Season’s Greetings! We hope you’re all enjoying the festive period and that work is winding down nicely for a few days of extravagance. But please spare a thought for one person whose job only gets harder at Christmas - Santa. Or, at the very least, the countless retailers for whom Christmas is the busiest time of the year.
For retailers, the busy period of Christmas brings with it a number of business challenges, and all of these issues essentially stem from not knowing just how busy things will get.
Fortunately, retailers (and, we assume, Santa), have an ace up their sleeves to help counteract this uncertainty - predictive analytics. In this article, we’ll specifically look at how and why retailers use demand forecasting, a branch of predictive analytics, to help them prepare for the Christmas rush.
What is Predictive Analytics?
Predictive analytics is a field of data science that uses past and current data to estimate future trends. It combines multiple disciplines of data science, such as data modeling, machine learning, artificial intelligence (AI), and more to build accurate estimations of future trends and events.
What is Demand Forecasting?
Demand forecasting is the discipline focused on trying to predict consumer demand for particular products or services. This generally entails looking at specific data sets regarding sales and coming up with informed estimations of future trends.
The concept of demand forecasting can be very useful to almost any organization when deployed appropriately. This, coupled with the fact that the technology behind it is becoming ever-more accessible, means it is being used with growing frequency.
For retailers that are preparing for upcoming periods of high demand, such as Christmas, demand forecasting can be absolutely crucial in helping to determine staffing levels and the ordering/production of sufficient stock.
The Role of Predictive Analytics in Demand Forecasting
In essence, demand forecasting is simply predictive analytics applied to a very specific context. Being so closely linked, demand forecasting involves using many of the same techniques such as machine learning and AI.
While some form of demand forecasting would technically be possible without employing the modern statistical techniques of predictive analytics, it would be very difficult to use it effectively at scale. Many retailers operate across multiple countries, serve millions of customers, and stock huge product lines, so any predictions need to take into account a vastly complex range of factors.
This is where modern analytics, powered by AI, become indispensable. To analyze the data sets produced by large-scale retailers, automated solutions are required. The latest analytics software is capable of automating data collection, data categorization, predictive modeling, and even provides prescriptive suggestions on the best courses of action to benefit a business.
How Does Demand Forecasting Benefit Retailers
Improve Customer Experience
Something that all retailers want to avoid at all costs is running out of a product while there is still a demand for it. This results in unsatisfied customers, not to mention the direct loss of profits. Demand forecasting can massively reduce the likelihood of understocking, so more customers will come away satisfied. Customer satisfaction is crucial in sustaining a strong reputation as a business and it also encourages repeat custom.
One of the most significant impacts that demand forecasting can have on a retailer is to reduce wastage. In the past, retailers were forced to rely on inaccurate forecasting and a large amount of guesswork. This meant that it was common to overproduce or over order certain products which then failed to sell. Modern demand forecasting, driven by machine learning, and enormous data sets, has helped to reduce this wastage. Less waste means more profit for companies, as well as a reduced environmental impact.
Retailers that incorporate demand forecasting into their operations are able to greatly improve their overall efficiency. Much of the work related to predicting future demand would have previously required a lot of human input, such as market research and surveying, just-in-time purchasing, and so on. With large portions of this automated or made redundant, companies can essentially get more insight with less input, freeing up time for staff to focus on other important tasks.
Does Demand Forecasting Have Any Limitations?
Demand forecasting will never be able to predict the future with 100% certainty. Its accuracy depends on many factors, such as the size of the data set, the number of variables, and how far in the future you are looking. But this shouldn’t take away from its usefulness to retailers.
The amazing thing about demand forecasting, and analytics in general, is that it is only getting more accurate with time. The more relevant data is fed into the systems, the better it gets, so it’s advisable to invest as early as possible to ensure the most accurate results.
Here is a recent case study of how our team was able to provide granular forecasts for a leading telecom provider in Germany, to better allocate SKU level handset inventories, increasing the sell-through rate by 18%, minimized purchases of low-demand handsets and reducing inventory cost by 7%.
Want to Upgrade Your Retail Business?
Retail is all about understanding your audience, and demand forecasting is an excellent solution to help achieve this. At Lynx Analytics we provide cutting-edge demand forecasting solutions that are tailored to your business, along with a wide range of other solutions to give your organization crucial insights to grow your business.
Our experts will work with you to develop the perfect solution for your challenges, utilizing the latest in machine learning and AI technology .
Talk to one of our retail solution experts to get started today.