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Description

Using Data Analytics to Optimize Pricing Strategies in Retail: A Mixed-Methods Study

Abstract

Presently, the study focuses on how data analytics is optimizing pricing strategies in retail. Data analytics is the process of examining data to support decision-making. However, this practice allows businesses to enhance their products by collecting, identifying, and visualizing trends and patterns. Additionally, data analytics help create frameworks for storing collected data to improve strategies. This boosts business by gaining insights into consumer behavior and marketing targets. Hence, retailers are increasingly utilizing data analytics to understand consumer behavior and analyze market competitors. This specifically leads to the optimization of pricing strategies in the retail sector.

A mixed-method approach combines quantitative and qualitative to learn more about the optimization pricing strategies. Moreover, the qualitative method talks about consumer demographics and sales data, which also analyzes product demands at locations in the market. Besides, this method mainly includes sales data that analyzes product demands, sales trends, and seasonal fluctuations that optimize that price. Consumer demographics identify consumer segments based on their locations and purchases. In addition, this is a pricing strategy through knowing about the competitor’s pricing that helps to monitor the pricing decisions based on the market positions. Besides, the qualitative method talks about consumer surveys by gathering the data in the form of feedback from the consumers on pricing and products.

Furthermore, focus groups through collections of consumer segments to understand pricing concerns. Monitoring consumers towards pricing by using social media platforms. Therefore, this research talks about how data analytics optimize pricing strategies in the retail sector.  Data analytics helps to improve business decision-making and evaluates product demand in the market through and treads the sales. Hence, conducting surveys and gathering feedback from consumers helps to make better changes and improve product growth in the market. In conclusion, the study suggested some recommendations and strategies to improve retail pricing.

Read more about the topic

Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness

Optimizing Real-time Dynamic Pricing Strategies in Retail and E-commerce using Machine Learning Models

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