If you are interested mainly in saving your own Tweets, using Google Reader is perhaps the best alternative. You simply need to locate the RSS feed for your (or anyone else’s Twitter account, if you’d like) and subscribe. Just locate the “Browse for stuff” option under the “All Items” drop down menu in the upper left hand corner of the Google Reader screen, click on “Search”, and enter the username of the Twitter account. The feed will then appear in the search results. Simply click on “Subscribe”, and you’re ready to go! All of the tweets from the account will then be saved from that point forward. This makes your archive of Tweets searchable and pretty much ageless (if you don’t expect Google to be destroyed in the near future).

TwimeMachine is another alternative that will  pull your older tweets into a single web page for you, starting with the most recent. However, it is restricted to you last 3,200 tweets and you can only view 25 at a time.

TwimeMachine

Snap Bird is a more powerful way to search through tweet history. You can use it to view your old tweets dating back several years. You simply enter your Twitter username in the search box and leave the search term blank to get Snap Bird to pull up all of your old tweets. You’ll get a list of 100 tweets to start, and you can continue to go back by 100 tweets at a time.

Snap Bird

The Archivist is the best and most flexible tool for saving and analyzing tweets, structured around searches. The Archivist offers two different ways to save tweets, in an online or desktop version.

The Archivist - Online Version

The online version will archive tweets beginning from the point in time when you initiate a search. It will periodically and automatically update the search based on the amount of activity for the search term. At any time you can observe the most recent tweets and some key statistics about all of the tweets in the archive, including tweet volume over time, top users, the percentage of tweets vs. retweets, top words, top URLs, and top sources. It also offer the opporutnity to make the archive public so that you can share it with colleagues.

Here’s how it works:  First you do a search—using Twitter’s own search syntax (for example, from:yourusername). It will then return a list of matching tweets. Your first search will return a maximum of 500 matching tweets. You can then save that search, which will continue to be updated until you delete it.

There are two problems with the online version. The first is that, since you don’t control when the search is updated, you may lose some of the tweets you want to archive. The second is that, due to Twitter’s terms of service, you cannot export the tweets to archive them in files on your own computer.

Both of these problems can be overcome if you download the desktop version.

The Archivist - Desktop Version

With the desktop version, you gain control over the frequency of the search updates. Once you have started a search, The Archivist will continue to monitor that search term while you leave it open, refreshing itself every ten minutes. You can save the results from your search and reopen it at a later time. Once you save the results to your file system, The Archivist will automatically save any new tweets that come in, so you only need to click save one time.

If you would like to have multiple searches going simultaneously, you can launch multiple instances of The Archivist. However, if you have too many instances of The Archivist open, you could get rate limited by Twitter.

If your search term has a lot of Twitter traffic, you can choose to leave The Archivist running, otherwise there is a chance you will miss some tweets. For example, if you do a search, save the results, close The Archivist and then reopen that search the next day – if there have been more than 1500 tweets since the last time you ran the search, there will be a gap in your archive.

Another convenient feature: if you would like to see the Twitter homepage for a user of a given tweet, you can click their avatar, which will launch a browser that takes you to the person’s Twitter homepage.

Most important of all, if you’d like to perform deeper data analysis, you can export The Archivist data to Excel. When you click Export To Excel, The Archivist will create a tab delimited text file which you can then open in Excel. If you are more tech savvy, you can save the data in an .xml file for further analysis.

The Archivist is a perfect tool for saving, creating a transcript, and analyzing a Twitter chat. If you have created a hashtag for an event, you can collect the tweets about the event to determine more about the attendees and their attitudes about your event than you could from any survey.

Both are on display in a brilliant study conducted by Seth Stephens-Davidowitz of Harvard. As reported in the Wall Street Journal, using freely available data from Google Insights, skillful research, and clever thinking, he was able to determine that in the 2008 presidential election, racial attitudes reduced the number of votes garnered by President Obama by 3%-5%. His method of reaching this conclusion, which we’ll review here, represents techniques that can be used by all marketers in gaining insights into topics such as forecasting product demand, buying attitudes, geographical preferences, and buyer demographics.

Stephens-Davidowitz performed the study because of the notorious unreliability of surveys to capture the true racial attitudes of voters. Participants in surveys are highly likely to misreport their true attitudes due to embarrassment. Google-based measures of racial bias are more likely to accurately reflect voters’ attitudes, since they perform Google searches online while likely alone. In addition, information about Google searches is available at finer geographic levels, uses data that is more recent, and aggregates information from larger samples as compared to typical surveys.

The method used in the study was as follows:

1. Choose a search term that represents the underlying attitude. In this case, Stephens-Davidowitz used a certain well know racial epithet that began with “n” for the representative search term.
2. He had to make sure that the term represented a strong proxy for racial bias; he did this by:
   • Examining some of the output from Google Insights, which includes the top related search terms including the word. From the list of related terms, it was clear that the search was motivated by racial bias.
   • Verifying that Google search volumes correlate well with demographics one would more often expect to search the term. For example, the percent of a state’s residents who say they believe in God explains 65% of the variation of the search volume for the word “God”. The table below gives further examples:

   

   • Finally, the major potential bias with racial attitude survey data – misreporting due to embarrassment – is unlikely to significantly bias Google data. As mentioned previously, the conditions under which people search -online and likely alone – limit this concern. The following table documents substantial search volume for various terms that researchers suspect may be under-reported in surveys.
     
   • He then used Google Insights to determine the geographic variation in the use of this term in searches. Quite a wide variation was found by media market:
   

   Markets with high racial bias have darker colors

3. Stephens-Davidowitz next sought to arrive at an estimate of how this bias translated into votes. In order to do this, he arrived at a first estimate by comparing voting results by media market in the Obama – McCain election with results in the Kerry – Bush election using linear regression.
4. To verify that his estimate of racial bias was a strong predictor of the difference in voting patterns between the two elections, he then added additional variables to his analysis that are known to affect voting outcomes.Stephens-Davidowitz concludes that

Estimating the effect of racial animus on voting is complicated by surveyed individuals’ propensity to misreport socially unacceptable attitudes. This paper sidesteps surveys using area-level Google search data and administrative voting records. I find that racial animus played a major role in the 2008 election. Relative to the attitudes of the most tolerant area, racial animus cost Obama 3 to 5 percentage points of national popular vote.

More details are offered in the full study, The Effects of Racial Animus on Voting: Evidence Using Google Search Data. The method described here can be used in a host of marketing applications, including forecasting product demand, buying attitudes, geographical preferences, and buyer demographics.

In one of my previous posts, I examined the question “When is the best time of day to tweet?”. It turned out that there was no one universal answer to that question. The best time to tweet depended on what time of day your particular set of followers were active on Twitter.  Recent evidence regarding Twitter usage patterns illustrates exactly how important it is to time your tweets so that you are reaching as large and audience as possible.

So what is the effective lifetime of a tweet? Sysomos, a leading provider of social media monitoring and analytics technology, analyzed 1.2 billion tweets to find out how many of them generated some sort of reaction. The key points from the Sysomos analysis:

• 92.4% of all retweets happen within the first hour of the original tweet being published. Thus, if your Tweet is not retweeted in the first hour after it is posted, it probably won’t be.
• 96.9% of @ replies happen within the first hour of the original tweet being published
• 23% of tweets generate replies, while 6% generate retweets.
• Of all tweets that generated a reply, 85% have only one reply. Another 10.7% attracted a reply to the original reply – the conversation was two levels deep. Only 1.53% of Twitter conversations are three levels deep.

The following graph summarizes these important findings:

Like many things in life, on Twitter, timing is everything. If you want your message to be read, to engage your audience, and to be retweeted, you need to know when your followers are online. Every group of followers is different in terms of the periods of peak activity during the day. Remember that:

• A single tweet will only reach a fraction of your followers.
• 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.

What is groundbreaking about this research is not the realization that friends and colleagues influence what you buy, but the unprecedented ability in today’s connected world to track, measure, and quantify the effects of social influence. This newfound capability calls for a dramatic overhaul of the way that businesses determine the value of their customers.

Time evolution of the iPhone adoption network. One node represents one subscriber. Node color: represents iPhone model: red=2G, green=iPhone 3G, yellow=3GS. Node size, link width, and node shape (attributes which are visible in Q3 2007) represent, respectively, internet volume, weighted sum of SMS and voice traffic, and subscription type. Round node shape represents business users, while square represents consumers. Source: Product Adoption Networks and Their Growth in a Large Mobile Phone Network (http://www.sundsoy.com/asonam_product_spreading.pdf)

The Lifetime Value of a Customer

Traditionally, determining the lifetime value of a customer has long been the starting point for calculating  the ROI of a marketing campaign. The lifetime value of a customer is defined as the net present value of the profit a business will realize on the average new customer over a period of years from that customer’s purchases. This number is critical, because it indicates exactly how much it is worth to acquire a given customer. Armed with this information, a business can manage its marketing programs not as an expense, or for short term profits, but as a long-term business investment.

A New Metric – The Network Value of a Customer

As the research on iPhone adoption illustrates, with the rise in the popularity of social networks, it has become increasingly clear that the true value of a customer goes beyond how much he or she might buy from you directly. Traditional measures of customer value ignore the influence a customer may have on how much others buy. For example, if a customer buys your product, and then, based on his recommendation, three of his colleagues buy your product as well, his effective value to you has quadrupled. On the other hand, if a prospect makes his decision based purely on what others tell him about your product, you will be better off spending your marketing dollars on his colleagues.

The implication for marketers means that the lifetime value of a customer can no longer be considered to have captured the true value of a customer.  The advance in the understanding of how social influence effects purchase decisions has lead to the creation of a new metric – the network value of a customer.  The network value of a customer is the expected increase in sales to others that results from marketing to that customer.

The Factors That Determine The Network Value of a Customer

Which customers have a high network value? There are few businesses that have access to the kind of data that the Telenor researchers had at their disposal – billions of call records. However, by considering the characteristics of customers that have a high network value, there is data that you can collect that will begin to help you identify and target the customers that you have with the highest network value. The customers with high network value share these common characteristics:

1. A high level of satisfaction with your product
2. Is highly likely to recommend your product to others
3. Is highly connected to other potential buyers
4. Is highly influential, an opinion leader

How to Target Customers With High Network Value

Even if you don’t have access to billions of records detailing the social connections and behavior of your customers, like the researchers at Telenor, there is data that you can easily collect about your customers that can help you target the customers that you have with the highest network value. They include:

• Collect a Net Promoter Score from each customer – The metric is simple to collect and straightforward to determine, as described on netpromoter.com:

By asking one simple question — How likely is it that you would recommend [Company X] to a friend or colleague? — you can track these groups and get a clear measure of your company’s performance through its customers’ eyes. Customers respond on a 0-to-10 point rating scale and are categorized as follows:

• Promoters (score 9-10) are loyal enthusiasts who will keep buying and refer others, fueling growth.
• Passives (score 7-8) are satisfied but unenthusiastic customers who are vulnerable to competitive offerings.
• Detractors (score 0-6) are unhappy customers who can damage your brand and impede growth through negative word-of-mouth.

Source: Netpromoter.com

With this one metric you can capture the first two characteristics of a customer with high network value – they 1) have a high level of satisfaction with your product, and 2) are likely to recommend it to others.

• Collect social network information about your customers – many companies are starting to ask customers for their Twitter and/or Facebook usernames, in addition to other contact information such as email address. The very fact that a customer is willing to give you this information is an excellent indicator that the customer is actively involved with you product. In addition, it allows you to invite them to follow/friend you on Twitter and Facebook. Also, in the case of Twitter, it allows you to follow them, and collect vital publicly available information about them that indicates how many friends and followers they have, how many tweets they have made, and their bio. This will give you a measure of the third characteristic of high network value customers – how highly they are connected to other buyers.

• Perform a social network analysis of your Twitter and Facebook followers – you can analyze your own Facebook and Twitter followers to determine which customers:
  • have the highest number of connections
  • are most likely to pass key marketing messages along to their followers
  • have the highest influence and are opinion leaders

This information allows you to fill in the final piece of information you need to get a handle on the network value of a customer – the fourth criterion, whether they are highly influential and an opinion leader. Now you’re ready to start testing and scoring groups of customers according to their network value.

Optimize Your Marketing Programs

Clearly, ignoring the network value of a customer may lead to suboptimal marketing decisions. By collecting the information you need to assess the network value of your customers, you can now model both the likelihood that a given customer will buy from you, and the influence that customer has on other’s buying decisions. Then you can select a subset of your customers, and determine not just how much they will buy from you, but the total amount of revenue that they might generate from their influence over others. This enables you to determine the optimal set of customers to market to that will generate the highest ROI.

The topics I covered included the following:

The Lifetime Value of a Customer – A discussion of how to determine the lifetime value of a conference attendee is followed by the an examination of the much more difficult question of how to determine the lifetime value of an exhibit attendee. These attendees usually attend at no charge, and usually generate revenue only indirectly by attracting exhibitors and sponsors. In addition, I review an example of how knowledge of the lifetime value of an attendee can be crucial in decision making.

Closed Loop Marketing – A closed loop marketing system allows event managers to measure the results of all the various components of their audience development programs.  With accurate measurement of program results, they can accurately gauge the ROI of marketing programs, run controlled tests to optimize ROI, and identify key leverage points.

Email Optimization – Email is the keystone of many audience development programs. It is vital to optimize the revenue and response generated by email marketing through a comprehensive testing program. Properly done, email optimization can improve response by 50% or more, and in some cases double or even triple response. The presentation provides examples of how to identify key email test elements, implement carefully designed tests, and analyze the results.

Customer Profiling – Using the information about attendees collected during the registration process, prospects can be targeted with increased accuracy, and the results of marketing programs can be markedly improved.

Predictive Modeling – Moving beyond simple customer profiling, models can be developed that accurately predict which customers are likely to respond to promotions, and which customers are likely to defect. A case study is included on how predictive modeling helped triple conference revenue.

Segmentation Analysis – A highly effective way to identify which customers will respond to which promotions. Event managers can create custom-tailored marketing messages that address the needs of each segment to increase response, lower the cost of customer acquisition, increase retention, and increase cross-sales, up-sales, and referrals. An example of how a segmented campaign increased response by 20% is reviewed.

Web Site Optimization – Small increases in conversion rates can have a dramatic increase in registrations. An example of how minimizing abandonment rates during the registration process helped increase registrations by 30% is discussed.

Social Media Optimization –  Analytics can help event producers amplify the results of their audience development campaign through the optimal use of social media. By mining social networks to identify influential customers and prospects, adding social media profiles to the CRM system, and using predictive modeling to target high probability prospects, an event increased attendance by 30%.

As more event producers take advantage of these analytics techniques, they’ll be able to attract more and better qualified attendees to their events. Face-to-face meetings, the original channel of social media, will remain a vital method of marketing. Here are the slides from the presentation:

The immediacy of Twitter has provided an unprecedented window into the collective mind of conference and trade show attendees as they share information on what they are doing and thinking right now. Just ask Evan Williams, the co-founder of Twitter. At his keynote at SXSW earlier this year, when he was interviewed by Umair Haque, Director of the Havas Media Lab, the negative comments on Twitter about the session came fast and furious while it was happening. “The guy behind us is snoring” tweeted one attendee, while another tweeted “walked out of the keynote…not very compelling”. This is not an isolated incident by any means. Recently, there was one call for banning Twitter at conferences, by a speaker who was dismayed that the audience was more engaged with tweeting than they were listening to the presentation.

Twitter has now made conference evaluation sheets and post-show surveys seemingly obsolete. If you really want to know what’s on the mind of your attendees, analyze the Twitter stream that flows from the attendees during a conference or trade show. There you will find the unfiltered and unvarnished truth about what attendees really think from the most vocal and most influential attendees at the event.

There is a wealth of information that can be gained from a Twitter stream during an event, well beyond the occasional negative comments that emanate from a keynote that goes flat. As an example, I archived the Twitter stream at a recent technology conference. In order to protect confidentiality, I’ll call it the Open Source Technology Conference. Let’s take a look at some of the information that can be gleaned from it.

The Most Influential Attendees

I collected a total of 1462 tweets that took place during the course of the event. 74% of the tweets were original tweets, 31% contain a @user reference, 39% contain hashtags, 33% contain a URL, and 26% were retweets. There were 312 distinct users that tweeted in the course of the event. Not surprisingly, the distribution of their tweets follows a power law (long tail) distribution.

The top ten most active users tweeting included the following:

What’s even more interesting are the conversational habits of the users, which can be illuminated by building a graph of their conversational patterns. The figure below shows a directed graph in which the users are the nodes and the edges represent mentions or replies between them.  In order to make the graph more visually intelligible, it shows only users shows users who have ten directed messages or more.

Each node in the graph represents a user. The size of the node is scaled to show the relative number of mentions and replies each user had. The color of each node ranges from red to blue. The more red a node is, the higher the authority value of the user – meaning that they are the users that receive the most mentions from others. The more blue the node is, the more the user tends to send @reply messages, and is thus more of a hub for conveying information to other users. The graph makes it clear that some users are more influential than others. The most important authority at the event was user “AmilCarta”, as is evidenced by the large red node representing that user’s interactions. This individual is an important person for the event organizers to recognize and interact with. The large size of user “theexhibitsgroup”, and its purple color, show that it is the second most important authority figure, but is also a hub that conveys important information to other attendees. All of the individuals on the graph, given their high level of interaction, are important for the event organizers to develop a close relationship with in order to ensure the success of their event.

Note that the volume of tweets generated by a user doesn’t necessarily mean that they interact with other users via mentions or replies. TSUS, the event organizer, was the seventh most active tweeter, but didn’t interact with attendees. TSUS used tweets to primarily make announcements about upcoming sessions, speeches, and awards programs. You can chalk this up as a major missed opportunity by the events organizer – by not interacting with attendees, it forfeited the opportunity to participate in the flow of the conversation.

What Attendees Were Tweeting About

The Twitter stream also sheds light on what topics were foremost on the mind of attendees. One way to get a handle on this is to look at the most frequently used hashtags by attendees in their tweets. By studying hashtags, we can determine what the key messages were that attendees want to spread via Twitter. The dataset for this event, in which 39% of the tweets contain hashtags, versus an average of 5% on Twitter as a whole, show a strong desire on the part of attendees to emphasize particular messages that will be found not only by other attendees, but by anyone interested in the particular topic represented by the hashtag. The top ten hashtags used at the event were as follows:

It’s not necessary to stop the analysis at this level. For instance, it’s possible to drill down into each of these topics and create a word cloud to get a better sense of the buzz around the topic. Below is the word cloud for the tweets containing the hashtag #ibm:

The word cloud gives an instant impression as to the content of the 94 tweets for anyone familiar with the event. It isn’t necessary to be limited by hashtags in trying to distill the content of the 1462 tweets. One can also use the text mining and data clustering techniques I described in my post  A New Way to Segment Your Twitter Followers With Analytics to discover the major themes of conversations at the event.

Even More Information…

I’ve really just scratched the surface as far as what you can learn about an event from analyzing its Twitter stream. There is much more that you can learn and implement:

• Find out more about the interests, sentiment, and affiliations of your attendees by analyzing the content of linked URLs within tweets.
• Get extra insight as to what activities generate buzz during your event by examining the timing of heavy periods of tweet activity.
• Identify unique communities within your Twitter network. Based on the graph of interactions displayed above, algorithms can be applied to the network structure to identify groups of attendees who tend to communicate with each other more frequently than with the rest of the group. These communities may have different interests than the rest of the network, which can be used to custom tailor your communications with that community.
• Cross reference and apply everything that you learn about the topics, conversational patterns, and communities of attendees that tweet during the event to your entire group of Twitter followers, and the friends, fans, and subscribers in all of your various social networks.
• Determine the network value of an attendee. The valuable information that you can learn from analyzing the Twitter stream at your event underscores the importance of capturing in your CRM system the social media user name and/or identity of your prospects and attendees. Once captured, you can begin to determine the network value of an attendee – how much that indivdual may influence others to attend within your network of prospects. These attendees can be targeted with custom tailored communications, referral program incentives, and rewards programs.

If you have more ideas about the information that can be learned by analyzing the tweet stream at an event, or have questions, please leave a comment or email me at rhodgson@predictive-marketing.com.

If you have a personal Twitter account, it’s easy enough to create the lists that might be most meaningful to you. For example, you might organize them by family, friends, golfers, wine enthusiasts, etc. When it comes to business, however, it’s a little less straightforward how to go about segmenting your followers. And if your business has thousands of followers, it can get pretty tedious trying to segment them manually.

Using Text Mining and Clustering to Segment Your Followers

There are several approaches to segmenting your followers on Twitter. The powerful approach I’ll illustrate here is to use classic text mining and clustering techniques to let the data you have regarding your followers organize itself into the most appropriate segments. For instance, you could use each follower’s Twitter bio as the data to be used in creating segments. I recently did this for a client that had over 3,200 followers on Twitter. An illumnating visual aid to use in this example is a word cloud. Here is the word cloud that resulted when combining all 3,200 bios.

As you can see, there are certain words that seem to stand out and suggest different segments, such as storage, marketing, virtualization, media, technology, etc. That seems straightforward enough, but not all of the key words I just listed necessarily appear only once in a bio.

For instance, how do you go about classifying the following bios?

• Solutions for data storage, protection, availability, virtualization and collaboration.
• I tweet about Internet marketing, social networking, business development, and technology.
• Strategy, positioning, PR and Social Media for tech, B2B, consumer. Focus on clean, semi, storage and start-ups.

Each of the examples includes more than one of the most frequently used words listed above. You can see that assigning a follower to a segment on the basis of a single world is not a simple matter. If you were trying to attempt the segmentation of these followers manually, you could easily spend a lot of time trying to decide the most appropriate segment in which to place them.

That’s where the power of text mining can save the day. Using text mining and clustering algorithms, it’s possible to classify the bios into segments not just based on the appearance of a single word, but on the frequency of the appearance of all the words in the bio, and their tendency to appear together in the same bio. It’s the same principle used to find relevant documents when you use a search engine. In that way, all of the information in a bio is used to create the segments of followers who are most alike.

As an example, let’s take a look at the resulting word cloud for the storage segment.

As you can see, the bios in this segment are indeed dominated by a single word: storage, and secondarily, data. The text mining and clustering algorithms combine to create a very pure segment of followers that are focused on storage issues. The beauty of the algorithms is that they are not limited to the presence of a single word. For instance, consider the following segment:

Once you see this segment, it makes perfect sense. While the words social and media appeared separately in the word cloud of all bios, it turns out, not surprisingly, that the two words appear very frequently together, and create another very pure and distinct segment of followers. While you might be saying, well, that one was obvious, would either of the following two segments be obvious from the original word cloud?

Putting it All Together to Listen to Your Customers

The algorithms are finding patterns in the data that may not be intuitively apparent. This is a great illustration of the ability of text mining and analytics – in this case clustering – providing considerable added value in finding unexpected and fruitful patterns in data.

And here’s something that’s a real time saver: once the segments have been defined, as new followers are added, a classification algorithm can be used to place each new follower in the best segment, making the process of determining which list to place a new follower automatic.

Now let’s get back to the original reason for placing your Twitter followers on a list: to listen to what a group of people with similar charcteristics are saying. So here’s the icing on the cake: after placing your followers on a list, you can then collect their tweets over a period of time and use the same methodology: text mining and clustering, to classify their tweets into common areas of interest or concern. When you consider that you can process thousands of tweets this way, and find novel and unexpected patterns  in their comments, you have the ultimate opportunity to really listen to your customers.

Summary

• The Twitter list feature has made it possible to listen to a group of followers with a common set of interests and learn what’s on their mind.
• It’s possible to use text mining and clustering algorithms to let the data you have regarding your followers organize itself into the most appropriate segments.
• The algorithms are finding patterns in the data that are not be intuitively apparent, providing considerable added value in finding unexpected and fruitful insights into the mindset of your customers.
• As new followers are added, a classification algorithm can be used to automatically place each new follower in the best segment.
• Text mining and clustering can then classify the tweets of individuals on the different lists into common areas of interest or concern, and find novel patterns  in their comments, providing the ultimate tool for listening to your customers.

]]> http://predictive-marketing.com/index.php/a-new-way-to-segment-your-twitter-followers-with-analytics/feed/ 2 http://predictive-marketing.com/index.php/a-new-way-to-segment-your-twitter-followers-with-analytics/ http://feedproxy.google.com/~r/PredictiveMarketing/~3/Uy62INN3fEs/ http://predictive-marketing.com/index.php/how-to-kick-start-your-viral-marketing-campaign/#comments Wed, 02 Jun 2010 19:29:31 +0000 Bob Hodgson http://predictive-marketing.com/?p=773 In my last post, I reviewed the ability of some of the most well known Twitter users to extend their reach through viral marketing. One well-known approach to viral marketing is to focus your message on a small number of highly influential people, who will then help to start a word-of-mouth chain reaction that effectively broadcasts your message to a wide audience at a low cost.  Using this strategy requires that you can identify the most highly influential individuals in your target market. New research is now available to help facilitate the indentification process. Four researchers at the Max Planck Institute for Software Services recently published a landmark paper investigating how to measure and identify influence in social networks.

Measures of Influence

The researchers focused on Twitter users. With the cooperation of Twitter, they compiled a dataset used for the research that comprised more than 1.7 billion tweets among 54 million Twitter users containing nearly 2 billion follow links.

The researchers compared three different measures of user influence on Twitter:

• Indegree Influence, or the number of followers that a user has, an indicator of that user’s popularity.
• Retweet Influence, the number of retweets in the dataset containing a users’s name, a measure of their ability to propagate a message among their followers.
• Mention Influence, or the number if tweets containing a user’s name, indicating the ability of the user to initiate and maintain conversations with others.

The Million Follower Fallacy

One of the most interesting questions tackled by the study was to what degree the three measures of influence were correlated.  The researchers focused on the 6 million most active Twitter users, and ranked each one according to each of the three measures. They then examined the correlation between the rankings, shown in the following table:

Correlation ranges on a scale of -1 to 1; a perfect positive correlation is 1 (meaning that a high rank in one measure tends to occur along with a high rank in another measure); a perfect negative correlation is -1 (meaning that a high rank in one measure tends to occur with a low rank in another measure); no correlation is indicated by a score close to 0. All three measures of influence were positively correlated. However, ties in rank among the lowest ranked in the 6 million active Twitter users artificially generated the relatively high correlation seen in the column “All” in the above table. The researchers therefore  isolated the top 10% and top 1% of users based on their number of followers, and examined the correlations between the three measures of influence. The researchers reached the following conclusion:

After this filtering step, the top users showed a strong correlation in their retweet influence and mention influence…This means that, in general, users who get mentioned often get rewteeted often, and vice versa. Indegree, however, was not related to the other measures. We conclude that the most connected users are not necessarily the most influential when it comes to engaging one’s audience in conversations and having one’s messages spread.

This phenomenon has been dubbed “the million follower fallacy” and is one of the most important conclusions of the study: if your goal is to identify the users who are most likely to repeat your message to others in a viral marketing campaign, don’t look for the users in your target market with the most followers, look for the users with the most retweets and mentions.

As you move event further up the rankings, the overlap between the top 100 ranked users according to the three different measures of influence becomes smaller:

There were 233 distinct users that made the top 100 ranking in one or more of the three measures, and 67 that appeared on more than one of the top 100 lists.

Influence Across Different Topics

Another key question the team investigated was whether a user’s influence varied by different genres of topics. To address this question, they examined top-ranked influencers for three different topics which were among the most mentioned during 2009: the Iranian presidential elections, the H1N1 flu virus, and the death of Michael Jackson. Among the set of users who tweeted about any of these topics, only 2%, a set of 13,219 users, tweeted about all three topics, demonstrating the diversity of the topic genres.

Once again, the team looked at the correlations between the rankings on the topics, this time looking specifically at retweets and mentions among the most popular users, as measured by Indegree Influence.

Given the relatively high correlations among the most popular users in their ranks for retweets and mentions across these diverse topics, the researchers concluded that opinion leaders can hold sway over a wide variety of topics. This means that the opinion leaders can help spread a message outside their area of expertise. This is consistent with recent efforts to insert advertising links into popular user’s tweets. The fact that the degree of influence of users is a long-tail (power law) distribution also leads the authors to conclude that it is more economical to target top influentials to kick start a viral marketing campaign, rather than a massive number of less influential users.

How to Become Influential

The team next looked at three groups of users who tweeted about only one of the topics, to determine what factors make ordinary users more influential. These three groups of users had from 3 to 180 times fewer followers than the highest ranked influencers. In comparison to the top ranked influencers across all topics, this set of users that tweeted about only one of the topics saw their influence rise to a much greater degree over an eight month time period in 2009.

This lead the authors to conclude that through concerted effort, and focus on a single topic, users had the greatest chance of increasing their influence over time.

Summary

The key findings of the research are as follows:

• It is easier to kick-start a viral campaign by focusing on top influencers, rather than large numbers of individuals with a small degree of influence. This follows from the fact that all three measures of influence fall into a long-tail (power) distribution.
• If you are targeting individuals with a message with a view to extending your reach through viral marketing, the number of followers an individual has is less important than the number of times they retweet, and in turn, are retweeted. The number of followers a Twitter user has may indicate popularity, but this measure has a weak correlation to retweets and mentions.
• The most influential users can hold influence over a variety of topics, as measured by retweets and mentions.
• Ordinary Twitter users can best gain influence by focusing on a single topic, and consistently including links to useful and engaging content in their tweets, as opposed to focusing on conversations with other users.

Unanswered Questions

Like all good research, the analysis presented by the authors suggests further areas for investigation:

• Are the same influence patterns evident in business-oriented communications, as opposed to the general interest topics specifically investigated in this research?
• The research focused on the users with the most rewteets and mentions. The data was not normalized to account for the number of their followers. What patterns emerge when the data is adjusted in this manner?
• Are retweets driven by the influence of the user, the content of the tweet, or the content of the link? If it is driven by all three factors, what is the relative importance of each?
• How can the research be used to predict the outcomes of social media campaigns?

The researchers are currently working with Twitter to make their entire dataset available to researchers. While the dataset is not yet available, you can check their website for updates on their data sharing plan. If and when the dataset becomes available, we can look forward to further detailed investigation of user influence in social media.

The Lure of Viral Marketing

Every marketer dreams of the following scenario: You convey your message to a select group of individuals. Each of these individuals then repeats your message to one or more of their friends, who in turn repeat the message to one or more of their friends, and before you know it, your message has successfully reached millions of people.

The most recent example of this dream scenario was the  Facebook campaign to have Betty White host Saturday Night Live.  A 29 year old man from San Antonio started the campaign with the modest goal of gaining 5,000 fans on the Betty White to Host SNL (please?)! Facebook page. He reached his goal in a month and wrote a letter to Lorne Michaels, the Executive Producer of SNL, to encourage the selection of Betty as a host. The story was then picked up by major news agencies. A few months later, the Facebook page had 500,000+ fans, Betty White hosted SNL, and the show grabbed its highest ratings in 18 months.

Viral Marketing on Twitter

Dream scenarios are by definition rare. How much can you reasonably expect to extend your reach beyond your group of followers through viral marketing on Twitter?

The vehicle for viral marketing on Twitter is the retweet. It’s the indicator of how many times your message is repeated throughout the Twittersphere.  To get an idea of what the typical results of viral marketing on Twitter are, let’s take a look at what some of the most retweeted users are able to achieve.

I have focused the above analysis on business or news oriented sites, since I am addressing the question of how business marketers can extend their reach through Twitter. Therefore, no celebrities or other non-business entities are included.

The data shows the total number of followers for each user, the average number of tweets they make each day, the largest number of retweets they generated from a single tweet during the week of May 13-19, and an estimate of the % increase in reach they achieved over and above their follower base with their best tweet of the week.

In the calculation of the percentage increase in reach, the analysis makes the assumption that on average, each user who retweets is followed by 300 people. Although 93.6% of Twitter users have less than 100 followers, I’ll use Hubspot’s estimate of an average of 300 followers for the most active 5 million Twitter users. This makes intuitive sense, since the users most likely to retweet are among the most active, and in a long tail distribution the average is higher than the median due to the effect of the users with the most followers.

The data is surprising. I would have expected that the number of retweets would have been much higher for the six users with more than one million followers. Mashable wins the award for most retweets at 1,018.  The award for the greatest increase in reach goes to a tweet by HubSpot at 135%, more than doubling its reach via retweets.  Although it was only retweeted 159 times, the much smaller number of followers of HubSpot in comparison with Mashable translates into a greater percentage increase in reach.

The data seems to indicate that the users with the relatively smaller following have the opportunity to gain the most in percentage reach. This is not surprising, since for users whose following exceeds one million, there is only so far that they can extend their reach.

The Limits of Viral Marketing on Twitter

The above examples show how much the best tweets of some of the most retweeted users are able to extend their reach via viral marketing on Twitter during a typical week. The two best case scenarios, for HubSpot and Avinash Kaushik, range from a 60% – 135% extension of their reach. In terms of the dream scenario for viral marketing, these gains may not seem like much, but in practical terms, any time that you can increase the effectiveness of your marketing by 60%+, that’s significant.

Twitter is not the best platform for viral marketing. Tweets are ephemeral; they come and go. Twitter lacks the permanence of a blog post or a Facebook page, making it harder to achieve the explosive exponential growth of a true viral campaign. And the dream scenarios of viral marketing are not achieved via a single marketing medium; they are achieved through a perfect storm of mutually reinforcing marketing media. For example, the Betty White campaign was heavily reinforced by traditional news media.

When it comes to Twitter, it may be best to remember Avinash Kaushik’s tweet: “Success on twitter comes fm participating in conversations & adding value. It does not come fm “social media campaigns”.

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.

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