Using location intelligence to make business predictions
In real estate, there is a saying about what matters most: location, location, location. This is also true for other business areas like retail, logistics, or finance. All these sectors can benefit from the advancements of artificial intelligence in the field of location intelligence (LI). This is an umbrella term for data derived from geo-special tags, which are further mapped in a spatial or chronological order. These are usually included in business intelligence tools to identify relationships between entities and create actionable insights.
Such real-life data allows stakeholders to create and optimize their strategies depending on their customers’ behaviors, the environment, and other parameters, usually captured by IoT sensors. The gain from using artificial intelligence to analyze such data is that it detects patterns while offering solutions, and that it can learn from past experiences, becoming better with each new data entry.
Applicability of Location Intelligence
The immediate applications of LI are in the areas which are determined by proximity, such as retail stores, roads, and production. However, next to these direct purposes the technology can be used for fraud detection, public safety, and improving recruitment processes.
LI in Retail
Even before AI was available for problem-solving, retailers needed to answer one simple but critical question: where to build or rent their next store space. This question can be answered by corroborating a variety of different factors, including the demographics of the area, the spending patterns, the average salary rates, and even crime rates. The algorithm can create clusters of customers based on these insights and pinpoint the options with higher chances of offering increased revenues.
Another useful application of LI in retail is creating personalized experiences with the customer’s permission. For example, based on the geolocation of the customer’s smartphone, a restaurant could have their meal ready as they walk into the premises. This is possible by combining real-time localization with traffic information, cooking time, and other potential delays to create an accurate prediction model.
The same reasoning can be used for creating more personalized customer care features such as indicating a free space in the mall’s parking lot or signaling promotions available in the customer’s proximity as they walk into a mall or cross a shopping street. A simple example of using location intelligence are the recommendations given by Google Maps and TripAdvisor based on nearby locations.
LI in Logistics and Transportation
Using patterns detected by artificial intelligence to make transportation more efficient and fulfill expectations better will soon become the norm in the logistics sector. Right now, just a few pioneering companies use these tools to fight ever-crowded roads and bypass dangerous areas, keeping their delays to a minimum.
From mass systems such a Waze, which is continuously updated by its users, to specific software based on GIS, transportation companies can see an exponential increase in the quality of their services if they deploy tools which offer accurate predictions of arrival times. This could act as a clear differentiator in an industry where the base service is hard to tell apart from that of competitors.
Such software is built by taking into consideration millions of data points recording the origin and destination of a vehicle, the time of the day, day of the week, month, holidays, type of roads, and many other such variables. Each of them can impact the final statistical model.
Not only logistics companies can benefit from location intelligence. Every driver could soon be safer and arrive faster thanks to apps using this feature. By communicating with other traffic participants, we could drive more reliable cars which will be aware of the dangers ahead and adjust speed or route accordingly, automatically.
InData Labs’ experts state that this is achievable through trend analysis and by identifying those patterns which lead to accidents or traffic jams. Once the system has these pieces of information, it can communicate with the traffic light system and other safety systems. The raw data for this analysis can come from CCTV cameras or even personal dashboard cameras, which could have a geo-tag of the vehicle’s exact position.
Final Tip: 6 Steps to Use Location Intelligence
If your company decides that it could benefit from using location intelligence, it needs to understand the problem and use a systematic approach for maximized results:
- First, you should define the problem and identify why the location component makes a clear difference in solving it.
- Next, you need to find the way to measure and access geolocation information. Additional investments like acquiring sensors might be necessary.
- Once you have the data, clean, tag, and put it in the right format for training the algorithm. This is one of the essential steps in the process, as the quality of the model is only as good as the quality of the data used for its training.
- Iterate many times, making slight changes to inputs to check for performance and accuracy even in non-standard situations.
- Don’t be afraid to let the model self-learn to potentially uncover other, not that obvious connections within the data sets.
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