Ecommerce Product Demand Forecasting

Have you looked for a particular product online, found that it’s out of stock, and looked elsewhere? Aside from shopping around for the best deal, customers have also come to expect products to be in-stock and ready to ship (without a hefty shipping fee). In order to meet these expectations, Ecommerce businesses need to find the balance between having enough stock on-hand (to avoid stockouts) with the risk of overstocking (to avoid being left with dead stock) . Either end of that spectrum results in lost or wasted revenue..


Luckily, Demand Forecasting executed at a product level can be leveraged to maintain this balance and even increase revenue (we’ll get into the details of this further in).


First things first, let’s break down what Demand Forecasting is.


What is Demand Forecasting?

For those who haven’t come across this term before, Demand Forecasting (sometimes inaccurately known as Demand Prediction) describes a type of data analysis that is used to estimate the demand for products for a particular period in the future. For a more in-depth explanation, head here.


Now that’s out of the way, let’s get into how Demand Forecasting can be used to forecast demand for products in an Ecommerce context.


Why should you use product-level Demand Forecasting?


While you might already be using Demand Forecasting at a broader level or in other parts of your business- including financial and sales forecasting - forecasting at a product level is crucial. Namely, product-level forecasts can be used to inform your other forecasts and can act as a source of truth for other products. These forecasts can help you understand and optimise your short-term replenishment plans and improve your inventory management overall. Additionally, understanding demand feeds into other business decisions, such as staff levels to avoid overstaffing - and wasted money on wages - and understaffing - which can lead to backed-up deliveries and unsatisfied (and vocal) customers.


Ecommerce businesses also have a wealth of data and data streams at their fingertips, including:


  • Product page views

  • Which products are featured on your homepage

  • On-site searches

  • SEO ranking


These data streams (and more) can be integrated into your Demand Forecasting models, and of particular efficacy with ‘low velocity’ products (i.e. those with sparse demand) such as whitegoods. By understanding what the demand for your products is likely to be, you can make more informed decisions regarding marketing strategies, inventory planning, and pricing. Demand Forecasting should also form an integral part of your product research to identify which products are your top performers and the optimal time. This is especially useful when you’re looking to:


  • Expand to new channels

  • Understand who your customers are and what they want


Let’s consider an up-and-coming online retailer selling several brands of shoes in a range of sizes. Stock comes from an overseas manufacturer, so lead time on orders are longer than those experienced by more mature retailers. Current stock also has to last until the next shipment comes in. Predicting demand for all of their products, the retailer can identify which ones will be the most popular - think specific brand, colour or even sizes - and prioritise ordering of those products over those that are predicted to be less popular. This would primarily be based on historical sales, but could include page views over a particular time period, and the number of conversions. To help make this process easier, Demand Forecasting software (such as our own) can integrate with Google Analytics and platforms such as Shopify to collect data and allow you to set-up Demand Forecasting for all of your products.


Other Considerations


One thing to consider when implementing Demand Forecasting is the length of time that you should forecast for. Generally, you’ll choose from:


  • Daily

  • Weekly

  • Monthly


For most Ecommerce retailers, weekly forecasting will be the most sensible. Daily forecasts can encounter issues with accuracy (unless you sell high volume products), and monthly forecasting is better suited to low velocity products or rare goods. Additionally, these low velocity products often require special models since the majority of methods and algorithms available are trained to make the safest prediction (i.e. a sale or two are unlikely to be predicted since the average is zero sales). This decision is also important for helping you choose which type of Demand Forecasting to use (which you can read more about here), since different methods are better suited to forecasting for particular periods.


Final Thoughts


The need for accurate planning and the ability to adjust to changes in the market is crucial for success. Demand Forecasting for Ecommerce products not only enable this, but is the first step towards ensuring the availability of products for customers (and in meeting customer expectations more broadly). And, Demand Forecasting can assist Ecommerce businesses that want to optimize their inventory management and mitigate the risks of over- and under-stocking, leading to reduced waste and (more importantly) increased revenue.


Not sure where to start? Lucky for you, our Demand Forecasting platform can help improve your forecast accuracy, resulting in the benefits mentioned above. Our platform comes with 22 different Artificial Intelligence models to choose from. Worried that your data won’t fit a single model? Never fear, our platform also comes with an Ensemble Method - a combination of different algorithms that agree on the best result for each SKU - and Automated Machine Learning (AutoML for short) options. The AutoML functionality iteratively searches Machine Learning models, chooses the right ones for your needs and tunes them. Plus, our Quality Assurance Team ensures that things don’t get out of hand and that you’re getting the best results for your business.


Want to find out more about demand forecasting? 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. Once you’ve had a look through those, why not check out our blog for the latest AI reads. Or, if you’re looking to implement Demand Forecasting for your business or improve your current forecasting methods, drop us a line here.