In my last post, I proposed the (somewhat tongue-in-cheek) term of “content grazing” to describe automatically extracting small bits of information from company documents and feeding them to prospects and customers. The notion had been on my mind for some time, prompted by a sense that traditional approaches to content creation and distribution are fundamentally too expensive to deploy in full. That is, tailoring large content streams for buyer segments and funnel stages is just too much work for both marketers (who can't afford to create the content and campaigns) and buyers (who don’t have time to read the results).
The good folks at Autonomy apparently had similar thoughts. Back in July 2009 they published a paper Meaning Based Marketing that describes using a collection of Autonomy technologies “to truly understand your customers—drawing on everything from transaction history to cross-channel interactions, user generated content, customer and community behavior, as well as third party content—and act on that knowledge to deliver the best performing, most accurate, and relevant content to each individual visitor. Automatically.” That pretty much says it all.
The Autonomy brief has a slightly different scope from my own concept, perhaps because it’s tailored to fit the Autonomy product portfolio. For example, it includes Web content archiving, which I wouldn’t have considered, and excludes creating content from relevant pieces within a larger document. I do consider the latter quite important, because it’s a key to reducing the content creation cost and in making the information more digestible for customers. But the core components of meaning extraction and self-adjusting content selection are certainly present in Autonomy's description.
Specifically, the paper outlines a fully automated process of:
- analyzing unstructured content to infer the views, needs and preferences of customers who create or read it
- generating customer profiles based on the contents and associated behaviors- uncovering customer segments within the data
- delivering a tailored customer experience (that is, content recommendations) based on its understanding of both the customer and the contents
- optimizing interactions through real-time multivariate testing
The list also includes market analysis, based on sentiments and trends, as well as the previously-mentioned content archiving. Both are useful additions to the vision.
The paper doesn’t go into much technical detail beyong mentioning that Autonomy unifies the data through its “Intelligent Data Operating Layer” (IDOL). But I suspect you could find much more information elsewhere on their site: Autonomy itself doesn’t seem to be conserving its content-creation budget.
Nor, come to think of it, did I notice any particular personalization or intelligent targeting on my own return visits to their Web site. Either they’re very good at this stuff – and know I’m not a real sales prospect – or they haven’t quite gotten around to deploying these services for themselves. Either way, the concept remains valid and it’s good to see that someone is working to make it happen.