Are You Getting the Most out of Automated Recommendations?
In the age of Big Data, there’s no shortage of historical information to crunch. You can spend countless hours analyzing visitor behavior, past purchases, customer ratings, and more—but smart marketers don’t stop there. They leverage all that data to make predictions about the future, and then optimize accordingly.
The most widely known example of predictive analytics is Amazon. Using past customer purchases, page views, reviews, and demographic info, the site offers targeted recommendations for cross-sells and up-sells. It also adds site-wide analytics to the mix, with its “What Other Items Do Customers Buy after Viewing this Item?” According to Venture Beat, these automated recommendations generate 35 percent of the site’s revenue.
Amazon isn’t the only site that can succeed with this strategy. Any business can mine historical data—from transactions to surveys to social media posts—to anticipate what prospects and buyers are likely to do next. And products are just the springboard: automated recommendations can also be used to serve up articles, trending news stories, videos, and other media. Of course, it’s easier when you have the right tools.
Maybe you already have product recommendations on your site—but are they working as well as they could be? Below are some of the essential capabilities to look for when choosing a recommendation tool:
Full automation: Product suggestions should be automatically generated based on a blend of historical and real-time visitor data, as well as global site data. Tools like Adobe Target offer auto-optimization delivery, which automatically serves up the top-performing products to the segments most likely to purchase them.
Marketer controls: You don’t want to have to rely on your tech support team every time you need to tweak your product recommendations. Built-in marketer controls allow you to change algorithm settings or product layouts on the fly, all in a single user-friendly interface. No programming or training required.
Effortless A/B testing: Inherent testing is essential to ensuring that you’re using the right algorithms and most effective placements for your recommendations. With built-in A/B testing, you can compare results and answer important questions, such as whether it helps or hurts to display cross-sells in the shopping cart.
Multichannel versatility: Cross-sells and up-sells should persist across all of your marketing channels, from your website to email campaigns to mobile apps.
Easy integration: You want a recommendations engine that integrates seamlessly with your other tools. For instance, if you’re using Adobe Analytics for data reporting, you can bring it into Target for segmentation and targeting. Target even plays nicely with third-party tools.
In today’s data-driven marketplace, predictive analytics is the future of marketing. Using automated, real-time recommendations, you can turn yesterday’s insights into today’s conversions.