When Harold Samuel coined the phrase “location, location, location” in 1944 when he founded Land Securities, one of the United Kingdom’s largest property companies, I am sure he didn’t realise how profound that phrase would turn out to be, as location intelligence is fast becoming the darling of business intelligence.
Location intelligence (LI) is not necessarily new, with the first real-world example of it surfacing in London in 1854, when an outbreak of cholera was tracked and managed by mapping the cases overlapped by specific water pumps.
Since then, LI has been enriching business data by adding critical context to shape the future of business decision-making. Data-driven maps and location applications enable trend-spotting and prediction to support market assessment, site selection, risk management, asset tracking and other core business needs.
When it comes to LI, data points are linked to physical locations and times − an address with a timestamp − but location isn’t the only common thread that connects these disparate data sources, and that is what helps us break down these data silos.
Location data can often provide the most transformative insights when looking at data that typically is not connected to an x/y coordinate.
Many leading organisations use location data analytics to solve business problems and uncover new opportunities by combining datasets that have a single common thread of causality. This is what makes LI solutions a crucial addition to business intelligence tools.
Geographic information system (GIS) technology (the backbone of LI) provides a software framework to manage, visualise, analyse and ultimately understand the significance of location information. Rooted in science, GIS helps businesses gain actionable insight by integrating all types of data.
LI, powered by GIS, transforms businesses and organisations across a wide range of sectors.
The applicability of location intelligence by sector differs. However, the results achieved are similar. For example, logistics and delivery companies report, on average, a 5% to 11% reduction in delivery costs and a 6% to 11% increase in customer satisfaction, when primarily using location intelligence for route planning and real-time vehicle tracking.
Companies that have adopted a clear location data strategy are defined as location intelligence leaders, and those that are in “early stage” have been defined as location intelligence followers.
We are on the precipice of a geospatial transformation.
In a study complied by BCG, leaders reported improvements that were 1.3 to 2.0 times as great as those achieved by followers.
Who benefits from location intelligence?
Apart from the obvious logistics use case for location intelligence, there are myriad use cases for almost every other industry as well:
Insurance: Spatial data visualisation increases proactive behaviour for insurers and allows them to be more accurate in writing policies based on real-world data, reserving and allocating capital, and preventing damage from impending natural disasters. This helps insurance data analysts and policyholders mitigate risk for a mutual benefit.
Financial services: Banks and financial institutions can optimise the placement of their branches and ATMs, as well as more effectively detect fraud, avoid flagging legitimate charges, and promote special offers to their client base. This makes them far more client-centric and assists in a much-needed rejuvenation of the reputation for financial services institutions being completely devoid of customer-centricity.
Public sector: Arguably the richest area of location intelligence data lies within the public sector, where we see a plethora of use cases. Location intelligence can help police departments optimally allocate resources and understand crime patterns in comparison to law enforcement movement. Government entities can combat fraud, waste and abuse using geospatial data to identify inappropriate spending and fraud.
Retail: Here retailers can use their location data to map consumer purchasing behaviour patterns in-store. From this, they can optimally locate certain products, as well as shift mobile offers based on shoppers’ location in-store or historic buying behaviour. When this is combined with internet of things data, seamless inventory management can be analysed with other locations or warehouses for supply chain and logistics optimisation.
Higher education: Universities and colleges can use location data to optimise their student recruitment and retention process, alumni and donor tracking, campus site selection, and for indoor and outdoor campus mapping efforts.
Customer experience: Real-time location intelligence for customer experience enables large venues, where people congregate − like stadiums, holiday resorts and airports − to enhance customer experience by providing quicker product delivery, effective queue management, and in our current conditions, alerts for COVID-19 track and trace, as well as hotspot notifications.
We are on the precipice of a geospatial transformation. We cannot rely on transactional data devoid of location intelligence any longer. Applications’ location data must be fully integrated with data from a plethora of sources to unlock rich new insights, power better decision-making and increase the speed of delivery for new products and services across all industry sectors.
This has the potential to add millions to the South African economy, boost quality of life and wellbeing among our communities, and generally help protect our planet
By Jonathan Houston, General Manager: Commercial MAPIT