Integrating Twitter Into Web Analytics [Inside Omniture SiteCatalyst]
At last week’s annual Omniture Summit, there was a lot of buzz about social media and Twitter specifically. In my SiteCatalyst Power User session, one of the things I covered was an idea about how you can leverage Omniture SiteCatalyst to monitor your company’s brand reputation in tools like Twitter. This concept seemed to really resonate with the Summit audience, so much so, that I was given an opportunity to share it at the closing session. The following will describe the concept in greater detail for those who could not attend my Summit session. DISCLAIMER: What I presented is a “proof of concept” and is no way a formal introduction of a new Omniture product.
So this idea started with me thinking about how cool Twitter is and how it could be used for marketing purposes. As an Omniture Twitter ambassador (Omni_Man), I see people talking about Omniture all of the time on Twitter. Sometimes this chatter is positive, sometimes it is negative. I usually try and send my colleagues at Omniture “tweets” that I think might be relevant to them, but this can be very time consuming. So I said to myself, “SiteCatalyst has a Data Insertion API that is used to inject non-website data into SiteCatalyst and Twitter has an API associated with its search.twitter.com website, so if you put the two together, why couldn’t you pass Twitter information into SiteCatalyst?” Doing this would allow you to do many cool things which I will describe below. I enlisted the help of one of our Omniture Consulting geniuses and within 24 hours, we had a working prototype. The following examples of this functionality will use Comcast (who I co-presented with) as an example, but these represent test data and is not meant to imply that Comcast is using this functionality. In my session, I posed the following hypothetical business scenario: You are the web analytics manager at Comcast and your CMO returns from an executive retreat where he/she has learned all about Twitter and believes that Comcast needs to do everything it can to monitor what its customers are saying on social media sites. The CMO calls an emergency “all hands” Marketing/PR meeting and demands to know the following:
- How often Comcast is mentioned on tools like Twitter
- If there is ever a spike (positive or negative) in brand-related terms (in a week, day or even hourly)
- Who are the people most often mentioning Comcast on social media tools and who are they communicating with the most?
- When are people on social media tools mentioning key Comcast product/service features that Product Managers should know about
So at this point, the CMO turns to you and asks if there is anything you could do to help… What would you say? Not sure? Let’s tackle them one at a time…
Monitoring Brand Comments
The next thing your CMO wanted to know who are the most active Twitter users that are tweeting about your brand. Are there some really good brand advocates out there? Are there people who are repeatedly bashing your brand? Are any of your employees “going rogue” and confusing the marketplace with mixed messages? Again using the API, it is possible to extract the Twitter user name associated with every tweet. In our proof of concept we did our best to extract the author and the recipient, with the latter being more difficult since there are times when there is no recipient or multiple recipients (we are still working on this). However, by placing both in separate Conversion Variables (eVars), we could breakdown the “Brand Tweet Comments” Success Event metric by author to see who is twittering about your brand the most. We decided to take this one step further by creating a Conversion Subrelation between the author and the recipient so you could break one down by the other (note that if there is no recipient we used “[No Recipient]”). This allowed us to see who was tweeting with each other the most often. I imagine that this could be useful to see what types of people have formed virtual communities and some companies might consider contacting the key members of this virtual community to gather product feedback/suggestions or to leverage them for brand promotion. You could also use SAINT Classifications to group Authors into meaningful buckets once you knew who they were (i.e. Customers, Vendors, etc…). The following is a screen shot of the subrelation report we created:
Mining Important Product Keywords
The final thing the CMO tasked us with was discovering when, in addition to our brand name, social media users were mentioning specific keywords that are product or service related. For example, if someone “tweets” about Comcast and in the same tweet mentions “speed” it is likely that this tweet is related to high-speed internet access and could be interesting to the Internet product manager. Alternatively, if a “tweet” mentions Comcast and also mentions “Tivo” or “DVR” it is likely they are expressing an opinion in the digital TV recording arena that would interest the associated product manager. So you have millions of opportunities to read what your customers are saying, but who wants to scan through all of those “tweets” to find the relevant ones, especially if this has to be done manually?
This got me thinking about SiteCatalyst’s search functionality. If we had all of the “tweets” in SiteCatalyst, you could perform a keyword search and let SiteCatalyst find all of the comments that mention a specific keyword. For example, let’s imagine we use the Data Insertion API to pass all Comcast “tweets” to a Conversion Variable (eVar) and then conduct a search for the phrase “Tivo.” SiteCatalyst would isolate those “tweets” and then you can bookmark that report and schedule it to be e-mailed to the appropriate product managers at whatever time interval you desire (hourly, daily, weekly, monthly, etc…). This way, no one at your organization would ever have to use or look at Twitter, but instead, the information they need to see would be pushed to them automatically. Best of all, there is no limit on how many searches and bookmarked reports you can create so you can create hundreds of different keyword searches and send them to different groups of people using Publishing Lists. The following screen shot provides an example that shows “tweets” having been sent into SiteCatalyst and a user entering the phrase “Tivo” in the search box and a highlighting of one “tweet” that would be found:
As you can see, if you know a lot about SiteCatalyst including API’s, Conversion Variables, Subrelations, Searching, Bookmarked Reports, etc… you would be able to amaze your CMO by answering all of his/her questions and be a rock star!
Track Multiple Brands
Another concept related to this that we have explored is the idea of tracking multiple brands. There is no reason why Comcast, in this example, could not also capture “tweets” about its competitors or subsidiary brands to see them side by side. This would require the use of an additional eVar or potentially some additional Success Events, but we got this working in our prototype.
As mentioned previously, all of this was done as a proof of concept, but as you can see, the concept has great potential. We at Omniture are going to explore this topic more and hope that you do the same. We hope to add more information about this to the Developer Connection. We will also continue to explore new ideas related to this, but I encourage you to leave comments here with your ideas on how this concept can be extended.
If you want to learn the implementation details of this solution, please refer to the following technical blog post: http://blogs.omniture.com/2009/02/24/implementing-twitter-data-tracking-in-omniture-sitecatalyst/
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