The growing importance of online retailing is changing the balance of power in the retail market. It is now hard to imagine an aspiring retailer who does not have an e-store. E-commerce poses many challenges in the area of sales management and back office. The ability to accurately predict the future, including demand and sales, was until recently a kind of super power, left to only the largest players in the market. Today, this capability is available to companies of all sizes.
Data-driven present and future of retail
Any changes in customer behavior, competitors’ offers and in the organization’s closer and farther environment have a significant impact on corporate decisions. The more uncertain the environment, the greater the risk. The greater the pressure on price and the smaller the margin, the less room for error. The more competitive the market, the greater the challenges for each of its participants.
The retail industry is among those facing challenges from all of the above areas. Companies focused on retail, with a certain scale of operation, are in a comfortable position, i.e. they can collect data describing a huge amount of customer behavior and business conditions. With the use of appropriate IT tools, it is possible to objectify decision-making. Automation of business processes, including decision-making, enables making decisions quickly – faster than the competition.
Efficient data processing is made possible through the use of advanced algorithms based on artificial intelligence and machine learning. Accurate and rapid prediction of the future is a key issue for business today, especially in the face of new challenges – broken supply chains, or shortages of raw materials and workers.
The use of Big Data technology, artificial intelligence algorithms and machine learning allows retailers to build competitive advantage, increase sales and reduce costs. Thanks to advanced data analysis, they can react faster and more effectively to changes in the market environment, caused, for example, by a pandemic or economic turmoil.
Michał Koziara, Chief Executive Officer 3Soft S.A.
AI available for retail businesses of all scales
Recent years have seen a very dynamic development of digital technologies. Particularly noteworthy, from the perspective of data analysis, is artificial intelligence and machine learning based on it. In retail, so much data is collected that the use of digital learning systems is very much possible and allows one to achieve satisfactory results.
The development of this field means increasing its availability. Today, advanced data analytics can be used by companies representing the midmarket, not just the big players. The budgets needed to implement AI/ML-based systems are smaller, and consequently the technology has become more accessible to medium-sized and growing companies. The Occubee platform, which allows sales and demand forecasting as well as automation of replenishment and demand management processes, fits into this technological megatrend.
It’s not just the largest corporations and multinational retailers that can take advantage of AI’s potential. Occubee is the best proof of this. We have created this platform primarily for medium-sized and developing retailers who have historical data to forecast future business events. The SaaS model makes it financially attractive and business efficient. In this way, we lower the barrier to entry and make it easier to implement AI in smaller-scale companies.
dr inż. Kamil Folkert, Chief Strategy Officer 3Soft S.A.
Business benefits of sales and demand forecasting using AI algorithms
According to McKinsey Digital, by forecasting demand based on AI models, lost sales due to inventory shortages can be reduced by 65% and warehousing costs can be reduced by 10-40%.
Analyzing customer behavior and historical sales allows merchandising stores so as to minimize out-of-stocks and overstocks. By acting proactively, based on forecasted economic events, companies can reduce the frequency of deliveries to stores, thus optimizing logistics, including human labor associated with picking goods in the warehouse and receiving deliveries in stores. Increasing the availability of products at points of sale also has a positive impact on the customer experience – it allows to meet the needs of customers “here and now”, counteracts customer loss, and supports brand loyalty.
Generating orders to suppliers based on demand forecasts allows maintaining optimal warehouse stock, which translates into reduced storage costs, increased turnover in the warehouse, and, therefore, reduced allocated capital. With no negative impact on sales. Forecasting demand in the long term allows better planning and more accurate decisions, including strategic ones. An example could be renegotiating contracts with suppliers in view of more predictable sales, and, therefore, better planned orders. Especially in the wake of changing market conditions, including the impact of the COVID-19 pandemic, the ability to make faster and more accurate predictions becomes particularly important.
It’s also worth noting that AI/ML-based IT systems enable the automation of business processes while maintaining control over their progress. This allows applying the standardized and professional approach to the entire business, even for thousands of products on offer and hundreds of points of sale, and on a daily basis, taking into account the specifics of a given product and a given store. By entrusting repetitive and labor-intensive tasks to artificial intelligence, employees gain time and space to address key challenges based on expert knowledge. For instance, challenges related to expanding the company’s offer, expanding into new markets, or launching new sales channels. This allows them to react efficiently in critical situations that are important to the business.
