5 Free Ways to Archive and Analyze Your Tweets
Your Twitter stream is a moving target. After a couple of weeks, tweets disappear, unrecoverable via Twitter search. Fortunately, if you want to collect, save, and analyze Tweets, there are several alternatives that are freely available.
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.
On Twitter, Timing is Everything
Twitter is a medium of the moment. The life-span of a tweet is exceedingly short. If a tweet it is not read quickly after being posted, chances are that it won’t be read at all. The lifetime of a tweet appears to be social media’s answer to the mayfly.
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.
A Vital New Marketing Metric: The Network Value of a Customer
New research is underscoring the influence of social networks in marketing. Researchers at Telenor, a mobile phone carrier in Scandanavia, developed a map of social connections based on calling patterns between subscribers to analyze the adoption of the iPhone since 2007. The research showed that an individual with just one iPhone-owning friend was three times more likely to own one themselves than someone whose friends had no iPhones. Individuals with two friends who had iPhones were more than five times as likely to have purchased an iPhone.
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:
- A high level of satisfaction with your product
- Is highly likely to recommend your product to others
- Is highly connected to other potential buyers
- 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.
Twitter at Events: Find Out What Attendees Really Think
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.
