Analysing the ant-heap
Not to be confused with 'Location Based Services' where users opt in to have ads or 'offers' shoved at them depending on where they are, 'Location Insight Services' focuses on the location, not the transient mobile user rambling through it.
The idea is that telcos already get a mountain of location and customer data as a sort of by-product of running the mobile network. Properly sifted, sorted and anonymised it can be turned around and analysed from the perspective of the locations.
Simple example. You're researching whether or not to locate a retailer in a particular building on a particular corner. Obvious useful information includes foot-fall: how many walk past, when do they do it, where have they come from, where are they going, do they go to other shops, what sex, age and demographic are they?
Most of this information could feasibly be gathered by any of mobile network operators covering that corner by mashing together network location and customer data, completely anonymised and statistically valid (although all the network operators sharing data could conceivably provide a more granular picture).
According to JDSU and UK-based research firm, STL Partners, which today released a “Location Insight Services” report, LIS will be an $11 billion opportunity for network operators by 2016.
The alternative real-time Location Based Services will already be worth $12 billion by 2014, but much of that cash has been won by over the top (OTT) players. With Location Insight Services the network operator will have a clear advantage and the ability to hold onto the market, according to Mike Flanagan, CTO of Arieso, a company specialising in geolocated intelligence. Mike worked on the research project with STL Partners.
Won't OTT players find a way to generate that information themselves, perhaps by using an 'opt in' client on smartphones?
"They just won't be able to match the sheer volume of information the operator already has," Mike told me.
"Telcos can aggregate a huge volume of anonymous location data and deliver it in batches on a daily basis or even in close to real time. OTTs just can't match that."
The study points out, however, that only a few network operators have begun to tackle the technical challenge of transforming the data to a usable form or are developing a viable business model to make the best use of it.
This is despite the huge number of use cases out there for the analytics the data could yield - retail, transport, and advertising industries could benefit hugely from LIS, claims the report.
Could it be that large operators are wary of reputational issues around user privacy? Given recent history this wouldn't be surprising - ensuring that data is anonymised and, more important, seen to be anonymised, is something that the industry needs to work on.
The more I think about the use cases, however, the clearer it becomes that network location and associated data could ultimately see service as an M2M or Internet of Things stream - the 'machine' (or thing) being the mobile network. Think real-time data on commuter movements for a 'Smart City' transport and scheduling application, for instance. LIS is obviously an area we're going to hear more about.