Campaign plans
Why this matters for your business
Building a multi-platform ad campaign by hand is a half-day of setup work — different audience builders per platform, different ad-set structures, different bid strategies, separate UTM schemes that have to match for unified tracking. Most teams either skimp on rigor (run a Meta-only campaign because multi-platform is too much work) or commit to the cost of a specialist who can do it right.
Campaign plans on this platform make rigor cheap. You describe the goal in plain English ("acquire 500 new customers in the US for under $40 CAC, focus on women 25-44"). The AI drafts a complete plan: audiences per platform, ad-set structures, creative angles, bid strategies, expected ROAS, projected spend allocation. You review the plan as a single document, make adjustments, click approve. The system pushes consistent tracking across all platforms and launches.
The deeper unlock: you go from "I think we should run a Meta acquisition campaign" to "here's a multi-platform plan with math behind it" in 20 minutes. Strategy stops being gated by ad-ops bandwidth.
What this typically unlocks
| Outcome | Result |
|---|---|
| Time from goal → live campaign | 2 hours vs. half a day |
| Multi-platform launches | 5× vs. single-platform-default |
| UTM consistency across platforms | 100% — system handles |
| Plans rejected at approval | ~20% (rejection rate is healthy — humans tweak before ship) |
| Campaign-launch errors | near 0 vs. ~10% manual |
What you actually get
A plan, drafted by AI, structured for human review:
| Section | Description |
|---|---|
| Goal | Your stated objective (acquisition, retention, win-back, etc.) |
| Budget allocation | Suggested split across platforms with reasoning |
| Audiences per platform | Lookalikes, interest, retargeting — built from your Customer 360 cohorts |
| Ad-set structures | How campaigns group within each platform |
| Creative concepts | AI-suggested angles + tested-template references; you provide actual creative |
| Bid strategies | Per-platform recommendations (e.g. "tCPA $35 on Google", "lowest cost with bid cap on Meta") |
| UTM scheme | Auto-generated, consistent across all platforms |
| Expected outcome | ROAS, CAC, customer count projections with confidence ranges |
| Risk flags | Audience saturation warnings, creative fatigue alerts, budget pacing concerns |
How it works
Defining the goal
You describe what you want, in plain English:
"Acquire 500 new customers in the US in 30 days. Target CAC
under $40. Focus on women 25-44. Total budget $20K. Avoid
audiences we've already saturated."
The system parses the goal and grounds it against your Customer 360 + predictive LTV data. "Avoid audiences we've already saturated" → checks audience overlap, recent campaign recipients, fatigue posture.
The drafted plan (example)
GOAL: Acquire 500 new US customers, women 25-44, $40 CAC target
BUDGET: $20K over 30 days
ALLOCATION:
Meta: $10K (50%) — strongest in the demo
Google: $7K (35%) — search intent on category queries
TikTok: $3K (15%) — younger end, brand-discovery
PLATFORM DETAIL:
Meta ($10K):
Audience 1: Lookalike (1% US) of top-LTV customers — $4K
Audience 2: Interest stack [yoga, skincare, indie brands] — $4K
Audience 3: Retargeting — site visitors no-purchase 30d — $2K
Google ($7K):
Search: branded + category keywords — $4K
Performance Max: catalog feed + audience signals — $3K
TikTok ($3K):
Spark Ads: top organic content + lookalike — $2K
Brand awareness: top-of-funnel — $1K
CREATIVE CONCEPTS:
- Angle 1: Founder story (works on Meta + TikTok)
- Angle 2: Product demo with social proof (Meta + TikTok)
- Angle 3: Comparison vs. category leader (Google search ads)
- Note: Provide 3-5 creative variations per concept
UTM SCHEME:
utm_source: {platform}
utm_medium: paid_acquisition
utm_campaign: 2026-05-acquisition-women-25-44
utm_content: {ad_id}
EXPECTED OUTCOME:
Customers: 540 (range 410-680)
CAC: $37 (range $29-$48)
ROAS at 90d: 3.2× (using predicted LTV)
Confidence: medium-high — similar campaigns last quarter
achieved $35 CAC
RISK FLAGS:
- Meta lookalike 1% may be near saturation
(you've used similar in 3 of last 5 campaigns)
- TikTok creative needs vertical 9:16 ratio (existing
library is mostly square)
SUGGESTED REVIEW POINTS:
- Confirm $40 CAC target is firm or aspirational
- Decide whether to allow Meta to scale into 2% LA if 1% saturates
- Approve creative budget separately if needed
You review this as a single page. Edit any section ("change TikTok to $5K, drop Meta to $8K"). Approve.
Auto-launch + tracking
On approve, the system:
- Builds the audiences in each platform (lookalikes, interest stacks, retargeting pools)
- Creates the ad sets / campaigns with the bid strategies
- Pushes the UTM scheme into every ad creative
- Schedules the spend pacing
- Connects post-launch tracking back to Customer 360 for unified attribution
You don't touch a Google / Meta / TikTok console.
Real merchant scenarios
Scenario A — Founder runs first multi-platform campaign
Setup. Solo founder, $400K/year skincare brand. Has run Meta-only ads for 18 months. Wants to try Google and TikTok but doesn't know how to structure cross-platform campaigns.
Process:
- Stated goal: "$3K total, 30 days, drive new customer acquisition"
- AI drafted plan with $1.8K Meta / $700 Google / $500 TikTok
- Founder approved (modified Google angle to focus on branded search, not category search)
- Launched in 35 minutes total
Result (30 days):
- 92 new customers (vs. 60 from Meta-only at same spend baseline)
- CAC $32 (target was $40)
- TikTok was the surprise — 18 customers for $500 = $28 CAC
Founder's takeaway: "I would never have built the TikTok campaign on my own. The plan made it easy enough to try."
Scenario B — Mid-market brand scales acquisition
Setup. $12M brand. Wants to grow new-customer count 40% without exceeding $50 CAC.
Plan output:
- Diversify spend: 45% Meta / 35% Google / 20% TikTok
- New audience strategy: Lookalikes seeded only on top-decile predicted-LTV customers (not all customers)
- Creative concepts: 3 angles, 5 variations each = 15 creatives
Result (90 days):
- New customers up 47% (vs. 40% target)
- CAC $44 (under target)
- TikTok scaled to 28% of spend (more than planned — optimizer reallocated based on performance)
Scenario C — Agency cookie-cutters plans across clients
Setup. Agency builds a "Q4 holiday acquisition" plan template and applies it across 8 client brands.
Operationally:
- Plan template defines structure (3-platform, 60% acquisition / 40% retargeting, specific UTM scheme)
- Per-brand: AI fills in client-specific audiences, budgets, creative concepts
- Each plan reviewed by the brand's marketing lead
- All 8 launched in 2 days (vs. 2 weeks of manual work)
Best practices
✅ Be specific in the goal. "Acquire customers" → vague. "Acquire 500 new US women 25-44 customers under $40 CAC" → AI can plan against this.
✅ Review the risk flags carefully. Most plan iterations happen because the saturation warning was real.
✅ Separate creative budget from media budget. The plan covers media spend; creative production is a different line item.
✅ Approve plans weekly, not monthly. Tighter feedback loops let the optimizer learn faster.
❌ Don't approve plans you don't understand. If the AI proposes something unusual, ask the copilot for the reasoning before approving.
❌ Don't tweak the UTM scheme manually. The auto-generated one ensures cross-platform attribution works; ad-hoc changes break it.
Plan tiers
| Capability | Free | Starter | Pro | Agency | Enterprise |
|---|---|---|---|---|---|
| AI campaign plans | — | — | ✓ | ✓ | ✓ |
| Multi-platform launch | — | — | ✓ | ✓ | ✓ |
| Auto-UTM consistency | — | — | ✓ | ✓ | ✓ |
| Plan templates | — | — | — | ✓ | ✓ |
| Plan history + reuse | — | — | ✓ | ✓ | ✓ |
| Custom plan rules | — | — | — | — | ✓ |