As the Principal Customer Success Manager of our Media and Publishing Vertical, I work with many publishers that face a common challenge: a significant number of one-and-done visitors who leave their site after reading just one article. To drive growth and profitability, it’s essential for publishers to increase the average number of page views per session. By encouraging readers to explore additional content during their session, publishers can establish a stronger relationship with their audience and foster brand loyalty.
Delivering personalized content recommendations based on first-party data holds the key to helping publishers turn casual visitors into devoted readers. In this blog post, we will explore three strategies that can help publishers like you increase reader engagement, retention, and revenue through personalized content recommendations. For more in-depth information on using personalized content recommendations, including how you can get started, download our complete guide now.
Understanding the Value of Higher Page Views per Session
Before diving into the strategies, it’s important to understand why increasing the average number of page views per session matters. It's not just about boosting numbers – it's about achieving tangible results:
Progressing casual visitors to devoted followers: Turning one-time visitors into regular readers is crucial for subscription-based publishers. Higher engagement leads to more conversions from non-subscribers to paying subscribers.
Guiding readers to premium content: Personalized recommendations can guide readers toward locked content and paywalls, increasing the propensity to subscribe.
Amplifying ad revenue: Publishers reliant on ads benefit from more page views. By using content recommendations, you can increase the average number of page views per session and thereby increase the number of ad impressions per session. You can also drive readers to sponsored content, boosting traffic and revenue.
In essence, regardless of your revenue model, personalized content recommendations can significantly enhance reader engagement and revenue generation.
The Role of Unified Profiles in Recommendations
While editorial teams and content management systems are often responsible for curating content recommendations, a customer data platform (CDP) like BlueConic can offer distinct advantage. By automatically collecting and storing various data points about individuals in unified, actionable profiles, a CDP enables you to understand your readers in real time and automatically deliver personalized content recommendations based on their behaviors and preferences.
Strategies for Effective Personalized Content Recommendations
#1: Timing
A common starting point for content recommendations is exit intent prompts. These pop-ups appear when visitors indicate their intention to leave your site. Exit intent prompts can encourage various actions, whether it's reading another article or signing up for a newsletter. With a CDP, you can precisely target individuals with personalized calls to action based on where they are in their journey, such as encouraging content exploration for first-time visitors and newsletter subscriptions for more engaged readers. If you’re not sure where to start, try conducting A/B test to determine which exit intent prompts perform better for your audience.
#2: Placement
Once exit intent recommendations prove effective, consider testing other locations on your site, such as the right rail, middle of articles, or end of articles. Testing will enable you to understand which locations perform best. Then, and as you mature, you can use advanced CDP capabilities, like lifecycle orchestration and next-best-action modeling, to optimize placements for each reader.
#3: Algorithms & Audiences
Finally, various algorithms offered by a CDP like BlueConic can help you vary your approach to personalized content recommendations. For example, a breaking news algorithm suggests articles with the highest views within the last few hours, making it suitable for first-time visitors. A personalized recommendations algorithm tailors recommendations based on the reader's past content consumption, while the collaborative filtering algorithm suggests articles read by similar readers. Publishers should run A/B tests with different algorithms to identify the most suitable approach for various audiences.
Leveraging Return on Investment (ROI)
Before setting up your first personalized content recommendation test, it’s important to understand what an additional page view is worth to your organization. Are you looking to boost ad revenue? Increase subscriptions? Create operational efficiencies? Using a CDP can have a substantial impact on all three of these key performance indicators, so make sure you design a personalized content recommendations strategy that’s aligned with your business goals.
Let’s Get Started!
Personalized content recommendations hold immense potential for publishers seeking to elevate reader engagement and revenue. By harnessing the full potential of their first-party data, publishers can turn casual visitors into devoted followers and amplify their revenue streams. Download our guide to help you get started on your journey toward optimized engagement today, and if you need additional guidance, please don't hesitate to reach out – we're here to help!
