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‘Big Data’ Can Be Hard to Harness
Online retailing has existed for nearly 20 years, and product recommendations have been around for almost as long, due to the pioneering work of Amazon, which applied for a patent on its collaborative filtering technology in 1998. Since then, a majority of large retailers have adopted online product recommendations. Yet many of these implementations are still fairly primitive because they fail to understand an online shopper’s real-time product needs.
That’s likely to change, however, thanks to the power of “Big Data.” Major ecommerce players like Netflix, Wal-Mart and eBay are leveraging affordable, open-source Big Data tools to deliver real-time, personalized shopping experiences. And they say the efforts are paying off, with higher customer spending and improved retention rates.
“In addition to monitoring shopping behavior on their sites for clues about customers’ immediate purchase intentions, these early adopters are also trying to gain insight into broader consumer trends based on the likes and interests people express on social media,” said Jeffrey Grau, eMarketer principal analyst and author of the new report, “How Retailers Are Leveraging 'Big Data' to Personalize Ecommerce.” “Retailers see opportunities to use this data not only to better personalize product recommendations but also to influence merchandising decisions on their sites, and in the case of multichannel retailers, at the local store level.”
Half of the US web retailers in the 2011 edition of Internet Retailer’s “Top 500 Guide” said they were using personalization on their ecommerce sites, up from 32.6% of retailers that said so in the 2010 edition.
But many retailers’ personalization efforts are unsophisticated. “Sites still have a long way to go to improve product discovery and findability and to streamline their purchasing processes,” Dan Darnell, senior director of product marketing at Baynote, a provider of retail personalization platforms, told eMarketer.
Retailers themselves recognize the shortcomings of their efforts at personalization. When the e-tailing group surveyed 131 mostly large and mid-sized US web merchants in Q3 2011, it found that more than half gave themselves poor marks.
Various obstacles can derail retailers from effectively using Big Data. Organizational issues, problems making data actionable and difficulties getting down to a personal level were behind the top three challenges marketers faced in using Big Data, according to a February 2012 joint survey of US marketers by Columbia Business School and the New York American Marketing Association.
“The good news for smaller, less-technically savvy retailers is that a new group of product and service providers has come along with platforms that offer some of the personalization muscle being flexed by the industry leaders,” noted Grau.
eMarketer forms its estimates for advertising spending on Twitter and LinkedIn through a meta-analysis of estimates on consumer usage, marketer usage, ad pricing and impressions, as well as revenue estimates from research firms, company reports and other sources. eMarketer also conducted interviews with industry executives who provided perspective on their companies’ advertising business and revenues.
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