You may have heard about mobility data, location data, mobile location data, and device data. But what is the difference between these types of data, and why does it matter?

This guide will provide you with a better understanding of what mobility data is and what distinguishes it from other related kinds of data. We’ll also explore the industries where mobility data is most relevant as well as the specific use cases for those kinds of organizations. We’ll also note many of the challenges surrounding mobility data such as consumer privacy. Lastly, we’ll discuss the many benefits this data can provide to both your organization and to society.

What is mobility data?

“Mobility” refers to how people navigate the world around them. Therefore, mobility data may be defined as data related to transportation and traffic, contextually enhanced (or enriched) telematics data, and/or data related to how people move around places like points of interest (POIs) or neighborhoods (at Arity, this data is aggregated and anonymized – information is collected in a way that doesn’t identify individuals, ensuring privacy).

What is device data?

Device data, on the other hand, is any data that a mobile phone or mobile device collects; this includes location data (where a person is in a point of time) based on global positioning system (GPS) sources. When a device owner opts in to share device data, they may be granting access to trillions of data points from apps on their mobile devices.

Due to technological improvements over the last decade or so, device and location data can be further contextualized with other data points. When device data becomes further enriched with information it is called mobility data.

What is telematics data?

Telematics is a relatively new term; the earliest known use of this phrase was in the 1970s. The word “telematics” combines the words “telecommunications” and “informatics.” The concept, according to the Oxford English Dictionary, includes “the use of computing, information technology, and telecommunications for long-distance transmission of information.” Telematics data, then, refers to mobility data that gets transmitted. Although there are many use cases for telematics, we’ll be focusing specifically on mobility data pertaining to driving. Decades ago, this data was downloaded from sources such as OBD-II (on-board diagnostics) devices plugged into vehicles. Now, mobility data gets transmitted wirelessly by, for example, mobile apps, connected cars, and more.

Telematics data includes driving behavior and device data, including location data. With contextual enrichment, telematics data can then include the where, when, and how of traffic and transportation data. It may include adding a layer of temporal facts: weather, speed, moving direction, and geospatial constructs. For example, telematics data from a mobile device may indicate that someone appears to be driving–but if they are also at an airport and on a runway, the conclusion may be that they are in a taxiing airplane rather than a personal vehicle.

Contextual information superimposes those additional layers of data. But that contextual enrichment may also include observations, theories, or models, which are scenarios or use cases in the form of algorithms that interpret telematics data into useful insights. One example of these insights is crash detection functionality, which, when included in a consumer app, can help connect a consumer with emergency services and/or their emergency contacts.

Sources of mobility data

Mobility data can come from a variety of sources including private companies, consumer mobile apps, and Internet of Things (IoT) devices. Private companies, such as technology, mobility data, or transportation companies can gather mobility data through mobile app platforms once consumers agree to share their data.  Finally, sensors and IoT devices, such as smart traffic lights, road sensors, and connected vehicles, can also generate mobility data.

Mobility data for auto insurance, advertising, and beyond

Now that we’ve explored what comprises mobility data, let’s dive into how and why organizations can use it to meet their goals.

For businesses, mobility data can provide comprehensive, aggregated, anonymized driving behavior to inform strategic decisions. Instead of knowing where drivers tend to drive, mobility data can deliver a moment-by-moment picture of how drivers get from point A to point B – including whether they suddenly accelerate, hard brake, drive while distracted, and/or typically pass by points of interest (POI). These deep insights on customers can help businesses understand customers’ driving risk and driving patterns.

Mobility data for auto insurance companies

Auto insurers can use telematics data and mobility data to ultimately drive profitable growth.

• Strategy: Understand in near real time how driving behavior is changing so that you can better predict shifts in frequency and severity.

• Marketing: Convert the highest potential lifetime value customers, not just the highest volume of customers.

• Pricing: Avoid adverse selection on your book of business by segmenting at time of quote so that you can improve your quote to bind ratio.

• Retention: Insurance shopping may increase, so offer competitive rates combined with a simple and easy experience to avoid losing customers.

• Claims: Claims rates are rising due to more expensive parts for cars and other factors. Combat these costs for your organization by offering features like crash detection.

Mobility data for mobile apps and retail

Businesses also rely on insights from mobile location data to better understand and connect with their ideal customers. Mobile location data provides information on driver location from GPS sources. This data is a foundational component of mobility data but lacks key context. Mobility data enables more robust insights about driving behavior and can help inform more relevant consumer experiences and deeper customer connections. For example, when businesses such as retail mobile apps can understand consumers’ typical points of interest, these companies can serve more relevant ads at more opportune times, such as when the consumer is most likely to drive by their brick-and-mortar retail location,  Unlike mobile location data, mobility data provides an unprecedented amount of information that is vital for understanding customers.

Whether businesses seek to examine big-picture behavior trends or street-by-street traffic problem areas, a deep dive into mobility data can unlock key insights. By understanding why, when, and where behaviors are happening, businesses and members of the transportation ecosystem can make better, more strategic decisions.

Mobility data for city planning and road infrastructure

Real-time data can be used for city planning and traffic management. This mobility data can inform infrastructure development, helping planners create more efficient transportation networks. By analyzing traffic patterns, local government leaders can determine where to build new roads, bike lanes, or public transit lines. Real-time traffic data can also optimize traffic signals, reduce congestion, and improve road safety. Additionally, smart traffic management systems can adjust signal timings based on real-time conditions.

Mobility data for marketing and advertising

Using the real-world insights derived from mobility data, marketers and advertisers can present highly targeted offers in their apps for goods and services that their ideal audiences already use, based on their individual preferences and behaviors. This not only increases reach, but it also reduces advertising dollar waste. Additionally, more targeted advertising provides more value to consumers because the ads feel native and relevant to the consumer. These offers are affiliated with their habits and desires.

Mobility data benefits

Mobility data helps organizations understand how people move so that they can develop better, more useful products and experiences; manage the business, especially when transportation is an important component of that business; understand, predict, and manage risk; and develop better value propositions based on POIs.

Challenges

While mobility data offer numerous benefits, there are challenges that must be considered as well. For example, the collection and analysis of mobility data can raise privacy concerns, especially when tracking individuals’ movements is involved. For this reason, data anonymity and compliance with regulations are essential.

Arity has created a principle-based framework to guide data collection, partnerships, and best practices to ensure that we instill confidence with end users so that they understand and trust when their data is in the hands of Arity and our partners.

Conclusion

Mobility data has the power to impact industries and companies for the better by providing them with valuable insights into how people move. By leveraging this data, business and government leaders can more strategically provide customers and the public with much-needed services and support.

As technologies advance, collection and analysis of mobility data will continue to improve, becoming even more precise and presenting new opportunities and challenges. Strategic use of mobility data can enable organizations to remain relevant and competitive in an increasingly data-driven world.

About Arity

Arity is a mobility data and analytics company that provides data-driven solutions to companies invested in transportation, enabling them to deliver mobility services that are smarter, safer, and more economical. Arity collects and analyzes trillions of miles of driving data to create a greater understanding of how people move. With the world’s largest driving dataset tied to insurance claims collected through mobile devices, in-car devices, and vehicles themselves, Arity derives unique insights that help insurers, developers, marketers, and communities understand and predict driving behavior at scale.