Automation in Media & Entertainment: How to Boost Time Spent on Site with Personalized Recommendations?

A visitor reads a single article on your portal and bounces. Their visit lasted 90 seconds, and your ad algorithm only managed to serve a single banner ad. Meanwhile, on a competitor’s site, that same user might get up to five recommendations, two of which will keep them engaged for another 8 minutes.

Marketing automation in the media and entertainment industry solves this exact problem more effectively than any other tool. In this article, I will show you how.

Why Time Spent on Site Is a Metric You Can’t Ignore

Time Spent on Site (TSS) per session is a critical metric that directly impacts your bottom line. In CPM and programmatic advertising models, more time translates to more impressions and higher ad revenue. In subscription models, an engaged user is one who renews their plan. In VOD models, high TSS correlates with higher view counts per episode and organic word-of-mouth recommendations.

Media industry analysts agree that TSS is one of the strongest indicators of user retention—even more powerful than unique visits or newsletter CTR. It makes perfect sense: a user who spends 10 minutes a day on your platform is worth significantly more than one who drops by once a week for 30 seconds.

However, TSS doesn’t grow on its own. Content must reach the right user at the exact right moment. And doing that at scale requires marketing automation—with thousands of users, you simply cannot manage this manually.

How Does Marketing Automation Drive TSS?

The mechanism is straightforward: a marketing automation platform collects behavioral data (what users read, watch, or click), processes it in real time, and serves content tailored to their specific needs—either via email or through dynamic on-site personalization.

The key components here include:

• Next-content recommendations (articles, episodes, or videos) delivered immediately after the current piece of content ends.
• Dynamic “For You” blocks placed on the homepage and across landing pages.
• Personalized push notifications and emails designed to bring users back to the platform.
• Engagement scoring that detects exactly when a user starts slipping away, automatically triggering a retention workflow.

Mechanisms of Personalized Content Recommendations

There is no one-size-fits-all recommendation algorithm. Effective marketing automation systems combine multiple approaches simultaneously.

Behavioral Recommendations

The “most honest” form of data; it measures actual rather than declared interest. It is based entirely on what the user has historically done on the site—which articles they scrolled to the bottom, and how much time they spent in a given category.

The system quickly learns that a user reading three consecutive articles about the Premier League wants more football content, even if they haven’t explicitly set any preferences. The next recommendation becomes obvious.

Contextual Recommendations

These recommendations factor in the current context of the session: time of day, device type, location, and even the weather. A news portal might display different content in the morning (short news bites to go with morning coffee) and different content in the evening (deep-dive analyses). A streaming service at 10:00 PM will suggest an action movie rather than a nature documentary for kids.

“Users Like You”

This approach groups users with similar content consumption patterns and recommends what other similar cohorts rated highly. It works exceptionally well in VOD and music streaming services where the content library is massive.

However, its main weakness is that it requires a large volume of data, and the system knows very little about the preferences of completely new users.

RFM Segmentation in Media

The RFM (recency, frequency, monetary) framework, well-known in e-commerce, also works in media with a slight modification—we swap monetary for engagement value.

• Recency – when was the user last active?
• Frequency – how often does the user return to the service?
• Monetary / Engagement – how much content does the user consume? Do they pay for a subscription? Do they click on ads?

A user with a high RFM score is a premium subscriber who reads content daily. Conversely, a low RFM score indicates someone who hasn’t visited in a month. Naturally, the latter requires a completely different communication strategy than a regular visitor.

iPresso Marketing Automation allows you to build dynamic segments without the need to manually update lists; the segment updates in real time along with the user’s behavior.

“The most common mistake in media? In my opinion, it’s that all users get the exact same recommendations,” says Jakub Wyciślik, marketing automation expert at iPresso. “This happens because segmentation is done once and never updated. But people’s interests change.”

Recommendations Based on Browsing History and Sequences

Advanced marketing automation systems don’t just look at isolated events; they look at behavioral sequences.

Example: A user reads an article about a TV series, then browses reviews, and then searches for information about the actors. They are clearly ready for a recommendation of the next season or a similar title. The sequence tells a much deeper story than any single action alone.

Real-World Applications of Automation in Media & Entertainment

News Portal: Retaining a User After the First Article

The typical problem: A user lands on a specific article from Google, reads it, and leaves. Bounce rate hits 70–80%, and TSS is under 2 minutes.

The solution: An automated trigger is fired when the user reaches the end of the article. In real time, the system fetches their profile (if logged in) or analyzes the current session (if anonymous) and displays three personalized recommendations in a dynamic block right below the content.

The real-world impact: Portals that have implemented behavioral end-of-article recommendations report a 30–60% increase in page views per session. TSS grows proportionally.

VOD Service: Combating “I Don’t Know What to Watch”

Paradoxically, a frequent reason for canceling a VOD subscription is the paradox of choice—having a massive content library without a proper guide. In reality, a user wastes 15 minutes browsing the catalog, fails to choose anything, and logs off.

Marketing automation solves this by:

• Personalizing content on the homepage.
• Providing “Continue Watching” recommendations.
• Sending push notifications when new content drops in the user’s followed categories.
• Triggering an automated email after 7 days of inactivity (e.g., featuring a list of missed premieres).

Online Publisher: Reactivating Inactive Subscribers

A user paid for access but hasn’t logged in for three weeks. Their subscription is about to expire, and there’s no sign they intend to renew. In a traditional setup, you might miss this, but with marketing automation, a reactivation workflow kicks in.

Example of a reactivation workflow:

• Day 7 of inactivity – a personalized email with a roundup of the best content from the past week.
• Day 14 of inactivity – a push notification featuring a new article from their favorite category.
• Day 21 of inactivity – a special offer or an invitation to a new topic-specific newsletter.

This type of automation is standard in platforms like iPresso. You set up the workflow once, and the system automatically decides which message to send and when, based on real-time user behavior.

Music/Podcast Platform: Session-Level Personalization

Music and podcast streaming has the advantage of background listening, so TSS can span hours. However, the hurdle arises at the start of the session: what should you recommend?

The algorithm takes into account:

• Time of day and day of the week.
• Recently played content.
• Skipped tracks (this is a negative signal—just as vital as clicks).
• Trending content among similar users.

“Data about what a user doesn’t want is just as valuable as data about what they click. A system that ignores skips only learns half the story,” points out Jakub Wyciślik, marketing automation expert at iPresso.

Data and Integrations: The Technical Foundations of Effective Personalization

No recommendation engine can work without a solid data infrastructure. In practice, this means:

• Real-time data collection.
• A unified customer profile combining data from various sources.
• Seamless communication between the marketing automation platform, CRM, and analytics systems.
• Continuous A/B testing of recommendations.

Data Protection vs. Personalization

GDPR in Europe regulates how behavioral data is collected and processed:

• Collecting only the data necessary for personalization.
• Obtaining consent prior to profiling.
• Providing the option to withdraw consent and delete the profile (the right to be forgotten).

The good news is that a significant portion of high-value personalization relies on anonymous session data or data from logged-in users who have already granted the necessary consents.

FAQ

Does marketing automation work for smaller portals, or is it only for large platforms?

It works for both, though the scope of implementation differs. Large platforms (like Netflix, Spotify, or Onet) build their own advanced recommendation engines. Smaller portals and publishers leverage SaaS tools like iPresso, which provide access to segmentation, automated emails, and reactivation workflows without needing to build a system from scratch. Even a basic workflow based on two or three user segments can significantly boost TSS and retention.

How long does it take to implement personalized recommendations?

It depends on the complexity and the state of your data infrastructure. Basic segments and automated reactivation emails can be launched within a few days. Full, real-time personalization with dynamic on-site blocks is a project that takes a few weeks, primarily due to integration with your CMS and analytics data layer.

Does personalization require the user to be logged in?

No, but logging in enhances the quality of recommendations. For anonymous users, the system uses session data (behavior during the current visit) and cookies (history from previous visits, provided they consented). This is called anonymous personalization—it’s less precise but still highly effective for contextual recommendations.

How do you avoid the “filter bubble” effect (showing users only what they already know)?

This is a real risk in systems driven solely by consumption history. The solution is incorporating “discovery content”—material outside the user’s typical comfort zone, introduced in a controlled manner (e.g., one out of every five recommendations). A well-tailored algorithm strikes a balance between exploiting known preferences and exploring new territories.

What types of content are best suited for automated recommendations?

Content with natural thematic links or an episodic nature works best: article series, TV show seasons, playlists, or episodic podcasts. One-off breaking news is harder to recommend because recency matters more than thematic similarity. In practice, robust systems combine both logics: recommendations of related evergreen content paired with a box featuring current news tailored to the user’s interests.

Want to see how it works in practice?

If you manage a media site, VOD platform, or online publishing house and are wondering how to implement personalized recommendations, book a free iPresso demo.

During the session, we will show you:

• How to build your first user segments based on your data.
• How to launch automated reactivation workflows in just a few days.
• What integrations are required for real-time personalization.

Fill out the brief and book your demo.

No obligations. No generic presentations—just a practical conversation focused on your specific use case.