During my travels over the past week (AdTech NY and Qualcomm’s LTE Direct Summit) the same idea came up a few times which started to make me think.  Could all of the signals emanating from mobile device (see “big data meets local“) unlock better local yield management?

In other words, could knowing how far away someone is to a business — and several other variables — enable predictive modeling about their probability of transacting. This isn’t necessarily new but takes on new flavors if worked into an equation that defines their price sensitivity or elasticity?

From there, it becomes clear about offering different pricing to existing customers, repeat customers, faraway customers, nearby customers, customers with green eyes and love of craft beer, etc. This gets us closer to mobile’s promise of more effectively driving offline commerce.

It’s especially relevant within the context of perishable inventory (think empty movie theater or restaurant). This is of course nothing new, and gets to the yield management endgame of the daily deals craze of 2010-2011. But continuing advancements in big data push it further.

It boils down to segmenting consumers by willingness to pay for something — a function of location-oriented factors like weather, behavior, time, product category, etc. This makes it a juiced up version of the airline model that maximizes revenue with demand-driven variable pricing.

Groupon’s mobile efforts take this to heart. There’s also Thinknear (situational targeting), xAd (Smartfences) and Retailigence (local product availability). These each reveal variable demand, impacted by location-related variables and time. The next phase is optimizing pricing accordingly.

This will all be at the center of a lot of the things we continue to look at and write about at the intersection of mobile, social, local and big data. The emerging world of mobile shopping and payments (subject of my recent report), is central to all of this. Stay tuned for more.