Retail can be a volatile industry, so having an understanding of what future sales might look like can be crucial for success. That’s where retail Demand Forecasting comes in. And, as retailers face a shrinking margin for error, both online and brick-and-mortar retailers can utilise Demand Forecasting to cut costs, spend capital more effectively, and enhance customer experience to build loyalty.
What is Demand Forecasting?
Unsurprisingly, Demand Forecasting is all about being able to predict (or forecast) demand. This is usually performed using quantitative and qualitative methods, which you can read all about here. These methods consider historical demand - via historical sales or POS data, for example - and look for patterns, trends, and other behaviours that can help predict what future demand will look like.
For retailers, Demand Forecasting can be used across your business, including:
Budgeting and financial planning
Planning advertising and marketing efforts
Benefits for Retailers
The ability to accurately forecast demand can have a plethora of benefits across your business and supply chain. Two of the biggest benefits that come with retail Demand Forecasting are cost-effectiveness and enhancing customer experiences.
Let’s give them a closer look.
Knowing how much product you’re likely to sell means that you can reduce how much you spend on unnecessary inventory, which in turn can reduce money spent on storage of your products. Conversely, you can also minimize the risk of stock-outs and even negotiate better deals with suppliers by avoiding making last-minute (and expensive) orders.
You can also use Demand Forecasting to meet sales targets more effectively. For example, you might invest more in marketing and advertising when products aren’t meeting predicted sales, or replenish or cross-promote related products for those that have outperformed expected sales and run the risk of stocking out.
Enhancing Customer Experiences
We might be preaching to the choir here, but developing customer loyalty and ensuring that customers don’t need to turn to competitors is a priority for retailers big and small. Aside from ensuring that there is enough stock of your products when your customers want them, Demand Forecasting can also be used to make informed staffing decisions so that you have enough staff to field customer queries in-store and package and send products for timely delivery, but not so many that you are overstaffed and wasting money on wages.
Demand Forecasting can also be used to manage deployment of products across store locations and distribution centers to fill orders and give customers multiple options for picking up products or getting them delivered.
Considerations for Retail Demand Forecasting
Whether you are a building your first Demand Forecasting model or you’re a practised forecaster, there are some considerations you need to account for every time you want to predict demand.
These tie into the idea that there is a big difference between plain old Demand Forecasting and accurate Demand Forecasting. Accuracy (and usefulness) come from integrating internal and external data into your forecasting models. This data should include the variables that drive demand, such as:
Consumer behaviour and sentiment - the preferences and needs of your customers and their reactions to economic changes are huge drivers of demand, especially when these needs and wants change.
Demographic data - including everything from population demographics and household income and debt, this information can be useful for understanding your customer base, how they might change over time, and even how much disposable income they have.
Macroeconomic influences - what’s happening in your industry and the wider economy can have a big impact on your sales, and you can use this information to identify changes as they start to occur and develop contingency plans for anything from supply chain delays to global pandemics.
One way to do this is to integrate Machine Learning into your forecasting. Benefits include the ability to consider multiple variables that relate to demand, identify patterns and effects of external and unknown factors, and improve the overall accuracy of your forecasts.