Advanced Analytics: Cohort Analysis for X Growth

Most 𝕏 analytics treat your audience as a single undifferentiated group. Follower count goes up or down. Engagement rate rises or falls (see what counts as good engagement). The metrics show what happened without revealing why.

Cohort analysis provides deeper insight. By grouping followers based on when they joined or how they found you, you can understand which growth strategies produce the best results and which followers become your most engaged audience.

What Cohort Analysis Reveals

A cohort is a group of followers who share a common characteristic, typically the time period when they followed you or the content that attracted them.

Analysing cohorts reveals patterns hidden in aggregate data:

Which months produced your most engaged followers? A spike in followers during a viral moment might have added volume without quality.

Which content themes attract lasting engagement? Followers who arrived through your productivity content might engage more consistently than those who arrived through your hot takes.

Where does retention break down? Understanding when and why followers disengage helps you address the root causes.

How do different acquisition channels compare? Followers from replies, threads, viral posts, or external sources may behave very differently.

Setting Up Cohort Tracking

𝕏's native analytics don't support cohort analysis directly, so you'll need to build a simple tracking system. Start with monthly follower snapshots by recording your follower count on the first of each month and comparing month-over-month changes to identify periods of high growth. Add content logging by tracking what you posted each week and noting which posts performed above average, creating a record you can correlate with growth periods. Finally, do engagement sampling by periodically noting who engages with your content. Are these long-term followers or recent additions? Manual sampling provides qualitative insight that numbers miss. A spreadsheet is sufficient for basic tracking. More sophisticated approaches require external analytics tools that offer historical data access.

Time-Based Cohorts

The simplest cohort analysis groups followers by when they joined.

Analysis approach: Compare engagement rates across followers from different periods. If followers from six months ago engage more actively than followers from last month, your recent content might be attracting a less aligned audience.

Several patterns are worth watching for in this analysis. Declining cohort quality over time suggests your growth tactics are prioritising volume over fit. Consider whether recent content or engagement strategies are attracting the wrong audience. Improving cohort quality suggests your targeting is sharpening and your content is reaching increasingly relevant people. Specific periods with unusually high or low quality cohorts point to particular content or events worth analysing in detail.

Source-Based Cohorts

Grouping followers by how they found you reveals which acquisition channels produce the best results.

Common sources include viral posts that reached beyond your usual audience, threads that demonstrated deep expertise, reply engagement that caught attention, external mentions from larger accounts, and cross-promotion from newsletters or other platforms.

To the extent you can track it, note which new followers mention how they found you (some will say in their follow or DM). Over time, patterns emerge about which sources correlate with lasting engagement.

If viral posts produce followers who quickly disengage while reply-acquired followers stay active, that insight should inform your strategy allocation. This often explains the virality versus growth tension.

Topic-Based Cohorts

Followers who arrived during periods when you focused on specific topics may have different interests and engagement patterns.

Analysis approach: Correlate content themes with growth periods. If your productivity content attracted followers in Q1 and your marketing content attracted followers in Q2, do these groups engage with different subsequent content?

This analysis has several applications. Understanding which topics attract your best audience helps prioritise future content. This informs your content pillar decisions. Identifying topic mismatches explains engagement drops, since followers attracted by an old topic may disengage if you shift topics significantly. Planning content around audience preferences rather than just your interests can improve retention.

Retention Analysis

The most actionable cohort insight often concerns retention: when and why do followers stop engaging?

30-day retention. What percentage of new followers engage with your content within 30 days of following? Low retention suggests either content mismatch or poor follow-up.

90-day retention. How many followers from a cohort are still engaging after three months? This filters out temporary interest and shows lasting connection.

Churn patterns. When do followers typically disengage? If most churn happens after specific events (like content pivots or posting gaps), you've identified intervention points.

Practical Limitations

Pure cohort analysis on 𝕏 is challenging because the platform doesn't provide the underlying data directly. You're working with proxies and samples rather than complete information.

Acknowledge uncertainty in your conclusions. Directional insights ("followers from Q2 seem more engaged") are more reliable than precise measurements ("Q2 followers have 23% higher engagement").

Focus on actionable patterns rather than precise quantification. Even approximate cohort understanding improves decisions.

Applying Cohort Insights

Insights without action are just interesting observations, so connect cohort analysis to strategic decisions. For content strategy, double down on topics that attract high-quality cohorts and reduce investment in content that adds followers without engagement. For growth tactics, allocate effort toward acquisition channels that produce lasting engagement, recognising that a smaller number of engaged followers often beats a larger number of passive ones. This often means investing more in reply-based growth. For retention interventions, address periods when retention drops. If followers disengage after three weeks, consider what happens at that point in their experience. For timing decisions, if certain periods produce better cohorts (perhaps when you're posting more consistently), prioritise maintaining that consistency.

Building the Habit

Cohort thinking is as valuable as formal analysis. Train yourself to notice:

When new followers arrive, what brought them? What does their profile suggest about their interests?

When engagement changes, is it across all followers or concentrated in certain groups?

When you shift content or strategy, how does it affect different audience segments?

This ongoing awareness provides continuous insight that periodic formal analysis can't match.

The Compound Benefit

Understanding your cohorts helps you build a more valuable audience over time. Each decision informed by cohort insight improves your trajectory slightly. Compounded over months and years, these small improvements create significant advantages.

The accounts that grow most effectively aren't just adding followers. They're adding the right followers and keeping them engaged. Cohort analysis is how you measure and improve that quality dimension. For broader context on what drives profile visits and follows, see diagnosing low profile visits.