What Should Online Retailers Wish for This Holiday Season?
Success in understanding social referral traffic will determine the future winners and losers in e-commerce.
Success in understanding social referral traffic will determine the future winners and losers in e-commerce.
Peak season in retail is off to a fantastic start with Thanksgiving Day, Black Friday, and Cyber Week all reporting a major boost in sales from 2010. This holiday season has reinforced the fact that while the hottest gifts change from year to year, the transaction drivers in retail remain the same. The five continue to be:
Despite this continuity, however, the pre- and post-purchase activities of shoppers have fundamentally changed as a result of social content and social networks, which can now be easily accessed from anywhere via mobile and tablet. Brands and retailers who test, track, and learn as much as possible about their social referral traffic patterns this holiday season will be in a position to reap tremendous benefits next holiday season. Social – and knowing how to navigate it – will produce winners and losers.
At this year’s Web 2.0 Summit in San Francisco, Mary Meeker shared a staggering statistic: the number of people on social networks today exceeds the number of Internet users in 2006. Seventy percent of the individuals on social networks today use Facebook. The influence of social content and social networks (particularly Facebook) on the buying process has been well documented in 2011. Consumers are using social prior to making decisions about what to buy, which brand to buy, and where to buy. More and more, consumers are engaging in the pre-purchase shopping stage, which has been fueled by the 60 percent-plus increase in mobile use this holiday season. Post-transaction, buyers are then sharing their experiences and writing reviews via social channels.
While these activities are increasing at an exponential rate, however, marketers do not have the traceable fact base that quantifies all of this social activity. Few have hard facts around the direct impact of social to document the case for social commerce. As Forrester Research’s Nate Elliott outlined in his November 2011 report, “It’s Time To Make Facebook Marketing Work,” Facebook offers limited data to marketers. Many CMOs can readily tell you how many Facebook fans their company has, but will offer blank stares when asked what their Facebook referral traffic is. But in reality, the most important measurement in social commerce is referral traffic, which can be easily tracked and reported. Without social referral traffic, there is no conversion or average order, and therefore, no sales sourced from social.
The core of the case for commerce: Sales = Traffic * Conversion * Average Order Value |
Attention CMOs: if you don’t already have one, get a login to your analytics and reporting suite (e.g., Google Analytics, Adobe/Omniture, IBM/Coremetrics). It’s important to note where your site’s traffic is being generated, and from what social channels your social referral traffic is coming from. Below is an example of an SMB’s Google Analytics Dashboard. This is a shot of Traffic Source/Medium with the confidential data and volume removed:
Here are a few simple but important takeaways from this screenshot:
In Q3, the SMB tested programming on the two social networks used most frequently by their consumers (see figures below). They ran LinkedIn programming the first three weeks in August and Facebook programming the last two weeks of September. During this two-month testing period, LinkedIn represented 7.25 percent and Facebook 1.78 percent of referral traffic, respectively. The SMB tracked and monitored the referral traffic, as well as the conversion, a critical fact base for their Q4 programming decisions. They continue to test and learn in Q4.
Investing time to experiment and learn about social as a net new source of traffic should be a high priority and an integral component to the business strategy of online retailers. Test it. Track it. Learn it. This time next year, those who have four quarters of data and learning experience from which to make programming decisions will be far more successful than those who do not.