How to Build a Customer Avatar with AI for Real Estate | Step-by-Step Demo

Full video transcript

This video demonstrates how to run the MarketSauce 9000 recipe, specifically Section One of the Master Buyer’s Blueprint, so you can create and build a customer avatar — also called an ideal target audience, ideal customer persona, or avatar. The goal is to create that persona, that character of your best customer, and understand what’s driving their buying decisions. This is shown using the recipe found in the Buyer Brief book, using the Google Doc version because it’s easy to copy and paste the instructions and prompts.

Part one of the book contains example inputs and outputs. For this demo, Part Two is used instead — a blank-slate recipe with no example outputs, so viewers can insert their own context and get started. Part two just contains the recipe itself: symbols indicating what’s a variable, where prompts start and run, and sections that are open to adjust and build on top of. The book also includes trainings and workshops as bonuses.

Before running the recipe, you go to the MarketSauce 9000 instructions and context section and answer some questions: who is your target audience, what’s their goal, why do they want that, what industries you’re serving, where they’re located, company size, and buying intent signals. You don’t have to fill out every prompt, but the more detailed and specific you can be, the better the outputs. At minimum, you can start with a short blurb about who you are, what you do, who you serve, and how you’re different — the key inputs are your user inputs, product or service, segment, niche, and context.

In the demo, the user input is copied into a new Claude conversation. The product or service is set to “luxury real estate broker,” the segment to “Millennial millionaires,” and the niche to “luxury real estate listing sales.” Additional context is added: “We provide white glove real estate listing services to sell our clients’ homes at top dollar while also helping our clients secure their next luxury home or investment property… we do this by leveraging our proprietary system and network of luxury buyers worldwide to make custom matches and ensure the listing being sold is positioned specifically to the ideal buyer.”

Note: if Claude doesn’t format outputs in H1/H2/bullet-point format by default, you may need to explicitly ask it to.

Next, the instructions section of the recipe is copied and pasted into the Claude thread so Claude understands the user input above is the product or service, and that the instructions define step one: honing in on who this market is and getting inside their heads.

Claude returns a buyer’s brief for the luxury real estate broker targeting Millennial millionaires — noting the proprietary system and strategic positioning — and describes the segment as 25 to 40 years old, net worth of $5 million plus, tech-savvy, well-educated, working in high-paying fields like tech, finance, law, or medicine, or having inherited wealth. The demo emphasizes checking that the output is staying on track, since AI can go off the rails if not directed. Psychographic details follow: valuing experiences over things, self-actualization, wanting their home to be an expression of refined taste and success, status-seeking, and fear of missing out.

The demo notes this is just one example — Millennial millionaires in luxury real estate — but the same approach applies to any market segment or niche by adjusting who you’re serving and how specific you get about your ideal clients.

Moving into Section One of the Master Buyer’s Blueprint itself, the same top-down copy-paste process continues: copying each part of the blueprint prompt into the Claude thread, letting Claude build on the context already established. This produces geographic, demographic, and psychographic detail, plus positioning language such as “the broker positions itself as the preeminent luxury firm of the future — high-tech, high-touch, high-exclusivity.”

The primary goal section is copied in next. Claude offers a list of possible primary goals (Dream Home Status, Wealth Preservation, Lifestyle Upgrade, and others), and the human operating the process chooses one — in this case, “Lifestyle Upgrade” — which then directs the rest of the blueprint output.

The complaints section follows, covering both primary and secondary complaints tied back to the chosen goal: limited inventory, lack of smart home features and the latest tech and amenities, time drain and coordination issues, difficulty finding designers who capture their vision, concerns about privacy and security, and lack of expert tax guidance. The idea is that addressing the top complaints in marketing and communications builds trust with the segment.

Positioning statements and goal descriptors are generated as well, such as “effortlessly elevate your lifestyle with a white-glove luxury real estate listing service.”

Because Claude conversations can hit message limits, the demo copies ahead through several more prompt sections at once — objections, negative feelings (frustration, disappointment, being annoyed, confused, impatient), and fears (settling for a subpar home, overpaying) — telling Claude to continue top to bottom rather than pasting each prompt individually.

Further sections cover mistaken beliefs holding the market back, the “enemy” the brand can position against (an antiquated approach, unresponsive agents, or unsavory agents only interested in a quick sale), the primary cause of the problem, and the consequences of not solving it — for example, “watching a dream home slip away while mired in decision fatigue.” The demo notes that any statistics Claude generates in this process should be fact-checked and validated rather than taken at face value, since AI-generated numbers aren’t guaranteed to be accurate.

The blueprint continues into false solutions the market has already tried and myths about success they may believe. Each of these prompt blocks can be run one at a time so you can check the output at each step, or run further ahead if you’re comfortable letting Claude continue without stopping. Message limits over a given time period can slow down running an entire blueprint in one sitting, so the recipe is designed to be run in stages if needed.

The completed Section One blueprint leads into Section Two, a creative brief that goes further into refining team messaging for a marketing campaign. The demo closes by noting the value of offer targets — different sub-segments within the broader market, such as dual-career power couples, tech startup founders after an IPO, crypto millionaires, social media influencers, or Millennial CEOs and serial entrepreneurs after exiting a venture — each with a different “why” behind their goals. The suggestion is to consider whether a business might be missing adjacent segments (professional athletes, entertainers, other dual-career couples) that aren’t currently part of its marketing.

The recommended workflow after running the recipe is to move the output into a document, highlight the areas that stand out, test that language in real marketing and messaging, and update the document as real-world feedback comes in — treating the blueprint as a living research document rather than a one-time output.