Monday, February 24, 2014

What does the customer really want?

The question of figuring out customer demand has been one of the core tenets of any successful business strategy since time immemorial.  In the article, we see two distinct, but highly divergent approach to figuring this out.
The first is detailed in the small clothing start-up, Blank Label.  Their approach to divining the wants of a customer is decidedly low-tech.  They just ask.  Their highly customizable, highly responsive method of production takes made to measure shirts to a level mostly unavailable in the internet marketplace. Doing so creates an interesting bit of disruption from a supply chain perspective.  As the article mentions, this lowers their overhead and inventory costs, as they only need to worry about raw cloth, not ready-made shirts.[1] 
Alternatively though, this is a much different approach to the retail model in general.  Previously, clothing makers had to develop a style that has mass appeal by intuition, or by attempting to calibrate their design to broader trends.  Blank Label has a totally different option available to it.  Their granular, piece-by-piece approach to design allows them to collect data on their customer’s preferences and tendencies.  Similar to how Netflix cobbled together the genre chimera that was House of Cards from its massive data mining of viewer’s habits, the same could be done with Blank Label’s shirts given enough time and data. [2]
On the diametric opposite of the “just ask” model is in the often mentioned big data.  Given enough crumbs of information about past customer behavior, manufacturers and service providers can accurately predict what customers may want before they even order it.  This consequently ripples back up the supply chain and enables much more efficient logistics, inventory management and procurement practices[3]   
The winning bets for larger organizations are still likely on the side of highly specialized analytics and becoming more sophisticated in how they identify customers and market opportunities.  However the expectation of more and more customizability, of catering to their specific needs is unlikely to go away.  So, as big data makes it easier for organizations to define the specific wants and needs of their customers, they are simultaneously reopening the door to small-scale, craft operations like Blank Label to fill those same highly specialized wants and needs in certain scenarios.

[3] Waller, M.A. and Fawcett, S.E. (2013) Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management,” Journal of Business Logistics, Vol. 34[2]

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