---
title: "Four lessons for optimizing Twitter"
description: "Data-driven analysis reveals optimal Twitter engagement: best posting times, impact of retweets, and how follower counts affect click-through rates. Key metrics for social ROI."
categories: ["content marketing"]
keywords: ["Twitter optimization","social media engagement","Tomasz Tunguz","content marketing","retweet impact","click-through rates","best posting times","social media metrics"]
ai_summary: "Discover four key lessons for optimizing Twitter engagement, including best posting times and the impact of retweets on click-through rates."
date: 2012-10-08
lastmod: 2026-07-23
canonical_url: https://www.tomtunguz.com/twitter-analytics/
author: "Tomasz Tunguz"
---

<p>Over the weekend, I analyzed my Twitter performance over the past 4 weeks. I wanted to determine what if any best practices I could tease from the data. Below are my four conclusions:</p>

<h2>The best time for me to tweet is 9am Pacific.</h2>

<p>On average, tweets at 9am generate 2.3 times the number of clicks as those in the 8am hour and 3.3 times those of the 12pm (lunch) hour. Below is a chart of number of clicks per tweet by hour of the day:

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/q_auto/f_auto/w_auto/image_226_896708992)
<h2>Each retweet is worth about 9 clicks.</h2>

<p>My median tweet receives 24 clicks and the average tweet receives 36 clicks. So the first retweet drives a 25 to 33% increase in clicks.  Below is a chart of tweets (marked by red dots) plotted by the number of reactions/retweets and clicks. The blue line is the linear trendline. </p>

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/q_auto/f_auto/w_auto/image_226_518142602)
<h2>Users with huge follower counts send tweets into the stratosphere.</h2>

<p>There is a step function apparent in the data. If one of my tweets is retweeted by @hnshah with 80k followers, I instantly receive twice the clicks. The chart below plots the reach of each tweet by the number of clicks. </p>

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/q_auto/f_auto/w_auto/image_226_765673969)

<h2>The ideal tweet, the one generating the most clicks, is between 40 to 70 characters long and sent between 8:30 and 9:30am.</h2>

<p>I use <a href="www.timely.is">timely.is</a> to programmatically send tweets out at the optimal times of the day. You can see from this analysis that tools like these don’t seem to provide much value aside from scheduling. </p>

<p>Below is a chart of tweets by time of day (y-axis) and tweet length (x-axis) with the number of clicks indicated by the size of the bubble. My most successful tweet generated about 190 clicks. It was sent at 9:21 am and had 46 characters. NB: I don’t send many long tweets, so there is a data bias. </p>

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/q_auto/f_auto/w_auto/image_226_849580926)

<h2>Summary stats</h2>

<p>Below are stats describing my tweets in a bit more detail. The key conclusion is that the upper bound outliers, the really successful tweets, skew averages much higher than medians - this is the viral effect in action.
</p>
<table>
<tr>
<td></td>
<td>Clicks</td>
<td>Reach</td>
<td>Retweets</td>
<td>Tweet Length</td>
</tr>
<tr>
<td>Average</td>
<td>37</td>
<td>11,882</td>
<td>2</td>
<td>65</td>
</tr>
<tr>
<td>Median</td>
<td>24</td>
<td>3825</td>
<td>1</td>
<td>61</td>
</tr>
</table>