For the last two decades, retailers have been challenged to meet the needs of increasingly fickle consumers. Thanks to mobile devices, today’s shoppers are always on, which empowers them to research available options, check ratings, compare notes with their personal and professional networks and switch brands. They now expect to interact with retail brands with the same immediacy that they interact on social networks.
This trend shows no signs of slowing, and to remain competitive and meet the expectations of today’s customers, retailers need to be able to provide “streamlined and personal experiences both online and in stores,” according to Forrester. This requires both established and modern retailers to adopt hybrid strategies that embrace physical and virtual stores simultaneously.
To help create the optimal customer experience, retailers are turning to data virtualization to facilitate their retail transformation and using the technology to support the following strategies:
Product Optimization: Retail companies need to be able to set up compelling seasonal assortments of products in each store, and in the right categories on the web, localized for each viewer. However, product information is often fragmented, hindering these efforts.
Data virtualization enables a unified, real-time view of products, inventory availability, color and size options, and other relevant factors across all data sources no matter how fragmented. This enables retailers to assemble effective product assortments that are tailored for each specific location, and potentially for each specific customer, in an online setting.
Competitive Intelligence: Retail companies need access to competitive intelligence, often in real time, to optimize pricing. However, gathering intelligence takes time, especially when activities involve manually scanning the web.
Data virtualization can assist in establishing a competitive intelligence hub that gathers all applicable sources. Because data virtualization accommodates unstructured data, such as competitive intelligence that may appear on the web, data virtualization can automate web harvesting efforts, and the technology can deliver the consolidated information over standard reporting tools.
Quality Control: Quality control is a challenge for retailers because of the sheer number of pass-offs and locations products make on their way from the manufacturer to the customer’s hands. To streamline such efforts, companies need to be able to visualize the entire journey, even going back to the original factory if necessary. But this data is fragmented across the many parties involved.
Data virtualization offers a seamless, non-intrusive way to unify data that is geographically, organizationally and technologically disparate, and present it in near-real time, so that stakeholders can gain full visibility into all necessary background data for prioritizing and fixing quality control issues.
Customer Propensity Analysis: Understanding customer needs and propensities is the key to providing better service and exposing more successful upsell opportunities. On the web, this process is challenging enough, but it becomes increasingly difficult to gain a complete view of the customer that combines online and in-store transactional activity, products purchased and calls made to customer service. This is because information is often stored in different, traditionally incompatible systems, such as analytical and transactional systems.
Data virtualization creates a unified view of each customer and makes it available to all representatives across the company, online and off, so customers feel as though the online and offline stores are part of the same company. Also, with an extremely rich amount of knowledge about each customer, each representative is in a better position to make relevant offers.
Multi-Channel Integration and Analytics: Similarly, if a customer contacts a company by phone, text, in person or over email, they expect each recipient to have the same information. Unfortunately, that is often not the case, because each channel reaches a different department within the company, and they might not have the same information at the same time.
Just as data virtualization creates a single view of the customer, it can also create an omnichannel view of the customer. By providing each channel recipient with the consolidated view of the customer, customers feel as though talking with the retailer is a seamless experience, no matter how they talk to the retailer.
Targeted Marketing and Product Customization: Retailers are trying to deliver more personalized marketing communications to customers, reflecting their proper segments and each customer’s unique history.
Again, data virtualization facilitates this process with complete, integrated, dynamic views of each customer in motion, drawn from myriad sources without replication.
Supply Chain Optimization: Finally, data virtualization can also play a key role in logistics. Retail companies can leverage data virtualization to create virtual data marts, serving data from all applicable systems to enable teams to access the required data ,and easily aggregate positions by currency, geography, products and other parameters.
In this way, data virtualization facilitates NSFR, LCR and other types of liquidity ratio reporting, and enables companies to quickly build inventories that combine numbers from a store, in transit, the factory, suppliers and other parties, to better predict stockouts and overstock.
The retail sector is undergoing a sea of changes in attitudes and operations, with the biggest changes happening in brick-and-mortar. Companies now realize how the power has shifted considerably away from brands and towards the consumer, fueled by the mobile, digital and social revolution. At the same time, some companies also have become savvy in using the power of information technology to reshape omnichannel customer experience, customer loyalty, contextual marketing, merchandising and pricing optimization.
Retailers now understand the value of data, but unlike traditional data integration solutions that move a copy of the data to a new, consolidated source, they are looking at data virtualization to create a completely different approach. Rather than forcing retailers to move data from different sources and then combining the data in a new location, data virtualization provides a view of the combined data, leaving the source data exactly where it is. Retailers no longer have to pay the costs of moving and housing the data, and yet they still get all the key benefits of traditional data integration. Because data virtualization accommodates existing infrastructure in its existing state it provides retailers with real-time data views needed to drive their digital transformation efforts.
Lakshmi Randall is Director of Product Marketing at Denodo, the leader in data virtualization software. Previously, she was a Research Director at Gartner covering Data Warehousing, Data Integration, Big Data, Information Management, and Analytics practices. To learn more, visit www.denodo.com or follow the company @denodo or the author on Twitter: @LakshmiLJ