Analytics and the Future of Shopping
Digitization lies at the center of leading retailers’ strategies, and each new digital commerce innovation is designed to deliver better, faster customer insights. For example, mobile wallets not only provide a convenient way for consumers to pay, but they also create new data streams upon which data science can be applied to reveal new layers of information for marketers.
Using these insights, retailers can share real-time offers with greater personalization based on customer purchasing histories, individual store locations and geographies. In the future, we may even see how this information allows for more granular marketing campaigns that are based on the items customers are looking at in-store.
Building the shopping experience
Using consumer behavior and omnichannel data, retailers can begin to tailor the in-store experience to meet customer expectations. According to the multi-channel shopping survey by PwC, more than 80 percent of shoppers conduct online research before they buy electronics, books, apparel and other items, and this research plays a critical role in driving traffic to physical brick-and-mortar outlets. This is why it’s imperative that retailers focus on ensuring the right information is available to consumers during this phase of the buying cycle.
Retailers also track the in-store journey of consumers using mobile apps. For instance, Hillshire brands uses iBeacons to track shoppers' journeys through the aisles of a grocery store and sends customized discount coupons or ads for their craft sausages when the shopper approaches that section of the store.
Beyond marketing more strategically, retailers armed with data science can achieve other business benefits, such as leaner operations and better control over enterprise-wide assets by taking advantage of predictive analytics capabilities to determine inventory, assortment and pricing models. Walmart is one such retailer that has updated its mobile app with a “Search my Store” feature. The application allows in-store shoppers to search using keywords and product names, to find real-time inventory, pricing and accurate in-store location. This gives the shoppers a digitally enhanced experience. The app also has the potential to sell ads in the future—for example, if a shopper searches for men's shoes, Walmart could sell Nike or Reebok an ad space at the top of the search results page to display their discount coupons or product offerings.
Both of these examples illustrate how analytics is being applied to help guide a customer’s experience in real-time.
All for analytics, and analytics for all
In many ways, the “store of the future” that centers on deeper penetration of analytics into every area of the store is here already. Each component of the shopping experience—from the types and assortment of products to pricing, upselling opportunities and identifying pain points—can be optimized, giving retailers the edge they need to thrive in a highly competitive marketplace. Ultimately, retailers will become so adept at using data to improve customer interactions that customers will come to expect and demand this refined personalization, rather than being wary of it.
Through leveraging both internal and external data, such as social media analytics and publicly available information, those retailers are able to better predict what marketing messages and offers will resonate with customers, leading to greater consumer loyalty. In lieu of chasing every trend or waiting for industry patterns to emerge from macro data sets, those retailers can strategically take advantage of opportunities that resonate most with their audience and maximize the impact of their marketing budgets.
Retailers that aren’t applying analytics to all areas of their operations, including marketing, fall a little further behind every day. The retailers that perform well are likely those with the largest technology investments as part of their business strategy. And since many retail brands are all competing for the same dollars from consumers, those that listen to data to figure out how, when and where customers are most likely to spend are best positioned for success.
Venkat Viswanathan is founder and chairman of LatentView Analytics, a global data analytics firm.