Growing competition, the development of multi-channel sales, global and local trends or logistical requirements are just a few of the many challenges faced by the home décor industry. However, artificial intelligence and Machine Learning come to the rescue, facilitating business decisions and turning challenges into growth opportunities.
The home décor industry has experienced rapid growth in recent years. According to Research and Markets, the global home décor market reached nearly $650 billion in 2020. This is the result of social factors, such as more affluent societies and investment in real estate. The wide range of housing on the market and thus more frequent moves are also contributing to the sector’s growth. In addition, more than half of the world’s population now lives in urban areas, which is not without its impact on demand for home furnishing products.
Hand in hand with the growing market potential, however, is an increase in customer expectations and demands, and this makes it necessary for companies to respond quickly and flexibly to new trends. This, in turn, raises challenges in terms of forecasting sales and demand, and ensuring adequate product availability in stores.
How do artificial intelligence and Machine Learning address the challenges of the home décor industry?
The challenges faced by the home décor industry are cross-cutting and diverse. On the one hand, demand is shaped by trends, which are, as a rule, volatile. Among them can be distinguished micro-trends, which usually close within one season. Others are macro-trends, which affect the market for up to several years. The display of large-scale goods in brick-and-mortar stores also causes difficulties. Related to this is also the decision to choose a sales channel for specific products or product categories, taking into account the very different shopping paths of customers. The industry is also affected by logistical challenges, the state of the construction industry, and not infrequently by… weather.
Artificial intelligence and machine learning are tools that home décor companies can use to make strategic and operational decisions. They are used in many areas, including analyzing trends, forecasting demand and sales, optimizing how orders are generated to suppliers. With their help, it is possible to track changes in customers’ purchasing preferences, optimize product offerings, manage stock in stores and warehouses cost-effectively and operationally.
Michał Koziara, a co-founder of the Occubee platform
How to accurately manage lead times and mimimum shipping requirements?
Furniture, like interior design items, require long production times. Decorative goods, on the other hand, are often ordered from geographically distant locations, where they must first be manufactured. Lead times in the home décor industry can range from one to more than six months. In addition, shipping requirements must also be kept in mind. It is common for manufacturers and distributors to impose minimum production requirements. Then – even though the retailer needs specific quantities of goods – he may be “forced” to supplement the order so that it can be fulfilled.
While AI does not directly have an impact on shortening the supply chain, it does make it possible to forecast demand for product categories at both the market and sales channel levels. This information can be successfully used to decide what additional products should be ordered so that the containers are completely, but also optimally from the perspective of sales opportunities, filled, the unit cost of transportation is as low as possible, and the stock in the warehouse matches the projected demand.
How to meet the ROPO effect and plan omnichannel sales?
The home décor industry is ruled by the ROPO effect – research online, purchase offline. The initial research and search for interesting products is done online, but the final purchase is made in the showroom, after seeing the selected model, checking its functionality and workmanship. Very often retailers selling both in the online and offline channels have a much wider offer in the online store. The question, then, is how to ensure optimal availability and presentation of goods in the offline channel, while having a much wider assortment offered in the online one?
Artificial intelligence, particularly the learning aspect, enables a dynamic approach to ensuring optimal stock levels for sales in each channel, rather than – as is sometimes the case with expert management – setting “rigidly” the number of available products in each sales channel.
Michał Koziara, a co-founder of the Occubee platform
Problematic display of large-size goods
In the traditional sales model, every retailer faces limited display space, especially if there are a lot of products on offer. This space must be utilized perfectly for a wide assortment not only within the store, but also within the sections defined in the stores.
So a legitimate question arises: which products to expose in the store space? The answer is usually far from obvious. All the more so if you are looking for the golden mean between the display of goods, the desire to show a wide and deep assortment, and the arrangement of a customer-friendly store space.
However, it is possible to optimally replenish points-of-sale and avoid out-of-stocks. It can be done using AI and Machine Learning models which, based on historical sales data, as well as a number of other internal and external factors (including marketing campaigns or weather forecasts), generate demand forecasts, which can provide the basis for making the right business decisions.
The answer to customer needs is in the data
Generating sales and demand forecasts based on machine learning algorithms, taking into account the nature of sales in the home décor industry, allows you to make accurate business decisions and thus build a competitive advantage. As a result, you can achieve the business goals of minimizing inter-store transfers, increasing sales and reducing costs.
Combined with expert knowledge, data-powered artificial intelligence algorithms and the automation of business processes are part of the technological megatrends that involve using the full potential of data to achieve better business results. Additionally, economies of scale cause even relatively small improvements in operations to significantly impact the financial performance and value of businesses.
Michał Koziara, a co-founder of the Occubee platform
