How to Match Tweets to Customer Records

Many organizations are analyzing Tweets for various purposes such as sentiment at an aggregate level.  For example, “generally what are people saying about us in the Twitter universe?”  This is a good baby step into Big Data Analytics but where organizations want to get to is “what is my customer John Smith saying about us?”  This customer-level analytics is much more valuable as it allows the organization to serve the customer better, identify “market of one” opportunities and so on.

You have to match Tweets to customer records as a pre-requisite to such analytics.  So what are the considerations in doing so?  It is a key capability of MDM hubs to address the problem of matching customers together by using structured data sourced from internal systems within the organization and applying traditional deterministic and/or probabilistic matching techniques.  But the problem shifts dramatically when trying to match Big Data together.  You need to re-think a solution given the problem has changed.

Many are familiar with Twitter and Tweets.  What some don’t know is that there is a set of metadata that is distributed with each Tweet.  Some of it is useful for matching purposes such as the user’s name, Tweet timestamp, high level location information and so on.  This information along with information in the text of the Tweet triangulated with internal information can yield high quality matches.

So below are some considerations in matching Tweets to internal customer records.

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