Learning More About Customers

An interesting article popped up in my brands/branding Google Alerts this week.  Published in Online Media Daily, the article is about how Condé Nast, the large media group, is mining its customer data to create 10 audience segments (or what it calls “catalysts”) to appeal to marketers trying to reach segments with like interests.

The catalysts are:

  • Prestige Pioneer – top buyers of beauty products and early adopters
  • Big-Basket Beauty – high-volume buyers of mass beauty products
  • Right from the Runway – luxury fashion followers
  • Eclectic Stylist – “high/low” fashion buyers
  • Alpha-Millennial – young influencers
  • Lovemark Mom – mothers who prefer brand names over generic brands
  • Motor Maven – consumers who are ahead of the car curve
  • Shopping without Borders – well-traveled shoppers
  • Tech-thusiast – early tech adopters and power users, influencers
  • On-The-Towners – Mostly single, highly social

The articles says that Condé Nast analyzed digital interactions, direct mail and brand research with its 55 million Preferred Subscriber Network customers to create these unique groups, each of which share similar interests and online behavior.  American Express and Neiman Marcus have already signed on to participate in the program to market their products.

I found the article interesting because the Catalysts are based on how people behaved, not necessarily demographic information.  Traditional methods of segmentation go something like this:  Our best customers are women between 35 and 45 years old, with family income of $150,000 or more per year.  Therefore, all women between 35 and 45 years old, with family income of $150,000 or more per year are our target segment.   There is tremendous waste in this approach because, as we all know, there can be many differences between two women who are the same age and have the same income.  These differences will impact what and where they buy.

Being able to target people with specific behavior characteristics is a far more effective way of sending them tailored messages than traditional segmentation approaches.  Of course this is not really new ground.  Amazon shows you items and sends emails based on products you bought already or viewed on the web site.  And Facebook shows ads based on products, sports teams, restaurants and companies that you “liked”.   These can be very effective because they focus on the individual, but they sometimes have a creepy, Big-Brother-is-watching effect on me.

The Condé Nast approach seems like it would be more subtle.  You’re a luxury fashion person, so you will see more luxury fashion advertising when you’re on the Condé Nast web site.  Big Brother is still watching, but he’s not going to be quite as in your face about it.

This is all a continuing evolution of big data and how marketers convert that data into useful information about their customers and visitors to their web sites.  Companies like Condé Nast have a unique opportunity to amass huge quantities of data due to the sheer volume of subscribers and viewers they have.  Segmenting this data into more useful information can help their advertisers target more effectively and can be useful to their subscribers and viewers by presenting ads that are more relevant to their interests.



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