Location Data: The New Frontier


Location Data:  Opportunity or Challenge

With the growing availability of location based GPS data from smartphones, the quest to gain a more granular and context aware understanding of consumer behavior has gotten more fascinating but at the same time much more challenging. On the surface, location based intelligence creates a dynamic real time window on consumer movement.  But location based data alone is neither the Holy Grail nor the key to understanding your customer’s purchase intent or advertising matches. The key to creating a beneficial data model is behavioral and demographic context.   Without this basis, mobile analytics leading to geo fencing (having knowledge of where a customer is at the moment) can be misleading.   For example, simply knowing that a person is passing by a Starbuck’s Coffee store, does not guarantee that person wants or is ready to buy a cup of coffee. Did that person already purchase a cup of coffee today? Is the women pregnant and not imbibing coffee? So if you keep sending them digital coupons for a free cup or discount, will it please them and build brand equity or simply frustrate them by the continuous prompting? Obviously, context makes the difference.

Location Data:  Where does it fit?

CIOs and Marketing VPs in this age of smartphones must carefully design and develop an approach to collect and analyze consumer information on a number of levels simultaneously.  The primary goal is to gain a deeper more robust understanding of customers using various data sources such as demographic profile, social graph, purchase behavior and search history, among other things.  The goal initially should not be directed towards gaining insight about a specific project, product or strategy.  The goal is to create a base case dynamic foundation which is the necessary cornerstone for all future contextual mining.  This cornerstone behavioral fencing is necessary before considering the inclusion of temporal factors such as time of day, weather, etc. or targeted input like location centric data.  The landscape can be seen in the chart below:

Location Data

Location Data:  Adding more context

Building this model requires time and a good bit of trial and error.  It needs to be agile and flexible enough to be directed depending on the project, product or goal. In many instances the cornerstone behavioral components will be enough to gain understanding into client attitude and in some cases predicting general consumer behavior and activities.  Adding GPS type input helps enrich the data base but location directed technology can be used, in turn, as an effective means to reach consumers with targeted information, offers/discounts or utilities, at an opportune or appropriate moment.  The growing population of smartphone users and the massive adoption of mobile applications, opt-in programs and transactions are forcing every consumer driven company to get much more serious about this pivotal area of data mining. With a good foundation of behavioral and demographic data, the algorithm can be programmed to learn from the continuous and steady input of geo related information thus expanding the iterative capabilities of the model for the business or client.

Location Data:  Opening for New Businesses

Help is needed to design the right approach. To be sure, there are numerous companies, large and small, touting software tools, recommendation engines and algorithms all under the heading of providing contextually aware predictive models.  Due to the various demographic, behavioral, temporal and location based data available, there probably is growing business opportunity for savvy start-ups to provide expert consulting and procurement services.  Someone that can listen and understand a company’s business, long range strategic goals and products and then pick and choose from a myriad of software providers, the right tools, best in class teams and approaches to build and  maintain a custom tailored agile and contextually aware data resource will be coveted.

Location Data:  Helping to secure the Holy Grail

The Holy Grail in all this is transforming this massive amount of behavioral, contextual and geo related data into an accurate ability to serve highly targeted content or advertising to a mobile consumer at the precise moment he/she is looking to buy, search or locate something.  Correctly anticipating customer needs and wants is the next Big Thing worth billions of dollars for companies that develop the tools and for enterprises that can use these tools to accurately assess their customer’s needs and desires on a real time basis.

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