Data Mining in Retail: Benefits & Challenges Case Study

Leveraging Data to Drive Retail Success

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Introduction

The report attempts to give out a comprehensive idea on data mining in retail. Data mining is an assimilation of processes that are deployed to understand certain underlying patterns and relevant insights from “large data sets”. It is often difficult to formulate and understand the interconnected patterns that are underlined in large data sets, and data mining is used in certain cases. Data mining has multiple types, likely, web mining, text mining, pictorial data mining, audio and video mining, and others. All these benefit organisations to take up an active role in market segmentation. Authentic and sound quality market segmentation by an organisation helps it to receive a better competitive advantage in the market. Data mining, in reality, helps sectors to gain the target market and provides better insight into functioning ability which eradicates competition for the organisation. Not only that, organisations have been able to detect fraud through the usage of data mining. The report here will analyse the incident of data mining in the retail sector. The existing uses, relevant trends, and challenges will be focused on here, and along with that, a list of recommendations will also be added.

Figure 1: Attributes of Data Mining

Existing Data Mining Uses in Retail

Data mining is significant in managing and segmenting the market and target audience in the retail sector. The retail sector deals with a huge amount of data, information, and database that generally covers the “records on sales, users shopping history, goods transportation, consumption, and service”(‌Tutorialspoint.com, 2021). Sectorial organisations automatically require mining techniques and use them for sound quality handling of data. The contemporary retail world is now based on e-commerce and the internet which has been significantly increasing data in general, making data mining techniques extremely relevant. Data mining can accumulate and identify similar patterns by which it can formulate similar customer behaviours, buying patterns, patterns of searching and preferred sites. All these provide benefits to the modern retail sector by personalising its customer’s experiences.

Uses

• Market segmentation

Data mining is used to segment their target market and customers based on their behaviour and buying patterns. This segmentation not only helps the retail sectors to increase their brand loyalty but also helps in increasing brand value. For instance, the retail sector of Target uses data mining to predict pregnancies in women. This might give its sales a boost (Kuhn, 2023).

• Construction of warehouse

As data mining retail deals with a huge amount of data, it is used for constructing relevant warehouses, as per the requirement (‌Tutorialspoint.com, 2021).

• Effective sales campaign

Retail uses personalised and multidimensional analysis for constructing advertisements and sales programs (‌Tutorialspoint.com, 2021). This is benefited by the data mining attributes of analysis that provide a systematic overview of functioning.

• Recommender system

Data mining is used for providing personalised experiences to their customers by identifying the patterns of similarity among big data of retail. It now is used for recommending to customers as per their search, purchasing, and buying patterns. For instance, Amazon has been deploying data mining uses for providing personalised experiences ‌(Pathak, 2020).

• Price optimisation

Data mining is used to understand pricing data, competitor pricing, and customer demand data to optimise pricing strategies. This allows retailers to fix the optimal price points for their products, increase revenue, and improve profit margins. For instance, Walmart has been using it for a long time to cut down its inventory prices and increase its profit margins (‌Chatterjee, 2022).

Trends

• Personalisation

This is the first trend that is found while discussing the usage of data mining in retail. Personalisation is the process in which retail sectors are proving their customers with the products and items they like and choose. This gives a personal feel to the customer experience and is only possible by undermining the big retail data, and customer records‌ (Bamshad Mobasher and Bamshad, 2007). Data mining provides “ a personalization system that aims to provide robust, accurate and useful personalized content to its users”(‌Kannan, and Suresh Babu, n.d.).

• Fraud detection

The second development is data mining for the purpose of detecting fraudulent financial transactions, unlawful or otherwise unusual behaviour, and even buying patterns (Hyperverge. co, 2022). Credit and debit card payment of fake accounts are often understood by data mining methods (Hyperverge. co, 2022).

• Supply chain optimisation

The supply chain and its management is a heavy and difficult task that required human labour for a long time. However, in the current scenarios, the management of the supply chain is done by data mining. This technique of data mining makes the system fast and glitch-free (USC Consulting Group, 2021).

New and Innovative Data Mining Uses

Data mining uses can be improved by certain recommendations and relevant solutions. Various methods are yet to be deployed in the data mining field for optimising customer experiences.

Social Media Data Mining

Social media is used by a large number of people in current times. Estimates show that there is always a rise in social media users than before, making it the most used platform in the world. People all around connect, shop, buy, purchase, rent and search on these platforms making it a hub of bug data. Social media data mining thus is a new data mining area that can be utilised by retail organisations (www.javatpoint.com, 2019). Social media data mining will not only help retail organisations to customise and personalise their experience for the customers but will also understand the ongoing trends, fashion trends, preferences and trends initiated by influencers. Big data are used in this data mining process with techniques called “classification, association, tracking patterns, predictive analytics, keyword extraction, sentiment analysis, and market/trend analysis” (Ausrine, 2021).

Internet of Things or IoT Data Mining

The modern world has increased its usage of the IoT or the Internet of Things. The retail sectors have been transforming for a long time, and the upgradation of the stores by applying customer care and reliable services optimisation tools such as “smart shelves, beacons, and wearables, that has automatically produced a wealth of data that can be mined for valuable insight and enhancing sales”. Extensive research on the IoT with cloud technologies enables the accumulation of massive data produced by this diverse setting and the subsequent transformation of that data into valuable knowledge through the application of data mining techniques, as shown in the study (Sunhare, Chowdhary, and Chattopadhyay, 2022). This has raised organizational performances and preferences in the market, and it has improved customer experiences overall by meeting customers' requests and requirements.

Visual Data Mining

Visual data mining is a process of understanding “abstract data” by the method of data analytics and reasoning (‌Simoff, 2009). This method of data mining can bring about the visual presentation of big data and abstract data which in turn will help organisations to understand and dissect the market according to their needs. Visual data mining, alike other data mining techniques provides patterns for customer behaviours and similarities which are of robust relevance and significance. These methods can be deployed vigorously in the retail sectors for optimising performance, generating revenue and enhancing customer services.

Real-time Data Mining

Real-time data mining is the process of generating and analysing real-time data for enhancing customer services. According to the studies, real-time data is beneficial as it helps not only to generate present or current data but also to predict future data generation (‌Sayad, 2017). Apart from that, the analysis requires outlining the past data, which again benefits the cause. Thus real-time data mining requires past data to analyse the current ones and to predict future ones as well (‌Sayad, 2017). These data mining abilities and techniques will increase the ability of retail sectors to enhance their performances. They can also detect malfunctions, disputes, and frauds in the system if there are any.

The Challenges of Data Mining In the Retail Sector

Data Quality and Accuracy

Retail as a sector is huge and for that reason, the data quality and accuracy often fail to generate sound results. As data mining is solely dependent on the data being generated by the organisation, system faults are often considered. The retail sector captures data from multiple channels, including POS, E-commerce, and online networks (UNext, 2022). This vast amount of data as they are poured from multiple channels often ceases to be issue-free and unproblematic. It has been seen that data resources from which data mining in the retail sector have collected are found to have issues. The issues can be “data inconsistency, missing data, and data duplication, which can impact the accuracy and reliability of the mined insights”.

Data Privacy and Security

The retail sector often uses the personal data of the customers to make the experience more personal. However, the handling of such personal data is risky. Data mining often leads to the handling of such sensitive and personal data of customers (UNext, 2022). There are multiple challenges in the implementation of data mining in retail without prior planning as it might result in “data breaches, unauthorized access, and data theft”. Certain serious challenges are there which hinder the implementation of data mining as it is considered a significant challenge in data mining for retail.

Complex Data

As has been already mentioned that data mining and the processes related to it are solely dependent on the issue of data and its management, then retail sectors often face problems with the generation of ideas that are difficult to deal with. Such an instance is complex data. Complex data are usually real-time true data, generated from innumerable outsources, likely from audio, video, image, sites and many others (UNext, 2022). All these provide an array of innumerable data that might be a difficult issue of governance and implementation.

Incomplete Data

Retail sectors have innumerable sources from which they receive data. However, one such challenge that this sector face is the generation of incomplete data. The generation of incomplete data provides little to no benefit to the data mining technique and it can be identified as a governance challenge. These inaccurate and unreliable data results are due to human errors and issues that are unsolvable, thus creating a lacuna in data mining processes (UNext, 2022).

Multiple touchpoints

This is a governance challenge that the retail sector faces in the data mining issue. The world is dynamic and has been changing ever since the introduction of the internet and social media platforms. Customer behaviour has also changed over time, and currently, they trust the feedback and peer reviews more than the brand itself (‌Vinculum, 2020). Thus the retailers require to be omnipresent in the current situation and must handle all the channels and data equally.

Redundant Data Points

Redutant data points are another governance issues that are connected with repositories. Retail sectors as they generate a huge amount of data automatically possess quite a lot of repositories. However, the upgradation of one repository calls for the upgradation of all the interconnected ones. However, if this does not occur then there is the generation of redundant data points ultimately having a “huge negative impact on the retail strategy and eventually the productivity”(‌Vinculum, 2020).

Recommendations to solve the challenges

Data Clearing and Integration

The issues of incomplete, inaccurate and redundant data generation the data mining techniques can be solved by cleaning the data of any possible errors, misconceptions, and fraud. However, this strategy can be time-consuming and requires excellent and talented retailers to deal with, but they produce productive results. Data validation is another form of data cleaning technique that is used to check data against predefined rules. Data profiling and normalisation are again the two methods by which data can be made active and useful. These can be attained by the application of the CRISP-DM that undertakes the following steps, “Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment” (‌Hotz, 2018).

Figure 2: The CRISP-DM

Data privacy

Data Protection is extremely necessary for providing a boost to data mining activities, which will boost the retail sectors. However, the only solution for solving the data exposure and ensuring safe data usage in data mining is to implement an “all-encompassing data governance application”(Vinculum, 2020). These data governance systems provide end-to-end encryption security, and other security factors like tokenisation that not only secure the personal data of the customers but also enables the retailers to use the data in the mining process to select the patterns of purchasing and preferences.

Redundant data

It is quite relevant to shift the redundant data to the active one by governance schemes of activating the data pipeline which will automatically help with the production of active data. The usage of active data is relevant in retail as it provides the sector to upgrade its customer experiences. The issue of multiple touchpoints can be solved by being transformative with time. As the time demands retail to be trend-centric, then the omnichannel presence must be adopted by them to fulfil the customer needs.

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Conclusion

The report here has outlined the issue of data mining in greater detail. Not only that the report here has provided innumerable insights into the uses, trends, and techniques in which data mining has been operating, but also provides the relevant challenges faced by the data mining factors, especially in retail businesses. The report here has also provided a list of relevant solutions for further understanding.

References

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