The FCB Grid and its flipside

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A while ago I talked about different views on how advertising works. Personally, I’ve never believed in a single theory, people evaluate different products differently. They also evaluate products differently depending on where in their product evaluation cycle they are. So, for instance, household cleaners are advertised informatively, while facial soap is sold with brand ads. Car dealers prefer informative advertising while car manufacturers prefer brand advertising.

One of these so-called ‘integrative models’ is the FCB grid, developed at Foote, Cone & Belding (now Draftfcb) and written about by Richard Vaughn*. This model divides goods and services into four categories, along two axes: the Think/Feel axis, and the High Involvement/Low Involvement axis.

Vaughn makes interesting generalizations about how marketers should address the consumer decision process in each of these four quadrants**. But those are sort of boring, so I overlaid my vague and general idea as to what marketing approaches work, instead.
On the internet, WOM is social media marketing. Direct Marketing is lead-gen and email. And sales promotion is couponing (among other strategies.)

I think this framework is also somewhat helpful in thinking about the various ways to help consumers find the right product (as opposed to selling it to them.)
Personally, I’d like to see a lot more articulation on this last. Helping consumers find the right product will turn out to be a lot more fruitful over the next ten years than figuring out better ways to sell them one.

* Vaugh, Richard (1980), “How Advertising Works: A Planning Model,” Journal of Advertising Research, 20 (September/October), 27-30; and (1986), “How Advertising Works: A Planning Model Revisited,” Journal of Advertising Research, 26 (January/February), 27-30. Sorry, no link.

** Later refined by Rossiter and Percy: Rossiter, John R., Larry Percy, and Robert J. Donovan (1991), “A Better Advertising Planning Grid,” Journal of Advertising Research, 31 (October/November), 11-21. Again, no link. Academic journals suck.


  1. Recommendation engines ask you questions and use your answers to tailor results. I’m using collaborative filtering to mean taking prior purchase behavior and use that to make recommendations.

    I don’t think low-involvement purchases will support the added user cost of explicitly specing what they want. OTOH, I don’t think the broad data collection of a Netflix or Amazon would be precise enough to help me choose a car. With high-involvement items, it’s probably just better to ask the consumer what they are looking for.

  2. Dear Jerry,

    I like your blogg which help me up understand some issues was not clear before. I would like to ask you if there is possibility to give me the reference belonging the model you insert in this page which talks about (personal selling, word of mouth…with high-low involvement)


  3. Saeed—

    That model is mine, and just my opinion. If you think about those marketing types, that’s just where they seem to fit. You can use me for a reference, I’m probably as reliable as anyone else in marketing :)


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