What This Means
The data paints a clear picture of how prospective students engage with peer communities on Instagram. Content posted during the day drives commenting through the afternoon. Then, hours later — typically between 8 and 11 PM — students act on that familiarity by following accounts and joining communities.
That 5-hour gap between peak commenting and peak following is the key insight. It means the right content strategy is to post during the day when engagement is likely, let that content marinate, and then have something waiting for students when they show up in the evening to follow through. The problem is that last part. When a student searches for and joins a peer community at 9 PM on a Wednesday — the single highest-intent action in this dataset — what do they find? An active community of peers? Or an account that last posted 4 hours ago with no one engaging?
The content side of the equation is working. The engagement side needs a different model.
Autopilot Student Engagement
The data makes a strong case that prospective student engagement can't be covered by a 9-to-5 workflow. But it also makes an equally strong case that it shouldn't need to be. Nobody should be expected to manage an Instagram account at 10 PM on a Wednesday. The solution isn't longer hours — it's infrastructure that doesn't require hours at all.
That's what peer-to-peer communities provide. When a MeetYourClass community account is run by prospective students themselves — posting their own content, commenting on each other's introductions, answering questions in DMs — the engagement happens organically on student time. There's no scheduling tool needed for 9 PM because that's just when students are naturally active. The community doesn't clock out because it was never clocked in. It's autopilot engagement.
This matters more now than it ever has. As early-action and direct-admit programs continue to expand, institutions are building admitted-student pools as early as October and maintaining them through the summer. That's a 9-to-12-month engagement window. No social media team has the bandwidth to sustain active, authentic community management across that entire cycle — especially not during the evenings, nights, and weekends where 67% of student engagement happens.
When you let students drive the community, the 9 PM follow doesn't land on a dormant account. It lands in an active conversation. The student who joins at 11 PM on a Sunday sees other prospective classmates posting, commenting, and connecting in real time. That's what builds belonging — not a scheduled post from 6 hours ago, but a peer who posted 20 minutes ago asking who else is applying for the nursing program.
The data says students are showing up on their schedule. Autopilot engagement means your institution has something waiting for them when they do — without requiring a single staff member to be online.
Methodology
MeetYourClass is a platform for prospective students to meet each other from application to move-in. MeetYourClass integrates with the most popular social media apps, automatically helping turn them into enrollment drivers.
Overview
We analyzed two types of Instagram engagement events from the MeetYourClass network: comments on community posts and account follows. All timestamps were normalized to each institution's local timezone so that 11 AM EST and 11 AM CST are grouped together, giving us a true picture of student behavior relative to their local time. Separately, we analyzed 136,692 posts from university Instagram accounts (distinct from MeetYourClass community accounts) using the same timezone normalization methodology to compare institutional posting patterns with student engagement patterns.
How Data Was Collected
Comments (102,490 events): We queried all user events classified as Instagram comments on MeetYourClass community accounts. For each event, we extracted the timestamp and the associated college ID, then converted the UTC timestamp to the institution's local timezone using the timezone stored in our college database. Events were bucketed by day of week and hour of day.
Follows (162,445 events): We queried all Instagram follow records for MeetYourClass community accounts where the account name contained "2029" or "2030" (indicating Class of 2029 and Class of 2030 communities). To avoid counting duplicate follow records from multiple data uploads, we grouped records by account and kept only the most recent upload batch for each account. Each follow event was then normalized to the institution's local timezone and bucketed by day of week and hour of day.
University Posts (136,692 posts): Separately, posts were collected from university Instagram accounts (the institution's own account, not MeetYourClass community accounts) tracked in the MeetYourClass platform. Publication timestamps were extracted and normalized to each institution's local timezone. Posts were bucketed by day of week and hour of day. This data is used for comparison only and is not part of the student engagement dataset.
Timezone Normalization
Each institution's timezone was pulled from our college database. The following timezone values were used: AST, EST, CST, MST, PST, and AKST, mapped to their corresponding IANA timezone identifiers. If an institution's timezone was missing, it defaulted to Eastern (America/New_York). Timestamps were converted using moment-timezone to extract the local day of week and hour.
Aggregation
For comments, follows, and university posts, we generated three levels of aggregation: by day of week (7 rows), by hour of day (24 rows), and by the full hour-by-day matrix (168 rows). Percentages are calculated against total events for each respective dataset.
