Analytics and eCommerce go together naturally. Before the advent of convenient eCommerce tools for analytics, simple site analytics could be informative for eCommerce site owners. But analytics have come a long way just over the past few years, and the more advanced analytics can help enterprise-level businesses make the most of their online stores regardless of size.
Standard eCommerce analytics include things like information on where your traffic comes from, page interaction and navigation, conversion rate measurements, and “fall-offs,” which detail how and why people leave your website. But you need more, like information on shopping behavior during sales, check out analysis, how site content affects sales, and which activities lead to more conversions.
Differences Between Site Analytics and eCommerce Analytics
Where site analytics tell you, for example, the sources of page traffic, eCommerce analytics tell you the origins of sales traffic. Furthermore, they can not only tell you about page interactions but about shopping cart interactions. In other words, you can ask, “Where do my visitors convert, and where do they not convert?”
Three important things you can track through eCommerce analytics include
• Multi-channel funnel reports showing which traffic sources convert best
• Shopping behavior analysis to determine why shoppers abandon carts
• Product performance reports to show where revenues are generated
As an example, multi-channel funnel reports help you identify first and last channels before conversions and your best channel combinations. This allows you to focus more on productive channels in terms of page optimization, content creation, or advertising.
Identifying and Addressing Problems Using eCommerce Analytics
eCommerce analytics also help you uncover and address problems before they can negatively affect sales numbers. Shopping behavior reports can identify phenomena like:
• Visits with no shopping activity
• Products that are viewed, but not added to carts
• Products added to carts but not purchased
• Ratio of sessions that check out to abandoned sessions
These indicators often point to simple problems that can be fixed easily, such as by content-to-product linking, better shopping cart visibility, or increased clarity of pricing or shipping information. Other actions taken may include increasing stock of best-sellers, investing more in promoting highly profitable products, analyzing pricing to ensure it’s on-target, and creation / timing of promotions.
User Behavior and eCommerce Success
Advanced eCommerce analytics help you understand user behaviour better so you can tailor your site more to customer needs. User behavior analytics can lead you to take actions like
• Using targeted display ads for specific products
• Focusing social commerce efforts on visitors coming to your site from top converting traffic sources
• Retargeting people who showed interest in specific products by offering special promotions on those products
• Identifying poorly performing products and adjusting marketing and social commerce efforts toward improving their sales performance
Segmenting Users for Better eCommerce Success
One-size-fits all marketing and social commerce strategies are not ideal. Your visitors are probably quite diverse, and your site analytics can let you know for certain. Advanced analytics can help you customize pages to maximize conversions based on traffic characteristics. Additionally, you can use segmentation as a framework for creating intentional, well-planned A/B experiments on your site to help you provide a more engaging shopping experience.
One example of segmentation that could be valuable to your eCommerce strategy is differentiating mobile from non-mobile users. You can even get tools that let you segment by device type so you can run experiments and be confident that a single device type isn’t skewing results. You can also segment based on things like repeat shoppers versus first-time site visitors, users who have purchased recently versus users who made purchases a long time ago, and high “cart value” purchasers versus casual buyers.
Predictive Analytics and eCommerce
Predictive analytics combines techniques including statistical analyses, data mining, and operations research to help you understand data in the proper context. Combined with forecasting models, you can use predictive analytics to make more accurate predictions about future buying trends. Predictive analytics used to be reserved for bigger enterprises, but today tools are available to enterprise level eCommerce organizations of all types and sizes, along with the great insights they can reveal through processing and analysis of big data. Predictive analytics solutions customized for eCommerce allow eCommerce managers to make smarter strategic and operational decisions. Predictive analytics combined with customer engagement is expected to ultimately lead to a world of “hyper-individualized experiences,” according to research firm Forrester.
Analytics are critical for your eCommerce and social commerce strategy. eCommerce analytics go beyond traditional site analytics, and today include exciting techniques like predictive analytics that can help your organization maximize sales numbers while wasting less time on techniques that are less beneficial.