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!
“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” – Mark Twain
In today’s rapidly evolving markets, you can never take anything for granted. The average lifetime of so-called “best practices” is shorter than ever.
I experienced this first hand recently on a couple of email tests designed to drive registrations for some upcoming conferences. Both conferences were targeted at highly technical IT audiences.
My past experience had always indicated that the best subject lines had offers and calls to action, especially when closing in on a limited time offer pricing deadline. So the following test results were as expected:
I had seen this result dozens of times before; invariably, the subject line that emphasized the dollar savings and created a sense of urgency had always emerged triumphant. Imagine my surprise, then, when I saw the results of the following test for a different conference directed at a similar audience:
Not only did the standard subject line offer no improvement on the alternative; it produced a result that was significantly worse. There is no doubt that, for this particular audience, the second subject line produced more conversions than the first.
The beauty of testing is that marketers don’t have to figuratively stumble around in the dark searching for the best way to communicate with customers. Test and learn strategies provide a way to find out directly, from prospects and customers, what they value and want most. There is no longer any excuse for a marketer to rely on hunches, anecdotes, and biased opinions in order to make marketing decisions. Even the seemingly most insignificant of decisions – the color of a registration button, for example – may have an effect on conversion rates which can be quantified.
However, when employing a test and learn approach to marketing, there is a trap to be avoided, which is illustrated by this example. Every business, and every customer set are different. There is no one set of best practices that apply in every situation. Think of the body of knowledge that you gain by testing to be a set of “best guidelines” rather than best practices. And know that no testing program ever arrives at a final best answer. Your customers are always changing. Always be testing.