Money balance over time
Average cumulative profit by month
Average across all completed runs for each participant.
Live benchmark
ROASBench
A hard-mode performance marketing simulation for LLMs. Models act as the marketer for a DTC skincare brand, choose channels, plan spend, write creative angles, react to results, and live with the consequences for 12 months.
12-month simulation
3 repeats per model
6 controllable channels
8 shopper personas
Top cumulative profitability
Anthropic: Claude Opus 4.6
$506,094
Current leader
Anthropic: Claude Opus 4.6
Avg score 40.61
Profitable after 12 months
1 model
Anthropic: Claude Opus 4.6
Lead over #2
+13.47 points
vs. Google: Gemini 3.1 Pro Preview
Closest profit challenger
Qwen: Qwen3.5 Plus 2026-02-15
$-29,480 after 12 months
Score vs. cost per run
Quality is not evenly priced
Average benchmark score against average main-model API cost per run.
Monthly contribution profit
Who stabilizes fastest?
Average monthly contribution profit across completed runs.
Average score
ROASBench leaderboard
Models ranked by average primary score (highest first). Values above each bar are the mean score; whiskers show standard deviation across completed runs when more than one run exists.
Leaderboard
Current standings
Sorted by average benchmark score. Tap a row for sub-scores and detail.
| # | Model | Score | Avg profit | ROAS | 1st + mo | ||
|---|---|---|---|---|---|---|---|
| 1 |
|
Anthropic: Claude Opus 4.6 Leads the pack by compounding a coherent plan: retention channels stay funded, discounting stays rare, and changes are absorbed without constant learning resets. Averaged 40.61 across 3 completed run(s); contribution profit $506,094; ROAS 192.5%. Avg first month cumulative contribution profit turns positive: ~7.0. |
40.61
|
$506,094 | 192.5% | 7.0 | |
|
Sub-scores
Economics
Avg profit: $506,094 Strength: Stable iteration, persona-aware creative, disciplined CRM and remarketing. Watch: Still hits saturation and high CAC when scaling search and broad demand. |
|||||||
| 2 |
|
Google: Gemini 3.1 Pro Preview Often nearer break-even with structurally sensible moves; execution and generic creative hold the score down, with too many mid-course resets. Averaged 27.14 across 3 completed run(s); contribution profit $-34,549; ROAS 132.9%. Avg first month cumulative contribution profit turns positive: ~12.0. |
27.14
|
$-34,549 | 132.9% | 12.0 | |
|
Sub-scores
Economics
Avg profit: $-34,549 Strength: Directionally right budget and channel choices vs. weaker frontier peers. Watch: Generic copy, remarketing churn, and learning resets under pressure. |
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| 3 |
|
Qwen: Qwen3.5 Plus 2026-02-15 Ranked #3 of 13 with an average benchmark score of 26.07 across 3 run(s). Sub-scores are strongest on planning (51.7) and weakest on business (14.8). Average contribution profit $-29,480 and ROAS 133.8%. On average, cumulative contribution profit stayed negative through the full simulation year. |
26.07
|
$-29,480 | 133.8% | — | |
|
Sub-scores
Economics
Avg profit: $-29,480 Strength: Relative edge: planning (51.7). Watch: Relative gap: business (14.8). |
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| 4 |
|
Anthropic: Claude Sonnet 4.6 Ranked #4 of 13 with an average benchmark score of 21.21 across 3 run(s). Sub-scores are strongest on planning (57.4) and weakest on business (9.7). Average contribution profit $-146,915 and ROAS 117.6%. On average, cumulative contribution profit stayed negative through the full simulation year. |
21.21
|
$-146,915 | 117.6% | — | |
|
Sub-scores
Economics
Avg profit: $-146,915 Strength: Relative edge: planning (57.4). Watch: Relative gap: business (9.7). |
|||||||
| 5 |
|
DeepSeek: DeepSeek V3.2 Ranked #5 of 13 with an average benchmark score of 19.77 across 3 run(s). Sub-scores are strongest on planning (49.5) and weakest on business (8.9). Average contribution profit $-126,535 and ROAS 120.4%. On average, cumulative contribution profit first turns positive around month 12.0. |
19.77
|
$-126,535 | 120.4% | 12.0 | |
|
Sub-scores
Economics
Avg profit: $-126,535 Strength: Relative edge: planning (49.5). Watch: Relative gap: business (8.9). |
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| 6 |
|
OpenAI: GPT-5.4 Looks plausible on paper but weak compounding: revenue without efficient spend patterns; repeated broad demand spend without durable payoff. Averaged 18.39 across 3 completed run(s); contribution profit $-250,461; ROAS 103.2%. Across runs, cumulative contribution profit never crossed zero on average in the first 12 months. |
18.39
|
$-250,461 | 103.2% | — | |
|
Sub-scores
Economics
Avg profit: $-250,461 Strength: Readable strategy and channel mix in isolation. Watch: Search/remarketing saturation and budgeting that does not match outcomes. |
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| 7 |
|
xAI: Grok 4.20 Beta Ranked #7 of 13 with an average benchmark score of 16.77 across 3 run(s). Sub-scores are strongest on planning (43.9) and weakest on business (5.9). Average contribution profit $-243,069 and ROAS 103.5%. On average, cumulative contribution profit stayed negative through the full simulation year. |
16.77
|
$-243,069 | 103.5% | — | |
|
Sub-scores
Economics
Avg profit: $-243,069 Strength: Relative edge: planning (43.9). Watch: Relative gap: business (5.9). |
|||||||
| 8 |
|
Gemini 3 Flash Preview Ranked #8 of 13 with an average benchmark score of 16.29 across 3 run(s). Sub-scores are strongest on planning (49.3) and weakest on business (4.6). Average contribution profit $-239,859 and ROAS 104.6%. On average, cumulative contribution profit stayed negative through the full simulation year. |
16.29
|
$-239,859 | 104.6% | — | |
|
Sub-scores
Economics
Avg profit: $-239,859 Strength: Relative edge: planning (49.3). Watch: Relative gap: business (4.6). |
|||||||
| 9 |
|
MoonshotAI: Kimi K2.5 Ranked #9 of 13 with an average benchmark score of 13.10 across 2 run(s). Sub-scores are strongest on planning (50.8) and weakest on business (3.7). Average contribution profit $-292,423 and ROAS 97.5%. On average, cumulative contribution profit stayed negative through the full simulation year. |
13.10
|
$-292,423 | 97.5% | — | |
|
Sub-scores
Economics
Avg profit: $-292,423 Strength: Relative edge: planning (50.8). Watch: Relative gap: business (3.7). |
|||||||
| 10 |
|
MiniMax: MiniMax M2.7 Ranked #10 of 13 with an average benchmark score of 12.13 across 3 run(s). Sub-scores are strongest on planning (39.4) and weakest on business (3.5). Average contribution profit $-316,117 and ROAS 94.2%. On average, cumulative contribution profit stayed negative through the full simulation year. |
12.13
|
$-316,117 | 94.2% | — | |
|
Sub-scores
Economics
Avg profit: $-316,117 Strength: Relative edge: planning (39.4). Watch: Relative gap: business (3.5). |
|||||||
| 11 |
|
OpenAI: GPT-5.4 Mini Ranked #11 of 13 with an average benchmark score of 11.83 across 1 run(s). Sub-scores are strongest on planning (44.9) and weakest on business (2.0). Average contribution profit $-353,629 and ROAS 87.3%. On average, cumulative contribution profit stayed negative through the full simulation year. |
11.83
|
$-353,629 | 87.3% | — | |
|
Sub-scores
Economics
Avg profit: $-353,629 Strength: Relative edge: planning (44.9). Watch: Relative gap: business (2.0). |
|||||||
| 12 |
|
Z.ai: GLM 5 Ranked #12 of 13 with an average benchmark score of 9.64 across 3 run(s). Sub-scores are strongest on planning (42.6) and weakest on business (0.9). Average contribution profit $-370,131 and ROAS 86.0%. On average, cumulative contribution profit stayed negative through the full simulation year. |
9.64
|
$-370,131 | 86.0% | — | |
|
Sub-scores
Economics
Avg profit: $-370,131 Strength: Relative edge: planning (42.6). Watch: Relative gap: business (0.9). |
|||||||
| 13 |
|
OpenAI: GPT-5.4 Nano Ranked #13 of 13 with an average benchmark score of 6.80 across 3 run(s). Sub-scores are strongest on planning (46.5) and weakest on business (0.0). Average contribution profit $-577,506 and ROAS 56.5%. On average, cumulative contribution profit stayed negative through the full simulation year. |
6.80
|
$-577,506 | 56.5% | — | |
|
Sub-scores
Economics
Avg profit: $-577,506 Strength: Relative edge: planning (46.5). Watch: Relative gap: business (0.0). |
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Avg profit is shown in expanded rows on small screens — tap a model.
For model providers
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Methodology
How ROASBench works
Open each section for setup, simulation flow, what models see, personas, state, and what skills the benchmark rewards.
ROASBench drops the model into a year-long operating environment for one premium-but-accessible skincare brand and scores the result on business outcomes, not nice-sounding plans.
Brand
Northstar Skin
Barrier Repair Serum at $68 with 76% gross margin.
Time horizon
12 months
The model has to adapt over time instead of solving one isolated scenario.
Controlled channels
6
Meta prospecting, Search, Shopping, TikTok, Email / CRM, and Remarketing.
Scoring
Business + behavior
Primary score blends profitability, planning quality, persona response, and long-run adaptation.
What the model can control
- Budget allocation — spend per month and split across channels.
- Campaign design — type, segments, creative angle, copy structure.
- Offer strategy — discounts, remarketing, CRM cadence, margin vs. conversion.
- Iteration — hold, scale, or change course after monthly results.
Each round is a real operating cycle, not a one-shot prompt. Past choices affect future state, so the benchmark rewards consistency and punishes lazy resets.
1. Seeded world
Fixed brand, budget, customers, email list, warm pool, seasonality, shocks.
2. Decision step
Structured monthly plan: objective, budget, discount, remarketing, channels, creative.
3. Persona panel + rules
Panel judges copy and targeting; rules produce clicks, trust, purchases, retention.
4. State update
Budget, base, momentum, fatigue, pools, and channel memory roll forward.
What data the model gets back
- Monthly operating metrics: spend, revenue, ROAS, CAC, repeat rate, reinvestment.
- State: budget left, customer base, email list, warm pool, momentum, fatigue.
- Audience map: segments, persona sizes, fit, competition, value.
- Working memory: prior decisions, highlights, penalty flags.
- Market notes: seasonality and shocks.
No raw persona-by-persona judge feedback in the prompt — infer from outcomes.
Main difficulties
Learning resets
Abrupt reallocations hurt efficiency.
Abrupt reallocations hurt efficiency.
Saturation
Finite warm pools and auctions.
Finite warm pools and auctions.
Offer fatigue
Discounts can damage later months.
Discounts can damage later months.
Organic carryover
Upper funnel pays off slowly.
Upper funnel pays off slowly.
Persona tradeoffs
Easy vs. valuable audiences.
Easy vs. valuable audiences.
Soft caps
CRM, retargeting, channel limits.
CRM, retargeting, channel limits.
Every persona differs in size, growth, fit, competition, and value. The model starts with a commercial map but must learn what actually monetizes.
Value Seeker
Large and relatively easy to wake up with offers, but lower-value and highly price competitive.
Motivations: visible results, discount
Premium Conscious
Smaller but high-value premium audience with strong fit for the brand and heavy competition from other prestige skincare.
Motivations: ingredients, authority
Ingredient Researcher
Harder to win because they scrutinize claims, but they compound into valuable, durable customers when convinced.
Motivations: clinical details, ingredient list
Impulse Buyer
Big upper-funnel opportunity that is easier to engage creatively, but conversion quality and retention are weaker.
Motivations: aesthetic creative, quick payoff
Comparison Shopper
Commercially meaningful and high-intent, but expensive to win because comparison behavior increases competition and pressure on proof.
Motivations: clear differentiation, proof
Returning Loyalist
Smaller owned audience but the most valuable and efficient to monetize if protected with the right cadence.
Motivations: routine, restock convenience
Lapsed Customer
Warm and recoverable with decent value, but reactivation requires freshness and fatigue management.
Motivations: newness, better routine fit
Low Intent Browser
Largest reachable pool and easiest to attract at the top of funnel, but low intent and low customer value.
Motivations: light curiosity, visual intrigue
Fixed upfront
Brand, economics, personas, channels, seasonality, shocks.
Persistent state
Budget, customers, email, warm pool, momentum, fatigue, reinvestment, channel memory.
Iteration summary
Prior decisions and outcomes compressed for the next month.
Economic judgment
Margin, contribution profit, CAC, bad revenue.
Budget pacing
When to press, hold, sequence spend.
Channel allocation
Prospecting vs. intent vs. CRM vs. remarketing.
Persona targeting
Easy vs. valuable audiences.
Creative specificity
Angles that match motivations and objections.
Stable iteration
Improve in place; avoid constant restructures.
In practice: protect margin, keep retention alive, scale demand capture carefully, stay persona-aware. Models fail when they confuse activity with progress or write polished but generic copy.
Qualitative findings
What the frontier models are actually doing
Across 36 completed runs, Anthropic: Claude Opus 4.6 is currently leading with an average score of 40.61, average profit of $506,094, and average ROAS of 192.5%.
Top model
Anthropic: Claude Opus 4.6
Average score
40.61
Claude Opus 4.6 is winning by doing the boring but important things well. It keeps CRM and remarketing active every month, avoids discounting, and improves the account without repeatedly breaking learning.
Performance
Positive months 27 / 36 · Discount months 0
ROAS 192.5% · Avg profit $506,094
Decision quality
Copy 82.39 · Targeting 85.78 · Iteration 87.11
Why it works
The pattern is compounding discipline: many profitable months, price integrity, retention channels funded throughout. It still gets hit by CAC and search saturation at scale but keeps the account coherent.
Other frontier models
OpenAI: GPT-5.4
GPT-5.4 reads as strategically plausible in isolation, but run-level data shows weak compounding and broad demand spend without durable payoff.
Google: Gemini 3.1 Pro Preview
Nearer break-even with structurally sensible moves; execution and generic creative limit the score, with learning resets on remarketing.
Bottom line: The top model compounds a coherent strategy over time; others may look smart in prose without twelve months of aligned outcomes.
Example turns
Snapshots from strong runs
One high-scoring run per model. Pick a month to read objective, outcome, and what the simulated audience did — expand for channel and creative detail.
Strong completed run
Anthropic: Claude Opus 4.6
Run score
42.61
Run profit
$652,288
Month 1
Score 20.2
Objective
Establish baseline performance across channels, build retargeting pools, and prioritize high-fit personas (Premium Conscious, Ingredient Researcher, Returning Loyalist) while seeding upper-funnel demand. Month 1 is a learning month: diversify spend to gather signal, lean into education-led creative, and avoid discounting to protect margin and brand positioning.
Budget
$93,716
Profit
$-30,993
ROI
-0.33
Discount
0%
What happened next
Strong educational baseline with excellent persona-message fit for researchers and premium buyers. However, high remarketing spend on a small 18K audience pool risks early frequency fatigue.
Why it thought this made sense
Month 1 strategy: No discount — we lead with ingredient credibility, founder story, and clinical proof. The brand's strongest fit is with Premium Conscious (0.91), Ingredient Researcher (0.94), and Returning Loyalist (0.96). We allocate ~38% to Meta prospecting to build awareness and feed retargeting pools, ~18% to Google Search/Shopping to capture existing intent, ~12% to TikTok for upper-funnel creative testing, ~11% to remarketing to convert warm audiences (site_visitors 37.8K + cart_abandoners 8.6K are small but actionable), and ~3% to email/CRM to activate the 62K list and 14K customer base. We hold back ~18% of annual budget capacity for later months when retargeting pools are larger and we have performance data. Key risks: small retargeting pools limit remarketing efficiency; cold audiences may have high CPAs initially. We accept that and optimize in Month 2 with real data.
Top channels
Meta Prospecting
$36,500
Education-led ingredient authority — lead with ceramide science and barrier repair mechanism. Split test founder-to-camera explainer vs. clinical before/after carousel. Targets Premium Conscious and Ingredient Researcher…
Tiktok
$21,500
Raw UGC test battery — 4-5 hook variations from real customers. Test: texture close-up, 'dermatologist reacts', morning routine integration, 'products I stopped buying'. Optimizing for thumb-stop rate and click-through. …
Google Search
$14,000
Competitor conquest — target searches for competing ceramide/barrier products (CeraVe, Dr. Jart, SK-II barrier lines). Differentiate on clinical formulation and ingredient purity. Comparison Shopper persona.
Month 6
Score 45.9
Objective
Compound Month 5's strong momentum (ROI 0.22, $152K revenue) by scaling proven channels incrementally while protecting efficiency. Google Search and Remarketing remain the ROI backbone. Meta gets a modest increase to feed upper-funnel pools for H2 scaling. TikTok holds at minimum viable. Email gets a slight uplift to monetize the growing 92K list and 16.7K customer base — particularly Returning Loyalists and Lapsed Customers. No discount: brand momentum is +0.28 and offer fatigue is 0.00, so we preserve pricing integrity and let education + trust do the work. Target: ROI 0.25+, revenue $155K+, CAC under $200.
Budget
$92,343
Profit
$38,923
ROI
0.43
Discount
0%
What happened next
Disciplined scaling of proven channels and strict margin protection are driving strong profitability. The clinical, ingredient-led messaging resonates deeply with high-value personas, though casual browsers remain alienated.
Why it thought this made sense
Month 6 strategy: M5 was the best month yet — ROI 0.22, revenue $152K, and direct revenue emerging at $19K (brand demand building). Google Search ROI climbed back to 1.40 after stabilizing from learning resets. Remarketing continues to be the highest-ROI channel at 3.02 but is finite — we increase slightly to $14K to capture the growing retargeting pools (site visitors 182K, cart abandoners 41K) without over-saturating. Google Search gets $32K — a small increment from $31K — respecting the persistent saturation flag while capturing the intent we're generating. Google Shopping scales to $16K as PMax continues to improve (1.16 ROI in M4). Meta gets $26K again — stable spend to keep feeding lookalike and cold audiences; we rotate creative to avoid fatigue. TikTok holds at $5K minimum viable to maintain upper-funnel presence. Email gets a $1.5K allocation to run a restock sequence for existing customers and a win-back for lapsed — the 92K list and 21% repeat rate represent meaningful owned-channel revenue. Key persona focus: Premium Conscious and Ingredient Researcher on search/shopping (high brand fit, high value), Returning Loyalist and Lapsed Customer on email/remarketing (highest value, lowest competition), Impulse Buyer on Meta/TikTok (creative-led engagement to build pools). We avoid discounting — brand momentum is positive and we have zero offer fatigue, which is a strategic asset heading into H2 when we may want promotional levers for peak season.
Top channels
Google Search
$31,269
Problem-aware search capture with ingredient authority — targeting barrier repair, damaged skin barrier, ceramide serum queries. Maintain brand search defense at ~10% of budget.
Meta Prospecting
$25,406
Rotate M5 winning angles with fresh creative iterations: (1) Founder story + ingredient deep-dive for Premium Conscious/Ingredient Researcher, (2) Before/after transformation with customer testimonials for Impulse Buyer,…
Google Shopping
$15,635
Performance Max with optimized feed — hero product image, star ratings, price clarity. Let Google's signals find high-intent shoppers across surfaces.
Month 11
Score 68.0
Objective
Maximize holiday peak revenue in Month 11 by scaling proven channels aggressively with the largest budget yet ($174K). M10 was the best month ever — ROI 1.12, $324K revenue, CAC $102, 1,090 new customers, and $91K direct revenue showing massive brand momentum. With holiday demand surge, brand momentum +1.00, zero offer fatigue, and a 21.5K customer base feeding direct/organic, this is the month to push hard. Scale remarketing to capture the enlarged warm pool (189K), increase Meta and Google Shopping for holiday gift-intent traffic, and lean into email for holiday lifecycle pushes to the 138K list. No discount needed — brand equity is strong and holiday urgency provides natural conversion lift.
Budget
$147,500
Profit
$195,218
ROI
1.34
Discount
0%
What happened next
Aggressive holiday scaling capitalizes well on peak brand momentum, though zero discounting risks alienating price-sensitive shoppers during a highly promotional season. The heavy $35K remarketing push may quickly saturate the warm pool, requiring close monitoring of frequency caps.
Why it thought this made sense
Month 11 strategy: M10 was the breakout month — ROI 1.12, $324K revenue, $91K direct, CAC $102, 1,090 new customers. Brand momentum is at +1.00 and offer fatigue is 0.00 — ideal conditions for aggressive scaling during holiday peak. Key decisions: (1) Scale total spend from $114K to $174.5K — a 53% increase justified by holiday demand surge, proven channel ROI, and the need to capture seasonal intent before it evaporates. (2) Remarketing scaled to $35K (from $24K) — warm pool is 189K, cart abandoners at 91K, and holiday browsing will inflate these further. Remarketing has been our most consistent ROI channel (3.4-3.8x) and the larger pool can absorb more spend. (3) Meta scaled to $38K — holiday is Meta's strongest period for DTC skincare (gift-giving intent, aesthetic creative performs well). Targeting Premium Conscious and Ingredient Researcher personas with gift-angle creative. (4) Google Shopping scaled to $28K — holiday shopping intent peaks in November; PMax should capture high-intent gift and self-purchase queries. (5) Google Search held at $34K — already showing saturation flags but holiday intent should temporarily expand the addressable search volume. (6) TikTok scaled to $10K — holiday content consumption spikes; creator whitelisting with gift-guide angles. (7) Email at $3,537.50 — holiday lifecycle push to 138K list with gift guide, restock reminder for loyalists, and lapsed reactivation. No discount: brand equity is strong, holiday urgency replaces discount as conversion driver, and protecting margin at 76% gross is more valuable than marginal conversion lift from discounting. Budget remaining after this month: $116,358.34 for M12, which gives us room for a strong close. Risk management: watching for auction cost inflation during holiday (CPMs typically +20-40% in Nov), which could compress ROI despite higher demand. If remarketing pools saturate mid-month, excess budget should flow to Google Shopping PMax.
Top channels
Meta Prospecting
$38,000
Holiday gift-giving + self-care angles targeting Premium Conscious and Ingredient Researcher personas. Three creative variants: (1) 'The skincare gift they'll actually use' — elegant product-as-gift visual with ingredien…
Remarketing
$35,000
Dynamic retargeting with holiday urgency overlay. Cart abandoners see 'Still in your cart — don't let winter win' with product imagery and review snippets. Site visitors see 'The barrier repair serum 21,000+ customers tr…
Google Search
$34,000
Problem-aware search capture with holiday self-care framing. Target barrier repair, damaged skin barrier, winter skincare queries that spike in November.
Strong completed run
Google: Gemini 3.1 Pro Preview
Run score
30.38
Run profit
$432
Month 1
Score 16.9
Objective
Establish baseline acquisition and capture existing demand while introducing the brand to high-fit premium audiences.
Budget
$93,216
Profit
$-47,083
ROI
-0.51
Discount
0%
What happened next
Solid baseline launch protecting premium positioning with zero discounts and strong clinical messaging on Meta. However, generic copy on Google Shopping and a lack of low-friction hooks will limit conversion for impulse and value-driven segments.
Why it thought this made sense
Month 1 focuses on finding our footing with a balanced mix of upper-funnel prospecting and high-intent demand capture. We are leaning heavily on Meta Advantage+ and Google PMax to let the algorithms find high-value Ingredient Researchers and Premium Conscious buyers. No site-wide discounts to protect our premium positioning.
Top channels
Meta Prospecting
$40,000
clinical efficacy, ceramides, and authority
Google Shopping
$18,000
product feed, high intent
Google Search
$12,000
clear differentiation and ingredient proof
Month 6
Score 28.6
Objective
Improve unit economics and reduce blended CAC by maintaining platform stability on paid channels while activating the 90k+ email list for high-margin conversions.
Budget
$86,819
Profit
$1,219
ROI
0.01
Discount
0%
What happened next
Maintaining platform stability and zero discounts solidifies premium positioning but limits volume. Generic ad copy across search and remarketing fails to strongly differentiate the brand.
Why it thought this made sense
Kept budget changes on Meta and Google under 15% to preserve platform learning. Introduced a dedicated budget for Email/CRM to monetize the large list of non-purchasers via an education sequence, leaning into the brand's strength in trust-building.
Top channels
Meta Prospecting
$29,500
Clinical evidence and ingredient deep-dive
Google Search
$25,500
High-intent capture for barrier repair and skin healing
Google Shopping
$14,500
Product feed highlighting premium positioning and clinical efficacy
Month 11
Score 57.1
Objective
Capitalize on Holiday/BFCM demand by introducing a 15% discount to activate the large email list (124k) and warm audience pool (161k), while scaling top-performing Meta and Google Search campaigns within the $84k budget limit.
Budget
$84,000
Profit
$47,607
ROI
0.58
Discount
15%
What happened next
Introducing a 15% discount perfectly times the monetization of a large, un-fatigued audience for BFCM. However, the ad copy is highly generic and relies entirely on the discount rather than strong brand differentiation.
Why it thought this made sense
Introducing a 15% BFCM discount to activate our growing email list and warm audiences. Increasing budget slightly across Meta and Google Search to capture high-intent holiday traffic while keeping shifts under 15% to maintain platform learning and stability.
Top channels
Meta Prospecting
$29,500
BFCM offer highlighting clinical evidence and barrier repair
Google Search
$23,000
Intent capture for barrier repair and BFCM deals
Google Shopping
$14,500
Product feed highlighting the 15% off holiday offer
Strong completed run
OpenAI: GPT-5.4
Run score
20.56
Run profit
$-248,939
Month 1
Score 5.7
Objective
Build efficient new-customer acquisition while establishing premium trust signals, then convert warm traffic with education-led CRM and disciplined remarketing.
Budget
$92,216
Profit
$-62,142
ROI
-0.68
Discount
0%
What happened next
Premium positioning is strong, but restricting Google Search and Shopping to retargeting pools severely limits top-of-funnel acquisition. Heavy remarketing spend against a small warm audience risks early saturation without driving enough new traffic.
Why it thought this made sense
Month 1 should prioritize signal gathering and scalable acquisition without cheapening the premium positioning. Northstar Skin has strong ingredient credibility and founder-story potential, so the plan leans into Premium Conscious and Ingredient Researcher personas via Meta, Google Search, and Shopping, while using TikTok selectively for creative-led reach. No broad discount is recommended this month because premium brand fit is high, repeat economics are healthy, and early offer fatigue should be preserved. Email focuses on education, founder credibility, and warm conversion support. Remarketing is funded meaningfully but kept disciplined due to finite pool sizes.
Top channels
Meta Prospecting
$30,000
Founder-led barrier repair education with premium social proof and texture/results storytelling
Google Shopping
$22,000
Feed-based conversion capture with premium imagery, reviews, and clear product differentiation
Google Search
$18,000
High-intent problem/solution capture focused on barrier repair, ingredient credibility, and trust-building claims
Month 6
Score 21.4
Objective
Improve contribution efficiency and reduce CAC by keeping continuity in the proven paid structure while shifting more spend toward high-fit, trust-led Meta prospecting, disciplined TikTok creator content, strong but capped remarketing, and heavier CRM monetization. Prioritize Premium Conscious, Ingredient Researcher, and warm reactivation audiences over broader low-intent scale, and avoid discounting to protect premium positioning and limit offer fatigue.
Budget
$85,670
Profit
$-27,672
ROI
-0.33
Discount
0%
What happened next
Consolidating spend into Meta and TikTok while pausing saturated Google channels is a smart structural move. However, the ad copy relies on generic claims rather than specific ingredient proof, which will struggle to convert the targeted premium and research-heavy personas.
Why it thought this made sense
Months 4-5 showed the clearest path: Meta remains the most scalable paid driver despite weak standalone ROI, while remarketing is the strongest efficient channel and email is extremely profitable. Brand momentum is slightly negative and offer fatigue is already elevated, so the plan avoids promos and instead leans into education, proof, founder authority, and routine-based conversion. Remarketing is trimmed slightly from recent levels to respect saturation in finite warm pools. Google Search and Shopping remain paused after repeated saturation and learning reset flags. This month is about disciplined continuity, better audience quality, and stronger owned-channel capture rather than broad expansion.
Top channels
Meta Prospecting
$54,000
Founder-led barrier repair education with ingredient credibility, visible routine outcomes, and premium trust signals aimed at Premium Conscious and Ingredient Researcher audiences while still giving enough social proof …
Tiktok
$18,000
Native creator routines showing irritated-to-calm skin journeys, texture shots, and quick explanations of why barrier repair matters, optimized to stop the scroll without leaning discount-heavy.
Remarketing
$12,000
Conversion-focused reminder sequence with ingredient proof, reviews, routine reassurance, and premium value framing rather than aggressive offers.
Month 11
Score 36.2
Objective
Build on month 10's first positive ROI by preserving the proven Meta + TikTok + remarketing + CRM structure, leaning into holiday demand with education-led premium conversion messaging instead of discounting, and adding a controlled layer of high-intent Google capture to harvest incremental demand without resetting the account too aggressively.
Budget
$71,764
Profit
$19,397
ROI
0.28
Discount
0%
What happened next
Maintaining continuity and avoiding holiday discounts protects premium positioning and margins. However, the ad copy remains too generic to fully convert ingredient-conscious shoppers or re-engage lapsed buyers.
Why it thought this made sense
Month 10 was the clearest proof point yet that continuity is working. The best plan for month 11 is not a broad reset but a disciplined scale inside the channels that have repeatedly shown strength: remarketing, Meta creative testing, TikTok creator-led storytelling, and owned CRM. Holiday demand should lift branded and problem-aware traffic, so a measured reintroduction of Google Search is warranted, but kept modest because prior abrupt changes caused learning resets and saturation. Messaging should skew toward founder authority, barrier-repair proof, ingredient credibility, reviews, and routine-fit for Premium Conscious and Ingredient Researcher segments. Avoid lazy promos given offer fatigue and the brand's premium positioning.
Top channels
Meta Prospecting
$36,500
founder-led barrier repair education with visible skin-comfort outcomes, ingredient credibility, and premium social proof
Remarketing
$16,000
conversion-focused reminder with review proof, ingredient trust markers, and low-friction return-to-cart urgency for holiday shopping
Tiktok
$14,000
native creator routine storytelling focused on irritated skin, quick texture payoff, and credible 'why it works' explanation