How To Maximize Your Reach on Twitter

Tweets are ephemeral. Chances are, unless a person is engaged with Twitter when you tweet, they aren’t going to have the opportunity to read it. Not only do people ignore or lose track of old tweets, they are dropped from the Twitter database. Unlike a blog, which is long-lived and indexed by Google for future reference, tweets are heavily time-dependent. If you’re running a business trying to reach as many customers as possible with your corporate message, the timing and frequency of your tweets are critical to your success.

In a previous post, I examined the question of what time of day is best to tweet. To determine the answer, I analyzed two sets of data representing the behavior of two different groups of followers. It turned out that each group had a different best time of day to tweet, and that a single tweet reached between 10% and 24% of the followers.

That brings up the question: if you employ multiple tweets, what percentage of your followers can be reached? Guy Kawasaki recommends posting your most important tweets 4 times, 8 to 12 hours apart, to reach as many of your followers as possible. Let’s take a look at this question for the same two groups of followers analyzed in my previous post.

A quick review: I collected data over the course of several weeks for two Twitter groups –  followers of a company supplying services to event professionals, and followers of a company selling CRM software. 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 follower’s availability to read tweets was whether or not they had tweeted within a given hour. I was then able to determine for any given hour of the day, how many unique followers were active, and presumably reading their Twitter stream.

To figure out the impact of multiple tweets on reach, I then ranked all of the hours in the day in order of how many unique twitterers there were during any given hour. Choosing each hour in order of priority, I then eliminated duplicates.

The results for the group of event professionals are as follows:

The graph displays the percentage of unique followers that can be reached for each tweet. For example, a single tweet during the best hour of the day can reach 24% of the followers, two separate tweets during the two most active hours of the day, 40%, and three 50%. The graph shows that using Guy Kawasaki’s rule of thumb, that you can reach 60% of your followers with four tweets (we’ll see later that these four tweets should not take place 8 – 12 hours apart). For this group of event professionals, it takes eight tweets to reach 80% of the followers.

Now let’s examine how reach is affected by multiple tweets for the CRM software group:

As you can see, there are dramatic differences between the two groups in the extent to which a given percentage of followers can be reached with the same number of tweets. For example, it takes ten tweets to reach 60% of the CRM software group, compared to the four tweets needed to reach 60% of the event professionals group.

Each group is different. If your business needs to make sure its message is reaching the widest possible audience, you need to develop a similar analysis for your group of followers.

The graphs above show only the number of tweets required to reach a given percentage of followers, but not what times to tweet.  The chart below reveals when each tweet should be deployed to achieve the reach shown in the graphs above.

Note that the first three tweets for each group, while not in identical order, occur in the 10:00 AM – 12:59 PM time period. After the first three tweets, the best time for additional tweets varies according to the group. In both cases, Guy Kawasaki’s rule of thumb – four tweets 8 – 12 hours apart – would not maximize reach. To be fair to Guy, his rule of thumb may well work for his group of followers. The point I’m making here is that you can’t generalize – there is a different strategy to maximize reach for each group of followers.

Summary

• If you are trying to maximize the effective reach of your message, the ephemeral nature of a tweet puts a premium on the timing and frequency of your tweets.
• A single tweet will only reach a fraction of your followers. For the two groups examined, the range was 10% to 24%.
• By analyzing the times during which your followers tweet, it is possible to develop a strategy to predict the percentage of your followers that you can reach with multiple tweets.
• It is also possible to determine the best times of day for multiple tweets. Note that the muliple tweets don’t necessarily have to take place during one day; they can be spread out over several days so as not to annoy your most attentive followers.
• Every group of followers is different. You need to analyze the tendencies of your followers to determine the optimal strategy for maximizing the reach of your most critical messages.

Predicting Market Demand With Social Media

Social media is now being extended beyond its original applications into a tool for predicting the future. The exponential growth in social media has helped create a large body of content that reflects the trends, experiences, evaluations, and sentiment of the marketplace. It is becoming increasingly apparent that this content can be mined and analyzed to help predict the size of markets, the outcomes of marketing campaigns, and marketing ROI. In this post I’ll take a look at three ways in which data generated by social media has been used recently for Predictive Marketing.

Predicting Movie Box Office Results with Twitter

A group of researchers at HP Labs recently published a paper describing how they used data captured from Twitter posts to predict box-office revenue at the movies. The researchers extracted 2.89 million tweets from 1.2 million users referring to 24 different movies over a period of three months. For each tweet, the timestamp, author, and tweet text were collected and used for analysis. The researchers focused on what they termed the “critical period” – the week before and the two weeks after the release of a movie.

An initial analysis of the tweets revealed that:

• The tweets built up in volume the week before the movie release; peaked at the time of the release; and fell during the two weeks following the release.
• The average number of tweets made by individuals about a particular movie was between 1 and 1.5.
• The distribution of tweets by individuals showed that a handful of individuals made many tweets; the distribution followed the “long tail” power distribution that frequently occurs on the web.

The team then proceeded to analyze the data for predictive power. First, what didn’t prove to be very good predictors of box office success:

• Prior to the release of a movie, studios promote the film heavily via TV, print, news releases, interviews with the stars, and trailer videos. The researches classified tweets according to whether they contained urls, indicating that they could reference trailers, movie reviews, or other PR about the movie. It turned out that although 22% to 40% of the tweets contained urls, such tweets were only mildly predictive of box office success.
• The percentage of retweets was in the 11%-12% range, and was even less predictive of box office success. This is surprising, given that retweets are indicative of word-of-mouth.

There were three factors that proved to be powerful predictors of box office revenue:

• The tweet rate, or the number of tweets about a given movie per hour. This is indicative of the overall attention and interest that the movie is generating. This factor is particularly important in predicting the box office reevnue for the opening weekend.
• Positive sentiment about the movie. The researchers created a customized method for analyzing positive and negative sentiment about movies for the purposes of this study. Sentiment proved to be an important factor in predicting box office revenue in the weeks after the opening.
• Distribution, or the number of theaters in which the film screened. The wider the distribution, the more opportunity that existed for revenue generation.

Using these data as a predictive model, the team was able to demonstrate that they could predict opening weekend box office revenue with 97.3% accuracy. This compared favorably with a well-known prediction tool for movies, the Hollywood Stock Exchange (HSX), that had 96.5% accuracy. The results for these two techniques are shown in the graph below:

The exciting implication of this study is not that this particular application of using social media to predict box office revenue, but that a model has now been developed to to use social media to predict a wide variety of outcomes from product sales to elections. The researchers conclude:

While in this study we focused on the problem of predicting box office revenues of movies for the sake of having a clear metric of comparison with other methods, this method can be extended to a large panoply of topics, ranging from the future rating of products to agenda setting and election outcomes. At a deeper level, this work shows how social media expresses a collective wisdom which, when properly tapped, can yield an extremely powerful and accurate indicator of future outcomes.

Using Social Media to Predict Election Results

The predictive model used to forecast outcomes using social media developed by the HP Labs team is one of the few that is customized, well-defined, and mathematically rigorous. However, that hasn’t stopped people from predicting outcomes from social media trends, even if they lack such a powerful tool.

One of the most stunning election outcomes in the past few years was the victory of Scott Brown over Martha Coakley in the special Massachusetts U.S. Senate election to replace the vacant seat created by the passing of Ted Kennedy. Larry Kim published a blog post five days before the election forecasting the upset for Scott Brown.

At the time, conventional polls suggested that the race was too close to call, despite the fact that the Democrat Coakley had been the early front runner and had a seeming lock on the seat, given the nature of the Massachusetts electorate. However, while Coakley coasted through the campaign, the hard work and grass roots effort employed by the Brown campaign paid huge dividends. Kim’s analysis of social media trends showed that Brown had developed a huge advantage is social media presence:

• 10:1 Advantage in YouTube video views
• 4:1 Advantage in Facebook fans
• 3:1 Advantage in Twitter mentions
• 10:1 Advantage in estimated web traffic

The trend in web traffic as measured by Alexa was particularly telling:

While the data looked overwhelming, Kim, to his credit, lacking a quantitative predictive model such as that employed by the HP Labs team, cautioned that the type of people who were heavy users of social media were undoubtedly a biased sample that was perhaps not representative of the electorate as a whole. However, the trends looked so overwhelming that, on the basis of this data, Kim concluded that Scoot Brown was headed for a victory. Five later Brown proved the prognostication accurate.

Using Social Media to Predict Fashion Trends

One final example of the predictive power of social media involves the notoriously fickle and unpredictable fashion world.

Luke Brynley-Jones of Our Social Times reports that Geoff Watts from Stylesignal has developed a new social media monitoring tool that helps to track new fashion trends. The tool is used to monitor the sites of opinion leaders in the fashion world.  The data collected by the tool is then analyzed offline to predict fashion trends. Case studies on the site claim that the StyleSignal has helped correctly predict the colors, shapes, and styles that become trend setters.

Summary

It has become increasingly clear that social media can be used to predict future events with accuracy. Now that predictive models have been developed for quantifying and measuring the accuracy of predictions, you should expect to see explosive growth in the use of social media in forecasting.