Many account managers running X (formerly Twitter) feel they don't know how much their posts are actually reaching people, can't figure out why engagement is low despite high impressions, or are looking for clear signals about what to improve. For these operators, the first tool to master is X's official analytics feature, "X Analytics." From per-post performance to follower trends and engagement breakdowns, it's the only standard tool that officially provides data-driven hints for account improvement.
On the other hand, beginners often have questions like "What changed in the 2024 update?", "How much can I use for free?", "How should I interpret impression and engagement numbers?", and "Which metrics should I look at first?" This article systematically explains X Analytics from the perspective of SNS operations practice—from the basic concepts and access methods, through screen layout, key metrics, purpose-based analysis procedures, advanced techniques, and common pitfalls. It's designed to help both beginners starting out with X Analytics and operators who already use it but want deeper insight.
Definition and Role of X Analytics
X Analytics is the official account analytics tool provided by X (formerly Twitter). It lets you quantitatively check performance data such as how many users your posts reached, what kind of reactions they got, and how your follower count is changing. It can be accessed from both the X app and the browser version, with data updated in near real-time to a few hours of latency, making it easy to check reactions right after posting.
The essential role of X Analytics is to provide a data foundation for identifying the gap between posts that performed well and those that didn't, and for forming improvement hypotheses. By tracking behavior-based metrics that surface-level numbers like follower count or "likes" can't reveal—impressions, engagement rate, profile clicks, link clicks—you move from running your account on intuition to running it on data.
The June 2024 Update | Paid-Plan-Only Access and New Metrics
You can't talk about X Analytics without mentioning the specification change made in June 2024. Before that, free accounts could view detailed data for the entire account, but after the update, account-wide X Analytics features became limited to users with X Premium or Premium+ paid plans. In Japan, the Premium plan starts at 980 yen per month, so serious use of X Analytics now generally requires a paid subscription.
The new analytics also redesigned the screen layout and visible metrics. It now uses a tab structure switching between "Overview," "Content," "Audience," and "Video," and new behavior indicators like "Bookmarks" and "Profile visits" are now displayed by default. Bookmarks in particular mean users actively saved a post to revisit later, so they're positioned as a key metric that the algorithm also tends to value highly.
Post Activity | Available Even on Free Accounts
Even without a paid plan, free accounts can use the per-post analytics feature called "Post Activity." Tapping the bar-graph icon (the activity icon) shown below each post reveals individual metrics for that post: impressions, engagements, likes, reposts, replies, bookmarks, profile visits, and more. You can't see account-wide trends, but it's plenty for understanding which posts performed well and what kind of content gets a reaction on a per-post basis.
What beginners should do first is use Post Activity before subscribing to a paid plan, to understand the tendencies of their own posts. By lining up the most recent 30 to 50 posts and comparing those with high and low impressions and engagement rates, you start to see the "growth pattern" of your own account. When you're ready to dig into account-wide analysis, upgrading to a paid plan to unlock full analytics features is the most cost-rational sequence.
How to Access X Analytics and the Screen Layout
Access from PC (Browser Version)
To access X Analytics from a PC browser, first log into X (x.com), then go through "More" → "Premium" (or the corresponding sidebar item) in the left-side menu and select "Analytics." Alternatively, you can type "analytics.x.com" directly into your browser's address bar. This brings up the analytics screen linked to your logged-in account.
The strength of the PC version is that the wider screen lets you survey multiple metrics at once, the larger graphs make time-series changes easier to read, and you can more easily export key metrics to CSV and aggregate them in other tools. For weekly or monthly reporting tasks, or for preparing materials for regular team meetings, working from the PC version is more efficient.
Access from Smartphone (App Version)
To access from the X mobile app, open the app, go to your profile screen, and select "Analytics" from the menu (≡) or the "Premium" section. If you only need per-post simple data, just tapping the activity icon (bar-graph mark) below each post opens Post Activity directly.
The strength of the smartphone version is that you can check quickly while on the move or out of the office, and you can instantly verify reactions right after posting. It suits real-time operational judgments, like watching how impressions grow within 1–2 hours of posting and deciding when to post a follow-up or extend a thread. On the other hand, the PC version is better for detailed analysis comparing multiple metrics or tracking time-series trends, so using each for what it's best at is the practical approach.
The New Analytics Tab Structure | Overview, Content, Audience, Video
The new analytics introduced after the 2024 update uses tabs at the top of the screen to switch between major sections. "Overview" is the account-wide summary, "Content" shows per-post performance, "Audience" covers follower attributes, and "Video" provides video-specific metrics—each tab represents a different angle of analysis.
Beginners should start from the Overview tab and first grasp the account-wide trends of impressions and engagement rate. Then move to the Content tab to check individual post rankings and extract common patterns from posts that grew. The Audience tab becomes useful once your follower count reaches a certain scale, and if you run video content, the Video tab is where you check things like completion rate—following this order keeps you from getting lost.
Key Metrics in X Analytics | What the Numbers Mean and How to Read Them
Impressions | Total Number of Times a Post Was Displayed
Impressions show the total number of times a post was displayed on a user's timeline, search results, profile page, and so on. If the same user sees the same post multiple times, it's counted that many times. This is a different concept from "reach" (the number of unique users reached). X Analytics doesn't display reach as a standard metric—impressions function as the basic indicator of how widely your content is spreading.
Higher impressions mean the algorithm is delivering your content to more users, which serves as a rough measure of "reach power" and "timeline exposure." However, high impressions alone with low engagement means users "saw it but didn't feel it was worth reacting to"—a sign to reconsider your content direction. The key mindset is that impressions and engagement rate should always be read together.
Engagement and Engagement Rate | Quantity and Quality of Reactions
Engagement refers to the total number of actions users took on a post. Specifically, it's the sum of likes, reposts, quotes, replies, bookmarks, link clicks, profile visits, hashtag clicks, and so on. The new analytics shows the breakdown of engagement by category, so you can also see what kind of reactions are most common.
Engagement rate is calculated by dividing engagement by impressions and multiplying by 100 (%). It indicates "what percentage of people who saw the post reacted to it" and is a key KPI for measuring content quality. Business accounts typically see engagement rates around 0.3–1.5%, while individual or growing accounts often achieve 2–5% or more. If impressions are high but engagement rate keeps falling below 1%, there's likely a mismatch between content and reader interest, and reviewing content direction becomes a top priority.
Profile Visits | The Number of People Who Thought "I Want to Know This Person"
Profile visits show how often users who saw your post opened the poster's (your) profile screen. This represents an active behavior—"this post is interesting, so I want to know more about the person who wrote it"—and is an important leading indicator that often connects to follower acquisition.
If profile visits are high but follower count isn't growing, the room for improvement is on the profile side—bio, header image, pinned post, recent timeline. Conversely, posts with low profile visits are being read as standalone content but aren't sparking interest in the poster themselves—a sign to rework the closing line or signature-like elements of the post.
Bookmarks | The Number of Times Saved "To Look at Later"
Bookmarks is a relatively new metric officially added to the visible indicators in the 2024 update. It shows how many times users saved a post to their personal bookmark list, signifying a more active and longer-term saving behavior than "likes." X's algorithm is said to be moving toward valuing "information worth saving," making posts with many bookmarks an important signal—a reproducible content format you can build on.
Practically, you should record the structure (list format, how-to, checklist, diagram, etc.) and theme of posts that got many bookmarks, then horizontally apply the same format to other topics—this is the proven path to account growth. Treating likes as "emotional reactions" and bookmarks as "evaluation of save-worthiness" gives you a multi-dimensional view of content quality.
Reposts, Quotes, and Replies | Indicators of Spread and Communication
Reposts (formerly retweets) show how many times a post was directly re-shared by other users—a direct measure of how broadly it spreads. Quotes are reshares with the user's own comment attached, indicating a more active reaction than a plain repost. Replies are the comments on a post, showing the level of communication with users.
Unlike likes and bookmarks, these metrics represent actions that appear on other users' timelines, so they directly drive spread. Posts with many reposts and quotes are likely to be distributed even more widely by the algorithm, leading to awareness expansion among new users. Posts with many replies indicate strong community character and can be used to deepen relationships with followers. Whether to design your posts toward "shareable formats (likely to be reposted)" or "conversational formats (likely to get replies)" depends on your account's goals.
Follow and Follower Count Trends
On the Audience tab and the account overview, you can see how followers have grown over time. By tracking daily and weekly net gains, you can identify when followers grew and when drop-offs occurred. If followers jumped the day after a specific post, you can judge that post contributed strongly to acquisition—a theme worth horizontally expanding.
It's equally important to observe patterns of increased unfollows. Unfollows tend to happen after a sudden surge in posting frequency that crowds the timeline, themes drifting away from the account's original context, or a streak of negative posts—useful as material for reviewing operational direction. Beyond absolute follower count, paying attention to the "quality of net growth" (growth rate relative to follow count) and the "active follower ratio" keeps you from being misled by surface-level numbers.
How to Use X Analytics by Purpose | 3 Common Scenarios
Scenario 1: Find Patterns That Grow
The most basic use is comparing posts that performed well with those that didn't, to extract the formats that grow. On the Content tab, sort posts from the last 4–12 weeks by impressions and engagement rate, then pick the top 10–20. Observe their commonalities—angle, character count, image presence, post time, format—and hypothesize a repeatable "winning pattern" for your account.
Example extraction axes include: (1) theme category (how-to / experience / opinion / question format, etc.), (2) structure (list format / conclusion-first / story format, etc.), (3) character range (within 140 / long-form / thread format), (4) media (text only / with image / with video), (5) post time (morning / noon / evening), (6) day of week. Lay these out in a table, find the combinations common to top posts, then run a cycle of consistently testing posts that incorporate that format and measuring results again.
Scenario 2: Identify Why Engagement Rate Is Dropping
This is the analysis flow when you sense "impressions are growing but engagement rate is dropping" or "followers grew but reactions thinned out." First, on the Overview tab, compare engagement rate between the last 4 weeks and the previous 4 weeks to confirm the magnitude of the drop. Then move to the Content tab, line up posts with low engagement rate, and verbalize the cause for each one in order.
Common cause patterns include: (1) themes drifting from the account's core (diverging from follower expectations), (2) post structure being too long and hard to read (causing drop-off), (3) timing not matching followers' active hours, (4) too many external link posts lowering algorithmic priority, (5) newly-acquired followers having different attributes from existing followers, causing mismatches. Once you identify the cause, the secret to sustained improvement is to drop it into simple operation: "test one hypothesis only over the next 4 weeks."
Scenario 3: Identify Posts That Drive Follower Growth
If you want to grow followers, the metrics to focus on are "profile visits" and the subsequent "follow conversion rate." On the Content tab, check profile visits per post and extract the themes and structures of posts with high numbers. Posts that drive visits have the power to spark interest in the poster, so they should be prioritized as core follower-acquisition content to expand horizontally.
Additionally, recording patterns where followers spiked the day after a specific post reveals the "format that grows followers." It's also crucial to check whether your profile's bio, pinned post, and header image are consistent with the theme of that post, so visitors instantly grasp "the reason to follow this person." Designing both axes—post growth and profile consistency—is what makes follower acquisition reproducible.
Advanced Techniques in X Analytics | What Sets Practitioners Apart
Make a Weekly 15-Minute Check Flow a Habit
Staring at analytics every day doesn't add much judgment material. Accounts that consistently turn analytics into results have built a "weekly 15-minute check" into their operations. The concrete steps: first, check the week-over-week change in impressions and engagement rate on the Overview tab; next, verify whether engagement rate is above 1%; finally, on the Content tab, pick out 1–2 of the most recent top-performing posts (especially those with many bookmarks).
Continuing this weekly flow builds the higher-level operational sense of "not getting swept up in daily numbers, judging account direction on a weekly basis." Leaving simple notes in the same format each week (URL of post that grew / hypothesis for the cause / format to test next week) creates a learning asset you can look back on after 3 or 6 months, accumulating operational know-how within the organization.
Structure the "Format" of Top Posts and Templatize It
Just discovering a post that grew, without going further, doesn't create reproducibility. Posts that ranked at the top in analytics should be broken down into four blocks—"opening hook (first line)," "middle development," "conclusion," "closing line"—and the structure of each templatized, making it easier to horizontally apply to other themes. For example, if the pattern "open with a counterintuitive question → list three concrete examples → present the common principle → propose an action to the reader" grows posts, apply that same pattern to a different specialty topic.
The key to templatizing is not copying the text itself, but tracing "the order in which the reader's emotions move." As long as the structure follows the same emotional curve, the post tends to grow even if the surface words differ. Reading X Analytics data not as a "language template" but as an "emotional curve template" lifts content reproducibility to a higher layer.
Align Post Timing with Followers' Active Hours
From data on the Audience and Content tabs, you can infer when your followers are most active. Compare the impression growth rate for each post time, identify the hours that get above-average reactions, then concentrate important posts at those times. Generally, individual BtoC accounts see peaks around 7–9 AM, noon, and 9–11 PM, while BtoB and business accounts see reactions in the 7–9 AM and 5–7 PM weekday windows.
That said, these are generalities—the right answer for your specific follower base only comes from your own data. Even just running an A/B-style test 2–3 times a month, where you post similar content at different times and compare results, will reveal optimal post timing in the data. Alongside content quality, optimizing post timing is a quiet but high-impact improvement.
Beyond X Analytics, there are many external SNS analytics and operations support tools like SocialDog, Hootsuite, Buffer, and Sprout Social. These tools provide things X Analytics doesn't easily give—estimating "reach" (unique users), cross-comparing multiple accounts, analyzing publicly available data on competitor accounts, automatic recommendations for optimal post times, scheduled posting, and so on.
The basic principle is: "Use X Analytics for verifying official data, and external tools for operational efficiency and competitor comparison." X Analytics is the official first-party data, so its accuracy is highest and it's positioned as the final evaluation axis for KPIs. External tools, on the other hand, are strong on operational features like bulk multi-account management, scheduling, and team sharing—making them necessary companions when running multiple SNS channels seriously. A cost-rational sequence is: master X Analytics first, stabilize operations, then consider introducing external tools.
Differences in Role from Search Console and GA4
Some operators familiar with web management think of X Analytics as "the SNS version of Google Search Console or GA4," but their roles differ. Search Console measures Google search performance, GA4 measures user behavior after a site visit, and X Analytics measures "post performance on X and the relationship with followers."
If you want to measure traffic from X to your website, check both the link clicks in X Analytics and the sessions for "twitter / referral" or "x / referral" in GA4's "Source / Medium" report—together they let you trace "clicked on X, then behaved how on the site" as a connected flow. Adding UTM parameters (utm_source=x, utm_medium=social, etc.) to the links you post greatly improves source identification accuracy, so it's a setup any account aiming to convert X traffic into business results must put in place.
What X Premium (the Paid Plan) Unlocks
Subscribing to X Premium (formerly Twitter Blue) unlocks full account-wide X Analytics, plus useful operational features like long-form posts (up to 25,000 characters), edit function, blue checkmark, higher display priority, and participation in monetization programs. Starting at 980 yen per month (price varies by plan), it's a high-ROI option for accounts you intend to operate seriously.
In particular, monetization mechanisms like the "Ads Revenue Sharing Program" and "Creator Subscriptions" presuppose Premium (or the higher Premium+), so it's effectively required for accounts considering side-business or commercial use. Since analytics use and monetization features are designed as a single experience, the royal road of modern X operations is: subscribe to Premium first, then watch the data while establishing your content format.
Common Pitfalls in X Analytics and How to Avoid Them
Just Staring at Numbers Without Connecting to Improvement Actions
The pitfall beginners fall into most often is making "opening analytics" the goal itself and ending up just looking at numbers. Watching impressions go up or down, followers go up or down, doesn't change the account at all. You need to reposition analytics as a tool not for "grasping the current state" but for "deciding what to do next."
The fix is to verbalize and write down one hypothesis to test in the next week, every time you open analytics. For example: "Top posts were mostly list format, so I'll post five list-format posts next week," or "Engagement rate is higher in evening posts, so I'll shift important posts to after 9 PM." Making it a rule to write out one concrete action turns data into behavior.
Getting Swept Up in Short-Term Number Swings
Another common pitfall is fluctuating operational direction based on the result of one or two posts or daily impression swings. X's algorithm causes daily impressions to fluctuate substantially due to timing, time of day, and external trending topics, so using short-term numbers as a measure of true performance leads to flawed judgments.
The fix is to set the evaluation unit not at "single post" or "single day" but at "median of last 4 weeks" or "average of top 10 posts"—units where noise is smoothed out. Daily spikes are easily swayed by external factors like viral moments, so building the habit of judging on monthly or weekly trends keeps your operational direction steady. Even just fixing the analytics graph to a 4-week view raises your perspective a level.
Putting Follower Count at the Top of Your KPIs
A final common mistake is putting follower count alone at the top of your KPIs. Follower count is certainly a measure of account scale, but if your followers don't actively react, it doesn't connect to business outcomes. Even in influencer marketing, it's known that engagement rate—not follower count—has higher predictive accuracy for PR effectiveness.
The fix is to design KPIs in layers based on purpose, not by follower count alone: "engagement rate," "profile visit → follow conversion rate," "link clicks (tied to business outcomes)," and so on. Treat follower count as a mid-to-long-term outcome indicator while evaluating daily operations on content-quality indicators like "are we putting out posts that are read and reacted to." That approach is what ultimately leads to sustainable growth. Understanding that X Analytics is a tool that provides the material for this multi-faceted evaluation lifts operational precision a step higher.
Summary | X Analytics Is the Starting Point of Data-Driven Account Operations
X Analytics is X's only official standard analytics tool. From per-post performance to follower trends and engagement breakdowns, it's the starting point for running an account based on data. The June 2024 update limited account-wide features to paid plans (X Premium, starting at 980 yen/month), but per-post Post Activity is still usable on free accounts, and beginners can start there to grasp the tendencies of their posts.
The screen uses a tab structure of "Overview," "Content," "Audience," and "Video," letting you check key metrics from multiple angles: impressions, engagement rate, profile visits, bookmarks, reposts, quotes, replies, follower trends. In practice, the three core scenarios are "finding patterns that grow," "identifying causes of engagement rate decline," and "extracting posts that drive follower growth"—and building a weekly 15-minute check flow is the tip for improving operational efficiency.
That said, three pitfalls to watch out for are: "just looking at numbers without taking action," "getting swept up in short-term swings," and "making follower count the only KPI." Reposition analytics as a tool for "deciding the next action" rather than "grasping the current state," judge on 4-week trends, and evaluate by combining behavior-based indicators like engagement rate and profile visits—doing this enables reproducible account growth. Use this article as your starting point to look at your own X account's data and start running a cycle of operational improvement grounded in evidence.
