Big Data Can Revitalize Retail Industry

by Sohini Bagchi    Dec 31, 2013


Today, more retailers are turning to Big Data and analytics solution to enhance the experience of the new-age customers that are increasingly becoming web, social and mobile savvy.In an exclusive interaction with CXOtoday, Sheshagiri Anegondi, Vice President, Technology, Oracle India discusses how big data and analytics is changing the face of retail industry. Excerpt.

How are big data and analytics influencing the retail industry?

The multiplication of retail channels and the increased use of social media are empowering consumers. With a wealth of information readily available online, consumers are now easily able to compare products and prices, whether they shop online or from physical stores. When consumers interact with companies publicly through social media, they have greater power to influence other customers or damage a brand. In order for retailers to capitalize on these and other transformations in the industry, they need ways to collect, manage and analyze a huge volume, variety and velocity of data. The insights can be leveraged to optimize product offerings and promotions, target consumers with give offers to specific customer segments and or even precisely target customers and drive higher returns and greater customer satisfaction. Even competitors’ customers can be tracked and analyzed to understand industry trends and customer propensity to buy certain products or services.

Big Data and analytics solutions can also help streamline functions like merchandising and supply chain and help them understand factors the performance of merchandise across channels. It can help them improve inventory availability, set a competitive, responsive pricing strategy and differentiate offers to specific customer segments and channels. Marketing can also use this technology to measure return on investment and report on performance from major initiatives to individual campaigns.

How should a retail company build a Big Data strategy?

To make the most of big data, retailers must equip their IT infrastructures to handle the rapid rate of delivery of extreme volumes of data, with varying data types. The infrastructure, required for analyzing big data, must be able to support deeper analytics such as statistical analysis and data mining, on a wider variety of data types stored in diverse systems; scale to extreme data volumes; deliver faster response times driven by changes in customer behaviour; and automate decisions based on analytical models. Some of the critical areas that a retail company should keep in mind while approaching Big Data are:

Align Big Data initiative with specific business goals: For this, he should properly align and prioritize big data implementation with the business drivers. This is critical to ensure sponsorship and funding for the long run.

Ensure centralized IT strategy for standards and governance: User departments may be tempted to buy in decentralized fashion which can result in IT standards and governance being compromised. Similarly, agility is another area that requires a greater attention.

Ensure security for Big Data - Retailers should invest in integrated security solutions to ensure that big data insights generated from the integration of old world structured data and the new world unstructured data is not compromised in any way.

Correlate Big Data with structured data - Analysis from big data in itself has limited relevance. It is when such findings are correlated with the existing enterprise data such as past purchase history, customer demographics etc., that the true value can be extracted in the form of for example better segmentation models or more targeted up sell schemes and so on.

Look at Big Data as an extension of existing information architecture: The real value is found when big data insights are combined with existing enterprise data but that is not easily done if separate silos are created which potentially do not follow the existing enterprise IT standards.

 How does Big Data help Retailers improve customer experience?

Retailers understand that improving customer satisfaction is vital. And it means more than simply tracking complaints. Combining structured data from sales, marketing and supply chain with unstructured or semi-structured data from surveys, syndication data and other outside sources can give retailers a new perspective of their customers. For example, merging structured with unstructured content to find underlying customer satisfaction issues allows enterprises to proactively monitor customer satisfaction levels. In many organizations, sales and customer service work in separate silos and customer feedback is often not allowed to flow freely between the different operations, resulting in ineffective distribution channels. However a COO would be interested in the convergence of sales information and call centre operations to get a holistic perspective of customer engagement. In addition, by tracking the social media and by analyzing feeds from twitter and Facebook, Retailers can find a correlation between product sales, support and customer voice to validate the true issues impacting customer satisfaction. Another customer satisfaction issue solved by Big Data is to identify the most valuable customers from a 360 degree view; to be able to reward them with offers and benefits relevant to a loyalty program.

How does Big Data analytics help an e-retailer?

The relevance of Big data solutions is even more for online retailers as they strive to balance back-end challenges while sprinting to deliver engaging, consistent user experiences that capture more mind share and wallet share. E-retailers cannot just assume that customers will visit their Websites. Instead, they must track those customers across a variety of digital devices and serve up the right information at the right point in time through e-mail campaigns, search engine optimization (SEO), search engine marketing (SEM), onsite search, and customized landing pages. In addition data analytic solutions are helping e-retailers identify for example the consumers who are more likely to make a purchase if offered certain promotions as well as those who would not be swayed at all. Such tools are especially important for an e-retailer in a scenario where only a small percentage of site visitors ever actually make a purchase.