5 Ways Location-Based Technology is Changing Business
Using location (or maps) as a way to digest and visualise information is now part of everyday life. Think about tracking the Uber driver on your smartphone or assessing an AirBnB property based on exact location as well as price.
We are already seeing far more brand interaction with consumers around location-based services, with specialist geospatial technology integrated into mainstream applications. But as well as the consumer market, location-based technology will have a major impact in the business-to-business sector.
Better connectivity has the ability to make a step-change in decision-making and reduce costs, or open up new revenue streams in a number of sectors. Such as…
There are already location-based benefits in retail with shops positioning products using shopper in-store movement heatmaps. But with a multi-channel approach, retailers can maximise online sales as well as traditional sales from “brick and mortar” stores where, despite the huge increase in e-commerce, the vast majority of sales still come from.
Retailers therefore need to find new ways of driving footfall into their stores whilst increasing customer experience. Wherewolf is a London based company that uses a location-based approach to help customers search for an item of clothing and then recommend a number of retailers nearby that stock that item, even opening up the option for “in the moment” marketing offers. The critical element is that they link into the retailer’s in-store inventory so that the customer knows that their size and colour preference is in stock before they arrive.
Transport & Logistics
Moving things around the world, whether by land, rail, air, or sea, requires a knowledge of multiple data sources to make the best commercial decisions around planning and risk. Those data inputs change on a regular basis so the ability to provide up-to-date, relevant information is critical, as is the ability to provide some context around knowing what data is relevant and when. Location is often that context.
Take, for example, a container ship travelling across the Indian Ocean. Risks are linked to weather forecasts and cyclones in the region, or even piracy incidents, so there is a need to enable decisions to be made in real-time to change routes to ensure that the ship reaches its destination safely, with minimal additional fuel costs and time delays. The coordination with ports and land-based freight operations can then be maintained to ensure that every transport used is maximised for capacity and cost efficiency, whilst maintaining security and traceability of goods transported.
In time, having relevant data feeds based around location will allow machine learning algorithms to start predicting what might happen, which in turn, drives even more optimum decision-making such as changing the intended destination of goods whilst en-route because of a predicted change in operations within the business.
Gaining insight from huge amounts of data streams that are now interrelated using location as a reference will drive new ways of thinking across cities. We’ve already benefited from GPS route planning in our cars for a number of years, and more recently up-to-date traffic information and re-routing. The next stage is for incidents (or even congestion build-up) to be identified and reported in real-time across a city – either crowd-sourced from commuters, flagged by road sensors, or predicted using artificial intelligence methods from CCTV and traffic video feeds.
This enables city authorities to automatically react to change traffic light configurations, open up diversions, coordinate emergency services or breakdown, and re-route traffic to keep the city moving. This takes us from time delayed, intermittent reporting with manual decisions, to automated real-time actions, using machine learning algorithms, to find the most optimal outcome which in turn benefits to city’s population and economy.
Assessing insurance claims has always been a time consuming operation with little options for digital transformation, until recently. The use of drones with geospatial software has enabled a number of insurance companies to visit areas of damage and fly a drone to accurately measure the extent of the damage. This is particularly relevant for agricultural insurance, where surveys use Near Infrared (NIR) to measure the extent of vegetation growth thereby preventing any fraudulent claims for crop damage.
Also, combining with historic and other relevant data, through the use of machine learning, means that any other assumption that is relevant in a policy pay-out decision can be considered far more easily to ensure more efficient processing. For example, an accurate flood risk or earthquake risk map. This means a faster pay out for customers.
Mobile Workforce Management
Companies such as utility firms have a huge amount of assets positioned across the country with mobile workers required to often work alone and remotely. The ability to accurately locate the assets (especially those underground, such as pipes and cables) and guide the worker to that asset will speed up time spent in the field. Also, the ability to monitor and check-in with the remote worker whilst they are working is a far safer practice.
For all the above use cases, location provides the context to analysing data streams to derive some unique insight which can be acted upon. These data streams will soon become more diverse with the wide range of sensor types that will capture that data. The real challenge will be how to discover all the data that is relevant, from a huge number of disparate sources, each with their own data protection and security requirements, using different data standards and models, and very much in their own silos.
Nabil Lodey, Envitia CEO