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How AI-Driven Analytics are Revolutionizing Retail HQ Decision-Making


Written By: Gargi Sarma 


In the past, experience and intuition played a major role in retail decision-making. For important choices on pricing, promotions, and strategies, retailers turned to seasoned managers and analysts. However, as time passed, procedures that gave these choices more structure started to appear. These procedures included conducting intricate tests, evaluating data, and modifying action plans using new information from several sources. The true revolution, however, occurred when AI-driven analytics emerged and replaced human judgment with machine-powered precision in retail decision-making.


Figure 1: Evolution of Pricing in Retail


The Transition: From Intuition to AI-Powered Insights


Figure 2: What Advanced Pricing Methods Improves


Retail decisions were first made on the basis of experience and intuition. To choose the best price, promotion, or store layout, retail executives would rely on their experience. However, this reliance on human intuition became less viable as the retail industry became more competitive and sophisticated. To manage the overwhelming amount of data and choices, retailers required more organized procedures. At that point, analytics became increasingly important.


In order to maximize decisions, retailers started conducting trials, A/B testing, and other assessments using simple mathematical models and algorithms. Exporting Point of Sale (POS) data, creating charts, and giving management insights into what worked and what didn't were the duties assigned to business analysts. Although the procedure was still labor-intensive and slow, these charts assisted teams in making well-informed decisions. Days or even weeks may pass before the required information is gathered, examined, and a decision is made.


The Emergence of AI: Automating Decision-Making


In the present day, AI-powered analytics have revolutionized the way retail offices make decisions. Many of the manual tasks that were previously necessary for data processing and decision-making have been automated thanks to AI. Consider doing a test-and-control experiment to ascertain whether a promotion was successful. It used to be a multi-step process that included exporting data, executing mathematical models, and presenting the results to managers so they could make decisions.


AI can now automate a large portion of this procedure. Retailers may easily determine the ideal price for test and control stores by using AI-powered solutions such as RapidPricer. Based on real-time data, the AI then decides whether the promotion was effective and automatically modifies the price. The time and effort needed to test and evaluate promotional methods is significantly decreased as a result.


Figure 3: The Impact of Improving Pricing Capabilities (Source: BCG Analysis)


Figure 4: Fresh Produce Dynamic Pricing Example


From Execution to Strategy: Shifting Focus to Human-Centric Tasks

The role of human workers in retail offices is evolving as AI continues to replace humans in the performance of daily activities. Employees are spending more time on more strategic, human-centric responsibilities rather than tedious, process-driven work. For example, senior management and executives are increasingly primarily responsible for long-term planning, market strategy creation, and vendor negotiations.


Consider how price analysts' roles are changing. These experts used to spend hours comparing prices, evaluating test results, and adjusting pricing plans. The bulk of the work is now done by AI systems, freeing up analysts to concentrate on improving pricing tactics, negotiating better prices with suppliers, and establishing long-term pricing targets based on more general company goals.


Industry Examples: How Retailers are Embracing AI


AI-driven analytics has been adopted by several top retailers to transform their decision-making procedures. For example, Walmart has led the way in utilizing AI for price optimization, demand forecasting, and inventory management. Walmart can cut down on excess inventory, optimize stock levels, and boost profitability by utilizing AI to forecast which products will be in demand and modify prices accordingly.

Figure 5: Projected Impact of Automation by Core Merchandising Activity (Source: McKinsey & Company)


Target is another example, of using AI to customize marketing for consumers. Target's AI-driven analytics can automatically modify prices in real-time and forecast which promotions would work best for certain customers based on data from customer interactions. In addition to increasing sales, this customisation strengthens client loyalty.

AI-driven solutions like RapidPricer are helping even smaller to bigger retailers by enabling them to estimate demand, optimize promotions, and automatically alter pricing without the need for technical know-how or a wealth of previous data. Retailers of all sizes may now compete with bigger companies in the market thanks to these technologies, which democratize data-driven decision-making.


The Future of Retail HQ Decision-Making


The way choices are made at retail headquarters will continue to change as AI develops. We may anticipate much more sophisticated AI systems in the future, ones that can not only make judgments but also predict changes in the market, customer behavior, and competition strategies. Real-time, highly educated decision-making by retailers would further lessen the need for human intervention and foster a more flexible retail environment.


More integration of AI-powered decision-making with other cutting-edge technologies, such as real-time analytics, Internet of Things (IoT) devices, and consumer feedback systems, is also anticipated. Retailers will be able to give their customers an even more responsive and customized shopping experience thanks to this connection.


Figure 6: Benefits of Artificial Intelligence (AI) for Retail Business Worldwide, 2022


Conclusion

Retail headquarters' decision-making is changing due to AI-driven analytics, which is replacing manual procedures and human intuition with automated, data-driven insights. Retailers can now make decisions more quickly and correctly, freeing up important human resources to concentrate on long-term growth and strategy. Retail decision-making will likely become even more automated, flexible, and sensitive to market situations as AI develops further, assisting merchants in maintaining their competitiveness in a sector that is becoming more and more complex.


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About RapidPricer


RapidPricer helps automate pricing and promotions for retailers. The company has capabilities in retail pricing, artificial intelligence, and deep learning to compute merchandising actions for real-time execution in a retail environment.


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