Finance — Grow Fragrance

Finance

Date Range:

YTD 2026

Profit & Loss

2026 YTD Net Rev

$316K

2025 Full Net Rev

$3.39M

Comparison year

2024 Full Net Rev

$2.48M

+36.7% YoY

NOI (2026 YTD)

-$156K

-49.4% of net rev

📋 P&L Summary — 2026 Monthly Detail vs 2025/2024 Full Year

Line Item	2026 Jan	2026 Feb	2026 Total	% of Rev	YoY Var	2025 FY	% of Rev	2024 FY	% of Rev
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Last updated: —

📊 Revenue by Channel

2024 FY

2025 FY

2026 YTD

MER & Performance Metrics

📈 Marketing Efficiency Ratio (MER)

Shopify Gross MER0.34

Ad spend / Gross revenue. Target: <0.40

Shopify Net MER0.29

Ad spend / Net revenue after fees

Total Ad Spend$95K

Meta $75K + Google $18K + Shopify Collabs $2.5K

Break-Even Analysis

🎯 Break-Even Framework — Monthly Revenue Target

Monthly Break-Even—

Net revenue needed to cover all fixed + variable costs

Current Run Rate—

Annualized from YTD actuals

Gap to Break-Even—

Monthly shortfall (or surplus)

Component	Monthly Budget	Monthly Actual	Variance	Type
Calculating break-even...

Break-even = (Fixed Costs + Marketing) / Gross Margin %. Fixed costs = G&A + Warehouse Rent. Variable costs (COGS, shipping, platform fees) scale with revenue. Budget from Nikita's 2026 plan.

🎯 Trend vs Target — 2026 YTD Action Gap

Line Item	TREND (Actual)	TARGET (Budget)	Gap	% to Target	Status
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Budget sourced from finance_budget_monthly (2,577 rows). Actuals from fact_orders + fact_ad_spend. Gap shows where intervention may be needed.

Cross-Reference: Revenue Validation

Shopify and Amazon revenue are validated against fact_orders in the Datahub. Shopify $277K gross = sum of all Shopify orders (Jan–Feb 2026, before discounts/fees). Amazon $95K includes pack decomposition. See the Dashboard for channel-level drill-down and customer metrics.

Cross-Reference: Ad Spend Detail

Meta and Google spend are pulled from fact_ad_spend in the Datahub. Weekly granularity. All amounts are actual spend, not attributed revenue. ROAS (revenue per $ spent) is calculated separately in the Quadruple Whale dashboard by attribution window (7d, 1d, etc.).

What this shows: P&L sourced from fact_orders and fact_ad_spend in the Datahub, plus BOM-sourced COGS and budget-sourced G&A. Revenue is net (gross − discounts − returns − platform fees). TREND = actuals (where the business IS going). TARGET = budget (where the business WANTS to go). The gap = Action Gap.

⚙️ Needs Config: Bank reconciliations and monthly close data. Monthly P&L is currently sourced from fact_orders + budget. When full accounting integration is available (QuickBooks MCP, Q2 2026), this will sync to actual GL balances.

Channel Profitability

📊 Channel P&L Waterfall — Annual Summary

COGS allocation method: COGS is allocated to each channel proportionally by unit volume (channel units sold / total units sold). This means channels with higher unit volume absorb more COGS. Raw material cost per unit is consistent across channels (same product = same BOM), but fulfillment labor varies by channel ($0.38 Shopify, $0.40 Amazon, $0.37 Faire). Shipping costs are channel-specific from ShipStation data.

📋 Channel P&L Detail

Line Item	Shopify	%	Amazon	%	Faire	%	Total
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ℹ️ Faire (wholesale) note: Faire's margin profile differs structurally from DTC channels — lower gross revenue per unit, no paid ad spend, 15% Faire commission, and net-30–60 payment terms. High operating margin % on Faire reflects the absence of ad costs, not superior unit economics. Do not compare Faire margin % directly to Shopify/Amazon without accounting for channel mix and AOV differences.

📈 Operating Margin by Channel — Monthly Trend

📦 Unit Volume by Channel & Format (Nikita #7)

Format	Shopify	Amazon	Faire	Total	% of Total
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💰 Per-Unit Economics by Channel

Metric	Shopify	Amazon	Faire
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Per-unit metrics derived from channel P&L annual summary (net revenue, COGS, ad spend, G&A) divided by total units sold per channel. YELLOW — Blended across all product formats; spray-only vs candle-only unit economics differ significantly.

⚙️ Assumptions & Parameters

Every number on this tab comes from the inputs below. Nothing is hidden or estimated without being labeled.

Revenue GREEN

Sourcefact_orders (net_revenue = gross − discounts − returns − platform_fees)

ChannelsShopify, Amazon, Faire — identified by order source

Ad Spend Attribution YELLOW

Meta + Google100% → Shopify (simplification — some Meta drives Amazon halo)

Amazon Ads100% → Amazon

Collabs100% → Shopify

FaireNo ad spend attributed

Yellow becauseMeta halo effect (~$0.54 Amazon lift per $1 Meta) not yet split. Future: 85/15 Shopify/Amazon.

G&A Allocation YELLOW

MethodMonthly revenue share (channel net rev / total net rev)

SourceQBO P&L G&A lines via finance_qbo_pnl

Yellow becauseProportional to revenue is simplistic — doesn't reflect actual resource usage per channel

Labor Allocation YELLOW

WarehouseBy unit share (channel units / total units)

OfficeBy revenue share (same as G&A)

Data Sources

Model scriptetl/export_channel_pnl.py

Output tablechannel_pnl_monthly (81 rows)

JSON pathfinance_data.json → cost_model.channel_pnl

Cross-Reference: Ad Spend Attribution

Meta and Google ad spend is attributed 100% to Shopify (they drive DTC traffic). Amazon Ads → Amazon. Faire has no ad spend. This is a simplification — Meta spend drives some Amazon halo effect (~$0.54 Amazon lift per $1 Meta). A future refinement could split Meta 85/15 Shopify/Amazon.

What this shows: Full P&L by sales channel — Shopify (DTC), Amazon (marketplace), and Faire (wholesale). Revenue from fact_orders, ad spend from fact_ad_spend (Meta+Google → Shopify, Amazon Ads → Amazon), G&A allocated by revenue share from QBO P&L, labor from payroll summary. This is the first place to look when asking "which channel is actually making money?" YELLOW — G&A allocation is proportional to revenue (simplistic). Fulfillment labor rates are BOM-Channel estimates, not task-timed.

Cost of Goods Sold & Bill of Materials

📊 COGS Trend — 2024 vs 2025 vs 2026 YTD (% of Net Revenue)

Line Item	2024 FY	2025 FY	2026 YTD	YoY Trend (24→25)
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🧭 BOM Inventory Summary

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🏪 BOM Cost by Channel

Channel	Products	Avg RM	Avg Labor	Avg Total	Min	Max
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Source: BOM - Channel v2.xlsx (Nikita). RM = Warehouse RM + Fulfillment RM Packaging. Labor = Warehouse Labor + Fulfillment Labor. Total = full landed channel cost.

💰 SKU-Level Margin Analysis

⚠️ Data Source & Method Disclosure

Avg Price — ACTUAL gross_revenue / units from order CSVs

BOM Cost — ACTUAL finance_bom_channel (Nikita BOM v2). Faire uses Shopify BOM as proxy (same product cost). SKUs without BOM show "—" with no cost deducted

Disc/Unit — ALLOCATED Order-level discounts split to line items by revenue share (Method A)

Ship/Unit — ACTUAL Real carrier fees from ShipStation. 95%+ coverage for 2024-2025

Fees/Unit — ACTUAL Platform fees per line item from Shopify/Faire

True Margin — CALCULATED Gross - BOM - Discounts - Shipping - Fees. Only reliable where BOM status = Full

Free shipping threshold: $70. "Free Ship %" = % of units where carrier cost was $0 (customer paid nothing). Period: 2025 full year, min 5 units.

Loading margin data...

Product ↕	Ch	Units ↕	Avg Price ↕	BOM/Unit ↕	Disc/Unit ↕	Ship/Unit ↕	Fees/Unit ↕	Margin % ↕	True Margin $ ↕	BOM
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Cross-Reference: COGS per Unit vs True Margin

The cogs_per_unit field in dim_products covers RM only (~$3–9/unit). The BOM/Unit column above uses finance_bom_channel.total_cost which includes warehouse RM + fulfillment RM + warehouse labor + fulfillment labor (~$6–19/unit). True Margin further deducts allocated discounts, actual shipping (ShipStation carrier fees), and platform fees. SKUs without BOM match show inflated margins — filter to "Full BOM Only" for reliable margin analysis. YELLOW — BOM coverage is —% of SKUs. Discounts use revenue-weighted allocation (not per-item breakdown).

⚠️ Data Quality Status

Raw Material Costs YELLOW: BOM refreshed March 2026 from Nikita's Purchasing Framework (370 RM pricing rows with MOQs and price breaks). Costs generally increased 1-11% (sprays ~1-2%, candles ~7-11%). Path to Green: Nikita spot-check review on key products.

Production Labor YELLOW: Per-type BOM rates (not flat): sprays $1.16, candles $2.20, inserts $2.15, 2oz $0.44, discovery sets $2.54, car fresheners $0.77-$0.93. 93.2% of production units matched. Path to Green: Drew task-timing validation.

Shipping Cost GREEN: Validated from ShipStation carrier data (108K shipments, $988K total, Dec 2023-Mar 2026). 95% Shopify match rate. Avg ~$3.22/line-item.

What this shows: Breakdown of COGS by category (Raw Materials, Shipping, Warehouse Rent, Warehouse Labor). Product costing sourced from Nikita's BOM (refreshed March 2026): formulations (363 rows), components (844 rows), RM pricing (370 rows with MOQs and price breaks), and per-type production labor rates. 186 products in dim_products (126 BOM-costed + 25 Amazon + 35 Shopify unmapped).

Cash Flow & Liquidity

💸 Cash Flow Statement (QB Actuals)

Category	Jan	Feb	Cumulative
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Source: QuickBooks Statement of Cash Flows · Jan 2025–Mar 2026 · LIVE DATA

🏦 Bank & Credit Card — Cash Flow by Category (Q1 2026)

Stacked bar = monthly cash movement by category. Inflows (green) from Shopify, Amazon, Faire payouts + owner transfers. Outflows split by Payroll, Taxes, Warehouse (RM + supplies), Marketing (ads + agency), G&A, Rent, and Vendors. CC payments excluded to avoid double-counting (spend shown via CC statements instead).

💳 Checking Account — Inflows vs Outflows

Month	Inflows	Outflows	Net
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📥 Inflow Breakdown by Source

Source	Jan	Feb	Mar	Total
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📤 Outflow Breakdown by Category CC payments excluded (see CC spend below)

Category	Jan	Feb	Mar	Total
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💳 Credit Card Spend by Category (Chase 6511 + Capital One)

Category / Subcategory	Jan	Feb	Mar	Total
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Spend categories from CC statements. Marketing = Meta/Google ads + agencies. G&A = software, professional services, office. Warehouse = raw materials, shipping, supplies. This is the underlying detail behind the CC payment line in the checking account.

Data sources: Chase 5880 checking (Jan–Mar 2026), Chase 6511 CC (Jan–Mar 2026), Capital One CC (Jan–Mar 2026). 809 transactions total. Categories assigned by vendor pattern matching. YELLOW — First load; category rules should be reviewed by Nikita.

📊 Working Capital Assumptions

Metric	Days	Notes
Days Sales Outstanding (DSO)	1–2	Shopify/Amazon are immediate settlement. Wholesale terms vary (net 30–60).
Days Inventory Outstanding (DIO)	35–45	Inventory turns ~8x/year. Seasonal peaks (summer, holiday) require higher working capital.
Days Payable Outstanding (DPO)	30–45	RM suppliers: net 30. Logistics: immediate or net 15. Depends on vendor mix.
Cash Conversion Cycle (CCC)	0–15	Generally positive due to quick AR and extended payables.

⚠️ Coverage: Q1 2026 only. Bank/CC data loaded from CSV exports (Jan–Mar 2026). For continuous coverage, Nikita should submit quarterly CSV batches or enable QBO bank feed integration.

What this shows: Cash flow model and liquidity analysis. This is a directional guideline based on the P&L, operating cycles, and working capital assumptions. Actual cash is driven by payment terms, customer deposits, and inventory turns, which vary monthly.

General & Administrative (G&A) Expense Detail

📊 G&A Trend — 2024 vs 2025 vs 2026 YTD

Category	2024 $	2024 %	2025 $	2025 %	2026 YTD $	2026 YTD %
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📋 G&A Detail by Line Item — 2025 Monthly

Account	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Annual Total
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Expanded G&A breakdown from QuickBooks P&L. Grouped by category (Bank Charges, Supplies, Taxes & Fees, Travel & Conferences, etc.). Click a parent category to expand/collapse detail items.

🎯 Key Observations

Office Labor jump (15.3% → 30.2%): 2026 YTD includes recent headcount additions in Customer Service, Product Development, and Technical direction. Full-year 2025 was spread over 12 months. In a full 12-month 2026 (annualized), expect ~18–20% of net revenue.

Consultant spike (3.5% → 6.3%): External consultants ramped for data architecture and warehouse/GRP strategic projects. This will normalize to 3–4% once projects complete. Purchasing & Finance Manager is a full employee, not a contractor.

What this shows: Detailed breakdown of G&A expenses: office labor, contractors, software subscriptions, facilities rent, insurance, legal, and miscellaneous. G&A is running 49.4% of net revenue in 2026 YTD vs. 30.3% in 2025 FY — a significant increase driven by headcount and consulting spend.

Warehouse Inventory Position

📦 Current Inventory Snapshot

Category	Count / Units	Status	Notes
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Source: WMS Control Board · LIVE DATA

🔢 Finished Goods by SKU (Top 20 In-Stock)

Product	Units	COGS Value
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📊 Inventory Turnover Ratio (Computed)

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Estimated from order-level COGS (dim_products × fact_orders units) divided by average inventory. YELLOW — uses BOM COGS which is directionally correct but RM costs need supplier verification.

Cross-Reference: GRP Integration

Current inventory snapshot will feed into the GRP (Grow Resource Planning) system. GRP tracks four queues: Fulfillment (outbound orders), Production (make-to-order), Assembly (kitting, labeling), and Purchasing (inbound). Each queue will have Green/Yellow/Red health status based on stock coverage and lead times. See GRP Design Spec (repo: docs/GRP_Design_Spec_v1.md).

What this shows: Current raw materials on hand (899 RM items) and finished goods (30,712 ecom-available units across 152 SKUs) from the WMS Control Board. Data refreshed whenever Nikita uploads a new WMS.xlsx. Ties to production forecasting and GRP.

Labor Model — 3-Part Decomposition

⚠️ Values are temporary pending task-timing validation. Production rates are BOM estimates (🟡 Yellow); fulfillment rates are BOM-Channel estimates (🟡/🔴 Yellow/Red). All numbers will be updated once Drew's time-trial study and Nikita's BOM review are complete. Do not use for external reporting.

Production Labor

$386K

72.9% of WH payroll

Fulfillment Labor

$83K

15.6% of WH payroll

Scale Variance

$60K

11.4% of WH payroll

📊 Monthly Labor Decomposition — Production + Fulfillment + Scale Variance

Stacked bar = total warehouse payroll. Green = production labor (absorbed into COGS via BOM per-type rates). Blue = fulfillment labor (variable cost by channel). Orange = scale variance (underutilization + overhead). Variance should shrink as volume grows.

📊 Monthly Production Volume by Format

Format	Total Units	% of Total	Avg/Month
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⚙️ Production Labor — BOM Per-Type Rates

Product Type	Rate/Unit	Annual Units	Annual Cost	% of Production	Quality
5oz Spray	$1.16	—	—	—	YELLOW
Candle	$2.20	—	—	—	YELLOW
Insert	$2.15	—	—	—	YELLOW
2oz Spray	$0.44	—	—	—	YELLOW
Discovery Set	$2.54	—	—	—	YELLOW
Car Freshener	$0.77–$0.93	—	—	—	YELLOW
Retail Spray	$0.56	—	—	—	YELLOW

Rates from Nikita's BOM (March 2026 refresh). 93.2% of production units matched to BOM rates. Remaining 6.8% are old/discontinued SKUs. Path to Green: Drew task-timing validation + Nikita review.

📦 Fulfillment Labor — Per-Unit by Channel

Channel	Rate/Unit	Annual Units	Annual Cost	Source
Shopify	$0.38	—	—	YELLOW/RED BOM-Channel estimate
Amazon	$0.40	—	—	YELLOW/RED BOM-Channel estimate
Faire	$0.37	—	—	YELLOW/RED BOM-Channel estimate

Rates from BOM-Channel.xlsx Shopify sheet (fulfillment labor column). Blended across product types. Path to Green: Drew fulfillment timing study (actual pick/pack/ship operations), ShipStation orders-per-hour analysis.

💼 Real Payroll Summary — Office vs Warehouse (2025)

Month	Office Cost	Office HC	Warehouse Cost	Warehouse HC	Total Cost
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Department-level aggregates from payroll records. Includes gross pay, benefits, taxes, and headcount. Office = headquarters staff (Owner, HR, Marketing, Technology, Customer Service, Product Development, Technical Director, Purchasing & Finance). Warehouse = production and fulfillment team (Warehouse Manager + staff). Individual salary data restricted for privacy.

📊 Monthly Production Volume (Last 24 Months)

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🔮 Path Forward: Task-Level Labor Model

Layer	Current State	Target State	Status
Production Rates	BOM per-type rates (7 types, 93.2% match)	Drew task-timed rates per process per SKU	BOM Rates
Fulfillment Rates	BOM-Channel blended ($0.37-$0.40/unit)	Drew pick/pack/ship timing by channel	Estimate
Scale Variance	Single residual bucket (11.4%)	Split: overhead vs true underutilization	Combined
Overhead Allocation	Scale variance absorbs all overhead	Explicit overhead line (meetings, receiving, cleanup)	Future

Drew is lead on task-timing validation. Dan defines overhead categories. Current model balances to $0.00 against actual payroll — accuracy improves as Drew and Nikita validate inputs.

⚙️ Assumptions & Parameters

Every number on this tab comes from the inputs below. Nothing is hidden or estimated without being labeled.

Production Labor Rates YELLOW

SourceNikita's BOM - Goods (Ecommerce + Retail Summary)

5 oz Spray (ecom)$1.16 / unit

5 oz Spray (retail)$0.56 / unit

8 oz Candle$2.20 / unit

6.5 oz Candle Insert$2.15 / unit

2 oz Spray$0.44 / unit

Discovery Set$2.54 / unit

Car Freshener$0.77–$0.93 / unit

Match rate93.2% BOM + 6.8% format-inferred = 100%

Yellow becauseBOM estimates, not task-timed by Drew

Fulfillment Labor Rates YELLOW/RED

SourceBOM - Channel (Shopify/Amazon/Wholesale sheets)

Shopify$0.38 / unit (blended spray $0.37 + candle $0.41)

Amazon$0.40 / unit (blended single $0.33 + multipack $0.46)

Faire$0.37 / unit (wholesale = spray rate)

Yellow/Red becauseBOM-Channel estimates only — not validated against actual pick/pack/ship timing

Scale Variance DERIVED

FormulaWarehouse payroll − production labor − fulfillment labor

Annual total−$4,406 (−0.8% of payroll)

InterpretationTeam is right-sized. Monthly swings (±$22K) are seasonal timing.

BOM rates embed overheadAbsorption >100% in 9 of 12 months — rates cover production + overhead time

Data Sources

Warehouse payrollfinance_payroll_summary (dept = warehouse, 2025)

Production unitsfinance_production_log (441,812 units, 2025)

Fulfillment unitsfact_orders (units sold by channel, 2025)

Model scriptetl/build_cost_model.py

Model outputdashboards/labor_model_data.json

Key Assumptions

Payroll year2025 (full year, 12 months)

Headcount7–9 (seasonal: 9 in Jan/Dec, 7 in summer)

Unmatched SKUsFormat-inferred: product name → type → BOM rate

Fulfillment blendingFlat rate per channel (not per product type)

OverheadNOT separated — embedded in BOM rates (confirmed by >100% absorption)

Cross-Reference: COGS Integration

Production labor flows into COGS via dim_products.cogs_per_unit (which includes BOM per-type labor rates). Fulfillment labor is a separate variable cost line, NOT in COGS. Scale variance is isolated as its own P&L line — it's the cost of underutilization, not a product cost. See the Channels tab for how these flow into channel-level profitability.

What this shows: Warehouse payroll decomposed into three parts: Production Labor (BOM per-type rates x units produced — in COGS), Fulfillment Labor (per-unit rates x units sold — variable cost), and Scale Variance (residual underutilization — separate P&L line). COGS uses Nikita's per-type rates (stable, not a blended average). This is the standard cost accounting framework. YELLOW — Production rates from BOM estimates, not task-timed. Fulfillment rates from BOM-Channel estimates YELLOW/RED.