Retail sale benefits from high seasons to optimize margin and inventory with the use of it

There are some key dates for retail such as downtown parties, mothers’ day, Valentine’s Day, Monday online, hot sale, among others, which derive from a significant increase in retail reflected in the purchase of these chains. Currently, artificial intelligence solutions (AI) and advanced data analytics have become strategic allies for stores because they allow for optimization of operations and enhancing their performance.

Although in Mexico and the retail chains of the Latin American region face challenges such as the instability of demand, the robbery of ants or the legitimate or fraudulent returns of products, among other things.

Accurate implementation of it as it is SAS can reduce their effects and even predict them to take actions that improve their sales and margin at the time of reducing losses in high demand contexts. Raising different, improving price strategies and even intelligent video camera analysis in stores will be used to improve consumer experience.

Given this panorama, retail sellers require efficiency to maximize sales, optimize inventories and meet customer requirements, where the implementation of it and advanced analytics has proven to be a differential factor by anticipating purchasing behaviors and personalization of bids, which contribute to an increase in sales and efficiency in management management and efficiency

What retail sellers use at high demand dates?

During this season, the retail sector is implemented Advanced claims of the request To anticipate products that will be better sold, avoiding excess inventory or absence; Price optimization and special offers to maximize boundaries and respond to competition; Customer predictive segmentation using it to identify purchase models and adjust offers for specific client groups; as well as Purchase basket analysisTo identify connected products and improve internet and offline sales strategies during high seasons.

Although everything may be perfect, the fact of applying it to the stores will not only yield results, as it is required professional advice and adapting to the needs of each chain, product or turning business, so companies of any size are recommended to require, beyond only standardized tools, and encourages entrepreneurs to seek advice to provide these new technologies.

He’s main challenges in Mexican retail sale

High demand seasons, in addition to being a great opportunity to increase sales, also present some challenges, four major challenges for the Mexican market that can be reduced or predicted by the correct use of it:

• Data Administration: Appropriate infrastructure and staff training are essential to use as much as possible from the data created by the tools.
• Instability: Uncertainty in the sales of popular products can generate overloaded or inventory absence problems.
• Return the product: An increase in returns can adversely affect both sales and customer satisfaction. These can be legal or even a deceitful and malicious order.
• Hive: It can have a significant impact on companies’ borders.

It can process large volumes of real -time information by optimizing decisions and ensuring that equipment is taken informed actions. Predictive models anticipate trends and regulate inventories, avoiding both overvoltage and absence. Moreover, purchase behavior analysis helps identify models and provide problems before they occur, while detecting transactions irregularities allows retailers to implement preventive measures more efficiently, reducing losses due to theft.

The role of iot

Integration of internal data with exterior online things (IOT) is the revolution of retail analytics. Store sensors and camera traffic analysis allow you to get correlations between shopping behaviors and external factors such as the weather, which optimizes real -time customer experience.

Sensor data can determine the links between customer purchases and tastes. Today, it is possible to analyze traffic in a store through cameras and heat analysis, or even have macroeconomic climate -related data from temperature meters, atmospheric pressure, wind speed and relative humidity. This allows for optimizing customer experience in smart cities, detecting buying models and adjusting real -time marketing strategies during high demand seasons.

High demand dates represent an invaluable opportunity for retail chains, and the approval of it and advanced analytics has become an essential pillar for success. As the economic environment changes, companies must be equipped with technology that not only allow them to use as many of these key dates, but also to manage any challenge that arises efficiently.

Read more entrance from the author here.