Introduction
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
Everything will be explained with simple words and major emphasis will be on how location data is able to boost your business activities, marketing, customer services and risk management. At the climax of this article, you will possess the means with which you can stitch a procedure that involves the exploitation of location information to enhance your performance in operations and functionality.
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 successful ones are the programs that can use a combination of different data to get the best coverage and accuracy. Moreover, any location data must be time stamped, standardized and labelled with an identifier (e.g., a device ID, customer ID or asset ID). The way such organisations should collect location data is by determining the purpose of such data collection, seeking user consent (where necessary), and setting up retention policies prior to collecting the data. In order to collect and analyse this data effectively in the future, organisations must implement clean ingestion pipelines on data, create validation procedures and create simple geocoding to support their location services. The absence of a rigorous methodology in data collection about the location will not allow future analysis of the data to be correct and useful to the business.
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.
Finally, appropriately aggregating the data to the appropriate level for decision making, for instance, hourly demand by zone or average length of stay by location. To build an audit trail, it is imperative that a full explanation of all assumptions made and transformations performed be provided as part of any reporting process to promote understanding of the data further. Preparing data correctly will ensure that the patterns identified are actual patterns and not artifacts of poor data quality or of inconsistently applying the same process multiple 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 are built on mathematical models to predict demand, arrivals and congestion (by location) to inform the planning process. Prescriptive analytics provide suggestions about the most optimal path, the most appropriate staffing requirement, or location choice to meet the needs of the business. The analysis of the gathered data is essential; maps, isochrones, and flow charts will be useful to portray analytical data to your non-technical departments.
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 location data, often combined with Real Estate Data Analytics to plan warehouse and hub proximity.
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 business organizations utilize location information to be in a position to easily detect individuals who are committing crimes as well as the point where the crimes are taking place.
Through the data, cities have the opportunity to determine traffic flow, formulate action plans to enhance the safety of the population, and create effective emergency response plans.
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 data of location may be wrong, prejudiced, and may invade privacy. As one example, the precision of GPS is strong in suburbs and rural locations, and signals are weak inside houses, and data may skew a certain group of people, resulting in misleading conclusions. Privacy and compliance should be prioritized in all instances in which GPS location data is being used.
As an illustration, an organisation is only required to gather as little data as is required, utilize moderate security to data stored but not that which is in transit and, where feasible, anonymize the location data in order to safeguard personal privacy. Besides that, the organisations should adhere to the relevant legislation regarding consent and opt-out measures, an unambiguous system of governance, which outlines access to the information, the period of its storage, and the purpose of its use.
Openness and honesty towards the customers and employees will establish trust. Technologically, organisations have to keep tracking the quality of location data and models to eliminate silent degradation. GPS location information should be ethically used not to make detrimental conclusions and cause tracking of people in sensitive places with no reasonable reasons.
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 merely the possession of maps and dashboards to transform location information into valuable insights. You should collect data, make it ready to be studied, and learn about the relationship of location data with business decision making. With such consideration on where and what happened, organizations are in a better position to comprehend demand, efficiency and risk. The best teams develop the use cases of location data and then apply them to everyday operations and assess the impact. In the process of doing this, they also emphasize on data quality, privacy, and governance to create and sustain trust. Location information will be a productive resource enhancing operations, marketing, investments, and customer experience when team members gather data on purpose, clean it regularly, analyze it sensibly, and responsibly.
