Meta Ads Strategy

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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."

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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.

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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.

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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

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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.

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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.

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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

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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.

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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

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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