Twitter has become an increasingly popular and important tool for businesses to keep in touch with their customers. Twitter is a medium unlike any other. Each tweet has a limited life-span – if it is not read within a short time of its being posted, the chances of it ever being read drop exponentially. The constant stream of new tweets from the group of individuals each twitterer is following makes it unlikely that the tweet will be read if it is a few hours old. For few twitterers capture all of their tweets in RSS feeds, or take the time to examine all the latest tweets from more than a handful of individuals. For a business hoping to broadcast a message that is read my the most followers possible, timing is of the essence.
So then, what is the best time of day to tweet? There have been several approaches to answer this question:
- Gary McCaffrey examined the Twitter referrals to his websites and concluded that any time between 9:00AM and 3:00PM is best.
- Malcom Coles claims that according to his survey, 4:01PM is the best time to tweet.
- Guy Kawasaki recommends posting your most important tweets 4 times, 8 to 12 hours apart.
- The Social Media Guide recommends 9:00AM Pacific Time as the single best time to tweet.
- Hubspot’s “State of the Twittersphere” report indicates that global tweets peak between 10:00 PM and 11:00 PM.
As you can see, there are a lot of different opinions about the best time to tweet. In order to develop the best answer possible to this question, I collected data over the course of several weeks for a business whose followers consist primarily of event professionals.
The data set consisted of several thousand tweets, including the username, the time and day of the tweet, and the tweet itself. For the purpose of this analysis, I assumed that the best indicator of a given twitterer’s degree of engagement was whether or not they had tweeted within a given hour. So in order to determine the best time of day to tweet, what is most important is not the number of tweets being posted at a particular time, but the number of unique users posting tweets. Here’s the data, in Eastern Time:
For this group of followers, there are actually two optimal hours to tweet – 10:00 – 11:00 AM and 12:00 – 1:00 PM. Tweets during these two hours reach 23.7% of the total number of followers, an 18% advantage over the next best time, 11:00 AM – 12:00 PM, and a 31% advantage over 1:00 PM – 2:00 PM. These increases in total available audience are highly significant to a business with thousands of followers.
Notice that, for this particular group, a tweet during the hour beginning at 9:00 AM, the beginning of Gary McAffrey’s time window, would only reach an available audience that is two-thirds the size of that available during 10:00 – 11:00 AM and 12:00 – 1:00 PM. Malcolm Cole’s suggestion of 4:01 PM reaches an available audience that is less than half the size – only 41% – of that of the best time to tweet. Guy Kawasaki’s formula of four tweets varied over 8 – 12 hour intervals is a hit-or-miss proposition. In this particular case, the Social Media Guide is right on the money – the hour beginning at 9:00 AM Pacific/12:00 PM Eastern is best.
But does this pattern hold for every group of followers? Or does each group of followers have a unique pattern, a sort of “time fingerprint”? To answer this question, I examined a second group of followers of a CRM company. Here’s the data, once again expressed in Eastern Time:
This group is far different! The group following the CRM company is much more likely to be active during the morning hours, and is more evenly distributed over the entire day. As a result, a tweet to this group reaches a maximum of 10.8% of the total available audience, as compared to the group of event professionals, which peaked at 23.7%. The CRM group reaches its maximum at 11:00 AM – 12:00 PM, rather than the hour before or after, as in the previous case. So while a close approximation, the Social Media Guide guideline of 12:00 PM Eastern Time would for this group reach an audience 17% smaller than the peak time period of 11:00 AM – 12:00 PM.
As these two data sets demonstrate, there is no one best time to tweet for every business. Each business has a unique set of followers with their own Twitter “time fingerprint”. You have to track the habits of your own set of followers in order to determine the best single time of day for your business to tweet.
Develop this graph for your own set of followers. How much different is your group compared to these two?
One of the most important insights from these two examples is that at any given time, you can only reach 10% – 24% of your followers with a single tweet. In a future post, I’ll examine what percentage of a group of followers can be reached with multiple tweets.
Conferences, exhibitions, and events were the original forms of social media. In recent years attendees have been more difficult to attract, due to the rise of the Internet, the increased hassle of travel, and an economic recession. But even as event producers have struggled against these forces to maintain or grow attendance levels, they have in many cases ambitiously attempted to increase revenue by increasing prices to attend their events. And as the recent experience at a number of events demonstrates, this can be a formula for failure.
One of the most forthright and savvy publishing operations on the Web, Mequoda Daily, recently discovered that price increases can backfire. In their own words:
Mequoda Summit: Rolling Back Prices to 2009
A 3-day program for the price of 2-days
After a multi-month test we have decided to reduce the price of the Seventh Mequoda Summit. We originally tested a theory explained in today’s Mequoda Daily post. It basically consisted of our desire to add more content to this year’s Mequoda Summit, to further enhance the experience for our attendees.
So we went forth with the test. This included increasing the content of the Summit by 25%. To be able to support the time and resources spent on this additional content, we decided to increase the price by 14%.
As a result we concluded that a 25% increase in content and a 14% increase in price yielded a 38% decrease in attendance.
In turn we have ended our test, and have shared our results with all of our loyal readers. We hope that you consider our findings when planning live events in the future. We are also offering admittance to the Summit for last year’s price, which is $200 cheaper than our original offer for 2010.
This decision by Mequoda Daily is at once smart and courageous. I’m sure they saw registrations and revenue increase immediately.
Test Multiple Price Points to Determine the Best Price
The best way to determine the appropriate price for an event is by testing several different price points at the start of your marketing campaign. The graph below displays the results of a price test I recently conducted for a client at conference fees that ranged from from $1,395 to $1,995. The test enabled us to determine that the best price was $1,595, which provided a projected incremental $60,000 in revenue over the next best price of $1,395, and more than $130,000 of incremental revenue over the worst outcome at $1,995.
Pricing is often a seat of the pants decision for an event producer. There are many methods that can be employed to determine the price for your conference – what your competitors charge, how many days it lasts, or how much content you have. Testing provides a way to find out directly from your customers what value they place on your product. The best strategy, and the one that will generate the most revenue, is to set your pricing through testing.
The Revenue Implications of Charging for Exhibit Attendance
Many conferences have an exhibit area that also provides a significant revenue stream. In order to maximize traffic on the exhibit floor, event management usually offers free passes to individuals who would like to visit the exhibits, but not attend conference sessions.
In some cases an event producer may decide to charge a nominal fee for passes to visit the exhibits to generate some additional revenue. This can be a major mistake.
Let’s take a look at some actual data and the revenue implications of charging for admission. In this case, the event producer decided to offer free admission to the exhibits if the attendee pre-registered, and charge a $50 fee if the attendee registered on site. This was a change in policy from the previous year, when admission was free regardless of when the attendee registered. The change in policy permits a year to year comparison that provides a dramatic illustration of what can happen when a fee is charged for exhibit attendance.
The first thing to note about the data is the significant drop in on site registrations, which declined from 397 to 103, a drop of 74%, in contrast to an increase of 81% in pre-registrations. One could assume that if the $50 fee had not been applied for on site registrations, they would also have grown by 81%. So attendance grew by 39% (to 2,067), when it should have grown by 81% (to 2,680). Actual attendance was 23% lower than it should have been.
The effect of this shortfall in attendance had a major, negative impact on the exhibit sales. Since the size of the exhibit floor grew by 50% (from $342,000 to $521,000), but attendance grew by only 39%, the density of attendees on the exhibit floor decreased by 11%. For an event, the size of attendance is perceived by the density of attendees on the exhibit floor – how crowded it looks. Even though actual attendance grew by 39%, because the size of the exhibit floor grew by 50%, it looked like there were actually fewer visitors in attendance.
The effect on exhibit sales and revenue was immediate. The percentage of exhibitors who signed contracts on site to exhibit at the next conference dropped from 79% to 59%, resulting in a revenue level that was $104,200 lower than it should have been. This revenue loss far exceeded the $5,150 in revenue realized from charging a $50 on site fee for exhibits passes.
This whole scenario could have been avoided by a simple price test on the exhibit pass at the start of the attendee marketing campaign. Event management would than have known the effect of the increase on price on overall attendance, and could have made the pricing decision accordingly.
It never pays to set prices first and react later. Always be testing!
Many email service providers admit that there has been a gradual decline in open rates over the past few years. While the open rate doesn’t tell the whole story on on email success, it is still vital to measure. After all, if your audience doesn’t open up your email, they have no chance to read it and respond to it.
One of the primary reasons cited for the decline is inbox clutter. According to Forrester Research, 60% of consumers believe they receive too much email. In another study, Customer Knowledge is Marketer Power, Forrester found that the chief reason that marketers who believe email will be less effective in 2 years is “too much clutter in consumer inboxes.” A belief that “SPAM” will drive the decline was cited by only 59%.
Clearly, we are all becoming increasingly numb to the steady stream of email arriving in our inboxes. A second, related reason often given for the decline in open rates is the increasing effectiveness of spam filters that help manage this flood of email.
A third reason, and a significant one, is technological. The way that opens are measured is by including a tiny image (usually a 1 pixel by 1 pixel gif or jpeg) within the email. Once the images that are embedded in the email are served, the email is recorded as opened. The problem is that there are a lot of email readers don’t automatically serve the images in an email. In fact, ExactTarget estimates that 50% of all email is now delivered to email readers that either don’t automatically render images or are unable to render images, such as Outlook, Gmail, AOL, and handheld devices such as Blackberries. Thus, there is an inherent bias in not detecting all of the opens.
If you’re running an email campaign, it’s important to know the true open rate, so you can gauge the true reach of your email message. There’s an easy way to do this. It’s based on the insight that click-throughs are always measured, even if opens aren’t. Even though the email reader may not be indicating an open, because it hasn’t rendered the images, the recipient of the email can still click on the links. That means that some recipients will be tracked as clicking through, but not opening an email. Let’s walk through an example.
Here’s the initial tracking information for an email:
Here’s how to estimate the true open rate:
- Download the list of the email addresses that have opened the email from your email service provider.
- Download the list of the email addresses that have clicked on a link in the email. Now match up the list of those who have clicked through, to see if they were tracked as opening the email. In the case above, it turns out that 105 recipients clicked a link in the email, but only 75 of them were tracked as having opened the email.
- Multiply the open rate above by the ratio 105/75. This gives an estimate of the true open rate, assuming the same click through to open ratio for the group that clicked on a link in the email, but was not tracked as having opened the email. The revised tracking information is as follows:
As you can see, because not all of the email reader render images, the estimated open rate in this case was actually 40% higher than reported. Here’s how you can use this information:
- In order to maximize your click through rates, make sure that message in your emails does not rely on images. That way, if the recipient of your email doesn’t see the images, they can still respond to your message. As demonstrated above, this can help increase your open rates by 40% – or more.
- It’s vital to know what the real underlying trends are for your email campaigns, so you can make adjustments as necessary. You’re in a better position to know that if you monitor the estimated open rate, as described above, because it eliminates quirks in the tracking system. You need to make adjustments in your strategy based on real changes in customer behavior, rather than changes in the way email readers render images.
- With the estimated open rate, you now have a better estimate of the cumulative penetration of your message to your target audience. For example, if the reported rate shows a cumulative penetration of 33% after several emails, and you actually have a 40% higher open rate, a better estimate of your penetration is 1.4 x 33% or roughly 46%. You can then make better decisions about how to most effectively reach the rest of your target audience.
Many marketers pay a lot of attention to email stats such as opens, click-throughs, unsubscribes, etc. When you have embarked on a campaign that involves multiple emails to the same list, one of the factors you should consider is how deeply you are engaging the universe of customers that you are trying to reach.
For example, it’s typical to see open rates that decline by a fairly constant percentage on each effort, such as 20% on the first email, 15% on the second, 11% on the third, and so forth. If you were to graph the percentage of opens as a function of the number of emails sent to this customer list, it would look something like this:
At one extreme, the minimum penetration would be 20%. In other words,after the first email, no one new is opening the email. The opens on the second through fifth efforts are coming from an ever decreasing proportion of the orignal 20% who opened the first email.
At the other extreme, the maximum penetration would be 61%. In this case, the prospects opening the email are different on every effort. No one opens any of the five emails twice.
As you might expect, the actual percentage of penetration is somewhere in between. Upon examination of the email addresses from the prospect list that opened up each email, the actual cumulative penetration is 33%. The penetration after each effort looks like this:
Several facts are clear from this. First, the 33% penetration is a lot closer to the lower of the two extremes. Second, it is clearly approaching a limit. Finally, the limit in this case, judging from the trend of the graph, is surely less than 40%, even if additional efforts are made.
There are a number of ways to use this information:
- To keep emailing this list with essentially the same subject lines, “from” field, etc. is like beating your head against a wall. Instead of continuing to beat your head against a wall, you can test new subject lines to try and motivate new prospects to open the emails.
- If additional information about the prospects is available, by statistically analyzing the differences in the characteristics of those who opened the emails versus those who didn’t, a predictive model can be developed to devise a messaging strategy to motivate those who didn’t open the email to open subsequent emails with the new message.
- This information can also be used in predicting which third party lists will be most responsive and in devising messaging strategies for other lists with similar characteristics.
- Even if you find that you can’t penetrate this list (or market) any further, you can save time and money by not trying to do so.
These graphs may look very different for your lists. But the information you need to analyze a major list in this way is readily available, and you can use it to your advantage.