You bought too much inventory for Christmas and not enough for BFCM. A revenue forecast would have changed both decisions.
Inventory planning, staffing, and marketing budget allocation all depend on a single question: what will revenue be next month? Most DTC brands answer this with a combination of last year's data, gut feel, and a spreadsheet that updates manually. The result is systematic over-investment in slow periods and under-investment in peak periods — a gap that compounds across a full trading year. AI forecasting does not replace your judgment, but it gives you a starting point that is substantially more accurate than the spreadsheet.
Why For e-commerce look for this solution
The real operational pain we solve
Problem
Traditional revenue forecasting achieves 65-75% accuracy — the equivalent of a 25-35% margin of error on every inventory decision
Problem
80% of online retailers now use AI for demand forecasting, a 270% increase since 2019 — the brands not using it are at a structural disadvantage (StayModern, 2025)
Problem
Manual seasonal adjustment for BFCM and Christmas is typically based on one or two prior years of data — AI models use all available historical patterns
Problem
Over-inventory in slow months and under-inventory in peak periods costs DTC brands 10-15% of potential annual profit on average
How Sales Forecasting helps
Sales Forecasting generates 30, 60, and 90-day revenue predictions from your Shopify order history, incorporating seasonal patterns, growth trajectory, and your planned marketing spend. Forecasts are updated daily as new data arrives. You see the prediction, the confidence range, and how it compares to the same period last year — so every planning decision is anchored to data rather than assumption.
Before Alpomi vs After Alpomi
From pain to clarity with Sales Forecasting
Before
October inventory planning: you look at last year's BFCM sales and add 15% for expected growth. You order inventory based on this estimate. BFCM comes and demand is 40% higher. You run out of stock on day three of Black Friday. Revenue and margin are lost.
With Alpomi
October inventory planning: Sales Forecasting shows BFCM revenue projection of £185,000 (vs £132,000 last year), with a confidence range of £162,000-£208,000. You order to the upper end of the range. BFCM demand is met. No stockouts.
Before
Budget planning for Q1: you allocate ad spend based on Q4 performance trends. Q1 is structurally slower — revenue drops and ROAS appears to decline. You cut budget in March, which was the most efficient spend period of the quarter.
With Alpomi
Budget planning for Q1: Sales Forecasting shows expected revenue by month, with Q1 seasonality modelled. You plan a lower ad budget for January-February and a higher budget for March when the model shows a recovery trend. Budget allocation matches actual opportunity.
What you get when you use Sales Forecasting
85-95% forecast accuracy at 30 days
AI-powered forecasting vs 65-75% for traditional methods — a meaningful accuracy improvement for inventory and budget decisions
Seasonal planning built in
BFCM, Christmas, and other seasonal peaks are modelled automatically from your Shopify history
$3.50 value per $1 invested in forecasting
Industry benchmark for AI forecasting ROI — better decisions compound across inventory, staffing, and marketing
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See how Sales Forecasting worksRelated features for you
These features work alongside Sales Forecasting for for e-commerce.
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