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The next metrics buzzword may be "fusion". As this well-written article explains, it's a kind of data mining (though the article doesn't use the term) which compares media consumption and purchase patterns across a mass of individuals to determine which media will actually reach those who buy a given product category, with a degree of precision that even the best demographic targeting can't equal.

For example, the media planner can use fusion techniques to target "true heavy beer consumers, as opposed to men 18 to 34."

Sounds good, doesn't it? But like any other approach, it's going to need some fine-tuning. At least in its simplest form, it's less useful for campaigns that seek to change rather than reinforce behaviour (how do you target "people who don't drink beer at the moment, but would probably like it"?). And of course there are groups that by its nature it can't identify, for example the lucrative first-time parents market (who by definition have never bought baby products before; indeed, a whole lot of their consumption patterns are about to change abruptly).

Most likely, the real value will come through correlating multiple behaviours. For example, consumers who purchase white wine and cola drinks might be good potential converts to beer: they evidently like chilled alcoholic drinks and are also not averse to drinks from cans.

All this, of course, presupposes that the presumption underlying fusion is correct  – that people exhibiting these behaviours are not randomly spread all over the place in terms of media consumption.

But if there's a trend toward this analytic technique, digital signage stands to gain (as, I suspect, do all kinds of specialist and niche media, at the expense of big-numbers primetime TV). The place-based nature of the medium already implies a certain degree of behavioural similarity on the part of its consumers, which the fusion approach should confirm. 

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