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4 ways Artificial Intelligence is reshaping demand forecasting in retail

Artificial intelligence (AI) is the technology of today, the story of 2010 and the excitement of tomorrow. The past decade will be reminisced as an era where machines began their journey on the path of intelligence – proficient in learning, executing, and ‘thinking’ like humans do.

4 ways Artificial Intelligence is reshaping demand forecasting in retail

The digitalization of the Retail Industry has been changing in recent years with augmented efficiency, rapidity and accuracy across every branch of business domain. Through prognostic analytics and innovative data exploration, we are now able to make all data-focused business resolutions. AI in the domain of retail has enabled industries to access high levels of data information which has improved retail operations and given business better opportunities.

Demand forecasting, a process by which sales data is used to forecast the expected demands of customers is optimized to increase customer satisfaction and improved efficiency of businesses through AI.

Here are some predictions that we will be likely to emerge in the future:

1.Inventory Management to Improve Efficiency of Demand Forecasting: AI has helped the retail industry gather deeper data and insights from the marketplace, from clients and opponents. Business intelligence tools created for AI are able to predict minutest changes in the marketplace, shifts in industry demand and supply chain management. Inventory management through AI tools also make hands-on immediate changes to the company’s marketing and business strategies through continuously exploring complex data gathered from consumers. The pricing of goods and services as well as the promotional planning of retail industry’s supply chain are positively impacted. Digital portals that provide e-retail services to the consumers based on AI inventory management can be analyzed deeply based on the shopping behavior, purchase history and current browsing. The evolved user’s digital experiences creates a platform for businesses to better the customer-inventory interaction and improve sales.

2.Data Analysis to Improve Accuracy of Demand Forecasting: Using advanced AI analytical tools, raw data gathered from all marketplace sources are converted into actionable visions. AI uses behavioral analytics along with customer acumen to develop different marketplace demographics of customer service sector domain. The types of data that are analyzed can be internal, external or contextual. The raw data that is used in demand forecasting are mostly historical sales of the business that influence demand factors and project a multi-dimensional scenario.

  • Internal Data: In demand forecasting, AI is used to predict the most valuable data source: the company’s internal data. The sales history, marketing strategies and promotion predictability are analyzed by the demand forecasting platforms. For example, should the business increase or decrease the prices of a particular good to increase revenue.
  • External Data: The analysis of external data is a very crucial part of demand forecasting in retail businesses as the choice of raw data can either help the businesses exponentially grow or vice versa. Certain consumer data types are virtually always beneficial for businesses like sales data from distributors. With a fully functional automated AI platform, it is easy to predict the demand from distributors and whether or not this demand can influence demand from the market.
  • Contextual Data: In the demand forecasting of retail businesses, context is essential. The consumer demographic data analyzed by AI tools helps the retail businesses define different contexts in which sales take place. For example, the wealth analysis of local consumers can increase sales forecasting in certain goods/ services categories by predicting which products can be in demand in the wealthier areas of society. Meanwhile, geographic forecasting can predict the number of similar stores in the vicinity or customer attraction to competitor’s merchandise. Such contextual data analysis can forecast various types of demands and AI algorithmic tools can help form patterns needed by the industry.

3.Product Analysis to Improve Capability of Demand Forecasting: In the e-commerce industry, retailers need information about all the characteristics of newer products introduced in the market to find impact on sales. Demand forecasting uses historical sales data to predict future sales, however, as the newer products are introduced frequently, AI algorithms are used to predict behavior patterns based on the sales of comparable products to develop a forecasting pattern. The analysis of products to increase revenues can be effectively achieved by the use of AI platforms.

4.Precision Analysis to Improve Results of Demand Forecasting: Along with inventory management, precision in retail industry is of paramount importance as products cannot be either overstocked or understocked. The profitability of retail industry is heavily dependent on the wastage percentages of the business. Hence precision in the operational management can be achieved through AI with minimal error.

According to McKinsey, AI in demand forecasting for retail businesses reduce the chances of error by 20 percent. The higher gains achieved by industries depends upon the forecasting model that is in place. The clear impacts of AI in demand forecasting has openly challenged businesses to invest further in digitalization of their forecast systems.

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