Machine Learning Analytics in Retailing Research

Introduction

Machine learning in retail goes beyond big data fundamentals. It’s been advised for years, data is king and that it should be used to make all decisions: what to stock, how much to purchase, and what brands to recommend to returning buyers. However, using machine learning analytics and performance metrics to do something about the data is just what marketers need to compete in today’s industry. In retail, data has been used in a variety of ways. Pricing optimization, personalization campaigns, fraud prevention, market prediction, and logistics support are only a few examples. Artificial intelligence (AI) and information technology are also attempting to move tactical tasks away from a human actor. Machine learning (ML) is an AI discipline that deals with data-driven learning enhancement and predictive analytics in retail. As a result, retailing and wholesaling, which are noted for having a high proportion of human labour while still having poor profit margins, can be seen as a natural match for the use of AI and machine learning software. The actual prevalence of machine learning in the industry is examined in this paper. The machine learning analytics, retail performance metrics, and data-driven decision-making models used in retailing analysis are discussed in this blog.

1. Retailing: Structure, Systems, and Information Flow

In any economy dependent on the division of labour, trade is in charge of managing the geographical, physical, qualitative, and quantitative distances between production and demand. Purchasing goods from various producers or retailers, shipping, storing, mixing them to form an assortment, and distributing them to commercial (wholesale) or non-commercial (retail) consumers without significant alteration or processing of the goods are all trade examples. Brick and mortar retailing (sales from a specific location such as a department store, shop, or kiosk), distance selling (and mailing), and internet retailing are the three main forms of retailing. This article relies on a comparison model to organize a retailer’s key processes to structure the study of the intent and future significance for the wholesale and retail industries. This overarching framework would aid in the structural grouping and reporting of results within a domain-relevant paradigm and retail analytics framework. The shell model of retail information systems [1] is a concept suggested as a reference model to characterize a retail mission. From the inside, it includes the master records, the technically constructed, value-adding heart, and the administrative and decision-making duties of the retail business supported by retail data analytics systems.

Figure 1. Shell model for a retail information system [1]

 

2. Retailing Research: Machine Learning Applications and Retail Analytics

The retail analytics sector has always had many appealing characteristics as a research domain, including its scale, (ii) its multi-faceted and diverse nature, (iii) the potential for researchers to use their domain expertise, (iv) comprehensive (but not always consistent) coverage by industry analysts, and (v) strong data availability. If the retail market isn’t the only one with (some of) these characteristics, their existence together makes it a very rich ecosystem to study using advanced machine learning models and predictive retail metrics. Some of the application of ML techniques for problem-solving in retailing are classification, estimation, clustering, optimization, anomaly detection, ranking, and suggestion are some of the main problem categories that can be solved using machine learning techniques and retail predictive analytics (Figure 2).

Figure 2. Machine learning tools for problem-solving, mapping use-cases, and diffusion throughout the world’s biggest retailers.

Retail is almost a large data sector by itself. Thousands of stores market hundreds of thousands of SKUs to millions of consumers in billions of sales at the macro level. Individual customers have evolved into walking data producers that leave a data trail every time they use their credit card, use a loyalty card, send a text message, or conduct a web search. Because of the big data that retailers already have on their customers’ orders, data-driven customization and retail personalization analytics have become viable options. Furthermore, by supplementing these data with information on inventory status in the supply chain, location-specific weather data, a combination of social media metrics, and sensor data, a retailer’s data typically exhibits a wide variety of both highly structured and highly unstructured data suitable for machine learning analytics.

However, there is a gap between the inherent importance of big data to the industry on the one side and the apparent ease with which such gains can be realized. Even though the industry is characterized by various data-rich chains at the forefront of big data analytics, most players are much smaller. They, therefore, have fewer resources to collect and process data and thus fully exploit big data opportunities and retail analytics metrics. On the other hand, larger retailers frequently lack a complete comprehension of the possible advantages of predictive analytics and machine learning models. They can either not spend at a commensurate pace with those benefits or fail to extract actionable consumer insights from the growing volume of data available.

4. Big Data in Retail: Possibilities and Challenges for Machine Learning Research

Retail analysts have benefited greatly from the big data boom and the adoption of machine learning analytics in retail research.

4.1. Difficult balance between technical complexity and managerial relevance

Many secondary databases’ huge scale and large dimensionality have introduced a new collection of numerical and statistical issues, such as scalability, noise aggregation, false correlations, and accidental endogeneity. Through proper treatment of these technical and statistical problems using advanced retail analytics metrics and machine learning evaluation techniques, retail researchers should first and foremost “keep their focus on the problem.”

4.2 Difficult to Listen to the Data in Large-Scale Retail Analytics

Although there is a desire to include model-free proof before getting in the heavy modelling artillery, the growing scale of the data, especially through columns and data sources, makes it more difficult to “truly listen” to the data. When faced with hundreds of columns of data, frequently obtained from initially unstructured data sources, more work is required on how to give the retail manager and academic reader a true sense of the most applicable model-free content. Researchers have a bias to explain their smaller data sets meticulously. In laboratory conditions, similar caution is exercised when discussing the data collection process and retail performance measurement metrics.

4.3 Use of Theories in Machine Learning-Based Retail Research

Retail analysis has a long history and a large body of knowledge. Even then, when faced with a large amount of evidence, there might be a temptation to disregard well-established theoretical observations in favour of arbitrarily “throwing in” all possible variables in the hopes of uncovering any meaningful results through data mining and predictive retail analytics. According to some scholars, the proliferation of data has made theory obsolete. In direct contrast to this viewpoint, others have made a convincing argument for the continued importance of theory in guiding researchers. Still, in certain situations, one cannot overlook that retailing theory might not be adequately mature to formulate systematic theories for all potentially relevant results, especially where higher-order interactions are involved. Theory, or empirics, is not recent, and it predates the big data debate. The rise in popularity of data-mining methods and the advent of very big data sets with multiple columns have resurrected the debate. Theories will continue to be important for academic researchers in reconciling or contrasting recent scientific observations with what is already understood. Big data sets do not, however, obstruct the advancement of strong theories and create definition in retail machine learning analytics research.

4.4 Collaboration Between Retailers and Academic Machine Learning Researchers

If more retail powerhouses develop their analytics teams, they will feel less compelled to share their data with academic researchers. Academics are also welcome to partner with retailers. Retail science has a long history of embracing innovative approaches and collaborating with other fields, specifically economics and psychology. The big data revolution can necessitate new skills that are less common among traditional retail researchers. There are five steps in most big data processes: acquisition and recording; retrieval, cleaning, and annotation; convergence, grouping, and representation; and two analytic stages (modelling and analytics; interpretation). The two analytic phases refer to the extraction of business intelligence from big data using machine learning models and retail performance metrics. Marketing researchers in general and retailing researchers can be in a special position to continue to be useful to the retail industry. Rather than seeing this as a competitive game, cooperation with these other disciplines could be more beneficial in identifying the most managerially important research problems and collectively developing improved and more rigorous machine learning analytics approaches.

Conclusion: Future of Machine Learning Analytics and Retail Performance Metrics

Machine learning enables retailers to simplify data collection and move beyond the surface to understand their clients, find trends in the data, and transform data into actionable information through predictive analytics and retail performance metrics. Instead of just realizing what their rivals’ assortments are and what their buyers have already purchased, they can help prepare their products and include what consumers desire before realizing they want it. Machine learning in retail brings big data to the next step, putting the pieces of the jigsaw puzzle we’ve been working on for years together.

It does so by integrating consumer data with industry dynamics to provide retailers with a comprehensive action plan for better targeting customers using advanced retail analytics models. Retailers are then more able to refine prices and forecast purchasing behaviour with greater precision using machine learning metrics and predictive modelling techniques. Machine learning’s main aim in retail is to accelerate sales growth more productively, and it is undoubtedly effective in doing so. Machine learning is transforming retail for the better. It allows for hyper-personalization by extending big data dependent on demographics and behavioural analytics. By taking in more reliable data to inform critical business decisions, machine learning facilitates data-driven retail decision making.

References:

1. Marnik G. Dekimpe, Retailing and retailing research in the age of big data analytics, International Journal of Research in Marketing, Volume 37, Issue 1, 2020, Pages 3-14, https://doi.org/10.1016/j.ijresmar.2019.09.001.
2. Xin (Shane) Wang, Jun Hyun (Joseph) Ryoo, Neil Bendle, Praveen K. Kopalle, The role of machine learning analytics and metrics in retailing research, Journal of Retailing, 2020, https://doi.org/10.1016/j.jretai.2020.12.001.
3. Weber F, Schütte R. A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing. Big Data and Cognitive Computing. 2019; 3(1):11. https://doi.org/10.3390/bdcc3010011