Algorithmic location modelling to enhance customer behaviour profiling for marketers


Location based services (LBS) or location based marketing (LBM) has seen an increase in adoption based on ongoing technology diffusion amongst consumers (e.g. GPS enabled smartphones  as well as service enablers such as Foursquare or Facebook places).

Gartner estimates about 800 Million active LBS users by the end of 2012, a figure to grow to 1.4 Billion by 2015, resulting in over $13.5 Billion LBS consumer induced spending. With overall mobile advertising to be expected to account for $20.6 Billion by 2015, consumer induced spending on LBS provides a strong indicator of future growth driven by navigation, social networks and location search. The increase of smartphone penetration in third world countries will most likely result in a 2nd growth phase for mobile marketing and with it, LBS.

Growth of LBS will be enabled by further mobile and and tablet adoption rates and thus placing location based targeting devices into the hand of consumers. LBS revenue is assumed to grow mainly on its main form of current revenue generation, advertising.

LBS development

While customer privacy remains an issue, current adoption rates of LBS suggests users starting to neglect privacy concerns and even access fully passive LBS services such as placeme. Current studies confirm this trend with 58% of consumers valuing benefits over privacy concerns.

Advantages of LBS / LBM for marketers:

> Enhanced listening features to gain access to consumer behavioural data
> Enhanced targeting features to create more relevant POS offerings
> Enhanced interaction features to elevate customer brand interaction with social, contextual and location relevant content
Emerging opportunities for higher level data mining:
> Behavioural mapping. This is something which should see strong developments. Similarly to the Google Page Rank, a location rank should gain access to algorithmic modelling of consumer journeys. E.g. with increasing data, patters are likely to emerge to attribute value to. A consumer coming from location a to location b might suddenly seem more valuable based on prior exhibited behaviour than a consumer from location c to location b.
Some examples:
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2 thoughts on “Algorithmic location modelling to enhance customer behaviour profiling for marketers

  1. the wise guy sagt:

    Interesting idea to combine location with values to measure a page rank effect in real life. you should work for google

  2. markusrach sagt:

    Hi wise guy, thx for your comment; sincerely appreciated. That was a thought I came up with this week – while thinking about LBS services and how to leverage them for brands. Kinda working on this idea at the moment – will see were it leads me to :-)

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