In an ideal world, running a business would be as easy as purchasing the perfect amount of stock, selling it at a price that customers are willing to pay, and generating a profit based on margin.
In this less-than-ideal world, this task is complicated by rarely being 100% certain of how many units (of whatever makes up your business offering) will sell. Luckily, continued advancements in Demand Forecasting can turn guesstimations of demand into informed predictions.
First things first, what is Demand Forecasting?
Beginner’s Guide to Demand Forecasting
If you haven’t come across this term before, head here for an in-depth explainer of all things Demand Forecasting (we’ll wait here until you return. Ready? Let’s continue). For those wanting a quick refresher, Demand Forecasting (often confused with Demand Prediction) describes a suite of analytical tools that use data to estimate future demand. Depending on your needs, finances, and the driving force behind why you’re forecasting demand in the first place, there is sure to be at least one type of forecasting that will suit you.
Let’s get into why Demand Forecasting is necessary.
While making decisions on the fly might work for some people, once a business reaches a certain size, success will depend on an understanding of the market, strong systems, and valid reasoning. For instance, having an understanding of what the demand for your products is likely to be in the near future can help you identify changes in customer behaviour - whether that’s in response to your most recent price change, a stock-out of a competitor’s product, disruptions from major crises such as COVID-19, or seasonal changes. This, coupled with the ability to predict upcoming sales from historical data and current market trends, can help you formulate realistic production and price policies and prepare for upcoming changes. Meeting these changes means you have the right product at the right quantity, ideally at the right price - and this lays the foundation for success.
Mid- to Long-Term Decision Making
Medium and long-term forecasting - think 3-6 months and greater than 6 months, respectively - can be used for strategic and big-picture decisions - from planning marketing and sales efforts to financial and investment strategies. The ability to estimate potential revenue and costs from forecasted demand can be useful for budget preparation, determining production capacity, or planning machinery replacement schedules. And, while plans can change (2020, anyone?), Winston Churchill had the right idea “Plans are of little importance, but planning is essential.” An informed understanding of the future demand of your products can help you evaluate the performance of your products, which could lead to decisions to improve product quality or invest in advertising and marketing to elevate demand for less popular products. Forecasting is equally helpful in telling you what not to stock, which leads into what marketing promotions to avoid, when to give staff time off, etc.
All in all, Demand Forecasting is an incredibly handy tool for making planning decisions across all aspects of your business.
The above benefits of Demand Forecasting are generally useful across industries. Now, let’s consider the benefits for specific businesses. As examples, let’s consider a brick-and-mortar business selling homewares, and an ecommerce retailer selling stationery.
The brick-and-mortar retailer can use historical sales, price, and promotion data to predict demand for their products over a particular time period. Additionally, the retailer can use Demand Forecasting to identify seasonal peaks - think weekends and the lead-up to major holidays - and troughs in demand, as well as other patterns that human eyes alone would struggle to detect - or detect only through gut feel (which is prone to inaccuracies). Able to glean all of this information from their forecasting, the empowered retailer can:
- Adjust stock levels and ordering - if 350 throw blankets were sold in autumn and only 5 were purchased in summer of last year, it makes sense to order more for the peak period. This can also be integrated into marketing strategies to elevate demand even more.
- Stratify products by popularity - if the forecast predicts that 200 green pillows and only 3 red pillows are likely to be sold in the upcoming sales period, the retailer might consider ordering the green pillows more frequently than the red pillows (or stop stocking them altogether) to avoid being left with dead stock.
- Introduce new products - looking back at historical sales, the retailer might find that the demand for new lighting products tends to climb rapidly, while the demand for new wall art starts off slow and gradually increases. And this information can be used for predicting the demand for new products in each category, affecting the amount of stock that needs to be ordered.
- Optimize staff planning - whether that involves hiring more staff during the holidays or simply rostering on varying numbers of people, the retailer can save money by not overstaffing and maintain customer satisfaction by ensuring that there’s enough staff on hand.
In much the same way, the Ecommerce retailer can utilise Demand Forecasting to identify seasonal trends across their product range, prioritise ordering of popular products, and plan the introduction of new products. But, the Ecommerce retailer can integrate additional, unique data sources into their Demand Forecasting models - think everything from page views and site traffic to SEO rankings and competitor pricing.
Unlike their brick-and-mortar counterpart, the Ecommerce retailer can also adjust prices with minimal hassle and use Demand Forecasting to implement dynamic pricing strategies just as easily. This involves adjusting prices based on site traffic, where higher prices are charged during peak periods - such as in the leadup to Christmas, Valentines’ Day, and other holidays - and are reduced again when traffic decreases. More than this though, current dynamic pricing is more nuanced than many have (largely negatively) experienced. By changing prices throughout the day - often minimal amounts - dynamic pricing captures greater value for you without offending the client, as they don’t notice a huge difference in price up or down. The logic behind this strategy assumes that, when traffic is higher, it is statistically more likely more customers are willing to pay that price than when there are fewer visitors to the site. Additionally, since this retailer is a small but rapidly growing business, their products are coming from overseas suppliers and, as a result, have long lead times (let’s say three months). By forecasting demand across their product categories - think pencils, pens, rulers, and pencil cases - and for specific colours and designs several months in advance, the retailer can spot their most popular varieties (which are more likely to run out first) and order sufficient stock further in advance (without overstocking). And, they can even negotiate better deals with their suppliers by avoiding last-minute orders. Plus, the Ecommerce retailer can use their forecasts to plan marketing strategies, including:
- Strategically feature certain products on their homepage in the lead-up to their peak demand periods or to elevate demand for low-performing and new products
- Identify and segment their customer bases - whether that’s based on their price elasticity, demographics, or other behaviour
And, in order to use the unique data sources available to Ecommerce retailers (such as those mentioned above), platforms (including ours) integrate Google Analytics and Ecommerce platforms such as Shopify to simplify the set-up of Demand Forecasting across the store.
If you’ve been following on so far, it shouldn’t come as a surprise that there is a real need for Demand Forecasting across industries and along the supply chain. Plus, the integration of Machine Learning into your Demand Forecasting models can further improve the accuracy of your predictions and enable your business to become more dynamic and quick to the jump when things change.
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.