If 2020 was a good year for anyone, it was for those in the ecommerce space.
The space had been growing, but rolling lockdowns changed not just buyer preference, but for many it was the only way to procure anything. Of course, Ecommerce had been growing in the decades preceding, and had shaped itself as a space with some very strong market attributes that attract customers. Ecommerce retailers attracts these customers by meeting expectations for convenience, price, and availability - and those who do it best rely heavily on the power of forecasting.
Ecommerce businesses (at least, those that have achieved a reasonable size) can forecast demand to find the balance between guaranteeing products are in-stock and overstocking (thus avoiding being weighed down with dead stock). And, accurate forecasting can help you prepare for different kinds of demand (when integrated into your Demand Planning process).
What is forecasting?
This is episode 10 of our series but if you’re landing here from a Google search, don’t worry! If you’re a novice when it comes to forecasting, level up your knowledge by heading here (and make a speedy return). For those who’d prefer a quick recap, Demand Forecasting is a type of analysis that is used to predict future demand for a particular product or service. And, forecasting is just one of the crucial tools you need for successful Demand Planning, which you can find more about here.
Why does Ecommerce need forecasting?
Since Ecommerce retailers have to be dynamic in order to succeed, having accurate forecasts that integrate information as it becomes available can give you a much needed heads-up for when things are changing.
For instance, let’s consider an online retailer selling eco-conscious products - think reusable straws, shampoo bars, bags, and keep cups. Since this is an up-and-coming retailer, their suppliers are overseas and, as a result, their products have long lead times (let’s say two months). By forecasting demand for each product category - straws, cups, bags, and bars - and specific colours or designs several months in advance, the retailer can identify which product varieties are the most popular (and likely to run out first) and prioritise ordering these products over the less popular ones. While historical sales will be heavily relied on in order to predict demand, the modern e-retailer can look at site traffic and other data streams (which we’ll touch on in the next section) to enrich their understanding of their customers and what they are looking for. And, with this information at hand the retailer can:
Roster a sufficient number of staff in order to pack and deliver products to customers,
Plan marketing and promotional campaigns to drive demand for specific products
Plan buffer stock levels [link to Ecommerce demand planning and forecasting]
Predict demand for new products by comparing them to similar products currently being sold
Additionally, forecasting at a product level can be incredibly useful for informing the rest of your forecasts - think sales and financial forecasting - and can act as a source of truth for other products. Overall, any level of forecasting is handy for informing decisions concerning all areas of your business, including inventory management, pricing, staff planning, and marketing strategies (just to name a few).
Or, another way to look at it is that any businessperson worth their salt wants the greatest understanding of what the future looks like.
Advantages over brick-and-mortar retailers
Every retailer benefits from forecasting, yet there are some additional benefits for Ecommerce retailers derive. The leading two are:
Data - everything from product page views and onsite searches to SEO ranking and site traffic
Competitor pricing of similar products - handy to know when it comes to understanding short-term demand and predicting sales
These data streams can be included into your Demand Forecasting models with the help of software (such as our own). From here, they are integrated with data from Google Analytics and platforms such as Shopify to give you a strong understanding of the future.
It is important to note that the benefits of including these data streams come with the assumption that the data is accurate, relevant (meaning that it has an impact on demand), and error-free - this is not always the case, so you should anticipate some cleaning if or when you decide to proceed down this path.
While we won’t go into it in this article, the author also can’t speak more highly selecting a forecasting method that utilises multivariate data (i.e. it can consider multiple variables that affect demand). Methods that do this are generally powered by Machine Learning, which you can read more about here. These methods can further improve the accuracy of your forecasts and tend to be more reflective of real-life conditions than traditional statistical methods of forecasting.
In the competitive world of Ecommerce, Demand Forecasting is one of the foundational tools a retailer needs to succeed. With the help of unique data streams and AI, Ecommerce retailers can rely on their ability to accurately predict which and how many products will sell to optimize their inventory management and maintain the delicate balance between having sufficient stock and generating revenue (all the while avoiding over- and under-stocking). Knowing what future demand looks like allows you to make informed decisions about other areas of the business too, plus retailers can keep their customers happy (and coming back for more). I could keep going on about the benefits, but I feel I must have made my point by now.