Demand forecasting has played a pivotal role across various functions in retail, including supply chains, inventory management, and customer service, by forecasting customer demands, anticipating market trends, and so on. However, it has become exceedingly difficult for traditional demand forecasting methods to leverage the huge influx of data coming in from various streams (internal, external, and contextual) to generate accurate forecasts.
Demand, data, and customer perceptions are ever-changing, and retailers now need to leverage historical as well as real-time data, examine market conditions, and consider economic factors to ensure dynamic decision-making.
Considering this, businesses are increasingly taking note of the crucial role played by AI in augmenting these capabilities. In fact, research has revealed that 73% of leading retailers recognize the role played by AI&ML in demand forecasting, enabling them to prepare for and adapt to severe demand fluctuations when compared to 35% of other retailers. 
Let’s take a look at how this is playing out in real-world contexts, and the advantages AI offers for operational efficiency in retail:
According to a report, AI spending in the retail supply chain is expected to grow at a CAGR of over 45% and reach $10 billion by the year 2025. 
AI can augment the capabilities of traditional demand forecasting methods by considering multiple factors influencing demand, including weather patterns, geography, promotions, customer reviews, and market conditions. Following this, the impact of seasonal changes can be addressed and mitigated, with data-driven solutions resolving critical pain points, such as shipping delays, over-stocking, and under-stocking, for the retail industry. Accurate demand forecasting can therefore help retailers bridge the gap between supply and demand, optimize product replenishments, bolster revenue, and much more.
As inventory is one of the most significant investments a business makes, maintaining an optimized inventory is crucial to ensuring profitability. According to a study, retailers lose nearly $1 trillion in sales because of poorly managed inventory – both through overstocks and out-of-stocks. 
Overstocked products are often marked down and considered lost sales, whereas out-of-stock cases often lead to disappointed customers and missed opportunities. Reports reveal that markdown alone costs $300 billion in revenue, while misjudged inventory contributes to 53% of unplanned markdown costs for retailers. 
Optimizing inventory, therefore, is key to ensuring a consistent cash flow, increasing turnover, and reducing incidental costs incurred on holding/maintenance. With data-driven tools, variations in demand, the potential performance of new products, and relevant market trends can be adeptly identified to help generate accurate forecasts and update inventory. According to 47% of the respondents of a survey, AI can significantly impact inventory management, ensuring effective cost levels and helping them respond to customer demand better.  .AI-enabled demand forecasting solutions for inventory can therefore revolutionize the way retailers plan replenishments as well as stock and store products according to customer needs
The impact of retail promotions on customer behaviour is significant – personalized marketing efforts can help retailers gain $20 return for every 1$ invested.  Designing the right promotional events to attract more customers, and also having the right technology to analyze the impact of the event, therefore becomes essential for retailers to ensure better ROI.
However, 59% of trade promotions do not break even due to inefficient planning and overlapping events.  Further, they are restricted by traditional forecasting methods, which cannot analyse real-time data and account for sudden demand fluctuations. This is where AI-enabled forecasts offer a significant advantage: analyzing a range of factors such as customer behaviour, appropriate promotion types, competitor offers, and so on, AI&ML can help retailers maximize their marketing efforts, tailor promotions, and design optimal pricing strategies.
According to research, 86% of buyers are willing to pay more for a great customer experience.  Designing new ways to enhance customer experience and interactions, therefore, plays a vital role in ensuring retention and loyalty. The unavailability of new or relevant products at the right place and right time has proven to lead to poor customer experience, greatly accelerating customer churn. In fact, 32% of customers will turn away from a brand they prefer after a single poor experience, research has found.  However, with AI-enabled forecasts, customers’ shopping experiences can be significantly improved by ensuring the availability of the desired products at the right time. In this regard, a leading online retail company used AI-driven solutions to predict exactly where a product needed to be stocked such that it could be shipped to the customer as early as possible. The algorithm used examined every single available data point to enable anticipatory shipping, i.e., understanding future product demand efficiently enough to predict product requirement and stocking needs before customers could make purchase decisions. In this way, the business was able to offer one-day or even one-hour deliveries, resulting in improved customer experiences.  In addition, accurate forecasts can help retailers improve staff scheduling: estimating the fluctuations in demand by the month, day, or even hour and utilizing data-driven staffing rather than intuitive guesswork. Following this, staff can best engage with customers, cater to their needs, and solve customer concerns, enabling a superior customer experience.
AI has proven to be a significant driver for the retail sector, helping businesses enhance their capabilities not just in terms of demand forecasting and operational efficiency but also by augmenting end-to-end shopping experiences. In fact, a study by Gartner indicates that 43% of retailers have already enabled AI in their forecasting solutions,  highlighting its many applications in keeping up with changing customer demand, market trends, demand patterns, and much more, for the retail industry.
Uma has over five years of experience in the analytics industry and is passionate about approaching and solving real-world business problems using Data Science. She counts building statistical models and data engineering pipelines, as well as working with SQL, JavaSE, and C++ among her many skills. Apart from work, Uma’s interests include long distance running and watching OTT series.
Urna is a data science professional with over five years of experience in the industry. Counting economic and quantitative skills among her core capabilities, Urna excels at problem solving and has led engagements across the Retail, Finance, and Insurance domains. In her free time, Urna can be found reading up on data science publications and travelling.