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How to Accurately Forecast Sales (and Maximize Profits) in 2024

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Weathercaster on TV pointing at red tags in different states, predicting sales across the country.

Top retailers know that one of the most important aspects of running a successful business is sales forecasting.

If you know how to accurately forecast sales, then you can:

    • Make better plans
    • Predict revenue
    • Optimize inventory levels (and reduce out-of-stock and over-stocks)

Your ability to accurately forecast potential demand is valuable to optimize operations and stay competitive in the marketplace.

Clearly, this is no easy task, especially if you’re dealing with millions of dollars of inventory on a daily, monthly, and yearly basis.

So, let’s explore the function of sales forecasting, starting with what it is.

What is sales forecasting?

Sales forecasting is the process of estimating the future sales of your products or services.

Said another way, sales forecasting is when you try to predict how many items you will sell within a specific time frame.

It involves analyzing data, such as:

    • Past sales
    • Seasonality
    • Market trends
    • Competition
    • Customer behavior

This data is analyzed and used to make informed predictions about how much your business is likely to sell in the future.

A sales forecast is used to inform your planning as you make decisions about inventory, marketing, staffing, and budgeting.

By having a good understanding of how much inventory you are likely to sell in the future, you can make more accurate predictions and avoid risks like running out of stock or overestimating your revenue.

However, you may run into snags along the way when trying to forecast your sales. Let’s take a glance at what some of those challenges are.

Challenges to forecasting sales

Retailers are never without challenges when creating a demand forecast.

Some of the factors presenting challenges to an accurate sales forecast include:

Data availability

Large amounts of data, including historical sales data, competitor data, consumer behaviour data, and economic data need to be readily available to analyze. If you want to maximize accuracy, you may also need detailed historical data on product prices by date (to account for the impact of prices on demand).

Consumer behaviour

Consumers are constantly changing their buying habits, and you need to stay on top of these changes. There is no foolproof way to do this, but some retailers have incorporated signals like social media trends to anticipate shifts in consumer behaviour.

Seasonality

Seasonal factors, such as holidays and short life cycles make predicting sales difficult because they either have disproportionately high spikes in demand or only sell during particular periods of time. Many holidays and events also move from year to year, making the reliance on purely historical data inaccurate.

Economic factors

Changes in the economy, such as a recession or inflation, can have a significant impact on consumer spending and should be factored into your sales forecast.

Inventory management

If you are relying on historical sales figures without accounting for your past inventory levels, you might be setting yourself up to repeat mistakes from last year.

For example, if you only sold 10 units of a product last year because you ran out of inventory, you could have sold 100 if it was in stock.

Competition

As always, you need to keep a close eye on competitors. Their sales strategies can impact your sales forecasts if they do something like lowering their prices for similar products that you carry.

Unforeseen events

You may face unexpected events, such as natural disasters or pandemics, which can significantly impact your initial sales forecast. While you can’t predict the unpredictable, a good forecast can react to these events dynamically as soon as new data comes in.

In reality, these challenges often take place simultaneously, and so you may be prone to make forecasting assumptions if you don’t know how sales forecasting is done or do it well.

So let’s talk about forecasting sales with something every retailer needs to do well–historical data.

How to forecast sales using historical data

Forecasting sales using historical data involves analyzing past sales trends and patterns to predict future sales.

simplified image of how sales forecasting is done

Here are some steps to follow when forecasting sales using historical data:

1. Collect and organize historical sales data

Gather data from past years and organize it by product, time period, and other relevant categories.

2. Analyze the data

Look for patterns and trends in your sales data, such as seasonality, changes in consumer behaviour, or the impact of marketing campaigns. Retailers sometimes use Excel, statistical analysis tools, or advanced software like AI-driven analytics to analyze large volumes of data, identify patterns and relationships, and make predictions.

3. Develop a forecast

Once you’ve analyzed your data, use it to develop a sales forecast for the upcoming period. This forecast should be based on the trends and patterns you’ve identified and should take into account any external factors that could impact sales.

4. Monitor and adjust your forecast

A sales forecast doesn’t have to be static. Once you’ve developed your forecast, you’ll need to monitor your actual sales data and adjust your forecast as needed based on any changes you observe.

So, we have seen how forecasting based on historical data is done.

Now is a good time to take a look at methods available to produce a sales forecast.

Methods used to forecast sales

Retailers can apply various methods to produce a sales forecast. While these methods all have their pros and cons, some methods are more accurate than others.

simplified image of how sales forecasting is done

Commonly used sales forecasting methods include:

1.  AI-driven analytics

The technological emergence of AI and machine learning in analytics has revolutionized sales forecasting by providing an unprecedented level of:

    • Speed
    • Scale
    • Granularity

This translates into a number of important benefits for you.

Pros

Increased profits 

The more accurate your sales forecast the better your profits. AI-driven analytics drives sales revenue by removing uncertainty from the sales process.

Algorithms highlight patterns in buyer behaviour as well as inventory sales performance right down to individual product SKUs.

When this information is accurate you can:

    • Manage cash flow more effectively
    • Anticipate future sales with more certainty
    • Plan your inventory investments
    • Ensure you have finances to cover obligations

Cost savings

Automating the sales forecasting process with AI-driven analytics can save time and resources, reducing costs. Instead of spending a big budget on a ton of manpower hours to manually analyze data, you can leverage AI algorithms to do the heavy lifting.

Faster decision-making 

In today’s fast-paced retail environment, making quick and informed decisions is essential to seize opportunities to make profits and prevent loss.

Advanced analytics provides real-time insights, enabling you to make faster and better-informed decisions based on data.

Scalability 

AI-driven analytics can handle ever-increasing volumes of data, making it ideal for your retail business as it grows to meet increased demand. The machine learning algorithms will also continually evolve to handle new and increased data without having to be directly programmed.

Competitive Advantage

If you use AI for your sales forecasts you are more likely to experience significant growth.

This is due to its ability to help you analyze customer data, recognize their behavioural patterns, and effectively market to the appropriate customers with the right products at the ideal time; giving you a competitive edge.

Cons

Technical expertise

Developing and maintaining an AI model requires specialized technical expertise, which may not be available in-house, so you might need to hire an outside expert or team; which can be costly. A simple way to circumvent this problem is to acquire a proven AI demand forecasting solution.

Data quality 

AI models rely on high-quality data, so inaccurate or incomplete data can affect the accuracy of the forecast. To resolve this issue, you can work with experts who have experience in areas like master data management and digital transformation.

2. Excel forecasting

This method involves using Excel or similar spreadsheet software to create a sales forecast based on historical data and other factors.

Pros

Accessible and customizable 

Most of the retail industry is familiar with using or has used spreadsheets to attempt sales forecasting.

Spreadsheets can be customized to fit specific business needs and integrated with other business data to provide a more comprehensive forecast.

Cons

Limited functionality

It’s not suitable for complex forecasting needs with thousands of data sets. It also relies on human input, which often introduces errors.

It’s not easily scalable for businesses experiencing significant growth or changes in their sales patterns.

3. Qualitative forecasting

This method involves making predictions based on expert opinions, experience, and judgment. It relies on qualitative data, such as subjective observations, opinions, and personal insights, to generate forecasts.

Pros

Considers influential factors

You can incorporate information that is not available in historical data, like customer feedback or market trends to add insight; which may lead to identifying new products or opportunities.

Cons

Subjective

Assumptions can be made and therefore influenced by personal biases. As a result, it’s difficult to measure the accuracy of the forecast. It is also costly and time-consuming.

4. Time-Series analysis

This method uses statistical models to analyze historical sales data and make predictions for future periods. It considers seasonal variations and trends in the data.

Pros

Scientific

The use of statistics makes it an accurate method. It accounts for seasonal and cyclical changes and can be used for more complex forecasting needs.

Cons

Expertise

It requires a strong understanding of statistical methods. It can also be time-consuming and require specialized software.

5. Market Research

This method involves researching and analyzing market data about consumer behaviour, preferences, and trends. This can be done through surveys, focus groups, or analyzing social media trends.

Pros

Understand your target market

You gain insights into customer needs, preferences, and behaviour. This information can be used to develop products and services that better meet customer needs and to create marketing campaigns that resonate with them.

Cons

Costly

Market research can be time-consuming and time is money.

Lacks agility

It may not account for sudden changes in market conditions.

Accuracy in sales forecasts increases profits

Sales forecasts are necessary for all retailers as they inform decisions about future inventory, budget, and goals and even help them make necessary adjustments when needed.

Illustration of someone presenting forecasting assumptions

The list of sales forecast methods above demonstrates that there are various ways to forecast sales–all having pros and cons as well as delivering various levels of accuracy.

Ultimately, what you need to keep in mind is the higher the accuracy of your sales forecast, the higher your potential to maximize profits.

A high-accuracy sales forecast based on data provides you with:

    • Detailed information
    • Clarity
    • Insights

So, you can make better data-driven decisions around planning, merchandising, inventory management, price optimization, and more–leading to cost savings and increased profits.

In our modern retail world, among all the sales forecasting methods to choose from, leading retailers are turning to AI-driven analytics as their preferred sales forecast method as it delivers the highest accuracy to date, with the most benefits.

If you want to learn more about sales forecasting and how you can do it better, contact our Team and ask for a Demo.

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