Day 26 Transcript

NOTE: Today’s transcript is followed by an AI prompt that can be used with your AI provider of choice. Just copy and paste it into ChatGPT or Perplexity and it will help you answer today’s questions for your specific side hustle… the way a human teaching assistant would help you in an Ivy League university. If you’re eager for more on today’s topic, I’ve included a Secret Dessert Course at the very end — a bonus section that isn’t directly covered in today’s video but has a lot of value practical, hands-on value. That dessert also comes with its own AI prompt.

—

Part 1: Pursue Unique Before Big

Your hustle is your castle. And guess what? You can't rent a moat. If you can buy it-- and fill it with crocodiles-- anyone can buy it and fill it with crocodiles. Because you can also rent crocodiles.

Welcome to week 4, Day 26 of starting your side hustle! We’re taking 28 Days-- 28 small steps-- to build a business that’s meaningful, impactful, and profitable. This week’s goal is scalability so today we’re going to talk about data strategy… specifically data moats. That’s a fancy way of saying “let’s focus on your data.” And I never use jargon without explaining it. A data moat– like a moat around a castle– is just useful data that you have that your competitors don’t. They can’t get into your castle… because you have a data moat.

Our real life example today is our second lady boss in two days: Katrina Lake, founder of Stitch Fix. Katrina’s idea was simple—personalized clothing recommendations—but what set Stitch Fix apart was its data moat. From day one, Katrina focused on collecting unique data from customer style quizzes, purchase history, and feedback. As competitors tried to copy the model, Stitch Fix’s proprietary dataset and predictive algos made their recommendations smarter and more personalized over time. That data moat became the company’s biggest competitive advantage, helping it IPO back in 2017. Since that IPO, they’ve had periods of strong customer base growth and profitability, but they’ve also faced slowing new customer acquisition, stock price volatility, competitive pressures, ultimately maintaining a valuation close to its IPO level so not exactly a breakout success but… infinitely better than not having a data moat.

So… The two questions we’re going to ask today:

1. What unique behavioral data are you collecting that competitors can’t replicate?

2. In all your data, what best predicts customer lifetime value 6 months out?

Let’s break down why these questions matter:

Most people obsess over “big data,” but real power doesn’t come from big, it comes from unique. And the best kind of unique is data that signals behaviors only you can see because of how your product or service is used, because of who your customers are, because of the workflows you’re embedded in. Everyone can buy the same data– the same mailing lists– the same industry contacts– and you shouldn’t with those examples because spamming kills trust. The point though is that if you can buy data, it's just not a moat.

If you can scrape data from someplace on the web… it’s not a moat. Everyone can scrape the same public info. Doesn’t mean you shouldn’t… it's just not a moat.

If you want a moat, you should always be thinking about how to collect data that’s specific to your users’ habits, preferences, outcomes. So question 1 is about not falling for the myth that more data is always better. The real gold is not more-more-more but different data.

Question 2. Most founders think their “moat” is the code, the brand, or even first-mover status. But those fade. You’re going to want a compounding advantage– the kind of data that gets more valuable as you grow— because more users means better predictions, smarter features, a tighter feedback loop. Most companies stop at “we have data” because they think they’ll eventually use it. Smart hustling is using it today, not saving it for later. It’s about asking, “How does every new customer make our product better for the next one?” That’s the secret to defensibility, to growth, to data driving strategy.

Ok. Take a moment and try to answer the Day 26 questions for your hustle without AI and before you listen to the next section-- the 28-Day Ivy League MBA. I personally think it's useful to try to answer questions without AI first, but if you'd rather do that: The AI teaching assistant prompt will drop with today's case study... in a couple of hours. If you don't know what I'm talking about, check out Lunch Break Millionaire Day Zero... or go over to superserious.com where I’m posting daily transcripts. The AI prompts are there too. That's it. Hustle smarter.

Part 2: 💼 Let Data Drive Strategy: Today's Ivy League MBA Skill

Day 26, Part 2 of Lunch Break Millionaire– where we turn whatever you're eating for lunch into an Ivy League MBA degree. It’s tacos everyday now– my favorite lunch of the week. And the MBA skill we’re going to pick up today is how to build your business around data strategy from day one… or in this case, day 26. And I’m not just talking to the viewers who are already techies building out tech shit. I’m talking to everyone– even if your hustle is baking cookies.

If you want your hustle to last, you can’t treat data as an afterthought. You have to “bake” data strategy into your business from the get-go. That means you’re intentionally making every decision about what data to gather, how to store it, and how you’ll use it to outmaneuver competitors. You don’t care– or shouldn’t care– about having the most data; because it’s all about having the right data, the kind that actually gives you insight, gives you an edge.

Think about Katrina Lake who we talked about earlier, the founder of Stitch Fix—she didn’t just launch with a cool idea. She made customer data the core of her business. She used quizzes and feedback loops to build a moat that competitors couldn’t cross.

So how do YOU do this?

Start by asking yourself: what unique data can you collect that no one else can? Maybe it’s behavioral data from how users interact with your product, detailed feedback from onboarding surveys, or even patterns in how people use your service over time. The key is to make data collection part of your product or service.

And don’t just hoard data—decide early how you’re going to use it. Are you looking for signals that predict who becomes a loyal customer? Are you tracking which features drive the most engagement? Are you using data to personalize experiences or spot new market opportunities before anyone else?

Tie this back to today’s questions. When you ask, “What unique behavioral data are you collecting that competitors can’t replicate?” you’re really designing your moat—figuring out your data strategy, figuring out what insights only you will have, and how you’ll protect and grow that advantage as your hustle grows. And when you ask, “Which leading indicators predict customer lifetime value 6 months out?” you’re thinking like a strategist with an Ivy League MBA. You’re building systems– processes– to spot tomorrow’s winners today, so you can double down on what works and fix what doesn’t before it’s too late.

Take a sec and map out your own data strategy. What’s the one piece of data you could start collecting right now that would be hard for competitors to copy? How will you make sure your data is clean, organized, and actually useful? And what’s one way you could use that data to make smarter decisions or deliver a better experience for your customers? If you’re not sure, look at your best customers—what did they do early on that others didn’t? That’s your starting point.

Keep telling yourself, the hustlers who win 1) move fast, and 2) move smart, 3) with data as their compass. Build your business around data strategy from the start, and you’re building a moat that gets deeper every day. That’s how you hustle smarter.

Part 3: Build Defensible Moats: The 28-Day Case Study

This is Day 26, Part 3 of Lunch Break Millionaire. This is the segment where we #BuildinPublic– where I answer the daily questions every hustle should– using the MBA skills we just learned– and showing my work– sharing how I’m building my hustle from scratch-no filters, just the real journey. You don't need to actually like or subscribe. I'm not doing this for the clicks. But if you’re leveling up from other creators you follow or know, introduce us. I want to learn from them and help them level up, too. We all deserve better than just making rich people richer.

Ok. I am way too old– have done way too much hustling in the last 25 years– to ever think that my “moat” would be my code. I wish. Code is a commodity—anyone with a credit card and a few weekends can copy features.

And I’ve never believed in gatekeeping. I’m the opposite– I’m open source through and through. So I’m not going to try to keep everything secret, thinking *that* would protect me.

Which begs the question: what is my real moat?

I think it’s the behavioral data. It’s the data I collect by embedding my tools in every user’s existing digital habits. I think a copycat could see similar data but not for the creators’ audiences on those creators’ platforms.

Let me explain. Imagine that I’m a content creator and I have 10 million followers. Today, I have a rough sense of what they– as a group– do when they’re browsing. If my content is nerdy or startupy, I can assume my audience is nerdy and into startups but I’m still in the dark. Now let’s say I get 5% of that audience to install the plugin. They get value by experiencing the web from my perspective. I get value because it deepens every follower’s loyalty and– if I ever want to monetize that relationship– if I want to sell something– being given the permission by that audience to be in front of that audience– well… it prompts additional purchases— an upsell, cross-sell, repeat order kinda thing. As that creator, I get value because I increase my followership’s lifetime value. I turn them into a brand advocate who refers others to my business. That– by the way– is the classic definition of the best possible outcome of advertising to your existing clients… except it’s with their permission.

But what does this have to do with data moats? Once that creator’s followers are using the creator’s plugin, that creator starts getting aggregate data– nothing specific about any one follower– but aggregate data from ALL their follower’s digital habits– where they live digitally, what content– that other people produce– do they find interesting. It’s a reverse Homer-bushes thing.

Nothing private or creepy. It’s aggregate data. But it helps answer a really hard question for creators: what should I—as a creator– write about– talk about. Forget fads! What would be meaningful to my specific audience– that would grow my relevance to my audience?

In other words, the creator’s platform is now capturing contextual, behavioral data– not to sell to some creepy advertisers but to help produce more engaging content. The platform answers questions like: what content are my followers consuming, what are they reacting to, what topics in their broader digital lives triggered the most engagement, and even what time of day are my followers most responsive.

Suddenly, I have a moat. The creator has a moat. My platform– the creator’s platform– isn’t just another curation tool. I’m building a goddamn feedback loop for creators: every follower makes the creators’ platform smarter.

That actually explains why earlier this week, when we were talking monetization and pricing, I was like “Hey, it turns out, creators loved our little analytics dashboard.” They’d never use the word data moat but that’s why they love it.

Competitors can copy those features, but they couldn’t copy my creator’s specific insights. Because every followership produces different data. Different insights. And the more creators use the platform, the deeper the moat gets.

The takeaway: Your moat isn’t the tech. It’s the proprietary, evolving dataset you build– intentionally– by being closer to your users– and their users– closer than anyone else. If you’re not collecting unique data, you can hustle smarter.

Prompt #1 - Make Your Business Defensible

○ Prompt #1 - Make Your Business Defensible ○

Today, you’ll learn how to build a “data moat”-a unique asset that makes your business harder to copy and more valuable over time. You’ll be guided by the writings and frameworks of Ivy League faculty whose research is foundational in digital strategy, data-driven business models, and competitive advantage:

- **Professor Karim R. Lakhani, Harvard Business School:** Expert in digital transformation and building defensible data assets.

- **Professor Sunil Gupta, Harvard Business School:** Specialist in data-driven marketing and platform strategy.

- **Professor Michael E. Porter, Harvard Business School:** Authority on competitive advantage and strategic moats.

**What Today’s Coaching Will Help You With:**

You’ll identify what unique data you can collect (ethically!), how to turn it into insights or features your competitors can’t easily replicate, and how to use that data to drive smarter decisions and deeper customer loyalty.

---

### Step 1: Reflection Questions

Please answer these questions in a few sentences each:

1. **What unique data could your business collect through normal operations that competitors don’t have?**

- Think about customer behaviors, preferences, usage patterns, or results that only you see.

2. **How could you use this data to improve your product or service-or to create features that get better the more people use them?**

- Consider ways to personalize, automate, or enhance the customer experience.

3. **What steps will you take to collect, protect, and use this data ethically?**

- How will you get clear consent, keep data secure, and be transparent with your customers?

---

### Step 2: MBA Skill – Building a Data Moat

Today’s MBA lesson is about building a defensible moat with data:

- **Data Moat:** A unique dataset or feedback loop that makes your product smarter, more personalized, or more valuable over time-and harder for competitors to copy.

- Start by mapping what data you naturally collect as part of your service (not just what you *could* collect).

- Think about how you could use this data to create a “flywheel” effect-where more users = more data = a better product = even more users.

- Always prioritize ethical data use: get explicit consent, anonymize when possible, and make it easy for users to understand and control their data.

---

### Step 3: Coaching & Action Plan

After you reply, I will use the writings of Professors Lakhani, Gupta, and Porter to:

- Help you identify your most valuable (and defensible) data assets.

- Guide you in designing features or insights that get better with more data.

- Suggest best practices for ethical data collection, storage, and use-so you build trust, not risk.

- Offer real-world examples of businesses that turned unique data into a lasting competitive edge.

---

**How to use this prompt:**

- Respond with your answers to the reflection questions and your ideas for data collection or features.

- I’ll help you refine your data strategy, spot opportunities for defensibility, and suggest next steps for building your moat.

Secret Dessert Course

Data’s fun– the kind of fun that can quickly get you into trouble. The AI prompt below coaches you through “Ethical Data Use,” helping you build trust with your customers from day one. It’ll walk you through the must-do’s for handling data the right way: getting clear consent, only collecting what you truly need, keeping info safe, and being totally transparent about how you use it. No legal jargon, just practical steps that show your customers you actually care about their privacy.

Just copy and paste the following prompt into your favorite AI assistant to enjoy Day 19’s dessert.

Prompt #2 - Commit to Ethical Data Practices

○ Prompt #2 - Commit to Ethical Data Practices ○

**Today’s Focus:**

Build customer trust by designing ethical data practices that prioritize privacy, transparency, and security. You’ll be coached by Ivy League faculty with expertise in data ethics, consumer trust, and digital responsibility:

- **Professor Latanya Sweeney, Harvard Kennedy School:** Pioneer in algorithmic fairness, data privacy, and founder of Harvard’s Public Interest Tech Lab.

- **Professor Jonathan Zittrain, Harvard Law School:** Authority on digital governance, privacy, and the ethical implications of emerging technologies.

- **Professor Andrea Matwyshyn, University of Pennsylvania Carey Law School:** Specialist in cybersecurity, data ethics, and consumer protection.

**What Today’s Coaching Will Help You With:**

You’ll learn to implement simple, ethical data practices that protect your customers and your reputation-no law degree required.

---

### Step 1: Reflection Questions

**Answer these in a few sentences each:**

1. What specific customer data do you *need* to collect to deliver your core service (e.g., email, location, payment info)? What’s optional?

2. How will you explain data collection to customers in plain language (not legalese)?

3. What’s your plan for securely storing data (e.g., encryption, access controls)?

4. How often will you delete data you no longer need (e.g., inactive accounts, old analytics)?

---

### Step 2: Ethical Data Action Plan

After you reply, I will use the writings of Professors Sweeney, Zittrain, and Matwyshyn to:

1. **Simplify Consent:** Help you draft a clear, one-sentence data permission request (e.g., *“We ask for your ZIP code to show local deals-we’ll never sell it. Cool?”*).

2. **Minimize Risk:** Recommend tools for anonymizing data (e.g., stripping identifiers from analytics) and limiting collection to only what’s essential.

3. **Secure & Delete:** Suggest free/low-cost security practices (e.g., end-to-end encryption via ProtonMail, automatic data deletion scripts).

4. **Build Transparency:** Guide you in creating a “Privacy FAQ” that answers *“Why do you need this?”* and *“Who else sees it?”* in <100 words.

**Example Framework:**

- **Consent:** “Ask clearly, explain plainly, make opting out easy.”

- **Collection:** “If it doesn’t serve your customer, don’t collect it.”

- **Security:** “Encrypt like your reputation depends on it (because it does).”

- **Deletion:** “Data hoarding is a liability- clean house monthly.”

---

**How to use this prompt:**

- Respond with your answers to the questions above.

- Your Ivy League panel will return a tailored action plan with tools, scripts, and best practices.

- You’ll leave with a trust-building data strategy that’s simple to implement and easy to communicate.

*(This exercise combines Sweeney’s data minimization frameworks, Zittrain’s transparency principles, and Matwyshyn’s cybersecurity insights.)*

Hood Qaim-MaqamiAugust 5, 2025