Although instability is par for the course for retailers, having some idea of what future sales could look like can ensure your customers can find the product they are looking for when they want them. By implementing retail forecasting can not only improve the odds of a customer buying from you over a competitor, but can help you cut costs, increase sales, and optimize your supply chain.
What is Forecasting?
If you haven’t come across this term before or just need a refresher, Forecasting (or Demand Forecasting) describes a suite of analytical tools used to predict future demand. These tools often rely on data to look for patterns and trends that can inform what the future is likely to look like.
For a deeper understanding of just why you need Demand Forecasting, head here.
Forecasting for Retailers
Generally, retailers can use forecasting to make all kinds of decisions across key areas of your business, such as:
Planning advertising and marketing campaigns
Budgeting and financial planning
Whether you are a brick-and-mortar or ecommerce retailer, there are some universal benefits and challenges that come with adopting Demand Forecasting into your business.
Accurately predicting future demand can be a boon for retailers, as well as their customers and stockists. If you know how much of a given product you’re likely to sell you can send orders to stockists well in advance of a sales period (and avoiding last-minute orders) can ensure that they have the materials needed to fill orders, which in turn ensures that customers can find the products they’re looking for when they need them.
There are plenty more benefits where these came from, which you can read all about here.
One of the biggest challenges that come with predicting demand is accuracy. This is primarily based on the quality of the data that you integrate into your forecasting model, in a phenomena known as the ‘Garbage In Garbage Out’ effect (or GIGO for short). One way to minimize the influence of this phenomenon on your accuracy is ensuring that your data is accurate, free or errors, and stored in a central, accessible location to avoid discrepancies.
Another way of ensuring your forecasts are accurate is to integrate Machine Learning into your models. These methods take and compare the output of a particular forecast against a measure of truth, adjust any calculations or parameters, and test to see whether these changes improve the accuracy of the forecast. Plus, Machine Learning models are multivariate, meaning that they can consider multiple factors that influence demand - such as internal and external factors - and they can capture recurring demand patterns.
The move towards online (and socially-distanced) shopping has meant that more retailers are offering multiple ways of purchasing products. Managing in-store, online, and even click-and-collect options also puts more pressure on retailers to ensure that products need to be available and orders should be fulfilled just as quickly across these channels so that customers enjoy the same customer experience.
To take some of the pressure off, retailers should factor in the differing demand patterns of each channel and be able to separate forecasts by sales and fulfillment channels to ensure that stock is available across the board. Again, integrating Machine Learning methods can make this process that much easier, especially when coupled with a forecasting platform (such as ours). Remi’s Demand Forecasting platform uses autonomous Machine Learning (AutoML) to choose the best models for all your product portfolios and offers automated replenishment to automate your replenishment decisions, increase your revenue, and reduce backorders by 70%.
What Retailers Need to Forecast Demand
When it comes to Demand Forecasting, you’ll need some things you’ll need to know and have at your disposal.
To get started, you’ll need to know:
Which products you’ll be forecasting for and what stage of the product life cycle you’ll be forecasting for
What the forecasts are going to be used for - think replenishment, marketing, or budgeting
What kinds of data you’ll need to produce the forecast
Product Life Stage and Type
The forecasting methods that you can use will vary according to which life stage your products will be in during the forecast period. For instance, you might consider using Machine Learning time series models or qualitative analysis to predict demand for new products, whereas statistical time series methods could be used effectively for well-established products with stable demand.
Products can also be classified by their ‘velocity’, with high velocity products experiencing frequent demand and low velocity products having more sparse demand. Understanding the type of demand your products are likely to have can inform your decisions regarding marketing, inventory, and pricing, as well as helping you choose a suitable forecasting method.
Want to know more about how product-type influences your forecasting and what data you’ll need? Head here.
The purpose of your forecasts will inform what kinds of data to include and the level of accuracy. For example, a retailer using forecasting to decide whether to enter a new business area needs a less accurate estimate of market size than a retailer using forecasts for budgeting, while retailers looking to use forecasting to decide whether to implement a given marketing strategy should include the effect of that strategy on sales in the forecast, such as discounts, offers, or other special events.
Knowing why you’re creating forecasts in the first place can also help you decide which Demand Forecasting models to choose. Along with balancing the cost to implement the method, the time needed to use it, you should also consider the kinds of data that the method allows you to input and how accurate the forecast model can be.
To find out more about which forecasting methods could be right for your products, head here.
When it comes to business success, Demand Forecasting can take your decision-making to the next level. By understanding the demand profiles of your products and what you’ll be using forecasting for, you can make smarter decisions across the board. On top of that, integrating Machine Learning into your forecasting can improve its accuracy, assist with replenishment, and reduce your workload.
Want to learn more about Demand Forecasting? Head here for a breakdown of what Ecommerce retailers need to know to implement Demand Forecasting, or here to find out more about Demand Forecasting powered by Machine Learning. Once you’re a forecasting expert, why not have a read through of our case studies and find out how we’ve used demand forecasting to help increase stock availability and improve the accuracy of forecasts, or check out the rest of our blog for the latest AI reads. If you’re looking to implement Demand Forecasting for your business or improve your current forecasting methods, drop us a line here.