By: Ramakrishnan Krishnan, Associate Vice President and Head of DNA Retail, CPG and Logistics Delivery at Infosys
Not so long ago, stores gathered data through loyalty schemes to gain unique insights into customer behavior and to tailor their campaign and marketing strategies. Since then, the retail industry has made great progress by realizing the value of Big Data Analytics and integrating it into its existing systems.
With the explosion of data across diverse channels, sources, formats and in varying volumes, it has become necessary to leverage Data Analytics to run businesses successfully. Most industries have caught on to this and data analytics solutions are helping run various business functions effectively.
As the commercial environment becomes more competitive with more players entering the market, it is imperative for retailers to draw on their past experience and apply detailed insights from customer interactions on top of the analysis derived from Big Data Analytics solutions to further bolster their findings.
We are currently living in a world where consumers are looking for instant gratification and expecting a variety of new digital-enabled experiences and customized services. Hence, competition is not the only business driver compelling retailers to change; they are also expected to be agile enough to serve their new-age customers better and enhance their user experiences. The ones that fail to do so often end up losing their most loyal customers.
Today almost all retailers have already moved to the cloud or are on the verge of doing so. This migration grants advantages beyond the obvious cost and storage benefits that the cloud offers. Cloud players and the ecosystem also enable seamless integration, ingestion and ease of insights generation to run businesses; factors that help retailers irrespective of size. It is critical for all industries, including retail, to have a clear and contextualized cloud strategy in place to address their business challenges. The best cloud strategies have a strategic combination of one or more of the following parameters:
Personalization and time-to-market
With a plethora of engagement channels and a competitive landscape, every consumer expects a customized retail experience even while choosing vehicle interiors or while deciding the finer details of a tailored piece of clothing, for instance. Personalization thrives on data which is integral to gathering insights about customer preferences, stock levels, manufacturing capacity, supply chain and more.
Progressive retailers are now offering consumers the ability to even design and personalize items, thereby creating differentiation in a crowded market and opening up valuable new revenue streams in the process. Data Analytics enables a ‘Do-it-Yourself approach’ for consumers by providing a limitless data landscape.
Know your customer
The most successful retailers are those who have an intuitive understanding of what their customers desire. In the digital age, data plays a crucial role in learning more about customers. But given the volume of data gathered from online sources, it is difficult for brands to separate valuable information from unimportant data.
Analytics can help retailers do just that. It helps them understand what’s truly important to their customers, and this enables them to tailor their offerings accordingly for larger market share.
Dynamic pricing, famously used in the airline industry for decades, is now gaining popularity in other industries. We have seen companies like Amazon and Uber routinely adapt prices in real-time thanks to the insights they glean from customer behavior and other external sources.
As retailers embrace dynamic pricing, they are required to have a 360-degree view of both their customers and competitors. This necessitates a tightly integrated data network between the physical and digital properties of the retailer – brick and mortar premises to social media pages – to gain the required data that can enable them to make informed pricing decisions. Analytics with the aid of Artificial Intelligence and Machine Learning are crucial tools for turning this data into actionable insights.
Improving the supply chain
While personalized shopping experiences are key to customer satisfaction, product quality and timely delivery are equally important. These are increasingly stretching the supply chain and making retail operations more complex.
Analytics enables retailers to gain better visibility into their supply chain and logistics operations, thus helping them identify poor practices that are causing delays. By correcting these anomalies, retailers can bring the right product to the right customer at the right time.