In recent posts 1, 2 & 3 2 of my colleagues have gone into great detail about tracking twitter.
The response to these posts and presentation of this material at our Omniture summit was apparently overwhelming - everyone wanted to know about how they could track and use twitter.
I felt these posts were fantastic at conveying how one could go about pulling this twitter data into omniture and making use of it. What occurred to me was that peoples interest was not some much about the fact that it could be pulled into SiteCatalyst, but that the data could be got at at all!
It strikes me that people didn't realise that APIs could be used to get this kind of data - hopefully Adam and Ben have shown getting the data is not only possible but a very good opportunity.
Once the data is in Omniture, it's not tied to any other behavioural activity, it really is just a trend of data over time for a given segment, keyword or set of keywords. This could just as easily be pulled out of the API and into your own database as it would be uploaded to Site Catalyst.
In order to "analyse" this twitter data you would want to compare trends side by side and see if you can determine any kind of causality between you online activities and the various "tweets" about your company (or vice versa).
Personally, I would love to do a proof-of-concept to run a "text mining" tool over the tweets and see whether they could be segmented in someway to provide more meaningful trends than just a general indication of volume. I think the text mining software may have some difficulty with the online vocabulary (lexicon) that might need to be used along with the varieties of grammar that we see online, but it would certainly be interesting.
However, my main point is that many different data sources are online now that can be incorporated into analysing the customer. Because other tools make that data available you should be looking to take advantage of that somehow. Over the coming weeks I will be attempting to pull together a list of the online data sources I would consider to be most useful to analysing customer behaviour or at least comparing trends.
Remember, unless you can map the data from these tools directly onto your own customers you are always going to be speculating about their causality. Once I get this "ideal" list of data sources, I'll then look at ways of joining the data in that list to your customer data.
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