Being able to accurately predict customer demand, and act on those predictions, is a key skill for any business. Yet, most businesses do not know how to do this correctly.
Retail businesses can be very difficult, as well, as there’s so much data to collect. If you’re a wholesaler, then much of the data doesn’t belong to you. If you’re a retailer, then you’re likely selling thousands of SKUs, making it hard just to collect data on them let alone analyze it in order to forecast demand!
But that doesn’t mean it’s impossible for retail businesses. There are things retail businesses can do to make sure that their demand is adequately forecasted. But how?
What Is Demand Forecasting?
Demand forecasting is how a business predicts how many of which produce or SKU customers will buy during a specified time period. Techniques for demand forecasting are wide ranging, dealing with historical data, competitive data, and predicts based on projected industry growth.
This process is essential for retailers, as figuring out what products are going to sell and how quickly they will sell will help maximize profits over the long run.
So how is demand forecasting done?
Common Techniques for Retail Demand Forecasting
Regression analysis: This purely statistical technique looks at the relationship between variables that affect demand. Then it draws a regression curve based on how the variables affect overall demand. By plugging values for each of those variables, it can produce an estimate.
Qualitative forecasting: This technique relies mainly on heuristics, expert opinion, and understanding of market conditions rather than statistical techniques. Because it doesn’t include numerical analysis, it doesn’t do a good job of producing exact numbers. However, it can find things that analyzing numerical data cannot, and could help retail businesses make leaps and bounds in understanding and forecasting their demand.
Time series analysis: This technique uses data over time to figure out how to model future values. It relies entirely on historical data and aims to use the past to tell the story of the future.
Important Considerations for Retail Sales Forecasting
Establish a baseline
To establish a baseline, use historical data from your business or, if you’re brand new, use competitive data based on the stage of the business. For example, take your previous year’s sales and see the change between this year’s sales and last year’s sales to date.
Understand your market
What is your customer like? How do they buy? Which sizes and colors do they prefer, and is that reflected in the SKU data? Which brands do shoppers in a specific category prefer? Understanding the answers to questions like these can help you forecast demand.
Select & Analyze KPIs
Forecasting demand requires that you take a look at certain KPIs. KPis such as GMROI (Gross Margin Return on Investment), shrinkage, stock turn, and sell-through rate can help you understand your demand.
Consider external factors
Not everything in your business is under your control. Supply chain shocks like the COVID-19 epidemic are difficult to predict, but other factors such as weather in a season or seasonal sales change can be easier to estimate.
This is where techniques like regression analysis can help estimate various outcomes. You could decide to combine multiple possible outcomes into a “Best case, Average case, Worst case” chart.
Use technology to your advantage
Technology is incredible, and there is a vast array of software available to help your retail business forecast demand. By centralizing your data and making as many processes as automated as possible, technology can help you collect and analyze much more data than before.