Chemi Katz is the CEO and co-founder of Namogoo, a digital journey continuity platform.
For more than a decade, business leaders have posited that “data is the new oil” and that comparison is more apt now than ever before. In the year 2022, the basic meaning of the saying—that data is essentially the fuel that can power business needs—is certainly correct. But even as data becomes more and more ubiquitous, it’s important to remember that, like oil, data in its crudest form actually provides minimal value; only by refining it can business owners fully maximize their data.
Ironically, the greatest challenge to extracting the full value of data is its quantity. In an increasingly digital world, our movements are being tracked almost constantly by smart devices in our homes and web tracking tools online. With a near-infinite number of data points, business analysts are presented with troves of information, only a small portion of which will actually generate helpful business insights.
And while this has been a trend for years, it’s increasingly true for retailers since the Covid-19 pandemic began in 2020. During the 2010s, e-commerce’s share of overall retail sales grew consistently, but online shopping was still a relatively small portion of the market. When the pandemic hit, and the world shut down, the trend accelerated, as consumers, suddenly forced to shop from their browsers, became more comfortable with online retail—and many continued to do so even after stores reopened.
Most traditional retail brands had e-commerce operations prior to 2020, but all of a sudden, the online channel became dramatically more important, and gleaning insights from web shopping data became paramount to improving conversion rates and answering other key questions.
Were customers typically navigating the site in the way it was designed? Were promotional discounts actually pushing the needle for customers considering making a purchase? While these were undoubtedly important questions, sifting through the massive troves of data to find the answers was a tall order. Further exacerbating analysis was the diversity of sources that data was collected in, from web analytics to social media ad platforms and much more—a range of different platforms, some of which compiled data in different formats, and none of which could easily “talk” to each other.
As data has exploded, big data solutions across business sectors have grown commensurately, and e-commerce is no different. Over the past year or so, a number of compelling technology solutions have emerged to bridge the gap between different data streams and then conduct large-scale trend analyses that can generate helpful insight.
With computing power exponentially more powerful than a human brain, these software solutions are already being used by retailers to great effect; their superhuman power also means that they can analyze these massive datasets extremely quickly when there is still time to pivot strategies, if appropriate.
For example, one North American retailer used a software platform that analyzed insights from various different data streams and was alerted to the fact that online shoppers with weak CPU strength were purchasing at a lower rate. Because lower computer capabilities would lead certain site elements—like photos and videos—to load more slowly, the customer journey was being disrupted for these shoppers, and that likely explained the low buy rates. The solution, determined by the brand, was to also feature a stripped-down version of the site that would specifically be displayed for slower computers and would improve the customer journey for those shoppers.
Another benefit to this sort of massive collection and analysis of data could include things like cross-checking shopping patterns with the weather—do people buy certain sorts of food more in the rain?—and then having eateries dynamically change their websites based on a shopper’s location and its weather forecast.
It should also be noted that for marketers, in particular, one of the most valuable aspects of these modern methods of data analysis is that the insights are derived from aggregated streams of generic data (the device being used, how the site is navigated, browser extensions), and not actual data about the individual shopper. As GDPR and other regulations proliferate, marketers have been charged with protecting consumer privacy. Modern software’s ability to analyze this non-PII (personally identifiable information) data and deliver meaningful suggestions is a godsend, offering personalization without breaching shoppers’ trust.
With software combining dozens of different data streams and then combing through them for insights, the value of data to online retailers is hard to overstate. In an extremely competitive business sector like retail, brands are always looking to gain an edge. And, looking five or 10 years down the road, there’s little doubt that the companies enjoying the most success will be the ones who learn quickly how to refine and harness the power of this data to deliver optimized digital journeys to their customers.