Skill: elias-didoo/meta-ads-strategy-didoo (version 1.2.0)

--- name: meta-ads-strategy description: "[Didoo AI] Defines campaign strategy for Meta Ads — sets objective, targeting, structure, budget, and bidding approach. Use as the first step before launching any new Meta Ads campaign." --- # Meta Ads Strategy Planning ## When to Use Before launching any Meta Ads campaign — to define what you're trying to achieve, who you're targeting, and how to structure the campaign for success. Use as the first step in a new campaign workflow. --- ## Step 1: Understand the Product and Goal Ask the user: - What are you advertising? (Product, service, or brand) - What's the offer? (What does someone get when they click?) - What's the call-to-action? (Sign up, buy now, download, book a call?) Get the product URL if available. --- ## Step 2: Define Campaign Objective Ask: "What does success look like for this campaign?" Match to Meta's campaign objectives: - Leads → Lead generation campaign - Sales / purchases → Conversions campaign - Traffic / clicks → Link clicks campaign - Brand awareness → Awareness campaign - App installs → App engagement campaign --- ## Step 3: Define Target Audience Ask about: - Location: Which countries/regions? - Age range: Who buys this? - Gender: Relevant or not? - Interests: What topics does the audience care about? - Behaviors: Any relevant behaviors (e.g., online shoppers, business owners)? - Custom audiences: Do they have an existing customer list to target? Be specific — vague audiences ("everyone") waste budget. Push for specificity even if it means starting narrow. --- ## Step 4: Define Campaign Structure Decide: - CBO (Campaign Budget Optimization) vs ABO (Adset Budget Optimization) **CBO vs ABO — Decision Table:** | Scenario | Structure | Bidding strategy | |---|---|---| | Testing a new offer | CBO | Lowest cost | | Same offer, multiple audiences | CBO | Lowest cost | | Need to control CPL / have a specific CPA target | ABO | Cost per result goal (Cost cap) | | Multiple geo, budget allocations differ by location | ABO | Cost per result goal (Cost cap) | | Scaling a proven winner | ABO | Target cost | | Always-on with stable performance | CBO | Lowest cost | **When to use CBO:** - You want Meta to find the best-performing audience automatically - You have 2–3 test adsets and want to optimize across them **When to use ABO:** - You have specific budget allocations per audience or geo - You need to control cost per result tightly - You want to run clean A/B tests with equal budget per segment **Number of adsets and ads:** - Start with 2–3 adsets, not 20. Each needs enough data to learn. - 2–4 ad variations per adset to start, not 10. --- ## Step 5: Define Budget and Bidding **Budget:** - What's the daily or lifetime budget? - Is this a test (smaller budget) or scaling (ready to increase)? - Rule of thumb: need ~50 results per week per adset to exit learning phase **Bidding strategy:** - Lowest cost (default): Meta finds the cheapest results — good for volume - Cost per result goal (Cost cap): Cap the cost per result — good for controlling CPL/CPA - Bid cap: Set a max bid — gives more control but may limit delivery - Target cost: Maintain stable cost — good for scaling --- ## Step 6: Competitive Positioning If relevant (for DTC, SaaS, or brands entering new markets): - Who are the main competitors in the ad auction? - What's the typical CPM and CPL in this category? - Any seasonal or market timing factors to consider? Use web search to research benchmarks if user doesn't know. --- ## Step 7: Document the Strategy Output a clear strategy brief with: - Product, Campaign Objective, Offer, CTA - Target Audience (location, age/gender, interests, exclusions) - Campaign Structure (number of adsets, budget type, daily budget) - Bidding Strategy - Timeline and decision point --- ## Key Rules - Always start with clear success metrics: what CPL/CPA is good for this business? - Structure for learning — give Meta enough data to optimize - Don't over-segment audiences — too many small adsets = not enough data per adset - Budget must be realistic — $5/day won't get meaningful learning data


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