Up Close & Personal with Dynamic Content Engines
Delivering personalized content to your audience not only makes them more likely to engage with your brand, but it also helps push them through the sales funnel faster.
The problem, however, is finding the right technology to deliver personalized results to consumers in real-time. Luckily, the Web is full of recommendation platforms that can be leveraged to distribute content based on factors like visitor interest, past behaviors and even someone’s place in the sales cycle. Learn more about five of these technology platforms below:
The BrightInfo recommendation engine identifies each visitor’s preferences in real-time and then displays relevant content options – from case studies to videos. The recommendations are displayed to visitors inside a small box, dubbed the BrightBox, and can boost leads by as much as 83 percent and content engagement by up to 182 percent with existing marketing material, according to the company.
Baynote’s content recommendation solution monitors visitor behaviors and interests and creates patterns from these observations. The platform then uses its observations to deliver relevant content to the visitor. This creates a personalized experience that can boost engagement and conversions. Moreover, the platform adapts as customer interests (or product inventories) change. According to the company, Baynote’s recommendations can increase the number of content pages viewed and time spent on site by up to 60 percent.
IBM Content Recommendations helps publishers deliver the right content to their audiences, based on factors like relevancy, business rules and timeliness. The platforms personalization components are 100 percent automated and continuously learn from crowd and individual data. It is important to note that the recommendations can be delivered through websites and applications, and publishers can measure the recommendations’ success through IBM’s analytics dashboards and native reports.
Monetate’s Media/Publishing solution helps users both attract and retain readers by optimizing content based on past and real-time behaviors, including content that has been searched for, viewed and shared. Plus, publishers can leverage advanced A/B/n and multivariate testing to learn what content recommendations works best for specific types of readers.
Outbrain is a bit different from the aforementioned platforms, as it can be used to recommend both internal content and third-party content. While recommending internal content will help publishers increase time on site and page views, recommending third-party content can provide publishers with a new revenue stream. The platform delivers content based on a variety of factors, including popularity, audience behavior, visitor preferences and context. In addition, publishers can distribute their content to other sites through the Outbrain platform, which can help them obtain more traffic and click-throughs.