Retail Analytics for Winning More Sales

With the growing adoption of data-driven systems, it is imperative for organizations to not fall in the trap of assuming that data is a silver bullet that will transform their business operations in one shot. It is critical to understand that the requirements of every organization are different. Therefore, there is a need for a powerful technological framework that can make effective use of data and cater to variable needs of a business.

There is an ever-growing competition in the retail sector and adoption of state-of-the-art technology can give organizations the much-needed competitive edge. The significance of data applications in the retail sector is evident from the fact that there is a dedicated branch named retail analytics to serve the growing demands of this sector. However, before defining retail analytics, it is important to look at retail data and all that it includes.

In the simplest terms, the information that quantifies retail business is referred to as retail data. It includes operational data, customer data, sales data, and inventory data, in addition to any other internal or external data generated by the organization. This data is typically collected by retail organizations through loyalty programs, card-based transactions, and login to ecommerce portals, besides others[1].

As is the case with any other data analytics system, retail data needs to be acquired, stored, processed, and visualized for it to make sense and be used for providing any actionable insights. These processes cumulatively fall under the umbrella term, retail analytics[2]. Therefore, retail analytics unleashes the power of retail data by using it to facilitate business operations in verticals such as customer experience, supply chain, logistics and inventory management, to name a few.

Big Data Use Cases for Retail Sector

As mentioned earlier, most of the retail data is consumer data, which includes information about the consumer identity and product or service consumption patterns. Analysis of this data can provide insights to customer preferences and feedback. This analysis can potentially be used by the organization for orchestrating the customer journey in a way that makes it more satisfying. Customer retention is always easier than customer acquisition and this can be a great tool for retail businesses to plan their customer retention strategy. 

One of the greatest challenges faced by any retail business is inventory management. Having said that, this is also one of the most impacted verticals in this sector when it comes to technological interventions. Automated insights have played an instrumental role in optimising the journey of products from factory to shelf. Stocking products with a clear understanding of supply-demand patterns allows retailers to have the right amount of stock to support sales without incurring undue losses by over-stocking.

Lastly, personalization of the user experience by comprehending user spending patterns provides extremely useful insights. An example of how systems can predict expenditure and personalise user experience is Amazon’s recommendation system. It recommends items to users based on past searches, buying history and product similarity. The inclusion of this functionality is known to have increased sales for Amazon by as much as 29%[3], making it one of the most popular uses of big data for retail and e-commerce. Along with this, it is crucial that retailers provide effective product search functionality to make sure customers find the items they wish to purchase.

How can Big Data help your retail business?

Retail businesses can make use of analytics to understand the demand patterns for their products across demographic locations, age groups and sections of the society. This type of consumer spending analysis will enable the development of an effective marketing strategy. In addition to adoption of business intelligence solutions for automating and improving the operational efficiency of business processes, the use of these solutions will also trigger significant cost reduction in verticals such as inventory management.  Historic log data of previous purchases can be integrated into product searches and recommendation to aid customers to find relevant items.

How can SOLVD help your business?

If you have any questions about big data adoption for your business or would like to discuss your specific requirements, you can email the University of Wolverhampton SOLVD team solvd@wlc.ac.uk or visit www.wlv.ac.uk/solvd. The SOLVD project supports Telford & Wrekin and Shropshire businesses with the adoption of digital technologies to improve productivity and growth. Eligible businesses can access 12 hours of fully funded support with academic experts.

Blog by Dr Samiya Khan - Research Fellow in Big Data, Artificial Intelligence & Edge Computing at The University of Wolverhampton.

References:

[1] https://www.macheye.com/blog/how-retail-analytics-will-boost-sales-and-win-you-more-customers/

[2] https://www.vendhq.com/blog/how-retailers-can-use-data-to-boost-productivity-customer-service-sales/

[3] https://www.yodlee.com/data-analytics/big-data-retail-analytics#:~:text=What%20is%20Retail%20Data%20Analytics,to%20human%20behavior%20and%20interactions.