Here's how you can use retail data to forecast sales for 2027

As retailers wrap up this year and prepare for 2027, analysing past retail performance becomes essential. Checking historical data helps retailers make informed decisions, from ordering stock to planning seasonal promotions. 

One way to do this is through retail sales forecasting. Retail sales forecasting is the process of estimating future sales using historical performance, current trading data, and expected changes in customer demand. It helps retailers improve stock planning, set realistic sales targets, and allocate resources effectively.

For instance, an independent retailer can estimate which products are likely to be in demand by analysing the previous season’s sales. These insights can help inform purchasing decisions, stock availability, and replenishment schedules for retailers.

Still, a sales forecast doesn't guarantee a specific number of future sales. It is only an estimate based on historical data, assumptions, and current trading conditions. Its reliability also depends on how frequently a forecast is reviewed and updated.

With this in mind, here's how to turn existing retail data into a practical sales forecast for 2027 and beyond.

1. Which retail data should you use to forecast sales for 2027?

Retailers can start by gathering data from past years, where available. This helps to check consistent patterns across multiple years rather than making assumptions about the next trading period.

Five useful data sources include sales history, inventory data, customer records, promotional offers, and returns and cancellations.

Combining these data sources helps retailers understand what influenced previous sales performance and identify factors affecting future demand.

For example, if revenue increased in 2026, you need to figure out whether this resulted from selling more products, a change in prices, or product mix. Each factor has a different impact on the 2027 forecast.

Related: Retail Reporting and Analytics

2. How can historical sales data help predict future demand?

Historical sales data identifies demand patterns and highlights the consistently performing products. Retailers can review sales by week and month to check peaks and quieter periods. Compare results across different years to determine consistent patterns.

If a specific product category sells strongly in spring and has a recurring pattern across several years, it gives an estimate of future demand for the same period. But retailers must check the context for historical figures.

A product may have sold well due to a one-off promotion, a competitor’s stock shortage, or an unusually successful launch. Before using those figures, check what influenced the results and check if similar conditions are likely to occur in 2027.

It is also important to identify sales revenue from profitability. High turnover doesn’t mean higher profits if purchasing costs, discounts, and returns also increase.

3. How should UK retailers account for seasonal sales trends?

UK retailers can forecast increases and decreases in sales based on previous sales patterns, in addition to holiday timings, promotions, and product-specific demand. The relevant periods may include Black Friday, Christmas, New Year, Easter, bank holidays, summer season, and back-to-school shopping.

Reviewing previous seasonal sales can help retailers identify previous shopping patterns to plan stock orders, staff schedules, and marketing campaigns for the year 2027. Always check weather-sensitive demands, but avoid assuming that the past season will repeat as it is. For instance, a garden retailer may order products relevant to the upcoming season, such as spring or fall, whereas a clothing retailer may order seasonal products before the start of the actual season, whether summer or winter.

The goal is to prepare for predictable trading patterns while keeping plans flexible enough to respond when actual demand differs from expectations.

4. How does inventory data improve retail sales forecasting?

Retailers can also check inventory data to see if recorded sales match customer demand or limitations in product availability. A product that sells out early in the season may appear like a least popular choice in a sales report because customers could no longer purchase it.

Similarly, slow-moving stock may indicate weaker demand, unsuitable pricing or an ineffective product range. Retailers should investigate these factors before deciding what to order next.

Before forecasting sales for 2027, check stockouts, supplier lead times, inventory movements, replenishment schedules and excess stock. Using only the sales figures without investigating stock availability could result in ordering less stock for the next season.

Let's take an example of a retailer who sold a few coats in winter because popular sizes were out of stock. Looking at both sales and inventory records can reveal this missed demand and help the retailer plan more appropriate stock levels for the next winter season.

Related: Saledock Inventory Management

5. How can customer data help forecast retail sales?

Customer data is another way retailers can forecast sales. It helps retailers understand details such as average transaction values, repeat buying, and customer purchase frequency. Transaction histories also help to identify patterns influencing future demand.

For example, if a bicycle retailer finds that customers buy helmets frequently and accessories alongside new bikes, it can forecast demand for related products rather than focusing only on bike sales.

Retailers can compare new and returning customers to understand how returning customers and repeat sales increase revenue. In case repeat purchases account for a significant number of sales, your expected estimates for 2027 will be different. But don't assume the same purchase behaviour if customers' preferences change due to an increase in the product pricing.

Lastly, follow the UK data protection requirements when gathering and analysing customer data.

6. Should promotions and market changes be included in your forecast?

Absolutely. Retail sales forecasts should always consider planned price changes, new product launches, promotional campaigns, and other relevant market changes. Check all the previous promotions to determine how they impact sales volume, gross margin, and revenue. A discount may increase the number of products sold but reduce the profit earned per item.

External factors such as supplier costs, competitor activity, inflation, and changes in consumer spending all influence product demand. However, predicting future effects is still uncertain. The right way is to prepare a forecast according to three scenarios, such as:

• Conservative forecast: Product demand is weaker than expected.
• Expected forecast: Sales follow your most realistic assumptions.
• Optimistic forecast: Demand exceeds expectations.

These three scenarios help prepare alternative ways to purchase and budget instead of relying on a single estimate.

7. How can you turn a sales forecast into a 2027 retail plan?

Next, connect expected demand to purchasing schedules. Consider supplier lead times, stock availability, and the time required to replenish popular products.

To turn a sales forecast into an actionable plan, convert estimates into monthly targets, purchase decisions, staff requirements, and budget allocations. Set realistic revenue and unit-sales targets on a monthly or quarterly basis. Retailers can break it down further by shop location, product category, and sales channel where your data support it.

Use busy periods to plan staffing and marketing expenditure and then compare the actual results with the forecast estimates each month. If you get an increase in sales compared to your estimates, check if you need to order additional stock or revise the set targets. But if performance falls short of the expected target, investigate the different factors like product availability, less demand, pricing, or something else.

8. Can retail management software help with sales forecasting?

Yes. Retail management software can make sales forecasting easy by connecting sales, inventory, and product data under one dashboard. Instead of collecting information manually and maintaining spreadsheets, retailers can review performance data by date, product, location, and sales channel through integrated reports. This is particularly useful for omnichannel retailers who are selling through physical shops, eCommerce sites, and online marketplaces.

Reviewing the data of different channels under one dashboard also helps to minimise manual work, helps in understanding performance, and shares a complete picture for taking decisions.

Automated forecasting features vary from one software provider to another, so before you invest in new technology, check if a few integrations can fill those gaps.

Related: How to sync your POS with eCommerce Store

Prepare your retail business for 2027

Retail data provides a solid foundation for estimating demand, managing stock and setting realistic sales targets. Combining historical sales performance with inventory records, customer behaviour and seasonal trends can help retailers make more informed decisions about the year ahead.

The goal is to treat forecasting as an ongoing process. Review assumptions, compare actual results with estimates, and adjust plans as trading conditions change.

Saledock’s retail platform, Celerant ONE, helps retailers connect key aspects of their in-store and online operations. By bringing retail information together, retailers can improve visibility across sales channels and make more informed operational decisions.

Interested in exploring how connected retail software could support your business?

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