Big data analytics is empowering retailers to transform the customer experience, optimize pricing and marketing tactics, and reimagine supply chain strategy.
American consumers’ shopping behaviors have shifted dramatically over the past year in response to everything from tariffs to the COVID-19 pandemic, and retail companies have needed to pivot quickly to adapt to the changing landscape and claim and/or retain competitive advantages.
For many retailers, finding ways to engage in data-driven decision-making has been the key to keeping up with consumers’ rapidly evolving demands and expectations. Analyzing data on their customers, sales, marketing tactics, and supply chains helps retail companies track trends, identify problem points, and build effective strategies that drive revenue.
Common Use Cases for Big Data in Retail
Big data is already “big” in the retail industry, as many major companies are already heavily invested in purchasing, collecting, and analyzing data. Retailers seeking creative, proactive ways to make the most of their data should consider leveraging it to achieve the following objectives:
Improve the Customer Experience
Retailers can capture data on their customers’ behaviors by implementing in-store mechanisms like IoT sensors and connected point of sale (POS) systems, tracking online traffic and purchasing patterns, and soliciting direct feedback through surveys. Gathering this data allows retailers to offer a more personalized shopping experience for every customer, whether in person or online (or in the transition from one to the other). Especially in the wake of the pandemic, many retailers are integrating tools like chatbots equipped with natural language processing capabilities into their e-commerce platforms. These types of AI applications provide more seamless, better-supported online shopping experiences to customers while also supplying retailers with additional consumer insights. A robust data analytics infrastructure is particularly essential for retailers who want to track online customer journeys and create more relevant web experiences featuring elements like promotions tailored to customers’ specific needs.
Retailers can also build more efficient and effective in-person shopping experiences by enabling salespeople to leverage real-time notifications on tablets and POS systems to provide known customers with targeted offers and assistance, or by changing how they stock their shelves based on current shopper preferences. On a larger scale, retailers can use data on peak shopping times, traffic patterns, and store hotspots to reimagine their approaches to store design.
Optimize Pricing and Marketing Strategy
Retailers can combine customer insights with inventory data and other sales data to optimize how they set pricing levels and target advertising campaigns. Using dynamic pricing can boost profit margins and help stores clear out excess stock. Retailers can use customer demand data, RFID-based inventory data, and digital price labels to optimize in-store pricing in real time. After Walmart’s industry-leading experiments with RFID tags, other major retailers have begun to deploy these tags in their stores to dynamically push promotions and discounts.
Further, retailers have access to more customer insights than ever before, which means they have more ways to optimize their approach to market segmentation. With increasingly cross-channel data on the customer journey and more powerful advanced analytics techniques, they can better pinpoint their most valuable customers and dedicate the bulk of their advertising budget to these high-value purchasers. Retailers can even cross reference real-time weather conditions, news cycles, and current economic patterns to identify additional targeting opportunities and key shopping moments.
Improve Supply Chain Efficiency
Big data analytics can help companies reimagine their end-to-end supply chain strategies, from manufacturing to distribution to transportation. To better balance supply and demand, they can leverage inventory data drawn from connected POS systems to dynamically adjust orders or request changes in production volumes. With real-time data, they can also make requests based on emerging situations, such as out-of-stock issues that require agile delivery rescheduling.
Retailers that control warehousing and distribution can also deploy remote sensors, RFID tags, and GPS to track the status of products well before they hit shelves. With this level of insight, they can better address supply chain bottlenecks and streamline processes. Some retailers are even speeding up their supply chains by using predictive analytics — Amazon, for instance, plans to ship some inventory to distribution points in anticipation of demand.
The Future of Big Data in Retail
Particularly in the wake of the COVID-19 pandemic, the future of retail is highly unpredictable. That said, come whatever may, leveraging big data can help retailers transform insights into action by shaping their strategies in near-real time in response to changes in the industry landscape.
As a growing number of retailers find innovative ways to put their data to use, the challenge for many retailers has become determining how to differentiate their data-driven decision-making from their competitors’. Doing so requires thoughtful strategy, experimentation, and cross-team collaboration as well as the technical capabilities to stand up data warehouses and/or data lakes that support easy, efficient data access — all of which is facilitated by working with a consulting firm with deep industry-specific expertise.
At SEI, we have extensive experience helping retail companies achieve game-changing results by leveraging data and analytics. Whether you’re just getting started or want to transform your existing approach, SEI is ready to help you develop a data strategy that keeps you one step ahead in a quickly changing retail landscape.
To learn more about how SEI can help your organization, contact us today.
