Shaping the Future of Retail: A Comprehensive Review of Predictive Analytics Models for Consumer Behavior

Introduction

The retail industry is transforming significantly due to technological advancements and evolving consumer buying behaviors. Significant transformations have occurred within the retail sector during the past few decades. Technological advances have substantially transformed the retail industry (Akhare et al., 2021). Enterprises endeavor to maintain competitiveness in the dynamic market by adapting to evolving consumer preferences and proactively anticipating future trends. Significant advancements in computational capabilities and novel database frameworks have facilitated the storage and management of data at increasingly granular levels.

Consequently, there has been a decline in the count data time series, characterized by progressively diminishing numbers (Kolassa, 2016). The capacity to forecast consumer behavior has emerged as a crucial resource for merchants seeking to tailor their strategy, enhance operational procedures, and enhance customer satisfaction. This research examines the application of predictive analytics models within the retail sector. The objective is to ascertain the proficiency of individuals in forecasting consumer behavior and their potential impact on the future of the retail industry. Large datasets, sophisticated analytics tools, and machine learning algorithms have provided fresh insights into consumer behavior in the retail sector. Decisions used to be made intuitively, but now it's all about the numbers. In this way, companies can examine massive datasets in search of previously unseen patterns and trends. This research hinges on the hypothesis that predictive analytics models provide a potent means of comprehending and foreseeing shoppers' actions, with the potential to revise retail plans and outcomes.

Figure 1.

Workflow for predictive analytics (a generalized approach)

The process of using data, algorithms (statistical and conventional), and advanced techniques like machine learning, data visualization etc., to identify/predict the behavior of future output for a process based on historical data and existing information is what predictive analytics is about. Predictive analytics involves:

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  Analyzing past/history and current/present data to predict future events or trends.

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  Trend or plot development using data modeling technique(s) is helpful to create an approximately accurate picture of the outcome.

As explained earlier, predictive analytics can be applied in various business domains like finance, e-commerce, healthcare, marketing, etc. The primary technical objective of predictive analytics is.

• I.

  To provide insights and forecasts to help organizations and individuals make informed decisions.

• II.

  Optimize processes and mitigate risks, thereby providing a constructive build for progression.