How to Predict Sales with Retail Demand Forecasting

Retail demand forecasting helps brands estimate what customers will buy and when demand is likely to increase or decrease. Inventory planners use forecast data to plan inventory levels, schedule staff, and prepare fulfillment before demand changes.

The NRF forecast that non-store and online sales would grow 7% to 9% year over year in 2025, reaching up to $1.6 trillion. As demand shifts between stores and ecommerce, incomplete forecasts can leave one channel understocked while another carries excess inventory.

This guide covers how forecasting works and how Shopify helps retailers use demand predictions for inventory planning.

What is retail demand forecasting?

Retail demand forecasting estimates future customer demand by product, channel, location, and time period. Organizations use these estimates to predict sales across physical storefronts and digital channels. 

Sales reporting shows what already happened. Demand forecasting uses past sales, current inventory, and other inputs to estimate future demand. These predictions guide areas including:

• Markdown schedules: Predictions suggest when demand for a product will drop so stores can lower prices to clear out older stock.
• Fulfillment capacity: Anticipating order volumes helps logistics teams prepare warehouse space and packing stations for busy periods.
• Staff scheduling: Stores use historical traffic patterns to schedule the right number of floor workers for peak hours.
• Promotion timing: Forecasts identify periods when a promotion may lift demand or clear slow-moving inventory.

Predictions rely on past sales numbers and point-of-sale (POS) transactions. They also incorporate online orders and current stock levels.

Shopify Analytics provides the historical data for these calculations. The unified Shopify admin dashboard tracks store activity and customer transactions. Teams review these reports to assess product performance and identify new merchandising opportunities.

Retail demand forecasting vs. demand planning vs. demand management

These terms all relate to stock control, but they are part of different stages of inventory management. 

A demand management strategy can reduce the risk of overproduction and shortages by adjusting pricing, promotions, product availability, and fulfillment options.

Teams can use Shopify to track sales and inventory, create purchase orders, and manage transfers. Use Shopify Flow or an inventory alert app to issue low-stock notifications.

Why forecasting is harder in omnichannel retail

Omnichannel retail complicates inventory forecasting because online and in-store demand influence each other. 

When sales and inventory data live in separate systems, omnichannel orders can make demand harder to read. For example, a ship-from-store order uses inventory from a retail location even though the order started online. Without connected data, the store may look like it had less in-person demand than it did.

Omnichannel forecasting tracks inventory by stock keeping unit (SKU), location, and sales channel. A product might appear slow-moving at one location, but it's still useful for fulfilling online orders.

For example, a retailer might notice that a winter jacket sells slowly at a storefront in Chicago. But the same item sells quickly to online buyers nationwide. If the Chicago store inventory is visible online, the retailer uses that slow-moving stock to satisfy online demand.

Connecting these data points requires a unified system. Shopify POSsyncs with Shopify admin to track orders and inventory across retail locations, the online store, and other active sales channels.

Benefits of retail demand forecasting

Retail demand forecasting provides three main benefits, enabling businesses to do the following:

• Reduce stockouts and lost sales
• Reduce overstock, markdowns, and cash tied up in inventory
• Improve fulfillment, staffing, and customer experience

Reduce stockouts and lost sales

When a product sells out early, standard sales logs stop tracking consumer interest. This hidden demand can lead retailers to underestimate future demand.

A 2025 academic study analyzed sales data across 898 stores,e and found that accounting for stockouts reduced systematic demand underestimation from 7.37% to near zero.

Organizations can identify these blind spots by reviewing Shopify inventory reports and POS data. Real-time inventory-by-location visibility shows where stock ran out, allowing retailers to adjust allocations before the next product launch.

Reduce overstock, markdowns, and cash tied up in inventory

Overestimating market demand affects liquidity by tying up cash in storage rooms. Excess stock generates warehouse storage fees and creates markdown pressure to clear shelf space.

Excess inventory can tie up cash that could otherwise be used for product development, marketing tests, or replenishment of faster-moving items.

Shopify inventory reports show inventory snapshots, quantities sold, percentage sold, sell-through rate, inventory remaining, and ABC analysis. Retailers can use these reports when making replenishment and inventory transfer decisions.

For more advanced AI demand forecasting or replenishment planning, Shopify App Store apps such as Inventory Planner and Rewize add SKU-level or multi-location forecasting features.

Improve fulfillment, staffing, and customer experience

Knowing when orders will arrive helps managers schedule store staff during peak hours and position inventory near regional buyers. 

Retailers apply these historical metrics to coordinate multichannel fulfillment models, including buy online, pick up in-store (BOPIS) and ship-from-store services. 

For example, Canadian travel brand Bentley used Shopify POS to coordinate real-time inventory visibility across more than 125 locations. The migration enabled smooth BOPIS and ship-from-store services for their customers. 

Data shows that Bentley achieved 129% year-over-year total revenue growth, 74% online sales growth, and 17% POS transaction growth.

Retail demand-forecasting data sources

Demand forecasts draw from several business systems. POS systems and ecommerce platforms feed historical figures into the model. 

Combining online and in-store data helps businesses avoid stockouts and balance inventory levels across channels.

Historical sales and POS data

Historical sales data creates the starting point for future projections. Segmentation breaks these records down by SKU and product category. 

Teams track variations across individual store locations or distinct sales channels to capture localized behavior. Analysis includes trends by day of the week, seasonal cycles, and active promotion periods.

Inventory, product, and fulfillment data

Inventory metrics compare current stock levels with sales velocity. Projections evaluate baseline demand and verify whether available stock supports incoming orders. 

Shopify inventory analytics provide specific reports:

• Product sell-through rate measures sales against received inventory.
• Days of inventory remaining estimates stock depletion timelines.
• Inventory value reports provide an ABC analysis by product.

These metrics show if products run low quickly or tie up capital.

Promotion, seasonality, and external demand signals

Marketing data clarifies demand variations that baseline sales history cannot explain. Active promotions or shifts in ad spend often generate temporary spikes that diverge from normal buying behavior. 

The Sales attributed to marketing report connects revenue to trackable campaigns. This breakdown isolates baseline demand from campaign-driven volume during inventory planning cycles.

Customer and channel data

Customer metrics show whether demand originates from repeat buyers or first-time shoppers. Repeat purchases follow more predictable patterns than random sales spikes. Customer profiles track individual acquisition and buying behavior over time. 

Channel analytics show where orders come from. For example, Shop analytics track metrics such as total sales volume, order counts, payment methods, and customer engagement.

Retail demand-forecasting methods and models

Retailers choose forecasting methods based on how much data they have and how many locations or channels they manage. 

These are the common forecasting methods to consider:

• Qualitative forecasting
• Time-series forecasting
• Causal forecasting
• AI and machine learning forecasting

Qualitative forecasting 

Qualitative forecasting uses human judgment when there isn’t enough sales history for a statistical model.

For example, an apparel brand estimates initial inventory requirements by cross-referencing broad category trends with current market research. In-store staff provide qualitative data by logging direct customer inquiries for unlisted sizes or colors. These notes can reveal customer interest that doesn’t appear in online transaction data.

Human judgment can introduce cognitive bias into forecasts, so teams should document assumptions and compare them with actual sales.

Time-series forecasting

Time-series forecasting evaluates historical demand patterns over scheduled periods. Models analyze past daily logs or seasonal cycles to estimate future product volume.

Common time-series methods focus on statistical markers:

• Moving averages smooth daily fluctuations to reveal weekly transaction patterns.
• Seasonality tracking identifies recurring demand spikes, such as holiday gifting or back-to-school periods.
• Trend lines display whether stock velocity rises or falls over time.
• Recurring event analysis models annual promotions and local shopping patterns.

These models work for products with enough sales history to show patterns. One constraint comes from the retrospective nature of historical analysis. Projections omit sudden changes caused by unexpected promotions or local price shifts.

Causal forecasting

Causal forecasting looks at factors that may affect demand, such as price changes, promotions, weather, and local events. Merchandising teams use these calculations to estimate how a planned discount affects sales volume.

Shopify marketing reports and campaign attribution tools link promotional activity to transaction metrics. The system displays sales figures and orders tied to tracked marketing deployments. These reports provide data points for projecting demand after future campaigns.

AI and machine learning forecasting

Machine learning (ML) models evaluate extensive datasets containing multiple operational variables. These models can update forecasts as new sales, inventory, and channel data come in. They’re useful for multi-location retailers with large catalogs, frequent replenishment needs, or changing sales patterns.

A 2025 academic study indicates that model selection depends on data quality and distinct demand patterns. Findings show that localized tree-based models perform well on brick-and-mortar retail records.

The Shopify App Store lists applications that automate demand projections and stock replenishment. Integrations like Inventory Planner or Stockie sync directly with Shopify records to manage purchase orders. Systems like Rewize offer multi-location planning features with AI-powered forecasting tools.

How to build a retail demand forecast step by step

• Define forecast scope and horizon.
• Clean and unify your data.
• Build a baseline forecast.
• Adjust for events, promotions, and constraints.
• Review actuals and improve accuracy.

1. Define forecast scope and horizon

Decide what you want to forecast. That can be the products, locations, or sales channels you want to review, along with the time period to analyze. 

A few questions to answer are:

• How many units should be ordered for the next eight weeks?
• Which stores need replenishment before an upcoming promotion?
• Which products need more inventory before Black Friday and Cyber Monday (BFCM)?
• Which SKUs should be transferred to stores with higher demand?

Use reports in Shopify Analytics to look at trends by date range and compare one period with another. If you sell through Shop or use Shopify POS, review channel-specific performance to keep the forecast focused on the parts of the business that matter most.

2. Clean and unify your data

Forecasting works best when your sales, inventory, and channel data are reviewed together. Shopify reports let you work from a single admin, and Shopify POS syncs with your online store so in-person and ecommerce sales can be considered in the same planning process. 

If you need a more tailored view, create a custom report or data exploration in Analytics, and add the relevant metrics, dimensions, filters, and comparisons to your forecast.

3. Build a baseline forecast

Your baseline forecast projects future demand under normal conditions. 

Review reports such as Sales over time, Total sales by order, and Inventory sold daily by product to understand sales averages and order trends to get a starting point. 

4. Adjust for events, promotions, and constraints

After building the baseline, adjust the forecast to account for known events that could affect demand. 

Factor in the following elements:

• Planned promotions and price changes
• Marketing campaign activity and product launch plans
• Supplier delays and product discontinuation plans
• Store events, holidays, and seasonal weather shifts

Gather all upcoming launch and promo schedules. Review past performance using Sales attributed to marketing reports to understand how previous trackable campaigns impacted volume. 

Then, cross-reference these expectations against inventory analytics like Days of inventory remaining and Product sell-through rate to verify that your current stock levels can support the expected demand lift.

5. Review actuals and improve accuracy

After the forecast period passes, compare actual sales to what you expected. In Shopify, you can revisit the same reports, change the date range, add comparisons, and save custom versions of reports as you refine your process.

Shopify’s forecasting guidance notes that forecasts are almost never 100% accurate. Target a 10% to 15% weekly margin of error as a practical starting benchmark.

How Shopify supports retail demand forecasting

Shopify Analytics and reports

Retailers can use Shopify reports to pull historical sales and product performance data before building a forecast. 

From a unified dashboard, teams can review:

• Store activity
• Web performance
• Transactions
• Business metrics

Use these reports to review sales trends, product performance, and customer behavior before making merchandising decisions.

Shopify POS and omnichannel inventory data

Demand differs by channel and location. For retailers that sell both online and in person, Shopify POS brings store and ecommerce data together in one system. This gives retailers one place to review orders and inventory across retail locations, their online store, and active sales channels.

Forecasting apps from the Shopify App Store

When forecasting needs become more advanced, the Shopify App Store can extend that workflow. Forecasting apps can add SKU-level demand planning, replenishment suggestions, and multi-location planning to Shopify data.

Advanced analytics for enterprise teams

For larger teams, Shopify’s reporting workflows offer advanced planning reviews. Using the ShopifyQL query editor, teams can build recurring views for comparisons of key performance indicators (KPI) for demand planning, as well as performance reviews and ongoing forecast analysis.

An example is global sporting goods giant Decathlon, which adopted ShopifyQL Notebooks to replace legacy tools and streamline their US market expansion. 

By moving to live data environments, the team cut reporting times by 50% and combined multiple KPIs into dynamic reports. ShopifyQL Notebooks helped Decathlon review year-over-year numbers, combine KPIs, and compare peaks or drops in sales more quickly.

"Without using ShopifyQL Notebooks, I would have done an extract in Google Sheets or Excel... The problem with that is it's just one shot. It's out of date. That’s why we use Notebooks—it’s specifically adapted to all of our data-mining and storytelling needs as an ecommerce brand,” says Tony Leon, chief technology officer at Decathlon USA.