Top 5 AI algorithms applied in retail
- In the following Blog post we will analyze what Data Science is and what are the Top 5 algorithms applied in retail today.
- What is Data Science?
- Data science is the field of study that combines the skills of programming, mathematics and statistics to extract valuable information from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, etc. To produce artificial intelligence (AI) systems that perform tasks that normally require human intelligence. In addition, these systems generate insights that analysts and business users can translate into tangible business value.
- The retail industry is embracing Data Science techniques to reduce costs, refine decision making, improve demand forecasting, optimize operational efforts and understand consumer behavior to increase sales and improve business profitability.
- Top 5 Retail Algorithmss
- Today, it is essential to capture the attention of consumers who are constantly evolving and leading innovation in the market. Customers, who are used to receiving services and products at their doorstep, are becoming increasingly difficult to please. The trend is driving retail companies to leverage algorithms to make faster business decisions, optimize their operations and increase customer satisfaction.
- Retail algorithms, specifically in the areas of predictive analysis, demand forecasting and omnichannel selling, are helping companies solve problems and make faster decisions by providing the right logic at the right time.
Inventory Management
- The use of retail algorithms enables predictive sales analysis and can solve inventory management and optimization problems by predicting future demands and market fluctuations. This helps to stock the right products based on demand and avoid stocking problems.
- An example of the use of algorithms for predictive analysis is the case of the supermarket chain, Walmart and its preparation for Hurricane Frances in 2004. Walmart had analyzed a large amount of stored data to find out customer behavior before and after the last hurricane and, thus, achieved efficient inventory management and anticipated potential stock problems.
Selecting the location and design of the store
- Often, major business decisions such as opening a first or second store in a city, are based on perceptions and estimations, without considering the totality of consumer and market data.
- To select the best location for a store opening, it is possible to use a model that analyzes the behavior of historical data. The algorithm identifies new store locations based on the current distribution of customers, their online and offline shopping patterns, item return behavior, remoteness of current locations, product preferences, consumer demographics, and so on. Thus, it is possible to identify the optimal location considering market and customer data.
Store distribution
- The application of algorithms to big data helps build intelligent predictive models that are fed from unstructured data collected from various market sources.
- So, if it's the winter season, based on geolocation, weather conditions and shopping activity patterns, your grocery store can plan supply allocation. For example, fresh vegetables at first, then bread, then beer, etc. In fact, you will be able to predict what your customers will buy and in what order, making it easier to navigate the store and making the shopping experience smoother and less time-consuming.
Segmentation in Marketing
- With the use of big data and retail algorithms, it is possible to accurately segment consumers and develop personalized content and products that bring greater value to the business.
- Companies such as Amazon, provide real-time recommendations based on user browsing activity, sending them messages and offering advertising on social networks. Instead of generalized segmentation based on geographic location or age and gender, retail algorithms consider a wider range of variables (market, social activity, purchase history, etc.) to provide a more accurate view of customer preferences.
Optimizing Channels
- Today, driven by the pandemic, the vast majority of customers are using smartphones, apps and websites to shop for their products or to browse the items they want to purchase in-store. Therefore, it is crucial to create an efficient, customer-pleasing omnichannel strategy. To do this, companies are using algorithms for data analysis, market-driven price optimization, shopping basket analysis and measuring in-store traffic patterns.
- For example, retail chain Macy's uses data and algorithms in an app that helps customers find items within the store, shop for products, order online, find them in-store or get them delivered to the door. The retail algorithms compares in-store inventory availability, calculating expected delivery time, combining online and physical store activity to find similarities and offer personalized options.
- Conclusion
- Algorithms in retail will continue to shape the market and customers will find themselves in an ever-evolving scenario where services are increasingly personalized and optimized.
- Specifically in the areas of in-store predictive analysis, demand forecasting and omnichannel selling, Algorithms in Retail will continue to help companies solve problems and make agile decisions while providing the right logic at the right time.
- Learn more about the algorithms used at Prisma, and the value they bring to improving existing workflows and making decisions that directly impact profitability.
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