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The Blueprint: How AI Shapes the ‘Ideal’ Customer😰

The Blueprint: How AI Shapes the ‘Ideal’ Customer😰

🎃🎃Spooky-smart or just smoke and mirrors? Let’s see how AI profiles us. 👻✨

Anusha Kannan
October 31st, 2024

Hey guys! Before we get into this week’s business update, HAPPY DIWALOWEEN! 🪔🎃✨ I had my first-ever trick-or-treating adventure yesterday with lil’ kids knocking on my door, and it was FUHH-UN. Today’s also Diwali and I couldn’t be more excited to celebrate!

But back to business (hehe)…

Oooo, as you may know, the tech girl in me is always digging into AI topics, because just like you, I want to stay in the loop! Marketers talk a lot about using AI, but here’s something I’ve been wondering: whenever I ask ChatGPT to help with an email (about 100 times 😂), I still feel like I’m training it to understand my way of communicating. But what if I shared that same ChatGPT with a friend who talks completely differently? Would it actually get her style? (without having to train)

This question got me thinking (yes, down the rabbit hole again 🐰) about the mechanics behind AI and Ideal Customer Profiles (ICPs)—how it learns from us but can still get things a bit narrow-minded.

So here’s a peek into the process:

Hold on, give me one second…

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Thank you, thank you! Back to the point…

The Process:

Data Collection: AI starts by gathering tons of data—think customer purchases, website visits, and social media interactions. It combines this info from customer records, ad metrics, and even third-party sources, creating a big picture of each customer.
Pattern Recognition: After collecting the data, AI hunts for patterns. For instance, it might notice that people aged 25-30 in cities are more likely to buy. So, it groups them together based on those traits.

Customer Clusters: Picture these groups as “clusters.” At the center of each cluster are the people who best match the “ideal” customer profile. These are the people brands target most often.
Scoring Potential Customers: Then, AI scores potential customers based on how closely they fit the ideal profile. Those at the center are prioritized, while others on the edge might get overlooked.
The Catch: Here’s where it gets tricky—AI can get too focused on patterns in a limited dataset, reinforcing the same customer behaviors repeatedly. That’s why diverse data is so key—it helps AI stay open to emerging customer types and new trends

If you’re curious to dive deeper into these mechanics, check out my latest episode, “E47: Is AI Deciding Who You Are?” where I dig into how this all works and what it means for brands (all in under 13 mins!).

THIS WEEK ON The Marketing Woman Podcast:

E47: Is AI Deciding Who You Are?

Guys, ever wondered if AI truly understands who we are, or if it’s just boxing us into patterns? In this episode, we’re diving into how AI builds “ideal customer profiles” and whether it’s truly capturing us or simply repeating what it’s learned.

Think about it: 70% of companies are using AI to predict what we’ll like, when we’ll buy, and even what we might browse next. But can AI go too far, filtering us into narrow categories that might miss out on new or diverse customer traits? We’ll break down five key insights on how AI shapes customer profiles, including:

How AI clusters customers based on patterns—grouping us into segments and sometimes overlooking emerging types.
Why diverse data matters for AI, keeping it from reinforcing the same profiles repeatedly.
The importance of human judgment alongside AI to make sure we’re seeing the full picture, not just what an algorithm deems “ideal.”

Plus, I’m sharing examples of how brands are navigating these issues and making sure AI is working for them, not limiting their outreach :)

If you’re ready to dive into how AI could be deciding who you are—and how we can keep it balanced—this episode is for you!

Until next time,

Anusha

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