How to Forecast Retail Sales Using Historical Data

Forecasting the future has long been a fascinating idea, particularly in the business sector. Although we won’t offer a crystal ball, we can present an alternative. So, if you’ve been wondering how to forecast sales using historical data, you’ve come to the right place.

In this article, we’re going to explore how you can use your past sales data to make spot-on forecasts. It’s one of the key business skills that will help you face the challenges and seize the opportunities that come your business’s way in the future.

What Is Sales Forecasting?

Forecasting in sales is the process of predicting your business’s future revenue, and it’s about making smart predictions. But don’t let the word “predictions” mislead you – sales forecasting is based on careful analysis of real data. For example:

• Your sales history (this is the foundation of your forecast).
• Market trends (what’s trending and what’s not).
• Economic conditions (is the economy thriving or not?).
• Your marketing efforts (how many people know about you?).
• Seasonal patterns (because snow shovels don’t sell in the summer).

But why is sales forecasting necessary? Here are some of the main reasons:

• Know how much inventory to stock. Sales forecasting is important for managing resources accurately. When you understand what your future sales will be, you can make better operational choices. Precise forecasts can help you avoid overstocking (which can lock up your budget and risk becoming outdated) and understocking (which might cause you to miss out on sales and leave customers unhappy).
• Provide your sales team with targets. Setting the right sales targets can be tricky. If your goal isn’t so ambitious, you might not use the full potential of your team. But if you aim too high, you might end up discouraging your employees. Sales forecasts are useful here because they give you the data you need to set those goals. They help you come up with targets that are challenging but still possible.
• Make smarter decisions. Acting strategically is way easier when you have reliable sales forecasts. They provide a vital context for big business changes. For example, if your forecast says you can expect steady growth in a certain product line or market segment, it could back up a choice to expand in this direction. On the flip side, if your forecast based on historical data shows sales dropping in a particular area, this might lead you to think about product line upgrades or even replacements.
• Impress investors. Investors always think ahead, and a solid sales forecast can help you grab their attention. It lets investors know you’re not just daydreaming about the future but have a real plan backed by good analysis. This can boost their confidence in your business performance and make your company look like a worthwhile investment choice.

Before we start discovering how to forecast sales using historical data, let’s also focus on the forecast itself.

Also: Inventory Forecasting

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What Is Historical Forecasting and How Does It Work?

Historical forecasting in sales is what uses past sales data to predict future sales. Basically, this method is based on the idea that history repeats itself. This doesn’t mean the future is going to be a copy of the past, but it does suggest that some trends may remain for a while.

So, how does a forecast based on historical data work?

• To start historical forecasting, you should examine your sales data over a long period. This might be a few months, a couple of years, or maybe even decades, depending on your business and the amount of data you have. The whole point is to identify patterns that keep returning, like seasonal ups and downs, growth trends, or cycles in your sales.
• Yet, historical forecasting isn’t just about noticing the obvious trends. Some statistical techniques can reveal hidden patterns that you might not see right away. These methods can take into consideration a bunch of factors all at once, like overall market growth, seasonal changes, and long-term trends.
• While historical forecasting is a solid tool, it’s not perfect. When there are big market disruptions or totally new events, historical sales data might not be as useful for predicting the future.
• Here’s why smart businesses usually combine historical forecasting with other prediction techniques. They could involve market research, expert insights, or scenario planning to build a stronger forecast.

Also: Top 5 B2B Marketing Strategies to Drive Sales

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6 Steps to Forecast Sales Using Historical Data

Let’s break it down and see how to actually use this technique in real life. Here are six key steps that will walk you through forecasting sales with historical data. Each step builds on the previous one. It’s a solid structure for understanding how your business will perform in the future.

1. Data Collection

When you start data collection, it’s essential to gather as much information as possible. You will require more than just the basic sales numbers; you need many details that will help your analysis. This could mean considering specific products or services sold, how much was sold, their prices, and the period when each sale occurred.

Looking at a longer period and researching the details can reveal some trends and patterns that you might not catch otherwise. For example, data collected over several years can let you spot long-term trends, while daily sales information can emphasize quick shifts or weekly patterns.

What’s more, these days, data can come from different sources:

• point-of-sale systems;
• CRM software;
• E-commerce platforms;
• financial records.

So, the challenge here isn’t just finding the data but also pulling it all together so that it makes sense.

2. Data Cleaning

Raw data is like a rough diamond; it needs a good polish to reveal its true sparkle. So, data cleaning lets you create a neat dataset that doesn’t contain mistakes, which could negatively impact your forecast based on historical data.

Take a look at some of the main challenges when it comes to data cleaning:

• Determining anomalies. This can be sales figures that are way higher or lower than what’s normal. Some of these odd numbers could show real jumps or drops in sales, while others might just be mistakes from entering data or issues with the system.
• Data consistency. It’s better to ensure that all the formats match up across different data sources, keep dates recorded the same way, and check that product names and categories are the same in the dataset. Standardization is critical for accurate analysis and comparisons.

The data cleaning process also provides a chance to enrich your dataset. For example, you can add relevant metadata, like linking sales to the corresponding marketing campaigns or emphasizing important events that can affect your sales success.

3. Pattern Identification

Pattern identification is when your sales data starts to share its story. Here, you’ll start noticing cycles and patterns hiding in your sales data.

When you begin to identify patterns, you may ask some important questions:

• Are there clear ups and downs over time?
• Do certain products always sell better than others?
• Is there a clear seasonal trend in my sales?

These questions are just the beginning. Some patterns might be obvious right away, like the sales surges during holidays for a retail shop. But, the real gold often lies in more subtle trends that you notice when you take a closer look.

For example, you might find out that:

• Sales for a specific product line go up consistently two months before a big industry conference.
• Customer purchases often follow a certain order, where one product tends to lead to another sale.
• There’s a link between weather changes and the sales of particular products.

However, identifying patterns isn’t also about grasping what they mean. Each pattern you find can be a potential tool for forecasting and making decisions. A seasonal trend could help you manage your inventory better, while understanding a product sequence might shape your marketing plans.

4. Statistical Model Selection

At this stage, you should pick the right statistical model to match your specific sales trends. The selection process starts with a close look at the patterns you’ve spotted. Think about the following:

• How complicated your sales trends are.
• If there are seasonal or cyclical trends going on.
• The quantity and quality of historical sales data you’ve got.
• The timeframe for your forecast based on historical data.

If your business has pretty steady sales patterns, then simpler tools will usually work. A moving average model, for example, can capture overall trends by smoothing out short-term fluctuations. This is great when you want to shine a light on the long-term movements in your sales data.

On the flip side, a lot of businesses have more complicated sales patterns that call for more sophisticated models. Exponential smoothing models, for instance, are appropriate for handling data with clear seasonal changes. These models put more emphasis on the latest observations, which lets them capture evolving trends.

For even trickier situations, you might want to explore some other projection methods, like:

• ARIMA (AutoRegressive Integrated Moving Average) models, which can deal with a lot of different patterns and are especially handy for short-term forecasts.
• Machine learning algorithms, like neural networks, which can find complex patterns in big datasets.

Bear in mind that choosing the right model also depends on how practical and understandable it is. A complex model might give you slightly better predictions, but if it’s hard to explain to your team or tough to incorporate into your business, then it’s better to opt for a simpler model.

5. Forecast Generation

Creating a forecast is the end goal of all the work. This step lets you convert your past sales numbers into predictions about the future. It’s when your chosen statistical model takes all the patterns and trends you’ve spotted and combines them.

Forecast generation includes a few important steps:

• Putting your historical data into the model you’ve picked.
• Using the model’s algorithms to spot trends.
• Creating predictions for future timeframes.

While your model is processing the data, it considers factors, such as:

• Long-term growth or decline trends.
• Seasonal variations.
• Cyclical behaviors.
• The effects of known events or changes in business plans.

What you get from this process is your forecast – a detailed overview of expected sales over a certain future period. Depending on how detailed your input data is and what your business needs, this forecast could show predictions daily, weekly, monthly, or quarterly.

If you wonder how to forecast sales in Excel based on historical data, you can use Excel’s Forecast Sheet feature. You can adjust parameters like the forecast end date, confidence level, and seasonal patterns. If you want more hands-on control, use functions like FORECAST.ETS() or FORECAST.LINEAR() straight in your sheet.

6. Forecast Refinement

This stage recognizes that while historical sales data helps set the groundwork for predictions, the future isn’t just a replay of the past. Refinement means you’re constantly updating your forecast.

The whole process involves a few tasks:

• Regularly checking actual sales against what you forecasted.
• Figuring out where things don’t match up and exploring why.
• Bringing in new data whenever it appears.
• Changing your forecast based on fresh insights.

As you work on refining your forecasts, you’ll probably run into different situations:

• Surprising market changes.
• Shifts in your product lineup or pricing strategy.
• New competitors emerging in the market.
• Economic ups and downs.
• Changes in consumer habits or preferences.

All these factors can impact your original forecast accuracy, so refining it is critical to keep it relevant and trustworthy. Plus, forecast refinement is a great chance to learn and get better. It allows you to reveal some valuable insights into your business operations and how well your forecasting methods work.

Also: How to Calculate Wholesale Prices & Profit Margins

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How to Forecast Sales for a New Product with No History

If you’re about to launch a new product line, you may also wonder how to forecast sales for a new product with no history. This situation can be tricky since the usual forecasting methods may not work here. There are numerous strategies you can use to create a solid sales forecast for your new product:

1. Conduct solid market research to understand who your audience is and what they might want.
2. Look at sales data from products similar to yours or from the same category in your industry.
3. Check out how similar products in your own lineup sell if you have any.
4. Use the Delphi method to get and combine insights from experts in different departments.
5. Come up with best-case, worst-case, and most realistic scenarios for your sales forecast.
6. Think about outside factors like the economy, seasonal trends, and market competition.
7. Create a system to monitor and update your forecast as you get actual sales data.

Forecasting for a new product is a process you’ll also keep refining. Your first forecast will rely on assumptions and indirect information. Further, it will get more precise as you collect real sales data and get feedback from the market.

Forecasting sales using historical sales data is a useful tool for planning and making decisions in business. There are six main steps in this process: collecting data, cleaning up this data, spotting patterns, picking a statistical model, generating forecasts, and refinement.

While historical data offers a good basis for your predictions, it’s vital to stay flexible and adjust your forecasts when new information appears. Whatever way you go, effective sales forecasting requires a combination of analysis and the ability to adapt to changes in the market.

And while you’re in the process of refining your forecasts, it’s time to find some trustworthy suppliers to meet your projected demand. Wholesale Central is the top B2B directory for wholesale suppliers and products. With a huge selection of vetted wholesalers, importers, distributors and manufacturers at your fingertips, you can easily find the perfect suppliers to meet your sales goals.

Start exploring Wholesale Central today and unlock endless opportunities to grow your business!

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