Ecommerce can be a cut-throat industry. A plethora of competitors sell similar (if not identical) products. Customers expect your products to be in-stock and quickly delivered (with a small shipping fee). If you’re out of stock or late on delivery, a customer has no reason to show brand loyalty, but you also need to ensure stock is available without being left with dead stock at the end of any sales cycle.
It’s not all doom and gloom, however. The obvious benefits are there for business starting out no need for a physical storefront, means you can get started and get out easily if the business doesn’t play out as you hope it would. For the established business, Ecommerce has a number of powerful tools at its disposal. The focus of this article, and your best bet to balance out the risks outlined above, is demand forecasting. If you read no further, read this: integrating Demand Forecasting into your business is your best bet for success both in the short and long term. Besides avoiding over- and understocking your products, Ecommerce Demand Forecasting comes with a host of benefits (read on to find out just what these are).
What is Demand Forecasting?
Demand Forecasting refers to a suite of analytical tools used to predict the likely demand for your products or services for a particular period of time in the future (for a more in-depth explainer, head here). And, there are different types of forecasting methods depending on the kinds of data at your disposal, the goals that you want to achieve, and the price you’re willing to pay (or, perhaps it’s less about willing to pay and more about “amount of money one could spend before the accounts team went crazy).
The ability to identify patterns and trends in historical sales data makes Demand Forecasting integral to decision-making across business. For Ecommerce retailers, forecasting models can also use a wide variety of data points combined to create unique forecasts that are more accurate and reflective of real-world conditions. These include (but aren’t limited to):
These data streams can be leveraged to understand future demand in different ways. For example, competitor pricing and trends in site traffic can be used to optimize product prices (especially in the short-term), while page views can be useful for predicting demand for low-velocity goods that customers might look at multiple times before deciding to purchase.
Benefits for Ecommerce
When accurate, Demand Forecasting offers significant benefits, let’s take a look:
Reducing Financial Risk
Since Demand Forecasting involves looking for trends in demand and projecting them into the future, knowing what your customers want and are likely to want in the future can work as evidence for justifying budgetary decisions.Let’s consider two retailers that sell cosmetics and are looking to launch new products. By looking at historical data for existing products, you might find that initial sales for new products tend to grow quickly for one retailer and more incrementally for the other. From this, the first retailer could decide to order a larger amount of inventory while the second could choose to order significantly less stock. The first retailer might end up making a larger profit than the second, the latter also saves money by avoiding holding onto dead stock that they know won’t sell (and can save on warehousing costs).
Meeting Customer Expectations
Understanding how demand fluctuates over time allows you to boost inventory levels in the lead-up to peak periods of demand, reducing the risk of stock-outs. Plus, ordering in advance can help to improve relationships with your suppliers and help you negotiate better rates. Overall, having an idea of what future demand will probably look like can lead to better demand planning and lets you be more prepared for your next sales period.
In a similar vein, Demand Forecasting can help you optimize your stock levels. Specifically, identifying your high and low-performing products and knowing how much you’re likely to sell in the near future could lead you to reducing the amount of inventory on hand (whether that’s for specific products or across your portfolio if you tend to overstock), reducing warehousing costs and any potential losses that come with needing to get rid of dead stock whilst still carrying items that you need to have that also don’t shift in high volume (ie stock you are contractually obligated to always have in stock).
Aside from understanding the demand each of your products, product-level forecasting can be useful for:
A source of truth for other products
Optimizing short-term replenishment plans
Overall improvements in inventory management
Crucially, this kind of forecasting (just like other levels of forecasting) is helpful for decision-making. For instance, identifying seasonal peaks and troughs in demand can assist with staffing - whether that results in short-term increases in staffing in the lead-up to your busiest season or reducing staff during slow periods.
As with anything, there are some challenges that need to be dealt with when integrating Demand Forecasting into your business:
Method choice - when choosing a forecasting method (or two), the best choice varies depending on the needs of your business, the purpose of your forecast, the kinds of data available, and even the life-cycle stage of each of your products
Data quality - to produce accurate forecasts, you need data that is accurate, up-to-date, and relevant for predicting demand for a given product. For instance, while a human can reason that ‘chocalote biscuits’ and ‘chocolate biscuits’ refer to the same product, an algorithm is likely to interpret them as separate and predict demand accordingly.
Unexpected changes - since search ranking and social media have an impact on the visibility of your site (and, thus, on site traffic), unexpected changes in this ranking, such as in response to really effective SEO or a change in ranking algorithms, can really influence demand (like moving your storefront from a quiet side street to the heart of the CBD (or vice versa).
Supply chain management - while you might know which products are likely to be popular in the near future and should be replenished soon, this information can be ineffective if you don’t factor in lead times (especially when they are on the longer side).
Demand Forecasting is vital for the success of Ecommerce and brick-and-mortar retailers alike. For Ecommerce, utilising unique data sources to understand customer demand and tackling the challenges of meeting customer needs without a physical storefront are just two of the benefits that come with accurate demand forecasts.
If you aren’t already undertaking forecasting, or would like to increase your accuracy, streamlining your forecasting can be made even easier by integrating a Demand Forecasting platform (such as our own) into your strategy. Platforms like Remi AI use Machine Learning algorithms to help you comb through forecasts and find patterns within your demand more rapidly than a human analyst, in a fraction of the time, with a greater degree of granularity. Remi AI also integrates with Google Analytics, Shopify, and other platforms to collect data and make setting up Demand Forecasting for all of your products simple.
Want to learn 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.