Emerging Technology : Big Data

 

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Big data is related to large, complex, and organized data sets that do not fit with traditional computational methods or algorithms. It attempts to detect hidden patterns and has helped to develop and turn a model research framework into a data paradigm. Big data has four dimensions: veracity, amount, and velocity, according to IBM researchers. With IBM, Gartner confirms that big data provides a great number of details, pace, and diversity, which needs sophisticated, cost-effective information processing to improve clarity and decision making (Nadikattu, 2020).

In addition to large data, some algorithms analyze social networks and trends in web browsing to predict the next major issue in the retail market.  For retailers to remain competitive retail sectors must make better buying choices, offer relevant discounts, persuade customers to trend new trends, and remember their customers' birthdays, all of these could be assessed with big data. Big retail data is crucial to targeting and retaining clients, streamlining operations, optimizing the supply chain, improving business decision-making, and saving money in the end. Before the cloud, companies only tracked what a person had been buying and when. Companies can use more advanced technology to collect a wealth of customer data like age, location, sex, preferred restaurants, other shops, books, or news. The list goes on and on. Retailers are now working on aggregating and managing the information with cloud-based Big Data solutions (Santoro et al., 2019).

Figure: Web Scarping in Big data process

Source: (Santoro et al., 2019)

Big data analyses may predict new trends at the appropriate time, target the correct customer, lower marketing costs and enhance customer service quality. The general advantages of retail use of big data are the following.

• To keep every customer's 360-degree view - Creates the kind of personal commitment that customers expect from knowing each person on a scale.
• Pricing optimization—Take advantage of future trends and know when and how much to reduce the off-trend price of products.
• Simplify the operation of the back offices — images keep perfect stock levels throughout the year and collect data from products registered in real-time.
• Customer service enhanced — unlocks the customer service information in recorded calls, security in-store images, and comments on social media.

Figure: Scope of Big Data

Source: (Dekimpe, 2020)

Big Data Analytics contributes to a cost adjustment in the store of customers in real-time. Here retailers are attempting to analyze the impact of price changes on different products. The analysis of "what if" helps to understand the impact of the sales price, the customer decision, the choice of product, etc. Big data analytics help retailers calculate the optimal price, increasing sales and generating maximum income. The analysis of customer data will help the focus customers to personalize the discounts/offers. This information is linked to buying history, search history, average bill value, the frequency of the business visit, etc. Big data analysis generates custom SMS and emails related to offers/discounts. Allow marketers to share a few important mottoes in the field of retail analytics such as market basket analysis, recommendation systems, classification and segmentation, predictive analysis, trend analysis, etc (Almeida, 2018).

Author: Thomas Devasia

Keywords

#Big data

#Retail Industry

#veracity

#velocity

#real-time business

#Big data analysis

References

Almeida, F., 2018. Big data: Concept, potentialities and vulnerabilities. Emerging Science Journal, 2(1), pp.1-10.

Dekimpe, M.G., 2020. Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), pp.3-14.

Kozinets, R.V., 2019. Netnography: The essential guide to qualitative social media research. Sage.

Nadikattu, R.R., 2020. Research On Data Science, Data Analytics And Big Data. INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND, 9(5), pp.99-105.

Santoro, G., Fiano, F., Bertoldi, B. and Ciampi, F., 2019. Big data for business management in the retail industry. Management Decision.

Welte, J.B., Badot, O. and Hetzel, P., 2021. The narrative strategies of retail spaces: a semio-ethnographic approach. European Journal of Marketing.