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Location analysis was considered one of the most critical technologies for business intelligence in 2017, ranking higher in importance than IoT, big data and social media analytics by Dresner Advisory Service. The big data boom has given location professionals access to more data than ever before. Now location-based decision making can be even more accurate, with access to supplier, customer, travel time and labour data. Here are 5 trends you don’t want to miss in 2017.
Millennials demand new analysis criteria
Deloitte lists the particular lifestyle of millennials as one of the commercial real estate disruptors for 2017. This lifestyle focuses on health and wellbeing, drinking less and exercising more than previous generations and engaging in practices such as yoga and mindfulness. The WELL Building Institute encourages businesses to take into consideration the following concerns when designing and constructing new office spaces: air, water, food, light, fitness and physical and mental comfort. They suggest, ‘the way that buildings are designed, constructed and maintained impacts the way we sleep, what we eat, and how we feel’. Taking these factors into consideration could prove financially beneficial in the long run, reducing absenteeism, encouraging innovation and increasing productivity. This could be particularly beneficial for the business’ health, wellbeing and environmental concerns. Creating a working environment in cohesion with the company’s values will attract and retain quality talent, as well as creating natural advocates for the brand. Analysts are capitalising on this trend by mapping healthy commuting options such as cycling and walking. For example, Walk Score provides data on walking, biking, travel time and pedestrian friendly routes. Real Estate Portal Zillow uses the Walk Score API on property listings, highlighting a property's transport connectivity and scoring it's pedestrian-friendliness. Analysts might also consider adding layers of air pollution, noise pollution and health facility locations to their maps.
Using mixed reality for organisational decision making
The Next Web predicts that the dawn of mixed reality will bring changes to the way we communicate, educate and entertain, becoming the ‘next big paradigm shift’. But what will mixed reality do for location analysis? Pokemon Go was a phenomenon that proved the power of mixed reality. The launch of the game saw users trekking to locations they’d never been to before. Maxwell's Bar and Grill in Covent Garden reported in July 2016, a 26% increase in sales owing to a search for Pokemon in the area. In short, mixed reality has the power to change the meaning of location. Analysts can capitalise on this by using Pokestop and Pokemon gym data to record and analyse mixed reality content helping to determine changes in foot traffic and predicting increased sales in an area due to mixed reality content.
Collecting and analysing data from IoT devices
As objects continue to collect and transmit information, location analysts can utilise the data to their advantage. Sensors configured in infrastructures such as roads and train lines allow for more in-depth data analysis. For example, The Smart Streets consortium, led by InTouch uses the IoT to create a hub of road maintenance information, from the state of roads through to maintenance schedules and meteorological data. This allows industry partners to share information to improve the efficiency of highway services. Similarly, the IoT enables data professionals to monitor any vehicle that has a GPS. Using IoT location data, analysts can identify bottlenecks for deliveries. This location information can be applied when optimising a city's plans or for a retail business planning a new store. The IoT can also allow for a customer’s buying preferences to be sensed in real time at a specific location and a particular price, comparable to other stores in different areas. This enables ‘analysts to determine if dynamic pricing may increase the odds of a purchase’ across various locations .
Location data using minutes not miles
House prices in major cities are creating a mass exodus from the city centre and making previously undesirable locations on the outskirts, the places to be. In 2016, Domain reported that ‘the number of people living on Melbourne and Sydney’s fringes is continuing to explode at a rapid rate’. Similarly, CNN reported that one-third of San Francisco bay area residents say they are likely to leave the region in the next few years over its lack of affordability’. The development of infrastructure to suburban areas makes this an increasingly appealing option for first-time buyers and young professionals. Property portals are capitalising on this trend by switching from distance-based to commute time-based search engines, such as UK property search Zoopla's integration of the TravelTime API. Analysts can use migration data to inform business decisions. For example, Remix allows businesses to evaluate potential transit routes in any city, helping their customers to ‘understand the cost and demographic impact of a proposed change’ from a set of live location analysis data including poverty rates, population density and existing transport routes.
Predicting the future of location data with machine learning
The use of machine learning to predict information is becoming increasingly advanced and extends to many location
analysis applications from predicting movements of people and animals across the globe to assisting the travel and
hospitality industry with long term predictions about the best places to holiday. My
Total Retail suggests that machine learning is allowing retailers to plan ahead with regards to
sourcing, buying and supply chain, marketing, merchandising and customer experience:
‘Take the example of a
company trying to predict what consumers will be buying next winter. Machine learning algorithms can determine
availability of materials from outside vendors, incorporate predicted weather conditions that would affect
transportation or create an increased need for outerwear, and recommend the quantity, price, shelf placement and
marketing channel that would best reach the target consumer in a particular area’.
Using machine data
to create dynamic data sets focused on location will ensure location intelligence is more up to date, improving the
accuracy of location-based decision making.
What does the future look like?
Location data trends require analysts to become more contextually aware, as they’re required to evaluate new infrastructure and new social and cultural priorities. In the future, it’ll be important to understand location data not just for their lat/long properties, but the mixed reality content too. New techniques promise an evolution in business models and marketing strategies across multiple industries.
Analyse location data using travel time
We've just launched a free tool that can draw isochrones to determine where's reachable within 15+ minutes by car, foot, public transport and more. Try free it now! Or if you're looking to learn more about location analysis check out this post that lists 5 analysis techniques you need to try.