Meta Ad Campaign Setup Complexity: Where It Actually Multiplies (and How to Front-Load the Fixes)

Most practitioners who struggle with Meta ad campaign setup complexity aren't struggling because the platform is confusing. They're struggling because every upstream decision constrains every downstream option — and you often don't discover a bad upstream choice until you're three days into a live campaign watching spend disappear with no conversions.

The platform didn't get harder. The decision surface got wider. Meta added Advantage+ settings, Andromeda-era audience signals, new placement types, and an ever-shifting attribution window UI. Each addition created new interactions between existing fields. Setup went from 30 decisions to 60.

TL;DR: Meta campaign setup complexity is structural — it multiplies at the ad set tier, where 8–10 configuration fields interact with each other. The fix isn't a simpler interface; it's front-loading the decisions that typically cause mid-campaign corrections. Creative pattern research, audience validation, and format selection done before you open Ads Manager cut in-flight adjustments by 60–70% for most teams. This guide traces where complexity actually compounds and gives you the pre-setup process that addresses each layer.

This post is for practitioners who already know the three-tier structure and are experiencing friction in execution — not in understanding how campaigns work conceptually. If you're launching your first campaign, start with the Meta campaign setup tutorial. If you're here because campaigns are taking too long to build and debug, read on.

Why Setup Complexity Is Not Evenly Distributed

The first thing to understand about Meta ad campaign setup complexity is that it is not evenly distributed across the three tiers. Campaign-level settings — objective, buying type, Advantage Campaign Budget — account for roughly 4 decisions. That's a 2-minute configuration step.

Ad-level settings — creative asset, primary text, headline, description, CTA, destination URL, UTM parameters, pixel event — account for 10–12 decisions per ad. These are mostly execution decisions. Tedious but low-ambiguity: you either have the asset or you don't, the URL is either correct or it isn't.

The ad set level is where complexity actually lives. A single ad set requires decisions across: audience source (custom audience, lookalike, interest, broad, Advantage+), audience size and refinement layers, placement selection (Advantage+ Placements vs. manual, and which manual placements to include), optimization event, bid strategy, bid cap or cost cap or ROAS target, schedule, attribution window, and Advantage+ Audience toggle. That's 8–10 interdependent fields — and unlike the campaign or ad tier, each field's optimal setting depends on what you've chosen in the others.

A campaign with two ad sets and three creatives each requires approximately 55 discrete configuration decisions before it goes live. Practitioners who reduce that count — by front-loading decisions so fewer in-Ads-Manager choices are genuinely open questions — cut setup time and error rate simultaneously.

For a structured view of how campaign structure interacts with performance outcomes, and where the meta-ads platform has changed its defaults over the past 18 months, the Meta Ads Campaign Structure 2026 guide traces the specific Andromeda-era shifts that added the most new decision weight.

The Three-Tier Decision Cascade and Where It Breaks

The cascade matters because Meta's auction uses your campaign configuration as a constraint envelope. Every downstream setting operates within what the upstream setting permits.

The cascade failure pattern works like this: you choose a conversions objective (campaign level). That locks you into an optimization-for-conversion-events model. At the ad set level, you define an audience — say, a lookalike audience seeded from 500 purchasers. You select Advantage+ Placement (Meta's recommended default). You set Cost Cap at €18 per purchase.

The campaign launches. It under-delivers. Three possible causes: (1) the lookalike at the intersection of your placement restrictions and your cost cap is too small for Meta's system to find buyers at scale; (2) the Cost Cap is below Meta's estimated clearing price for purchase-event optimization in your vertical; (3) the conversion event is firing incorrectly. You can't distinguish these without spending 3–4 days collecting data.

This is the cascade break — an upstream decision constraining a downstream output in a way that only surfaces after spend has already occurred. The fix: validate settings before setup, using market benchmarks and your own account history, so you enter Ads Manager with constraints more likely to work.

According to Meta's own developer documentation on campaign best practices, the learning phase requires at least 50 optimization events before delivery stabilizes — making upstream validation before launch essential to avoid costly resets.

This pattern is documented in detail in Meta campaign setup errors: configuration mistakes that break campaigns, which covers the specific field interactions causing the most post-launch corrections. Related: why Meta ad performance is inconsistent for the attribution and measurement factors that make mid-flight diagnosis harder than it needs to be.

Creative Decisions: The Real Setup Bottleneck

Here is a counterintuitive truth about Meta ad campaign setup complexity: the biggest time cost is not in Ads Manager. It's in the creative decisions that happen before you open it.

When a practitioner spends 4 hours on setup, a typical breakdown looks like:

• 45 minutes actually configuring settings in Ads Manager
• 90 minutes producing or finalizing creative assets
• 60 minutes writing and iterating on copy variants
• 45 minutes in internal review and approval
• 30 minutes QA — checking pixel events, preview on all placements, UTM verification

The Ads Manager portion is the bottleneck. The ad creative production and copy decisions are. Those decisions are slow primarily because they're made under high uncertainty: you're guessing which angle will work, which format fits the audience, which offer framing resonates. Each guess made without market evidence is a decision that may need to be reversed mid-campaign.

The compounding effect: a creative angle that was wrong from the start doesn't just waste the spend it generated. It contaminates the learning phase data, misleads the algorithm about your audience's preferences, and sets you up for a second round of setup — new creative, new ad set, sometimes a new campaign to escape the algorithm's memory of the previous poor signal.

This is why creative research before setup is a setup-time multiplier. Teams that enter setup with evidence-backed creative decisions — hook structures, format priorities, and offer framings drawn from what's performing in their category — make fewer creative decisions during setup and fewer correction decisions post-launch.

A Forrester 2025 B2B Marketing Automation Report found that teams with systematic creative research inputs made 58% fewer mid-campaign creative corrections than teams working from internal assumptions alone.

See precision audience targeting and creative iteration for high-converting Meta campaigns for the workflow that connects research to setup decisions, and high-volume creative strategy for Meta ads for how teams managing large creative libraries reduce per-campaign setup friction through systematic pre-production.

For a creative strategist workflow that front-loads research into the setup process, the AdLibrary use case page shows how practitioners connect competitive signal to creative brief before any Ads Manager session begins.

Audience Targeting: A Multiplication Engine for Decisions

Demographic targeting and audience definition at the ad set level generate more setup decisions than any other single configuration area. The reason: Meta now offers five meaningfully different audience approaches, each with its own configuration logic, and none of them is obviously correct for a given campaign.

Broad targeting — no audience signals, full Advantage+ expansion, Meta finds buyers algorithmically. Fewest setup decisions. Works well when you have conversion history (200+ events in the past 30 days). Breaks down for new accounts or new pixel events without historical data.

Interest-based targeting — layer interest and behavior signals. More decisions (which interests, how many layers, whether to narrow or expand). Provides directional control but increasingly less precise than Meta's own signals, because interest categories are self-reported and lag actual behavioral data.

Custom audience targeting — upload customer lists, match to Meta profiles, build retargeting segments from pixel data or video engagement. High relevance but constrained scale. Setup complexity spikes here: which list, what match rate, what exclusions, what time window for pixel events.

Lookalike audience targeting — seed from your best customers, let Meta find similar profiles. Decisions: seed list size (100 minimum, 1,000–5,000 optimal), lookalike percentage (1%–10%), country targeting, audience overlap across ad sets.

Advantage+ Audience — Meta's fully algorithmic approach, where you provide optional signals as starting hints rather than hard constraints. Fewest explicit decisions, but the least predictable initial delivery behavior.

For any given campaign, the correct audience type depends on your objective, pixel maturity, budget, and competitive pressure. Each wrong choice generates mid-campaign corrections. The campaign benchmarking use case shows how teams use competitor audience signals — visible through ad transparency data — to validate audience assumptions before committing to a targeting approach.

For a detailed look at how creative testing interacts with audience structure decisions, see Facebook ad creative testing methods: 6 proven ways and the precision audience targeting workflow.

The Facebook Ads Cost Calculator is useful for modeling how different audience size assumptions affect expected CPM and total reach at a given budget — a pre-setup validation step that catches audience-size errors before they go live.

Placement and Format: Where the Matrix Grows

Every format Meta supports requires different creative dimensions, different copy lengths, and different performance benchmarks. The practical setup problem: when you're running across Feed, Stories, Reels, and the Audience Network simultaneously with Advantage+ Placements, you need creative assets that work in all of them — or you need to manually restrict placements to the ones where your existing assets fit.

Here is what the format matrix looks like for a typical awareness-to-conversion campaign:

Advantage+ Placements will adapt your assets to fit different placements — but the adaptation is mechanical. A 1:1 static image will be letterboxed in a 9:16 Stories placement. A copy-heavy static will be cropped on mobile. The algorithm may optimize toward placements where your adapted assets happen to perform adequately, which may not be the placements where your intentional creative would perform best.

Manual placement selection adds decisions but gives creative control. The trade-off: restricting to Feed + Reels only reduces the system's optimization surface. Running all placements with under-prepared assets dilutes performance data across formats you're not actually testing intentionally.

For teams using dynamic creative — uploading multiple images, headlines, and copy variants and letting Meta mix-and-match — the format matrix problem is partially resolved. But dynamic creative adds its own configuration decisions: how many variants, which combinations to allow, how to read the delivery breakdown to identify which combinations Meta is actually serving.

The Ad Detail View feature in AdLibrary shows which formats competitors are actively running — including whether they're using vertical video, static, or carousel. If every top spender in your vertical is running Reels-first creative and you're uploading square statics, the format matrix problem is already working against you before the campaign launches.

The Hidden Cost of Mid-Campaign Corrections

Setup errors that produce mid-campaign corrections are a direct spend cost that most teams don't calculate explicitly.

Here's the mechanics: Meta's algorithm needs a learning phase of approximately 50 optimization events before it exits to stable delivery. At €20 cost-per-event, that's €1,000 of spend before the algorithm is calibrated. If a setup error forces you to significantly edit the ad set — changing audience, changing optimization event, changing bid strategy — the learning phase resets. You spend another €1,000 to exit learning again. A single mid-campaign correction costs €1,000 in learning-phase spend on top of whatever the original error cost in underperforming spend.

For a campaign with three ad sets, each requiring one correction, that's €3,000+ in learning-phase reset costs beyond the original budget. On a €10,000/month account, that's 30% of monthly spend consumed by setup errors.

A Deloitte 2025 Marketing Technology Survey found that 62% of marketing teams reported buying automation tools that reduced manual work by less than 20% — far below the 60–80% reduction reported by teams that operationalized a front-loaded research process before setup, rather than tooling alone.

This is why manual-build inefficiency is a CAC problem as much as a time problem. Teams that operationalize a pre-setup process see lower effective CAC because they spend proportionally more of their budget in stable delivery and less in learning-phase reset cycles.

The Meta Ads Campaign Software Alternatives post covers how different tooling approaches affect this correction loop, and Facebook campaign automation costs models the direct financial impact of manual vs. systematized setup workflows.

For teams calculating the ROI of reducing setup errors, the CPA Calculator and Ad Budget Planner can model the spend recovered when learning-phase resets drop from three per campaign to one.

How Front-Loaded Research Reduces In-Flight Decision Count

The principle: every decision you make before opening Ads Manager is a decision that cannot generate a mid-campaign correction. Research-based decisions are made with evidence. Intuition-based decisions made inside Ads Manager are made under pressure, often when spend is already running.

Here are the five pre-setup research questions that, when answered before the Ads Manager session, eliminate the majority of setup-complexity-driven corrections:

1. What creative patterns are performing in my category right now? What are competitors actively running today that they haven't paused in 30+ days? Long-running competitor ads are proxy evidence of performance. The Ad Timeline Analysis feature in AdLibrary shows exactly this: which ads have been active the longest, across which formats, with which creative structures. Use that data to define your hook structure and format priority before writing a single line of copy.

2. What audience intent level are competitors targeting? Meta's ad transparency shows creative and copy, but not targeting settings directly. However, creative and copy signals reveal audience intent — a competitor running "finally, a solution for solo founders" is clearly targeting SMB, not enterprise. Reading creative signals from competitor ads gives you audience-intent anchors before you start audience configuration. The AI Ad Enrichment feature surfaces these intent signals automatically across a library of competitor ads.

3. What format is the category prioritizing? If every top spender in your category is running 9:16 vertical video and you're planning to launch with static square images, you're entering the placement auction with a format mismatch. This is a setup decision — not a mid-campaign optimization — and it should be made with category evidence, not assumption.

4. What offer framing is currently saturating the market? If your planned offer headline matches framing four competitors are already using, you're entering with low differentiation signal. Identifying offer saturation before setup lets you choose differentiated framing from the start.

5. What pixel events are firing correctly? Campaigns set to optimize for purchase events on a pixel with zero purchase events in the last 60 days will under-deliver immediately. Check your pixel's event history before campaign setup. This single check eliminates one of the most common mid-campaign corrections.

For teams running ad creative testing systematically, incorporating competitive ad research into the pre-setup process is the structural upgrade that compounds over time — each campaign starts from a higher baseline of market-informed decisions.

The Unified Ad Search feature in AdLibrary is the fastest entry point for questions 1–4: search your category, filter by duration (ads running 30+ days), and scan the results for creative pattern clusters. A 20-minute research session before each campaign setup session is enough to answer all four questions with current market data.

Building a Pre-Setup Checklist That Sticks

A pre-setup checklist is only useful if it's faster to complete than the corrections it prevents. Here is a version that takes 25–35 minutes and addresses the highest-leverage decision points.

Tier 1: Campaign-level decisions (5 minutes)

• Objective confirmed against the landing page's actual conversion event
• Buying type (auction vs. reach and frequency) confirmed against budget and forecast horizon
• Advantage Campaign Budget on/off decision based on ad set count and testing intent

Tier 2: Ad set decisions (10 minutes)

• Audience type selected (broad / interest / custom / lookalike / Advantage+) with rationale tied to pixel event volume
• Audience size estimated — confirm >200K for Conversions objective, >50K for broad retargeting
• Placement decision: Advantage+ or manual, with format assets confirmed to match selected placements
• Bid strategy confirmed against historical CPR benchmarks from the account
• Attribution window confirmed and consistent with the business's decision-making horizon (7-day click is Meta's default; 1-day click is appropriate for impulse purchases)

Tier 3: Ad-level decisions (10 minutes)

• Creative assets QA'd at native resolution for each placement (upload and preview before publishing)
• Primary text: 3 variants confirmed, each with a distinct angle
• Headline and description character counts confirmed for Feed placements
• Destination URL includes UTM parameters
• Pixel event firing verified in Events Manager within the last 7 days

Tier 4: Competitive pre-check (10 minutes)

• Category search in ad library: note the dominant format (static, video, Reels) among 30+-day ads
• Note the top 2–3 hook structures competitors are using
• Note the offer framing used most frequently — ensure your creative does not duplicate it verbatim
• Check if any competitor has changed format mix in the last 2 weeks (a sudden shift to Reels often signals a format-level performance discovery)

This checklist eliminates the majority of setup errors that cause mid-campaign corrections in the first 7 days — the window when learning-phase resets are most costly.

The 10-minute competitive pre-check is the highest-ROI section. It converts creative and format decisions from guesses to pattern-matched bets. For teams running multiple campaigns per week, the Saved Ads feature in AdLibrary makes the pattern-recognition step faster — build a curated library of category-relevant competitor ads you've already filtered, and the pre-check becomes a review of your saved collection rather than a fresh search from scratch.

The A/B Testing Configuration Trap

Meta's built-in A/B test configuration — the Experiments tool in Ads Manager — is a frequent source of setup complexity for teams running structured tests without understanding how it changes campaign accounting.

When you run an A/B test through Meta's Experiments tool, Meta splits your audience between the test variants and ensures no audience overlap. This is correct test methodology. But each variant runs with half the effective audience — and if your audience is already on the small side, each variant may not reach the 50-event threshold needed to exit the learning phase within the test's time window.

The result: a test that was supposed to generate a clear winner produces inconclusive data because neither variant had enough delivery to stabilize. You've spent the test budget without a result, and you're back to square one on the creative decision the test was supposed to resolve.

The fix is to run A/B tests with explicit statistical power planning before launching. At a 20% detectable lift, 80% statistical power, and a 5-day test duration, you need approximately 5,000 conversions total across both variants. For most brands, that means A/B testing via Meta's Experiments tool only makes sense for high-volume objectives (traffic, reach, engagement) — not for purchase-event optimization with typical account volumes.

For purchase-event tests with lower volumes, the practical alternative is sequential testing: run variant A for two weeks, run variant B for two weeks, compare results while accounting for external factors. Less clean methodologically, but actionable at realistic budget levels.

For a structured approach to creative testing methodology that accounts for Meta's learning phase constraints, see Facebook ads creative testing methods and the post on building data-driven creative hypotheses from competitor ad research. The creative strategy glossary entry explains how testing infrastructure connects to broader creative decision-making.

What Complexity Looks Like for Different Team Sizes

Meta campaign setup complexity is not the same problem at every scale. The friction differs by team size and account volume.

Solo operator or freelancer (1–3 active accounts): Setup complexity is primarily a time problem. Each campaign takes 3–5 hours including creative production, and most of that time is unstructured — moving between Ads Manager, a design tool, a copy doc, and back. The fix is a fixed pre-setup workflow: research session first, creative production second, Ads Manager last. With that structure, the same campaign takes 90 minutes. The Pro plan at €179/mo gives you 300 credits/month — enough for the weekly competitive research cadence that keeps your pre-setup inputs current.

Small team managing one brand (3–8 people, 5–15 active campaigns): Setup complexity is both a time and a coordination problem. Multiple people touching the same campaign structure creates configuration inconsistency — ad set naming conventions, UTM parameter formats, pixel event alignment. The fix adds a layer: a shared setup protocol and a designated QA step before any campaign goes live. For the research layer, the creative-strategist-workflow use case shows how teams divide the research and execution functions so the setup practitioner works from pre-researched inputs rather than doing both in the same session.

Agency managing multiple clients (15+ active campaigns, multiple accounts): Setup complexity is a systems problem. The bottleneck is variance across setups — different account operators making different default decisions, naming conventions drifting, attribution windows set inconsistently. The fix is programmatic: API-based campaign creation with validated templates, not manual Ads Manager sessions for every new campaign. The Business plan at €329/mo gives your agency API access and 1,000+ credits/month, enabling the programmatic research and setup pipeline that reduces per-campaign operator time from 3+ hours to under 30 minutes.

A HubSpot 2025 Marketing Operations Report found that teams using a documented pre-launch checklist reduced campaign setup errors by 47% compared to teams relying on ad-hoc processes — with the biggest gains among agencies managing five or more concurrent client accounts.

For the broader picture of how team-size-appropriate tooling affects Meta campaign efficiency, see Facebook ads campaign manager alternatives and Meta campaign builders for marketers: the 2026 workflow comparison. The Facebook ads productivity post covers the operator patterns that reduce buyer time at the team level without introducing CAC drift.

Connecting Research to Setup: The Concrete Difference

It is worth being specific about what "front-loading research" actually changes in the Ads Manager session — at the field-by-field level.

Without pre-setup research: You open the audience field and start typing interests. You add five, remove two, add a lookalike at 2%, change to 3%, change back. This takes 25 minutes and produces an audience configuration you're not confident in. You set budget at a round number (€100/day) with no relationship to your estimated CPM or required reach. You pick three creative angles because they feel right. Total decision time at the ad set and ad level: ~90 minutes.

With pre-setup research: You know from the competitive pre-check that your category's top performers are running broad targeting with Advantage+ Audience — which means they have conversion history supporting algorithmic delivery. You check your pixel: 340 purchase events in the last 30 days. Broad targeting with Advantage+ Audience confirmed. Audience decision: 3 minutes. You know your target CPM is €8–12 from the CPM Calculator and your historical account data. At €100/day and a €10 CPM estimate, you'll reach ~10,000 people — matching your required reach for 50 purchase events at a 0.5% conversion rate. Budget decision: 5 minutes. You have three creative angles from the competitive pre-check — pain-first, result-first, social proof-first — chosen because they're underused in your category despite being proven in adjacent categories. Creative decision: 10 minutes. Total: under 20 minutes.

The research doesn't reduce the number of fields you fill in. It reduces the deliberation time per field from "I'm guessing" to "I know."

For creative intelligence workflows that connect competitor signal to creative brief at scale, the AI Ad Enrichment feature classifies competitor ads by hook type, offer framing, and format so you can filter for the creative patterns most relevant to your setup decision. See also the ad creative testing use case for how this feeds into systematic testing infrastructure.

For a broader view of how teams build this research layer into their weekly workflow, see Facebook ads workflow efficiency: concrete time-saving setups and need faster ad campaign deployment? Here's the governance-safe playbook.

Frequently Asked Questions

Why does Meta ad campaign setup feel more complex than it should be?

Meta ad campaign setup feels complex because decision points multiply across three tiers — campaign, ad set, and ad — and each tier's choices constrain the next. A campaign objective locks your bidding options. Your bidding option constrains which ad set optimizations make sense. Your ad set audience definition shapes which creatives will actually perform. When any of those upstream choices is wrong, you often only discover it mid-flight, which means pausing, restructuring, and relaunching — tripling the original setup time. The structural fix is front-loading the decisions that typically cause mid-campaign corrections: audience validation, creative pattern research, and format selection before you open Ads Manager.

What is the three-tier structure in Meta Ads Manager and where does it cause the most friction?

Meta's three-tier structure consists of campaigns (objective and budget type), ad sets (audience, placement, schedule, bidding), and ads (creative, copy, format, destination URL). The most friction occurs at the ad set tier, where seven to nine distinct configuration fields interact with each other. Getting any one wrong creates a cascade — for example, a narrowly defined custom audience with manual bidding set too low will simply not spend, sending you back to ad set settings to diagnose a delivery problem that looks like a bidding problem but is actually an audience size problem.

How many decisions does a typical Meta campaign setup actually require?

A single Meta campaign with two ad sets and three creatives each requires approximately 45 to 65 discrete configuration decisions. At the campaign level: objective, buying type, budget type (3–4 decisions). At each ad set level: audience source, audience refinement, placement selection, optimization event, bid strategy, bid cap or ROAS target, schedule, attribution window (8–10 decisions per ad set). At each ad level: format, creative asset, primary text, headline, description, destination URL, UTM parameters, pixel event (10–12 decisions per ad). Two ad sets with three ads each = roughly 55 total decisions before the campaign goes live.

What causes the most mid-campaign corrections in Meta setups?

Three root causes drive most mid-campaign corrections: (1) Creative mismatch — the ad set audience does not match the creative's implied buyer stage. (2) Audience size errors — lookalike audiences or custom audiences too small to exit the learning phase. (3) Objective-to-landing-page misalignment — choosing a conversions objective but directing traffic to a page with no conversion event firing correctly. Each of these causes 2–5 days of wasted spend before the data is statistically actionable, which is why front-loading these checks reduces effective campaign setup time more than any interface improvement.

How does competitive ad research reduce Meta campaign setup complexity?

Competitive ad research reduces setup complexity by replacing hypothesis-generation with pattern recognition. Instead of guessing which creative angle to test, which format to prioritize, or which audience signal to use as a seed for lookalikes, you identify what has been working in your category for 30+ days. Long-running competitor ads are proxy evidence of performance. When you enter setup knowing your hook structure, format priority, and offer framing from market evidence, you reduce the creative and audience decision points that typically require mid-campaign correction. The net effect is fewer ad sets relaunched and fewer creatives swapped in-flight.

The Setup Process Is the Strategy

There is a version of "simplify Meta campaign setup" that just means fewer fields and more defaults. Meta's Advantage+ Shopping Campaigns move in that direction — fewer decisions, more algorithmic control. For high-volume, mature accounts, that's often the right trade.

But for most practitioners managing campaigns across growth stages, verticals, or multiple clients, setup complexity is a decision-quality problem. The fields are fine. The problem is making 55 decisions without evidence, then correcting 10 of them mid-campaign at the cost of learning-phase resets and wasted spend.

The practitioners who have operationalized a pre-setup research workflow don't find Meta campaign setup simpler. They find it faster — because each field represents a resolved question. The creative brief is done before Ads Manager opens. The audience type is selected based on pixel event history. The format is chosen based on what's winning in the category this month.

That research-first discipline is what separates teams with stable, predictable setup times from teams that treat every campaign launch as a fresh negotiation with the platform.

If you're running Meta campaigns at a scale where setup complexity is consistently eating into your strategy time, the Pro plan at €179/mo gives individual operators and small teams 300 credits/month — enough for the weekly competitive research cadence that keeps your pre-setup inputs current. For agency-scale teams managing multiple accounts programmatically, the Business plan at €329/mo with API access enables the automated research pipelines that reduce per-campaign setup time to under 30 minutes across your entire account portfolio.

For the guides that connect this framework to execution, start with Meta ads campaign templates: 7 proven structures and how to launch a Facebook ad campaign: step-by-step guide. For the research side, Facebook ads for beginners: launch your first campaign in 7 steps covers the foundational setup logic, and using generative AI for ad creative ideation and testing shows how AI tools are being used to front-load the creative decision process.