teams. That just depends on what you know about your customers. Your existing customer knowledge should complement AI insights, not just be ignored in favor of them. Now, in addition to these four steps, I want you to consider some red flags to watch out for. Certain AI outputs should just immediately trigger skepticism. One of those is perfect correlations. That would be a red flag. Now, we talked about this at the beginning, but you know that real customer data, it's. Well, it's messy. And if AI finds a behavior that predicts outcomes with like 95% accuracy, you're going to want to dig deeper. You might just be looking at a spurious correlation like margarine and divorce rates and not a real pattern. Another red flag would be contradictory findings. So, like, if this month's AI analysis completely contradicts last month's data, don't Just accept the new result. We want to investigate what changed in the data or the methodology. Also, watch out for overly complex explanations. I see this one all the time. Like, if your AI identifies customer behavior patterns that require five different conditions to be true, you're probably looking at noise, not signal. Okay, one more red flag here. Behaviors that can't be acted upon. Sometimes AI will tell you that customers born in odd number years are more likely to turn. Well, that's not really actionable. Look for patterns you can actually influence. Okay, so just remember, AI excels at identifying patterns in large data sets that humans would miss, but it struggles with context and causation. The key is using AI for pattern recognition while applying human judgment for interpretation. Now, here's some good news. AI is trainable. You can teach it to be more skeptical by building evaluation criteria directly into your prompts. Let me give you an example. Instead of asking what customer behaviors do you see in this data, try saying what behaviors do you see? What evidence supports each pattern and which might be coincidental? You see, you see the difference in that prompt. And you can upload your customer interview notes and product documentation to give AI better context about your specific situation. You don't just need to provide customer data. You can give it information about your company, your mission, everything you're trying to do. Be sure to feed that into the AI so it has more context. The more AI understands your specific situation, the less likely it is to suggest that your customer churn is caused by lunar cycles.

Peaches are my lunar cycle fruit.

Think of it as training a research assistant. The clearer your instructions and the better the context you provide, the more reliable the analysis becomes. The goal is to make sure you're making your decisions based on reliable insights and not just statistical noise.