eCommerce Analytics
Wednesday, October 1, 2025
Curious about how eCommerce brands can leverage AI demand forecasting? In this article we'll dive into some powerful use cases and explore the best tools to help you achieve this.
You’ve seen it before: a trending product flies off the shelves, but you’re stuck waiting on a supplier. Or maybe you’ve gone deep on a seasonal line, only to have it sit untouched in storage, eating into margins. In eCommerce, forecasting demand with spreadsheets and “I’ve got a feeling” (shoutout to the Black Eyed Peas) is not going to cut the mustard.
That’s where AI demand forecasting comes in. It uses machine learning models to combine your historical sales data with real‑time trends, promotional calendars, marketing signals, and inventory variables - all to generate smarter, more dynamic forecasts. Whether you're planning 30, 60, or 90 days out, AI can help you align stock to demand, reduce waste, and protect your cash flow.
In this article, we’ll walk through what AI demand forecasting actually is, why it’s powerful for eCommerce, how it works under the hood (in plain English), and where it drives the biggest wins for your business. You’ll also get a practical checklist for evaluating tools, a spotlight on Conjura’s AI forecasting, and answers to common questions from teams considering the switch.
AI demand forecasting refers to the use of machine learning algorithms to predict how much of a product your customers are likely to buy, and when. Rather than using fixed rules or linear trends (like “last year plus 10%”), AI models identify complex patterns across dozens of signals. They learn from your historical data, adapt to real-time changes, and adjust dynamically as new information comes in.
This approach is particularly powerful in eCommerce (even more so for multi-warehouse, omnichannel brands). Unlike traditional retail, online brands deal with constant change: new product launches, fast-moving trends, shifting customer behavior, aggressive promotions, and an always-on marketing engine. AI can process more inputs and spot patterns that humans or even advanced Excel sheets will miss.
Where legacy methods rely on broad assumptions, AI provides specificity. It can generate demand forecasts at the SKU level, by sales channel, and across multiple time horizons. The result is better alignment between what you expect to sell and what you actually stock.
Let’s break down how AI forecasting works in a way that’s easy to digest, no computer science degree required.
The first stage is data ingestion. The model takes in a wide range of inputs: historical sales, product metadata (like color, category, brand), promotional calendars, ad spend, traffic data, stock levels, supplier lead times, and even weather or external trends where relevant. In short, it’s not just looking at what sold last week, it’s trying to understand why.
Once the AI model has ingested and processed all data sources, it can generate the forecasts: how many units of each product you’ll likely sell, when, and through which channels. This forecast is typically broken down by 30, 60, and 90-day horizons and often includes confidence intervals to help you understand risk or uncertainty.
Some tools go further by generating actionable outputs - like reorder alerts, exposure adjustments for fast-moving SKUs, or margin risk warnings. So instead of just handing you another dashboard, AI becomes an operational co-pilot. Brands could even use a tool like Owly AI to create strategic action plans for their business to implement based on the forecasts.
When AI demand forecasting is embedded into your planning process, it can unlock some pretty transformative business results.
First and most obviously, it helps you avoid stockouts. Running out of your bestsellers isn’t just a revenue hit; it can damage your customer experience, hurt your rankings on marketplaces, and delay reorders during long lead times. AI can detect when a SKU is trending toward a stockout, giving you time to act.
It also reduces overstock and associated holding costs. Unsold inventory ties up cash, increases warehousing costs, and often ends up discounted. By forecasting more accurately, you invest only in the stock you’re likely to sell - freeing up capital for growth.
Another key advantage is purchase planning. Instead of guesswork, you can place POs based on expected demand and supplier timelines. Finance teams get more accurate cash flow projections, and ops teams gain confidence in their reorder timing.
It also aligns cross-functional teams. Merchandising, growth marketing, finance, and operations all operate from the same demand signal, improving coordination and reducing costly mismatches between stock and spend.
Over time, as more data flows in and the AI model continues to learn, these benefits compound. Forecast accuracy improves, margin risk goes down, and your ability to plan weeks or months ahead becomes sharper, all while your competitors are still juggling spreadsheets.
AI forecasting isn’t just for giant enterprise brands; it’s built for real-world planning problems. Here are a few scenarios where it delivers clear ROI for fast-moving eCommerce teams:
1. Promotions & Campaigns
Launching a big email or ad campaign? AI can predict the lift from your marketing efforts and help you avoid the classic “promo goes viral, stock runs out” disaster. Instead of reacting after a surge, you plan ahead with confidence.
2. Seasonal Peaks
Black Friday, holiday gifting, spring collections - seasonal spikes can crush traditional forecasting models. AI learns from past seasonal patterns and adjusts for current market conditions to help you hit your peak with precision.
3. New Product Launches
New SKUs with limited history are notoriously hard to forecast. AI models can look at similar product attributes (category, brand, price point) and early sales or traffic indicators to generate early-stage demand curves.
4. Multichannel Allocation
Selling through DTC, marketplaces, and wholesale? AI can help you split inventory intelligently by predicting channel-specific sell-through, not just lumping everything together.
5. Long or Variable Lead Times
When sourcing from overseas or dealing with unpredictable suppliers, timing is everything. AI forecasts help you place purchase orders at the right time to avoid either late deliveries or excessive cover.
6. Buying Under Cash Constraints
Tight budget? AI helps you prioritize SKUs that are high-velocity and margin-friendly, so you put limited capital where it delivers the highest return.
7. Returns-Heavy Categories
In apparel, electronics, or other high-return verticals, AI can factor in expected return rates, helping you forecast net demand rather than gross orders.
Across these use cases, the value is clear: fewer surprises, smarter decisions, and better alignment across teams.
The AI space is super noisy, so how do you separate real value from marketing fluff? Here’s what to look for when evaluating AI-powered demand forecasting tools for your eCommerce business:
This checklist helps separate tools that merely “look smart” from ones that actually help you make smarter decisions.
Conjura’s AI demand forecasting is designed for what today’s eCommerce teams actually need: SKU-level visibility, marketing-aware forecasts, and direct actions, all in one place.
Using your real-time sales trends, historical data, marketing inputs, and product catalogue, Conjura predicts both unit demand and revenue over the next 30, 60, and 90 days. Forecasts are broken down by SKU and by sales channel, so you can see exactly where demand will come from, and when.
You don’t just get pretty charts. Conjura flags SKUs at risk of stockout, identifies underexposed winners, and recommends reorder quantities before the damage is done. If a product is trending hot after a promo, you’ll know early - not when it’s already out of stock.
What sets Conjura apart is how it connects with a variety of core data sources - eCommerce store, marketplaces, marketing platforms and ERP/OMS (Brightpearl, Linnworks, Cin7, Inventory Planner and more!) This allows Conjura to gather the full picture of your business performance - fuelling smarter AI forecasting.
Because Conjura already unifies your customer, product, and marketing performance data, AI forecasting becomes part of the bigger picture. You’re not jumping between dashboards - you’re forecasting in context.
Conjura’s forecasting is ideal for fast-growing DTC and omnichannel brands that need clarity on what to stock, where to stock it, and how to buy smart — without slowing down to run reports manually.
So, how do you actually roll this out?
Start by connecting the key data sources: your eCommerce store, marketing platforms (Meta, Google, TikTok, Klaviyo, etc.), GA4, and any inventory or operations tools (ERP, WMS, spreadsheets). Clean historical sales data is essential!
Most brands begin with a pilot: select a category or set of SKUs, validate the AI’s forecasts against known patterns, and layer in planner overrides. You can then gradually scale to include more SKUs, add channels, and use the insights for full PO planning or cash flow modeling.
Is AI demand forecasting accurate enough to trust?
Yes, especially when used with clean, complete data. Studies show AI models can reduce forecasting errors by 20–50%, especially in promo-heavy or seasonal environments. But no model is magic: think of it as a co-pilot, not an autopilot.
Can it forecast for brand-new products?
AI can’t invent data, but it could make smart assumptions based on similar SKUs that you sell.
How often does Conjura update forecasts?
Forecasts are updated regularly based on the latest performance signals, including changes in marketing activity, seasonality, and sales momentum. You can see SKU-level projections at 30, 60, and 90-day intervals.
Will this replace our planners or merch team?
Not at all. It augments them. AI takes on the heavy lifting of trend detection and scenario planning, so planners can focus on high-impact decisions, like trade-offs, supplier negotiations, or product strategy.
Do I need a data team to use it?
Nope. Conjura is built for commercial teams - it’s plug-and-play with eCommerce and marketing platforms. If you can use a Shopify dashboard, you can use Conjura.
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