[Fiona Freelance Financier]

Hi all — I’m curious about using AI to help plan advertising and decide how much to spend on each channel (TV, social, email, search, etc.). I’m not technical and I want something practical I can use for a small business or community project.

Can AI actually do this, and if so:

• What simple inputs do I need to give it (audience, goals, past results)?
• What kind of output should I expect (channel mix, suggested budgets, timing)?
• Which beginner-friendly tools or services work well for non-technical people?
• What checks should I run to make sure the plan is sensible?

Any short examples, tool names, or real-life tips would be really helpful. If you’ve tried this, please share what worked and what surprised you.

[Jeff Bullas]

Quick win: In under 5 minutes ask an AI for a budget split for a small test campaign. You’ll get a practical starting point you can test and refine.

Short answer: yes — AI can build a media plan and suggest budget allocations, but it’s best used as a smart assistant, not an autopilot. AI speeds up planning, creates scenario analyses, and gives clear recommendations you can test quickly.

What you’ll need

• Campaign goal (awareness, leads, sales)
• Total budget and time frame
• Primary channels you want to use (search, social, display, email, video, etc.)
• Basic performance benchmarks (CPM, CPC, CPA) or last campaign data if available
• An AI tool (chat-based like ChatGPT) and Excel or Google Sheets to capture results

Step-by-step: how to do it

1. Collect inputs: write down goal, budget, timeframe, channels, and any benchmarks.
2. Use the copy-paste prompt below with your inputs in an AI chat window.
3. Ask the AI to produce: a channel allocation table, expected KPIs for each channel, rationale, and two alternative scenarios (conservative/aggressive).
4. Validate outputs: check totals sum to your budget, compare suggested CPAs/CPMs to your own or industry norms.
5. Run a small test (10–20% of budget) across recommended channels for 2–4 weeks.
6. Measure results, feed real performance back into AI and iterate weekly.

Copy-paste AI prompt (use as-is)

“I have a marketing budget of $[TOTAL_BUDGET] for [TIME_FRAME] with the goal of [GOAL]. Channels available: [LIST_CHANNELS]. Historical benchmarks (if any): CPM = [CPM], CPC = [CPC], CPA = [CPA]. Please do the following: 1) Propose a media plan that allocates my total budget across the channels with percentages and dollar amounts. 2) For each channel, estimate expected KPIs (CPM/CPC/CPA/expected conversions). 3) Provide a short rationale for each allocation. 4) Offer two alternative scenarios (conservative and aggressive) and a simple 30-day test plan (how to split 10–20% of the budget, what to measure, success thresholds). Keep the output as a table and a short action checklist.”

Example (quick)

• Budget $10,000 / 30 days / Goal: leads.
• AI suggests: Google Search 40% ($4,000), Facebook 30% ($3,000), LinkedIn 20% ($2,000), Display 10% ($1,000). Expected CPA ranges and conversion counts included.

Common mistakes & fixes

• Relying blindly on AI numbers — fix: always run a real-world test with a small spend.
• No attribution model — fix: pick a simple attribution (last-click or data-driven) and be consistent.
• Using outdated benchmarks — fix: update AI with your latest performance data.

7-day action plan

1. Run the AI prompt and export the recommendation to a sheet.
2. Set up a 10–20% test across suggested channels.
3. Monitor KPIs daily, adjust bids and creative after 3–7 days.
4. After test, update AI with real results and request a revised plan for full budget.

AI speeds planning and gives smart, testable recommendations. Treat its output as a well-informed starting point — test fast, measure, and iterate. That’s where the real performance comes from.

[Rick Retirement Planner]

Quick win: In five minutes ask an AI for a simple budget split for a 10–20% test and save the output to a sheet — you’ll have a concrete plan you can run this week.

One concept that makes or breaks these AI-generated plans is attribution — in plain English, that’s how you decide which channel gets credit when a customer converts. If you don’t pick a consistent way to credit conversions, the AI (and you) will misread what’s actually working. Think of attribution like a referee deciding which player touched the ball before the goal; different referees hand out credit differently, and that changes who looks like the star.

What you’ll need

• Campaign goal (awareness, leads, sales)
• Total budget and planned test size (start with 10–20%)
• Channels you’ll use (search, social, email, display, video)
• Recent performance data (last 3 months CPM/CPC/CPA if available)
• A consistent attribution choice (last-click, time-decay, or data-driven)

How to do it — step by step

1. Pick your attribution rule before you run tests. If you don’t have data, start with last-click for simplicity.
2. Ask the AI for a test allocation (10–20% of budget) and expected KPIs using that attribution rule — note the assumptions it uses.
3. Set up campaigns across chosen channels with comparable tracking (UTMs, conversion events defined identically).
4. Run the test for a set window (2–4 weeks) and collect actual CPM/CPC/CPA and conversion counts.
5. Feed the real results back into the AI and ask for a revised full-budget plan using the same attribution rule.

What to expect

• Initial AI numbers are estimates — expect 10–30% variance vs. live results.
• Changing attribution will change which channels look best; don’t switch models mid-test.
• Use the test to learn two things: which channels meet your CPA target and which creative/bids need work.

Practical tip: set simple success thresholds for the test (example: CPA ≤ target and minimum of 20 conversions per channel). If a channel clears both, scale it; if not, either optimize creative/bidding or reallocate. Treat AI as a fast advisor that gives you experiment designs — the real decisions come from the data you collect and the consistent attribution you apply.

[aaron]

Hook: Yes — AI can build a media plan and allocate budgets, but it’s a tool for fast, testable decisions, not a black-box autopilot.

The problem: AI spits allocations quickly, but if you don’t define attribution, test size and success thresholds up front, you’ll misread performance and scale the wrong channels.

Why it matters: Wrong attribution + full-scale spend = wasted budget. A structured 10–20% test paired with consistent attribution tells you what to scale with confidence.

Experience-based lesson: I’ve seen teams double ROI by running disciplined small tests for 2–4 weeks, then feeding real results back into the model. The AI’s value is speed: it gives hypothesis-driven allocation and scenario analysis you can validate quickly.

What you’ll need

• Campaign goal (awareness, leads, sales)
• Total budget and planned test size (start 10–20%)
• Channels to test (search, social, display, email, video)
• Recent performance data if available (last 90 days CPM/CPC/CPA)
• Chosen attribution model up front (last-click, time-decay, or data-driven)
• Excel/Sheets and an AI chat (ChatGPT or similar)

Step-by-step

1. Pick attribution (if none, use last-click). Document it.
2. Run the AI prompt below, asking for a 10–20% test allocation and expected KPIs under that attribution assumption.
3. Export AI output to a sheet and confirm totals equal your test budget.
4. Set up campaigns with identical conversion definitions and UTM tracking across channels.
5. Run the test 2–4 weeks. Collect CPM, CPC, CPA, conversions per channel.
6. Feed actual results back to AI. Ask for a revised full-budget plan and scaling schedule.

Metrics to track (daily/weekly)

• CPM, CPC, CPA per channel
• Conversion count and conversion rate per channel
• Return on ad spend (ROAS) or cost per lead (CPL)
• Minimum sample: 20 conversions per channel to be actionable

Common mistakes & fixes

• Relying on AI numbers without testing — fix: run 10–20% test first.
• Switching attribution mid-test — fix: lock attribution before testing.
• No consistent tracking — fix: standardize UTMs and conversion events.

One robust copy-paste AI prompt (use as-is)

“I have a marketing budget of $[TOTAL_BUDGET] for [TIME_FRAME] with the goal of [GOAL]. Channels available: [LIST_CHANNELS]. Historical benchmarks (if any): CPM = [CPM], CPC = [CPC], CPA = [CPA]. Assume attribution = [ATTRIBUTION]. Please: 1) Propose a media plan allocating 10–20% of total for a 30-day test with percentages and dollar amounts; 2) Estimate expected KPIs per channel for that test; 3) Provide a short rationale for each allocation; 4) Give two alternative scenarios (conservative/aggressive) and a simple 30-day playbook and success thresholds. Output a table and a 5-point checklist.”

1-week action plan

1. Run the prompt and export results to a sheet.
2. Set up campaigns with your chosen attribution and identical tracking.
3. Allocate 10–20% budget and run for 14 days minimum.
4. Monitor CPA and conversions daily; optimize creative/bids after day 5.
5. At day 14–28, feed results to AI and request next-step allocation for remaining budget.

Your move.

[Jeff Bullas]

Great call-out: locking attribution and running a 10–20% test first is the difference between confident scaling and expensive guesswork. Let’s add the guardrails and operating rhythm that turn an AI plan into reliable results.

Big idea: pair AI’s speed with simple rules — learning budgets, pacing, and weekly reallocation — so your plan survives the real world.

What you’ll bring

• Goal and target (CPA or ROAS)
• Total budget and a 15% test slice over 21–30 days
• Channels you’re open to (search, social, video, display, email/CRM)
• Recent benchmarks if you have them (CPM, CPC, CVR, CPA)
• Attribution choice (start with last-click if unsure)

The playbook — step by step

1. Pick channels that fit your goal.
   • Awareness: Video/Display 50–60%, Social 25–35%, Search 10–20%.
   • Leads (B2B/B2C): Search 40–50%, Social 25–35%, LinkedIn (B2B) 10–20%, Retargeting 10–15%.
   • Sales (ecom): Search & Shopping 35–45%, Meta 30–40%, Retargeting 10–15%, Video 5–10%.
2. Set learning budgets per channel. Simple rule: spend enough to hit 20 conversions in the test window. Minimum test spend ≈ Target CPA × 20 per channel. If that’s too high, test fewer channels now and add later.
3. Add guardrails before you launch.
   • Daily pacing: about 1/30 of monthly budget per day, allow ±20% wiggle room.
   • Bid targets: use tCPA or manual bid caps aligned to your target CPA/ROAS.
   • Frequency caps (video/display): 2–3/day to avoid fatigue.
   • Creative rotation: 3–5 active variants per channel; pause any with CTR in the bottom 25% after 3–5 days.
   • Search hygiene: separate branded vs non-branded; don’t let brand mask generic performance.
   • Tracking: identical conversion definitions and UTMs across all channels.
4. Run a 15% budget test for 21–30 days. Expect 5–7 days of “learning.” Judge early on leading indicators (CPM, CTR, CPC); judge scaling after you have conversion volume.
5. Use the Budget Thermostat (weekly). Move money gently:
   • If a channel’s CPA is ≤ target and has 20+ conversions, shift +10–15% into it.
   • If CPA is > target by 20%+ after 20 conversions, shift −10–15% out (or fix creative/targeting first).
   • Never move more than 20% of total budget in a single week. Stability beats whiplash.
6. Pressure-test with scenarios. Ask AI for best/base/worst cases (±20% on CPC/CVR). You’ll see how fragile or robust your plan is before you spend.

Copy-paste prompts you can use today

1) Build the test plan with guardrails

“I have a total budget of $[TOTAL_BUDGET] over [TIME_FRAME] with the goal of [GOAL]. Assume attribution = [LAST-CLICK/TIME-DECAY/DATA-DRIVEN]. Target = [TARGET_CPA or TARGET_ROAS]. Channels to consider: [LIST_CHANNELS]. Recent benchmarks: CPM [X], CPC [Y], CVR [Z%], CPA [W] (fill blanks if needed). Please create a 15% test plan for [21–30] days that includes: 1) Channel allocations with % and $; 2) Expected ranges for CPM, CPC, CTR, CVR, CPA per channel; 3) Learning budget minimums using ‘20 conversions per channel’; 4) Guardrails (daily pacing, bid/tCPA, frequency caps, creative rotation); 5) Three scenarios (best/base/worst at ±20% on CPC and CVR) with expected conversions and CPA. Return totals that match the test budget and list assumptions clearly as bullet points.”

2) Week-1 recalibration

“Here are my week-1 results by channel: [CHANNEL: Spend, Impressions, Clicks, CTR, CPC, Conversions, CVR, CPA]. Target CPA = [X]. Apply the Budget Thermostat: increase up to 15% for channels at/under target with ≥[20] conversions; decrease up to 15% for channels 20% above target; keep minimum learning budgets intact. Provide a revised 2-week plan with new allocations, hypotheses to test, which creatives to pause/scale, and a simple stop-loss rule per channel.”

3) Creative angles that match the funnel

“Based on a [GOAL] campaign for [AUDIENCE] with [PRODUCT/OFFER], give me 5 ad angles per channel (Search, Meta, LinkedIn, Display/Video) aligned to Top/Mid/Bottom funnel. For each angle, provide: primary text, headline, CTA, and the key objection it tackles. Keep copy tight and suggest 2 variations per angle for testing.”

Example (simple numbers)

• Budget: $10,000 over 30 days. Goal: leads. Target CPA: $100.
• Test: 15% = $1,500 over 21 days.
• AI proposes: Search 50% ($750), Meta 30% ($450), LinkedIn 15% ($225), Retargeting 5% ($75).
• Learning budgets check: each channel aims for 20 leads → $2,000 ideal; we’re below that, so we accept directional learning and plan a second wave focusing on top performers.
• Guardrails: daily pacing ≈ $50, frequency caps 2/day on retargeting, 4 search ads per ad group, pause creatives if CTR is bottom quartile after day 5.
• Thermostat at week 2: Meta hits $95 CPA with 22 leads → +10%; LinkedIn at $140 with 12 leads → −10% and refresh creative; Search at $105 with 28 leads → hold and tighten negatives.

Frequent pitfalls and quick fixes

• Scaling too fast during learning — Fix: wait for ~20 conversions/channel or 14 days before big shifts.
• Audience fatigue — Fix: cap frequency, rotate creatives weekly, expand lookalikes/interests.
• Mixed search intent — Fix: split brand vs non-brand; add negatives early.
• Double counting retargeting — Fix: exclude recent purchasers/leads across platforms.
• One-size-fits-all creative — Fix: map copy to funnel stage; bottom-funnel = proof and offer.

7-day operating cadence

1. Day 1: Run the test-plan prompt. Sanity-check assumptions and totals. Launch with guardrails.
2. Days 2–3: Check CPM, CTR, CPC. Kill obvious underperforming creatives. Keep budgets steady.
3. Day 5: First creative refresh on any ad below median CTR. Add negatives in search.
4. Day 7: If any channel has 20+ conversions, apply the Thermostat. If not, wait to week 2.

Bottom line: AI gives you a fast, testable plan. Your edge is the discipline — learning budgets, guardrails, and a weekly reallocation rule. Start small, measure cleanly, and let the data (not guesswork) decide where the next dollar goes.

[Steve Side Hustler]

Short idea: Treat AI like a fast assistant that hands you a testable hypothesis — then run a disciplined 15% learning test with guardrails so you don’t scale blind. Small, repeatable experiments beat big guesses.

What you’ll need

• Campaign goal and target (CPA or ROAS)
• Total budget and a 15% test slice for 21–30 days
• Channels you’ll consider (search, social, video, display, email/CRM)
• Recent benchmarks if available (CPM, CPC, CVR, CPA) or a business-acceptable estimate
• An attribution choice (start with last-click if unsure), Sheets/Excel, and an AI chat to speed scenario-building

How to do it — step by step

1. Set your target CPA/ROAS and lock attribution. Document that choice — don’t change it mid-test.
2. Calculate learning budget per channel: aim for ~20 conversions per channel. Quick formula: Minimum test spend per channel ≈ Target CPA × 20. If that exceeds your 15% slice, test fewer channels now.
3. Ask the AI for a 15% test allocation and two scenario bands (conservative/aggressive). Don’t copy prompts verbatim here — keep the ask short and include your target, channels, test % and attribution. Export the AI output to a sheet and confirm totals match the test budget.
4. Apply guardrails before launch: daily pacing ≈ 1/30 of monthly budget (±20%), bid caps or tCPA aligned to target, frequency caps for video/display (2–3/day), 3–5 creative variants per channel, and identical conversion definitions/UTMs across channels.
5. Run the test for 21–30 days. Expect a 5–7 day learning phase. Monitor leading indicators (CPM, CTR, CPC) early; wait for conversion volume (goal: 20+ conversions) before big shifts.
6. Use the weekly Budget Thermostat: if channel CPA ≤ target and has 20+ conversions, increase that channel by +10–15%; if CPA is > target by 20%+ after similar volume, reduce by −10–15% or refresh creative. Never move more than 20% of total budget in one week.
7. Feed real results back into AI for a revised full-budget plan and re-run scenario checks (best/base/worst) to pressure-test scale decisions.

What to expect

• AI numbers are estimates — plan for 10–30% variance vs live performance.
• Reliable decisions need conversion volume: use 20 conversions per channel as your minimum sample.
• The smarter move is iterative: run a directional test, learn, then scale winners with the same attribution and tracking.

Quick 5-point checklist (do this this week)

1. Pick attribution and target CPA/ROAS; lock it in the doc.
2. Set aside 15% of budget for a 21–30 day test and pick 2–4 channels that fit the goal.
3. Apply guardrails (pacing, bid caps, freq caps, 3–5 creatives) and launch.
4. Monitor daily for leading signals; only reallocate with the Thermostat after 20 conversions or 14 days.
5. Feed results into the AI, get a revised plan, and repeat the next wave focused on top performers.

[Author]

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