Stop flying blind with fragmented spreadsheets. The operating system for high-volume brands that unifies marketing attribution, inventory logic, and financial forecasting.
Scaling past $5M ARR breaks spreadsheets. Your Shopify data doesn't match Facebook Ads Manager. Your 3PL inventory count is lagging by 24 hours. Your finance team is guessing at Net Profit until the books close on the 15th of next month.
This is "Attribution Blindness." You are overspending on ads that don't convert and stocking out of products that do. You need a single source of truth.
Avg delay in accurate Contribution Margin reporting.
We ingest data from every corner of your stack—Ad platforms, Storefronts, 3PLs, and Returns—normalizing it into a standardized financial schema instantly.
Shopify + Meta + NetSuite
Identity Resolution
Unit Economics View
Sync ad velocity with 3PL levels. Automatically trigger POs when forecasted sales exceed current stock runway minus lead time.
Programmatic rules that pause campaigns when Contribution Margin drops below thresholds, or scale winners instantly.
Track LTV by acquisition channel and SKU. Know exactly how much you can pay for a customer based on 60-day payback windows.
Factor in returns, payment gateway fees, shipping variances, and duties automatically per order. No more estimated blended costs.
# Predict stockout date based on current ad spend velocity
def predict_stockout(sku, current_stock, ad_spend_daily):
velocity_model = load_model('sales_velocity_v4')
# Factor in seasonality and active campaigns
predicted_daily_sales = velocity_model.predict({
'sku': sku,
'spend': ad_spend_daily,
'seasonality_index': get_seasonality(month='NOV')
})
days_remaining = current_stock / predicted_daily_sales
if days_remaining < 45: # Lead time threshold
alert_ops_team(sku, days_remaining)
trigger_draft_po(sku, quantity=optimal_order_qty)
return days_remaining
Our models analyze historical seasonality and real-time ad performance to predict cash flow and inventory needs with 94% accuracy.
| Metric | Looker/Sheets | With WRKSHP |
|---|---|---|
| Data Freshness | 24-48 Hours | Real-Time |
| Inventory Stockouts | Reactive / Surprise | Predicted (-90%) |
| CAC Analysis | Blended Only | Per SKU / Creative |
| Forecast Accuracy | 65% (Manual) | 94% (AI Model) |
Schedule a data architecture review. We'll show you how to unify your stack and automate your P&L in 30 days.
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