Transformation in Retail Business through Big Data
- Vanessa Lam
- Mar 22, 2023
- 5 min read
Updated: May 7, 2023
Retail businesses are relying on big data to make data-driven decisions and to transform the business. This blog gives an insight of what big data is and how it is important to transform the retail industry.
What is Big Data?
Big Data is the game changing technology in many industries over the past few years. It has played an essential role in many decisions making in the organisation’s strategy on sales and marketing and other strategic planning for organisation’s growth. It reveals patterns, trends and associations of consumers behaviour and interactions. (Barasch, 2019) Businesses are relying on data like fuel keeping the business engine running, and to gather insightful data across industries. Organisations are formalising the way they collect, curate and manage the data. In a nutshell, Big Data is the organisation’s data, owned, obtained and processed to produce value in the best way possible. (Matteson, Big Data basic concepts and benefits explained, 2013)
The initial 3 main concepts of Big Data are VOLUME, VELOCITY and VARIETY.
According to (Lutkevich & Wigmore, 2023), it cited that Gartner analyst Doug Laney introduced the 3 V’s concept in a 2001 Meta Group research publication, “3D Management: Controlling Data Volume, Velocity and Variety.”
FIGURE 1 THE 3 V’S OF BIG DATA
Source from (Lutkevich & Wigmore, 2023)
From the video developed by (Priyadharshini, 2023), it claimed that some 200 million of data are generated on the internet per minute. This vast amount of data is a lot for traditional computing system to handle and for a human mind to process. These are known as Big Data and they can be classified by using the concept of the 3V’s as stated above. New V’s have been added over the years such as Variability, Veracity, Visualisation and Value (SimpliLearn, 2023), Vinculation and Validity(Monroe).
Understanding the aspects of the 9 V’s
Volume is the amount of data we have. Now it’s measured in Zettabytes (ZB) or even Yottabytes (YB). (Lutkevich & Wigmore, 2023)
Velocity is the speed at which data is processed and becomes accessible. (Lutkevich & Wigmore, 2023)
Variety refers to so many different types of available data. (Lutkevich & Wigmore, 2023)
Variability refers to data whose meaning is constantly changing. (SimpliLearn, 2023)
Veracity refers to the trustworthiness and importance of the data source, the reliability of the data, and its relevance to your business case. (SimpliLearn, 2023)
Visualisation is nothing more than using charts and graphs to visualize large amounts of complex data to make it easier to use. (SimpliLearn, 2023)
Value means that data needs to be important and useful, in other words, worth processing. (SimpliLearn, 2023)
Vinculation means the data has interdependent nature, it is bind together or attach in a relationship, example social networks. (Monroe)
Validity questions how sure or confident are we in the data collected, and how much truth in it. (Monroe)
How does Big Data impact the retail industry?
Marketers can have all the creative ideas behind a campaign to drive sales, but with data and analytics, they complement to the work to perform better. Smart marketers are now relying on data more than ever to inform, test and devise their strategies. (Virmani, 2022)
Retailers collect data through loyalty programmes, credit card transactions, IP addresses, user logins and more. With the information collected, retailers can use the analysed data to make informed business decisions such as pricing promotions and catalogue movements, predict future trends and spendings and make personalised recommendations. Customers will enjoy enhanced shopping experience and in turn become loyal customers.
The strategic techniques in using Big Data (Virmani, 2022)
Make Informed Business Decisions:
Companies need to rely on one single and trusted source of information for consolidated data about products and customers to help them make strategic decisions. Retail dashboards will give a high-level overview of important competitive performance metrics.
Predict Future Trends:
Retailers can use economic indicators and demographic data to the market demands.
Make Personalised Recommendations:
Retailers can predict what the customers are likely to purchase next from the customers’ purchase history. Machine learning model (Tavasoli, 2023) are trained on historical data which allows retailers to generate accurate recommendations.
Utilizing Market Basket Analysis:
This analysis helps retailers figure out what products customers are most likely to buy together. Retailers can now analyse more data using Hadoop(Zhasa, 2023).
Optimizing Pricing:
Example of retailer investing in real-time merchandising systems is Walmart. It works on a private cloud that will track millions of transactions everyday such as inventory levels, competitors, demands and market changes.
Listening to social media:
Platforms like Hadoop facilitate the analysis of vast amounts of unstructured data that helps retailers to listen to what customers have to say on social media. NLP or natural language processing is used to extract information from social sites, machine learning is then used to make sense and give the retailer an edge over the competition.
Example of a successful retailer who transforms its business through Big Data is Sephora.
The turn-around of Sephora using Big Data
From manual retrieving of sales and inventory updates to implementing a data analytics platform, Sephora speed the process in 2 weeks to make it for Black Friday. The system allows them to measure where they were versus their targets each of the markets they were managing. They were able to monitor their stocks inventory live so that the marketing team need not advertise if the stocks are running low. (Barbaschow, 2018)
Figure 2 diagram found in (Columbus, 2016) shows with the support of Big Data and its affiliate technologies, we see intelligence in contextual marketing. Integration of data and process level in customer management, sales, services, and channels needs are now more possible than before. Increasing quality sales leads, improving prospect list accuracy, strategic territory planning, increasing win rates and price optimisation, all these made possible, thanks to advances in big data algorithms and advanced analytics techniques. (Columbus, 2016)
Conclusion
Big Data is massive, and it is expanding by the minute. Retailers who know how to ride on the wave to tap on the vast amounts of information tends to have a competitive edge over their competitors. It is plays a significant role in shaping the future of the retail industry and it is here to stay (Virmani, 2022).
References
Barasch, R. (14 January, 2019). The Power of Retail Analytics.
Barbaschow, A. (23 August , 2018). How Sephora focused on data to prepare for Black Friday.
Columbus, L. (9 May, 2016). Ten ways Big Data is revolutionizing marketing and sales. Enterprise Tech, Forbes.
Lutkevich, B., & Wigmore, I. (2023). 3 V's (volume, velocity and variety). TechTarget.
Matteson, S. (25 September, 2013). Big Data basic concepts and benefits explained. TechRepublic.
Monroe, B. L. (n.d.). The Five Vs of Big Data Political Science. Introduction to the Virtual Issue on Big Data in Political Science .
Priyadharshini. (2023). What is Big Data and What are its Benefits? SimpliLearn.
Tavasoli, S. (09 March, 2023). Top 10 machine learning algorithms for beginners: Supervised, and More. AI & Machine Learning, SmpliLearn.
Virmani, A. (12 December, 2022). How Big Data is transforming retail industry.
Zhasa, M. (30 January, 2023). What is Hadoop? Components of Hadoop and How does it Work? Big Data, SimpliLearn.
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