Demand forecasting – Part 1: [.blue]Basics & benefits to eCommerce businesses[.blue]

February 6, 2023

min read

No matter whether your brand is in the start-up stages or experiencing rapid high growth, demand forecasting is a critical aspect of managing an online business.

Without knowing what to expect in orders you might receive in the coming weeks or months, it’s hard to make important business decisions. These decisions can range from when to replenish your inventory, to what budget should be allocated to develop, launch, and promote new products. That said, forecasting how many orders you will receive over a given period is difficult to get right – brands that do benefit from a healthier bottom line, whilst those that don’t struggle to win customers and stay competitive.

What is demand forecasting?

Demand forecasting is the process of predicting the future customer demand for a product or service. In eCommerce, demand forecasting typically relates to how many orders a brand is estimated to receive within a specified timeframe like the days and weeks leading up to Black Friday or Christmas. This information helps eCommerce brands make more informed decisions regarding manufacturing, product development, pricing strategies, marketing, operations, staffing, inventory management, and various other areas of business. 

Perhaps most importantly, demand forecasting enables online businesses to optimise their inventory by ensuring they have enough stock on-hand for peak trade periods but avoid overstocking unnecessarily when sales are expected to dip. Failure to optimise your inventory can have disastrous consequences in the form of stockouts and dissatisfied customers – not to mention overstock eating into your profits, with your business taking a further financial hit if products require disposal before they can sell. 

Unless your business has a crystal ball, demand forecasting will never be 100% accurate. However, the more accurate you can be, the better chance you stand at improving supply chain efficiency, reducing operational costs, driving sales, and providing a better fulfilment experience (FX) to customers. 

5 common demand forecasting methods

There are various methods that eCommerce businesses can follow to forecast demand – all of which leverage data and analytics over specific periods of time. Here are 5 common demand forecasting methods:

  • Time series analysis uses historical sales data to forecast future demand. It’s based on the assumption that past demand patterns will continue in the future. For example, time series analysis may show an eCommerce fashion brand that sales of their winter coats tend to increase in the months between October and December each year.
  • Causal forecasting uses external factors such as product promotions, weather events, and market trends to predict demand. This method is useful for eCommerce businesses that sell seasonal products or whose sales may be impacted by external factors beyond their control. A retailer that sells toys and games, for instance, may see orders for paddling pools increase in the summer months. 
  • Machine learning uses algorithms to analyse and learn from historical demand data to make predictions, which is why it’s particularly useful for businesses that hold large amounts of data and experience complex demand patterns. An online grocery business might use machine learning to predict demand for produce based on factors such as current consumer spending habits and purchasing behaviour, as well as general market conditions. 
  • Regression analysis relies on identifying the relationship between demand and one or several independent factors to forecast demand. These factors can include product information, marketing data, and consumer behaviour, amongst others. This makes regression analysis useful for businesses that want to understand the impact of specific factors on demand. A beauty and cosmetics retailer might use regression analysis to predict demand for sunscreen based on factors such as weather conditions and upcoming holidays.
  • Croston’s method is specifically designed for businesses that experience intermittent demand (i.e. demand that is unpredictable and occurs sporadically, which is common for brands that specialise in seasonal products). It uses the historical data of when demand occurs, in addition to the volume of demand in order to make sales predictions.

Factors that can impact demand forecasting

It’s important to remember that demand is a fickle beast – a product that might be selling extremely well one week may flop the following week, especially in the fast-changing world of eCommerce. Many online brands have experienced these dramatic peaks and falls in demand over the course of the COVID-19 crisis, when the average online share of total spending rose sharply from 10.3% in 2019 to 14.9% at the peak of the pandemic, and then fell to 12.2% in 2021

The reality is that there are many factors at play when it comes to demand forecasting, and it’s important for online businesses to take them into account if they are to be as accurate as possible with their predictions and maximise sales. Some of the factors that can impact demand forecasting include:

  • Seasonal trends
  • Marketing and promotions
  • The types of products you sell
  • The lifecycle of your product(s)
  • The geographical locations of your supply chain and prospective customers
  • Competitor activity (e.g. price changes, product launches, promotions)
  • Economic factors (e.g. inflation, interest rates, consumer spending patterns)
  • Customer preferences (e.g. changing tastes, needs, values)
  • Advances in technology

Demand forecasting in practice

The scenarios below illustrate how eCommerce brands can put demand forecasting into practice, no matter whether the business retains a conservative growth plan or is looking to significantly and rapidly scale up. 

Scenario #1: [.blue]Conservative growth[.blue]

An established sports nutrition brand is looking at the sales trends from the previous January in order to optimise its inventory for the coming January, which is when there is likely to be a peak in consumers hitting the gym. The team looks at sales for specific products, including meal replacement shakes, protein powders, and capsules.

The data shows that the brand experienced a profitable sales period. However, one of the brand’s biggest competitors recently launched a new range of weight loss products, which are being heavily promoted through social media ads and influencer endorsements. The team are unsure how this campaign will impact demand in January and whether current or potential customers will buy from their competitor as a result.

Nevertheless, more and more consumers are taking up weight loss activities, meaning the brand is still growing by an average of 3% month-on-month since their competitor’s new product range was launched. 

The team plans to offer attractive promotions and increase their ad spend across multiple sales channels in order to undercut their competitor and position themselves as the go-to sports nutrition company for weight loss products, thus boosting sales and revenue. Their demand forecast projects an 8% increase in sales from last January.

Scenario #2: [.blue]High growth[.blue]

A fast-growing subscription box business currently has 10,000 subscribers. Each month, their subscribers receive a mystery book, alongside small collectible items ranging from stationery and art prints, to mugs and candles. Based on their subscriber waitlist, past sales data, upcoming marketing activities, and current industry trends, the brand expects to take on an additional 5,000 subscribers next year. 

As the team plans its subscription boxes 3 months in advance, they are currently holding 150,000 units of stock across 15 SKUs in preparation for the next 3 months of orders. On this basis, they restock their inventory by SKU level at a rate of 90 days. Given the brand’s demand forecast, however, the average number of units the business stocks is expected to increase next year, although their reorder rate will remain the same. This means that in Q4, the brand will need to order 225,000 units of stock across 15 SKUs if they are to start meeting next year’s demand.

The company plans to continue growing at this pace, which is why they are exploring whether to outsource fulfilment to a 3PL provider to ensure they keep up with demand, whilst maintaining a consistently high-quality fulfilment experience for customers. Working with a 3PL provider like Zendbox – who has extensive experience in helping fast-growing businesses scale – could be all the brand needs to meet its strategic goals.

How to forecast demand in 4 steps

Now that you know how important it is for eCommerce businesses to forecast demand, how can you start to do it more accurately? Here are four steps you can follow: 

1. Define your objectives

Demand forecasting is a pointless exercise without knowing exactly what you are trying to achieve, so it’s important to set clear objectives before you start. Then choose a timeframe, the specific product(s) you want to look at, and whether you are forecasting demand across all your customers or a particular subset.

2. Collect and record data

Start recording data to inform your forecast. Although the data you collect will depend on the forecasting method you choose, the more you gather, the more accurate a forecast you can make. Historical sales data is useful, but you can collect data from other sources too, including promotional campaigns and market research. 

3. Measure and analyse data

Once you’ve collected the data, you can measure and analyse it manually or using automated forecasting tools in order to draw some conclusions. Either way, be sure to review your findings as much as possible to identify any notable patterns or trends that could influence your forecast whether positively or negatively. 

4. Make ongoing adjustments

Leverage your forecast to make adjustments within your business in order to achieve your goals. For example, if you forecast a demand increase for a particular product based on current market conditions, increase your inventory so you have enough stock on-hand to meet demand whilst ensuring unnecessary overstock doesn’t eat into your bottom line. 

What does the future hold for your business?

Demand forecasting is all about predicting the future – albeit without a crystal ball! Getting it right means you can make more informed decisions about your business and ensure it is better positioned to meet or exceed your customers’ demands, thus driving revenue and growth. Check out part 2 of our demand forecasting blog series to find out how you can use simple Excel formulas to forecast future order volumes.

If you’re looking for a reliable eCommerce fulfilment provider to help you improve demand forecasting and deliver the online shopping experiences your customers deserve, contact Zendbox today.

Micah George
Marketing Specialist at Zendbox

Micah assists in developing and implementing innovative marketing campaigns that promote the products and services at Zendbox. She also produces articles, eBooks and other useful resources to help online retailers optimise their eCommerce operations and grow their business.

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