Google and Overture are fairly regularly in the news for their service of placing ads based on the content of the Web page a visitor is viewing. Amazon is known for recommending items based on an analysis of what other buyers of the same item have purchased. Netflix recommends film titles based on prior selections. What does any of this have to do with law firms?

Law firms know a lot about their clients. But they do not typically apply this know-how systematically. Many large firms have purchased CRM (Customer Relationship Management) systems in an effort to manage client relationships more systematically and sell more services.

Firms also have a lot of documents they have written on behalf of clients. In theory, one could “extract” client needs and interests from work product by using semantic or full-text software. As lawyers write new work product and as the firm collects external information (such as new case law or industry updates via Web feeds or crawlers), it ought to be possible to apply similar analysis to these new and incoming documents. If new internally created or externally obtained documents match client “interest” as defined by prior work product, then the software could alert the client relationship partner about a possible “match.” That partner could then make a judgment call as to whether to contact the client to provide an update.

This approach goes well beyond clients subscribing to law firm e-newsletters. Like Google, Overture, Amazon, and Netflix, it would require “mining” and analyzing prior data against new data. I have not seen software to accomplish this, but many knowledge management tools rely on sophisticated textual analysis and categorization. So it seems likely that such a capability could be developed if it does not already exist.

From a knowledge manager’s perspective, this would facilitate cross-selling and repeat business – a good way to make KM more client-facing and produce a more visible return on investment than may currently be possible.