How is Data Science Transforming Retail Sector?

With the advent of Data Science, retail brands are exponentially resorting to the volume, variety, velocity, and value of the big data every year. Major players in the retail sector are aware that each one of these factors contributes to generating profits. They are now increasingly relying on information like consumer behaviour and preferences to notify, examine, and devise their strategies.

Data that serve as a means of multiplying revenues are now utilized scientifically to create effective marketing campaigns. As per the reviews of McKinsey, retailers can transform insights taken from data science into profitable margins by building insight-driven plans, investing in data science talent, and capitalizing on the existing workforce. In this new and emerging era of high performing real-time analytics, data analysis has evolved itself into a significant field of study for both researchers and practitioners. It helps to reflect the impact and magnitude of issues associated with data to be resolved in the retail industry.

More than 60 percent of the data available in digital form can be used by retailers to boost their operating margins. Moreover, a survey by JDA Software Group and PricewaterhouseCoopers (PwC) says that 86 percent of retail executives want to increase investment in data science tools in the future.

With the spike of the Internet, multimedia, and social media, the marketing industry is joining forces with companies who capture data. This will help the retail marketers enhance significantly in the near future. High-speed internet connections, evolutions in e-commerce product lines, and Smartphones and other online technology advancements have paved the way for the growth of retail purchases including both e-commerce and m-commerce. On the basis of an estimate, both large organisations and consumers produce 2.5 billion GB data every year. This figure is growing at the rate of 40 percent by every passing year.

According to a research study titled ‘Impact of Big Data On The Retail Industry’ conducted by A. Seetharaman, the Late Dr. Indu Niranjan, and Varun Tandon of SP Jain School of Global Management, there are four factors, such as data source, financial and economic outcomes, data analysis tools, and data security and data privacy. These factors play a significant role in gauging their effect on the Data Science of Retail Industry.

Business Intelligence and Analytics

Over the past two decades, the related field of data science analytics like Business Intelligence and Analytics (BI&A) have gained significance in both academic and business communities. Data worth collecting and strategic assets can be determined on the basis of such retail business transaction data. For instance, ninety seven percent of companies were found to be exceeding the mark of $100 million by using business analytics, as suggested in a state of business analytics survey by Bloomberg Businessweek.

This analysis had been expedited by the advanced tools of data techniques and analysis, including visual data exploration that permits the data visualization to form insights and develop new hypotheses. Apart from granting direct involvement to the users, visual data exploration is capable of offering several fundamental benefits as compared to machine learning and automatic data-mining techniques in statistics.

Organizations undertake a qualitative approach for data, analysis generally by employing statistical procedures. A continuous process of data collection and analysis are performed in real time to take strategic decisions. The adopted qualitative approach helps to determine the kind of analysis to be performed on data. The category of data to be analysed comprises documents, videos, field notes, audios, and much more.

Another important aspect of data analysis is maintaining data integrity. In order to develop the research findings, appropriate and accurate data analysis help preserve the integrity of the data. Issues such as statistical and non-statistical data are pertinent to authorize data integrity. Any disparity in data analysis can lead to negative outcomes on the scientific judgements and general perception of the public about the research.

Data Source

Companies engaged in any form of e-commerce business view customer’s data as a source of a strategic asset that carries a potential competitive advantage. However, the quality of information fetched from a source directly rely on the extent which they are administered by the schema. ‘Schema’ is defined as the structure or organisation for a database. The quality of data is also governed by integrity constraints that help to monitor permissible data values. Enormous data is pulled in as raw materials by the businesses to determine customer searches by using data analysis tools. It is then grouped as per the required criteria for targeted marketing in the future.

Data Analysis Tools

Data analysis tools play a major role in fetching previously unknown data or buried information from vast databases. By using various criteria, it lets the tools discover patterns and relationships. Data-mining and data-profiling are two types of data analysis tools. A significant amount of commercial tools back the process of extraction, transformation, and loading (ETL) for data warehouses. Recently, text categorization can be accounted as one of the important methods for handling and structuring data in textual form. Information derived from this analyses later contribute to support decision-making, prediction, estimation, and forecasting.

Data Security and Data Privacy

A study conducted by PWC in 2000 reveal, approximately two-thirds of the consumers would most likely shop online if they were told retail websites would not touch their personal information. The data collected by companies are regarded as assets of utmost importance. Therefore, its privacy and security hold priority for the companies from the customers as well as the business point of view.

Most importantly, data privacy and data security are one of the most important facets of electronic commerce (e-commerce).  It has gained and maintained the trust of consumers. This trust not only influences the decision to purchase but also directly influences the effective behaviour of purchasing comprising of cost, reference, the frequency of visits and the level of profitability offered by each customer. Moreover, the analyses reveal that the trust in the Internet is specifically built due to the security level apparent to the consumers, based on the way their private data are dealt with.

Financial & Economic Outcomes

It is seen that companies leveraging even the moderate level of analytic capabilities are most likely to offer their stakeholders with twenty percent higher returns in comparison to their non-analytic-oriented competitors. On the other hand, companies using advanced level of analytic capabilities in data science are most likely to generate fifty percent higher returns. This implies that the data collected from the transactions of consumers is growing by forty percent every year. In addition, retail bigwigs of the market are now using the latest technology, like big data analytics to ascertain five to ten combinations or new as well as existing data sources to make better decisions when fused with sophisticated real-time analytics.

Inference

Data analysis, therefore, has brought a major revolution in the retail industry. As a tool of Data Science, it is all set to be processed for implementation by many firms who did not consider it earlier. As suggested by a research, several operational challenges faced by a company can be tackled with the implementation of the data analysis. Not only does it gives the firms a competitive edge over the others in the business but it also accelerates their growth.

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About the author: Pragati Shrivastava
Pragati Shrivastava is a content writer at SP Jain School of Global Management. She is a skilled blog writer with over seven years of work experience. Currently placed in the Marketing team at one of the Top B-Schools, she is committed to delivering day-to-day programs related information through her work. She has worked across verticals ranging from sports to real estate in the past. She is a huge Netflix buff and loves reading fiction in her free time.

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