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.
Solved by Sales Forecasting — Know what revenue to expect — 30, 60, and 90 days out.
What Sales Forecasting does
Sales Forecasting generates AI-powered revenue predictions for the next 30, 60, and 90 days, drawing on your historical Shopify sales data, seasonal patterns, growth trajectory, and marketing spend trends. Traditional forecasting methods achieve 65-75% accuracy. AI-assisted forecasting consistently reaches 85-95% accuracy at the 30-day horizon (SelectedFirms, 2025). For e-commerce businesses making inventory, hiring, and marketing spend decisions based on expected revenue, the difference is material. Sales Forecasting is available on Pro, Agency, and Enterprise tiers.
- 30, 60, and 90-day revenue forecasts with confidence intervals
- Seasonal pattern detection: BFCM, Christmas, summer peaks built into the model
- Marketing spend correlation: forecasts adjust based on your planned ad spend
- Product-level forecasting: which SKUs are trending up or down
- YoY forecast comparison — see how this year is tracking against last year
- Forecast accuracy tracking: see how predictions compare to actual results over time
Works with
Why teams look for a Sales Forecasting solution
The real operational pain that drives people to Alpomi
Traditional revenue forecasting achieves 65-75% accuracy — the equivalent of a 25-35% margin of error on every inventory decision
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)
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
Over-inventory in slow months and under-inventory in peak periods costs DTC brands 10-15% of potential annual profit on average
What you get when you use Sales Forecasting
Real outcomes from teams using this feature in production
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
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.
Built for your segment
See how Sales Forecasting solves problems specific to your business type
For DTC brands
Stop guessing what next month's revenue will be. Your Shopify data already has the answer.
AI forecasting achieves 85-95% accuracy at 30 days compared to 65-75% for traditional methods — and the ROI on better forecasting is well-documented: $3.50 in value for every $1 invested. For DTC brands making inventory, staffing, and ad spend decisions based on expected revenue, that accuracy gap is the difference between a tight month and a profitable one.
- 30-day forecasts at 85-95% accuracy based on your Shopify history
- Seasonal forecasting built in — BFCM and Christmas peaks are modelled automatically
- Marketing spend included: forecasts reflect your planned ad investment, not just organic trends
Get started in three steps
Connect your Shopify store
Sales Forecasting uses your full Shopify order history — including seasonal patterns, product trends, and growth rate — to build an initial forecast model.
Link your ad platforms for spend correlation
Connect Google Ads and Meta to include planned marketing spend in the forecast. Predictions adjust based on your ad investment levels.
Review 30, 60, and 90-day projections
Forecasts are generated automatically and updated daily. View projections with confidence intervals, YoY comparisons, and product-level breakdowns.
Frequently asked questions
- How much Shopify history is needed for accurate forecasting?
- Forecasts work with as little as 90 days of history, but improve significantly with 12+ months of data, as the model can identify seasonal patterns. Brands with 2+ years of history get the highest accuracy.
- Does the forecast include BFCM and Christmas peaks automatically?
- Yes — the model detects seasonal patterns from your own Shopify history and incorporates them. If you have run BFCM campaigns previously, the seasonal uplift is modelled into the forecast automatically.
- Can I see forecasts at product or category level?
- Yes — product-level forecasting is available, showing projected sales velocity for individual SKUs and product categories. Useful for inventory planning and marketing budget allocation by product.
- What is the confidence range in the forecast?
- Each forecast includes a confidence interval showing the range of likely outcomes. A typical 30-day forecast might show a central estimate of £85,000 with a range of £72,000-£98,000, reflecting both upside and downside scenarios.
- Is Sales Forecasting available on all tiers?
- Sales Forecasting is available on Pro, Agency, and Enterprise tiers. It requires a Shopify connection, which is also available on Pro and above.
- How does including ad spend improve the forecast?
- Historical data shows the correlation between your marketing spend and revenue outcomes. When you plan higher spend for BFCM, the forecast adjusts upward to reflect the expected revenue impact of that investment.
Explore related features
These features work alongside Sales Forecasting. See how they fit together.
Ready to see Sales Forecasting in action?
Book a demo and we'll show you how Sales Forecasting connects to your stack and solves your reporting and attribution challenges.
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