Boost App Engagement with Channel Pattern Analytics in PubNub Insights

Let's say all your private chat channels just dropped 20% in user engagement this week. Would you even notice? You probably wouldn't if you're looking at your channels one by one.

Nothing stood out on its own. But grouped, the pattern is clear — and clearly a problem. Spotting these kinds of shifts early is essential for taking real-time action before the damage adds up — whether that means employing a new strategy to improve user satisfaction, reallocating resources, or rethinking your approach altogether to improve retention rates.

You know that saying, "you can't see the forest for the trees"? It’s when you’re so focused on the little things that you miss what’s going on. That’s exactly what can happen when you’re digging into individual metrics — you might miss the bigger trends that actually matter.

But it can go both ways. Sometimes you’ve got the forest but no idea what the trees are doing. For example, say your dashboard shows a drop in user retention for all video-room channels. You see something’s off, but you can’t immediately tell which specific room is underperforming. So you ping someone on the data team or dig through logs to isolate the channel. You’re stuck waiting, context-switching, and losing momentum — all because you couldn’t easily zoom in from the big picture to the details.

In PubNub Insights, we introduced a new feature called Group by Channel Patterns, which instantly shows both the forest and the trees. In other words, you don’t have to choose between high-level clarity and low-level control when you aggregate data – you have all of the real-time data from your backend that can help improve monetization, reduce churn, and optimize your real-time communication for your platform.

Let’s break down what this means for you, why it matters, and how teams in data-rich industries like sports, media, and entertainment already use this to their advantage to better understand user behavior and improve user interactions.

The bottom line: You need both

The real power of channel pattern grouping lies in how easily you can move between the forest and the trees. The high-level view tells you where to look. The detailed view tells you what to do.

With Insights, you don’t have to switch tools or build new dashboards just to connect the dots. Everything in one place allows you to streamline your operations — whether you’re adjusting creative on a single campaign or planning your next big strategic move to increase your monthly active users (MAUs).

When to look at the forest: The high-level view

Imagine standing in the middle of a dense forest. You can see the trees around you, but without a map, it’s easy to lose your bearings. Grouping your metrics by channel patterns gives you that map. It lets you zoom out and view your digital landscape from above, spotting trends and strategic signals across your business.

Why it’s powerful:

• See cross-channel patterns without getting lost in details

• Identify macro trends like engagement spikes/drops across channel types

• Uncover group-level anomalies (e.g., all “public channels” underperforming)

• Make strategic decisions fast, such as where to increase engagement, shift resources, or test

Use it when:

• You’re kicking off your analysis and need assistance in onboarding.

• You're reporting to leadership on overall performance

• You’re making strategic decisions or allocating resources

• You're planning or reviewing multi-channel promotions

When to zoom in on the trees: The detailed view

Once the big picture points you in a direction, it’s time to zoom in. This is where you get into the specifics, because the real action often hides in the details.

Why it’s powerful: Spot channel-level issues or standouts within a group Identify which specific channels need attention Make precise, tactical decisions instead of broad assumptions

Use it when: You’ve spotted a pattern at the group level and want to investigate deeper You’re fine-tuning performance on specific platforms or creators You need to isolate performance for A/B tests, new launches, or experiments