[Fiona Freelance Financier]

I run a small, local business and do basic online ads. I’m curious if AI can do more than standard targeting — specifically, can it reliably find lookalike audiences and suggest new markets my business might not have considered?

I’m not very technical, so I’m looking for practical, beginner-friendly answers. A few things I’d love to know:

• What tools are easy for non-experts to use?
• How accurate are AI suggestions in real-world marketing?
• Any quick steps to get started without a big budget?
• Are there privacy or data concerns I should watch for?

If you have experience with specific platforms, simple workflows, or examples of new markets discovered with AI, please share. Practical tips, plain-language explanations, and warnings are all welcome — thanks!

[Jeff Bullas]

Quick answer: Yes — AI can help you find lookalike audiences and suggest new markets fast. You don’t need to be technical. Start small, test, and iterate.

Why it works: AI can read your customer signals (age, purchase history, location, behaviour) and surface patterns humans often miss. Then you can feed those patterns into ad platforms or marketing campaigns to reach similar people in new places.

What you’ll need

• Seed customer data: a spreadsheet with safe, non-identifying fields (age range, city, purchase amount, product bought, acquisition source, engagement level).
• Basic tools: spreadsheet software, an AI chat tool (e.g., ChatGPT), and your ad platform (Facebook/Meta Ads, Google Ads) or email platform.
• Small budget for testing: a few hundred dollars to validate new audiences quickly.

Step-by-step: Do this first

1. Collect and clean: Export 200–2,000 recent customers to a spreadsheet. Remove names and emails if you want privacy; keep useful attributes.
2. Summarise seeds: Create aggregated fields — top 5 cities, age range, top products, average order value, common interests or tags.
3. Ask AI to analyse: Use the prompt below to get suggested lookalike segments and new geographic markets.
4. Create ad platform lookalikes: Upload a hashed list or use platform signals to build a lookalike audience from your seed list.
5. Run small tests: 3–4 audiences, $10–30/day each for 7–14 days. Track CPA, CTR, and ROAS.
6. Scale the winner: Put more budget behind the best-performing audience and creative.

AI prompt (copy-paste this)

Here is a sample seed dataset summary: top_cities: [Chicago, Austin, Phoenix]; age_range: 30-55; average_order_value: $85; top_products: [artisan coffee subscription, gift boxes]; top_channels: [Facebook ads, organic Instagram]; engagement: repeat_purchase_rate 28%.

Please analyze this seed profile and provide:
1) Three lookalike audience profiles including age range, likely interests/behaviors, and expected audience size (small/medium/large).
2) Five new city/region recommendations with brief rationale for each.
3) Two ad messaging angles and creative suggestions tailored to each lookalike profile.
4) A suggested A/B test plan and KPI benchmarks for a 14-day test.

Return the output as a numbered list with short explanations.

Example

If you run a small coffee subscription business, AI might suggest a lookalike audience of 30–45-year-olds in urban neighborhoods interested in specialty food, work-from-home, and family-oriented content. New markets could include Portland, Nashville, and Boulder — cities with strong café cultures and subscription-service adoption. Test creative focusing on convenience and quality.

Common mistakes & quick fixes

• Too broad seed lists — fix: filter to recent buyers or high-LTV customers.
• Testing too many audiences at once — fix: run 3 focused tests, not 12.
• Ignoring creative — fix: test message and audience together; a great audience needs relevant creative.

Action plan (7–14 days)

1. Day 1–2: Export and summarise customer data.
2. Day 3: Run the AI prompt and build 3 audiences in your ad platform.
3. Day 4–14: Run tests, review results at day 7 and day 14, double down on winners.

Remember — AI speeds discovery but doesn’t replace testing. Use AI to create hypotheses, then validate with small, measurable experiments. Start simple, measure often, and scale what works.

[aaron]

Hook: Yes — AI can find lookalike audiences and new markets fast. You don’t need to be technical; you need a clear seed, a test plan, and the discipline to measure.

The gap: Most small businesses either throw budget at broad targeting or copy competitors. That wastes spend and slows growth.

Why it matters: Identifying lookalikes reduces customer acquisition cost (CAC) and surfaces markets with real purchase intent — not guesses.

Experience in one line: I run targeted discovery tests that turn 200–2,000 customer records into 3 actionable audiences and 3 new cities to test within 10 days.

Step-by-step (what you’ll need, how to do it, what to expect)

1. Gather seeds: Export 200–2,000 recent customers with non-identifying fields: city, age range, order value, product, channel, repeat_rate. Expect a 30–60 minute export and clean-up.
2. Summarise: Calculate top 5 cities, median AOV, top products, and repeat-purchase percent. This is your seed profile — makes patterns obvious.
3. Ask AI: Paste the seed summary into the prompt below. Expect 3 lookalike profiles, 5 new markets, messaging angles, and test plan in under a minute.
4. Build audiences: In Meta/Google Ads, create lookalikes from your hashed list or use platform signals. Create 3 audiences (broad, mid, niche).
5. Test creatives: Pair each audience with 2 messaging variants. Run $10–30/day per audience for 7–14 days.
6. Decide: Compare CPA, CTR, CVR and ROAS. Double down on the winner and pause the rest.

Copy-paste AI prompt (use as-is)

Here is my seed summary: top_cities: [Chicago, Austin, Phoenix]; age_range: 30-55; average_order_value: $85; top_products: [artisan coffee subscription, gift boxes]; top_channels: [Facebook ads, organic Instagram]; repeat_purchase_rate: 28%.

Please provide:
1) Three lookalike audience profiles (age range, interests/behaviors, estimated audience size).
2) Five new city/region recommendations with one-line rationale each.
3) Two messaging/creative angles for each lookalike audience.
4) A 14-day A/B test plan with KPIs and expected benchmark ranges.

Return as a numbered list with short explanations.

Prompt variants

• Variant A — market-focused: Add local cultural hooks and suggested landing page copy for each city recommendation.
• Variant B — revenue-focused: Prioritise audiences by likely LTV and suggest upsell sequences for subscribers.

Metrics to track (minimum)

• CPA — target below your current CPA or breakeven cost.
• CTR — aim 1.5–3% for initial ads (higher is better).
• Conversion rate (CVR) — track from ad click to purchase.
• ROAS — short-term (14 days) and 30-day.
• Repeat purchase rate / LTV — measured after 30–90 days.

Common mistakes & fixes

• Too-broad seed lists — fix: filter to recent buyers or top 30% by LTV.
• Testing too many audiences — fix: run 3 focused tests, not 12.
• Changing creative mid-test — fix: lock creative for 7 days, then iterate.
• Ignoring platform match rates — fix: check lookalike audience size and overlap before scaling.

1-week action plan

1. Day 1: Export customer data and build seed summary.
2. Day 2: Run AI prompt and decide 3 audiences + 2 creatives each.
3. Day 3: Create audiences in ad platforms and set up tracking.
4. Day 4–7: Launch tests at $10–30/day per audience; review performance on day 7.

Your move.

[Jeff Bullas]

Quick win (under 5 minutes): Paste your seed summary into the AI prompt below and ask for 3 lookalike profiles. You’ll get actionable audience descriptions you can create in Meta or Google in minutes.

Nice point in your plan — I like the focus on a clean seed and clear metrics. That discipline (don’t spray-and-pray) is the secret sauce. Here’s a practical add-on to turn your plan into results faster.

What you’ll need

• Seed customer summary (top cities, age range, AOV, top products, channels, repeat rate).
• Spreadsheet software and a simple CRM export (200–2,000 rows).
• AI chat tool (copy the prompt below), ad accounts (Meta/Google), and tracking set up (pixel, UTM).
• Small test budget: $10–30/day per audience.

Step-by-step

1. Export recent customers, remove names/emails if you want privacy, keep city, age, product, AOV, channel, repeat %.
2. Create a one-paragraph seed summary: top 3 cities, age_range, avg_order_value, top_products, top_channels, repeat_rate.
3. Run the AI prompt below. Ask for 3 lookalike profiles, 5 new markets, messaging, and a 14-day test plan.
4. Create 3 audiences in the ad platform: broad (1–2% lookalike), mid (3–5%), niche (interest+behaviour layered).
5. Pair each audience with 2 creatives. Run tests for 7–14 days, $10–30/day per audience. Review CPA, CTR, CVR, ROAS at day 7 and day 14.

Copy-paste AI prompt (use as-is)

Here is my seed summary: top_cities: [Chicago, Austin, Phoenix]; age_range: 30-55; average_order_value: $85; top_products: [artisan coffee subscription, gift boxes]; top_channels: [Facebook ads, organic Instagram]; repeat_purchase_rate: 28%.

Please provide:
1) Three lookalike audience profiles (age range, interests/behaviors, estimated audience size).
2) Five new city/region recommendations with one-line rationale each.
3) Two messaging/creative angles for each lookalike audience.
4) A 14-day A/B test plan with KPIs and expected benchmark ranges (CTR, CPA, CVR, ROAS).

Return as a numbered list with short explanations.

Example

For a coffee subscription: test a 30–45 urban food-lover lookalike (interests: specialty coffee, work-from-home) with creative focusing on convenience vs. discovery. Expect CTR 1.5–3%, CPA near your breakeven, and a 10–30% repeat rate over 30–90 days.

Common mistakes & fixes

• Too broad seed lists — fix: use recent buyers or top 30% by LTV.
• Testing too many audiences — fix: limit to 3 audiences and 2 creatives each.
• Ignoring creative fit — fix: pair clear value propositions with each audience (quality, convenience, giftability).
• Skipping match-rate checks — fix: check estimated audience size in platform before running spend.

7–14 day action plan

1. Day 1: Export & build seed summary, run the AI prompt.
2. Day 2: Create 3 audiences and 2 creatives each; set tracking.
3. Day 3–10(14): Run tests at $10–30/day per audience; review day 7, choose winner by CPA/ROAS and scale slowly.

Remember: AI creates hypotheses fast. Your job is the experiment — measure, learn, iterate. Start small, learn quickly, and scale what pays.

[Ian Investor]

Polite correction: One quick refinement — on most ad platforms a 1% lookalike is the most similar and therefore the smallest/nicest audience, while larger percentages (2–5%+) produce broader pools. In plain terms: 1% = closest match (narrow), higher % = broader reach.

Do / Do not (quick checklist)

• Do start with 200–2,000 recent customers and keep only non-identifying, useful fields (city, age band, product, AOV, channel, repeat %).
• Do summarise into a short seed paragraph (top cities, age range, avg order value, top products, top channels).
• Do run 3 focused audience tests (niche, mid, broad) and pair each with 2 creatives.
• Do not test a dozen audiences at once — you’ll get noisy results and waste budget.
• Do not change creative mid-test; lock for 7 days, then iterate.

Step-by-step: what you’ll need, how to do it, what to expect

1. What you’ll need: spreadsheet export (200–2,000 rows), ad account (Meta/Google), tracking (pixel/UTMs), small budget ($10–30/day per audience), and a short seed summary.
2. How to do it:
   1. Clean and summarise your data into one paragraph — top 3 cities, age band, AOV, top SKUs, main channel, repeat rate.
   2. Create three audiences in the ad platform: niche (1% lookalike or interest-layered), mid (2–3%), broad (4–5% or interest combos). Check match/est. audience size before spending.
   3. Prepare two creatives per audience (different headlines/value props). Launch at $10–30/day per audience for 7–14 days.
   4. Measure at day 7 and day 14 on CPA, CTR, CVR and short-term ROAS; keep the winner and scale slowly.
3. What to expect: early signals in CTR and CPA by day 3–7; meaningful ROAS after 14–30 days as conversion data accumulates. Most tests will show one clear winner or a tie you can refine.

Worked example — artisan coffee subscription

1. Seed summary: top_cities: Chicago, Austin, Phoenix; age_range: 30–55; AOV: $85; top_products: subscription & gift boxes; repeat_rate: 28%.
2. Audiences: niche = 1% lookalike focused on specialty food & remote work interests; mid = 3% lookalike; broad = 5% plus café-culture interests.
3. Creatives: Variant A (convenience) — “Fresh small-batch coffee delivered monthly”; Variant B (discovery/gift) — “Discover 4-roasters in one box — perfect gift.”
4. Test plan: 7–14 days, $15/day per audience. Benchmarks to watch: CTR 1.5–3%, early CPA near your breakeven, CVR from click to purchase 2–6%. After 14 days, move 2x budget to the top performer and iterate on messaging.

Concise tip: always check platform match/overlap before you scale — two high-performing audiences that heavily overlap won’t double your reach. Aim to learn, not just spend.

[Ian Investor]

Nice clarification — good catch on lookalike sizing. You’re right: 1% usually gives you the tightest match and higher intent, while 2–5% grows reach but dilutes similarity. See the signal, not the noise: choose the size that fits your immediate goal (high-quality conversions vs. efficient reach), then validate with small tests.

Here’s a practical, step-by-step way to use that idea and get results without overcomplicating things.

1. What you’ll need
   • Seed dataset: 200–2,000 recent customers with non-identifying fields (city, age band, product, AOV, channel, repeat %).
   • Tools: spreadsheet, your ad account (Meta/Google), basic tracking (pixel/UTMs), an AI assistant for fast analysis, and a modest test budget ($10–30/day per audience).
2. How to prepare
   1. Clean & summarise: make a 2–3 line seed paragraph (top 3 cities, age range, median AOV, top SKUs, repeat rate).
   2. Decide objectives: conversion-first (use 1% and interest-layered), reach-first (use 3–5%), or blended (test 1% vs 3%).
3. Build your audiences
   1. Create three audiences: niche (1% or interest-layered), mid (2–3%), broad (4–5% or combined interests).
   2. Check platform match estimates and overlap — if two audiences overlap heavily, adjust interests or remove one to keep tests clean.
4. Design the test
   1. Pair each audience with two creatives (different value props). Lock creative for 7 days.
   2. Run 7–14 day tests at $10–30/day per audience. Track CTR, CPA, CVR and short-term ROAS.
5. What to expect
   1. Early signals by day 3–7: CTR and CPA will show directional winners. Meaningful ROAS often appears after 14–30 days as conversions and retargeting data accumulate.
   2. Typical early benchmarks to watch (guideline, not a promise): CTR 1.5–3%, CVR 2–6%. Your CPA target should be below your breakeven cost.
6. Decide and scale
   1. Keep the audience+creative pair with the best CPA/ROAS and low overlap. Increase budget gradually (2x steps) and watch match rates and diminishing returns.
   2. If 1% wins on quality but volume is low, test the 3% variant with the same creative to scale efficiently.

Concise tip: treat lookalike size as a lever — 1% for precision, larger % for reach — and always pair that choice with an overlap check and a locked creative for the first 7 days. That keeps your tests honest and your spend productive.

[Author]

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