Location data provides information about people, property, and what is happening in real time. Proper use of location data enables you to see trends that lead to more intelligent strategy-making, reduced costs, and higher revenues. Although many organizations currently collect this type of information, they do not effectively turn it into action. It leads to the creation of dashboards that display great information but do not provide any value to the user. In this article, we will help you transform your raw data into actionable insights. We will:
● Define location data and show you how it is collected
● Show you how to perform analysis on your location data
● Present several different case studies from a variety of industries
● Discuss the importance of accuracy, privacy, and governance, so that you can be assured that your insights are credible and compliant
All the facts will be delivered in a straightforward language where the main attention is paid to how location data can be used to optimize your business processes, marketing plan, customer care, and risk management. When you finish this article, you will have the mechanisms required to design a process that will make use of the location data to enhance your operational and functional performance.
What is location data, and why does it matter?
Location data is a type of data that provides stories about where something exists, when it exists, and typically within a given timeframe (e.g., within an hour). Location data may come from mobile devices such as smartphones, vehicle location systems, sensor-based systems, access points, and transaction data. When used in isolation, location data does not provide a meaningful representation of what a business may do with it; however, when combined with information from the business context, the benefits are far more accurate. By integrating product and service demand and supply locations, businesses can gain a much better understanding of their customer base and locations.
Therefore, by having a better understanding of their customer locations, companies will be able to tailor their marketing efforts to customers who are farther away from the delivery location of their products and services (thus reducing travel time to deliver their products and services). More importantly, location data allows business leaders to identify emerging trends earlier than with traditional reports. In addition, location data enables business leaders to make future business decisions based on actual data rather than past assumptions about customer supply and demand.
How is location data collected in modern systems?
Location is gathered through many methods, each with different levels of accuracy and cost. Smartphones use a combination of GPS, cell towers, and Wi-Fi to estimate their position. Vehicles and equipment get their location from GPS tracking devices and telematics. Buildings can help determine location through beacons, cameras, and access logs. The Internet helps determine your approximate location through IP addresses and delivery addresses from your orders placed at online retailers or delivery services, among other things. Point-of-sale systems connect your transactions to a store, and apps track your check-ins and provide other background data as needed.
The most effective programs combine multiple data sources to achieve the best coverage and accuracy. In addition, all location data should be time-stamped, standardized, and tagged with an identifier (such as a device ID, customer ID, or asset ID). Organisations should first identify the purpose of collecting location data, obtain user consent (if required), and establish retention policies before collecting the data. To ensure effective collection and future analysis of this data, organisations need to set up clean data ingestion pipelines, develop validation processes, and build basic geocoding to support their location services. Without a disciplined approach to location data collection, future analysis of that data will not be accurate and will not provide business value.
How do you clean and prepare location data for analysis?
Location data is often misrepresented in terms of accuracy, duplicate points, and noise (differences resulting from other tracking devices). Validation begins by removing impossible coordinates, correcting missing values, and standardising formats. The next step is to remove duplicate points (i.e., multiple signals emitted from the same source). Map-matching aligns these coordinates with actual roads/buildings/regions on earth. Geocoding converts addresses to coordinates. Reverse geocoding associates locations (or coordinates) with cities/neighbourhoods/store IDs. Temporal smoothing reduces minor variations between locations within time frames. Grouping coordinates creates a meaningful stop or trip. Enrichment provides context for the data, such as weather, traffic, and demographics.
Lastly, the proper aggregation of the data to the correct level upon which the appropriate decisions are to be made, e.g., hourly demand per zone or average length of stay per location. In order to construct an audit trail, it is paramount that a complete documentation of all assumptions made and transformation done should be included in any reporting process in order to further facilitate further understanding of the data. To get the correct data, preparation will make sure that the patterns that are found are actual patterns and not a result of bad quality data or of inconsistently using the same process several times.
How can analytics turn locations into actionable insights?
Analytics uses prepared location data to both understand the past and provide probable future behaviours by analysing patterns and providing probable outcomes. Descriptive analytics provides insight into what has been observed through heat maps of foot traffic or routing effectiveness. In contrast, diagnostic analytics provide insight into potential bottlenecks or underperforming areas and how they may correlate with time, weather, or promotions.
Predictive analytics involves the use of mathematical models to predict demand, arrivals, and congestion (by location) to be able to do proactive planning. Prescriptive analytics will provide recommendations on optimal route, manpower, or location to meet the needs of the business. It is important to visualise the information collected, maps and isochrones as well as flow charts are effective in conveying the information you have gained through analysis to your non-technical teams.
Alerts and automation can close the loop in the analytics process by executing recommended actions when a set threshold is met. It is essential to identify and assign an appropriate decision owner to each insight and include a quantifiable result for that decision (e.g., reduced costs, increased conversions, improved service levels). Using analytics as part of your operations will help position your business for a continuous operational advantage based on location data.
What are the most valuable business use cases?
Using location data and insights to help businesses reach objectives. Data-driven location insights are valuable across industries. They allow retailers to make informed business decisions based on the actual foot traffic and trade areas of their stores.
Marketers effectively reached their target audience through geo-fenced online ads and tracked offline conversions from users who saw them.
Logistics companies can save time and money by optimizing routes and dispatches using real-time data.
Real estate companies use this data to identify new locations for store openings and to see where competitors have established stores during the same period.
Healthcare companies can better understand how patients travel and establish new clinics based on those patterns, thanks to the data provided.
Financial companies use location data to quickly identify people committing fraud and the locations where it is occurring.
Cities can identify traffic flow, develop strategies to improve public safety, and develop effective emergency response plans using data.
In all industries, value is achieved by connecting location data to a business objective (e.g., increased revenue, reduced costs, reduced risk, and improved customer experience).
Successful programs begin with a specific question and then determine how the information provided will affect the company’s decisions about that question. After implementation of the program, measure the impact of the data and determine the return on investment.
What challenges and ethics must be addressed?
GPS location may be unreliable, biased and infringe privacy. As an example, the accuracy of GPS cannot be achieved in the urban environment, the signal is weak indoors, and the datasets might disproportionately represent certain demographics, which results in biased conclusions. Privacy and compliance should be considered as the most important things in all instances when the GPS location data are used.
To illustrate, organisations should only gather as little information as they need, should implement a reasonable level of security on the data stored and not in transit, and, wherever possible, anonymize the location information to ensure the privacy of individuals. There are also several other rules that organisations should follow including the applicable laws on consent and opt-out and a well-defined governance framework that will clarify who is permitted to access the data, the duration of time it is held and its use.
Transparency with customers and employees will build trust. From a technology perspective, organisations must continue to monitor the quality of location data and models to prevent silent degradation. The ethical use of GPS location data is not to draw harmful conclusions or to track individuals in sensitive locations without a justifiable basis.
To address these issues, we can help protect people and organizations from unnecessary legal problems. This approach also helps keep things honest and sustainable by using insights from location data.
Conclusion
It is not enough to have maps and dashboards in order to transform location information into valuable insights. You must collect data attentively, get it ready to be analyzed and learn about the relationship of location data with business choices. Organizations which take into consideration where and what happened can have a better insight into demand, efficiency, and risk. The most successful teams develop concrete application of location data use cases, implement them in business processes and evaluate their impact. In the process, they also remain quality-conscious, privacy-conscious, and governance-conscious in order to establish and sustain trust. Once information about the location is gathered deliberately, cleaned on a regular basis, analyzed with a purpose, and responsibly used, the location information will become a treasure trove which will enhance the operations, marketing and investments as well as the experience of the customers.
