This holiday season, customer-centric brands have an opportunity to up the ante for their consumer shopping experiences with an ace in the hole: Big Data.
Savvy e-commerce brands, like Macy's, Sears, Wal-Mart, and eBay, are focused on capturing not just data but also the insights driven from the data, in order to make informed decisions about their customers' shopping habits and behaviors, both online and offline, from Black Friday through the New Year.
The emergence of new structures for rich data allows for the unprecedented ability for brand marketers to truly deliver highly personalized, one-to-one e-commerce experiences. As we know, today's consumers are pros at ignoring mass media and listening only to what they want to hear. But, with access to and effective use of Big Data, brands can enhance consumer experiences with contextual relevance that allows for engagement at precise moments when customers are most persuadable. While profile and behavioral data can identify in-market consumers, situational data attributes—including location and time of day, mood and social situation, product availability and remaining inventory, limited time offers, and seasonality—can reveal contextual insights that often hold more relevance for consumers than their persona, segment or online history.
Applying contextual relevance to digital customer experiences is often overlooked by marketers, yet it can make all the difference in delivering value propositions that get to the heart of a customer's buying criteria. The key—and often the challenge—is harnessing situational data in real time to deliver shopping experiences at the most appropriate engagement points throughout the customer lifecycle.
Personalized Shopping From Clicks to Bricks
Though Big Data is captured online, it doesn't mean the insights cannot be used in support of brick and mortar stores. A strategic imperative for many retailers today is bridging the gap between in-store and online shopping experiences, and e-commerce brands can use customer-profile, historical, and situational data to engage consumers within both channels.
If a visitor is researching electronics online and comparing prices on pure-play websites, a big box retailer can use that behavioral information to deliver a targeted, personalized advertising experience that uses store data, including the nearest physical location and available inventory, with a message such as, "buy now and pick up in-store."
In addition, if a retailer's goal is to generate foot traffic, using retail store data, such as in-store promotions or upcoming Black Friday sale dates, can drive the customer to purchase in the nearest retail location, too. Word to the wise, though: the ability to use Big Data here in real time is key. If store location, sale dates, promotions, or inventory information is inaccurate, the result is a negative customer experience and a lost conversion.