Harnessing Gig Mobility Data and Analytics for Growth Opportunities


Gig mobility data may be one of the most valuable business tools in the last few years. In the post-pandemic shift to hybrid work and working from home, traffic patterns in downtown areas have changed. Major residential communities are springing up in the suburbs, seemingly fully developed, as the commuter base around metropolitan areas creeps further from the interstate. For instance, what was once an hour’s drive from downtown Los Angeles to the outer suburbs during evening rush hour now often exceeds two hours. 

What does this mean for mobility-related businesses? How do rideshare companies determine surge policies and driver incentives in these quickly growing and continually changing areas? How does the competitive food delivery business determine market penetration strategies in a constantly expanding and changing environment? How do services like Amazon Flex and Roadie look at logistics analytics to determine their scheduling? 

Equally important, how does the investment community evaluate mobility-related opportunities powered by new technology and new ideas, especially in a business sector where change is the only constant?   

The secret is knowledge. Who’s driving where? When do they drive? What route do they take? Gig mobility data answers many of these questions.

Here's what we cover:

Gridwise Analytics gathers vehicle trip data across all significant gig service platforms, receiving information from over 500,000 gig drivers who have downloaded the Gridwise app. This input allows Gridwise to aggregate gig mobility data and translate it into transportation analytics. The result is the ground truth: What’s happening on our streets and freeways? This information helps businesses in all sectors operate more efficiently. 

What are mobility data, gig mobility data, and transportation analytics?

Before discussing the opportunities within mobility data, gig mobility data, and transportation analytics, it is crucial to understand the definitions and the differences between the applications of these terms. 

Mobility Data

This is the broader of the two terms, referring to any data related to the movement of people or goods across different transportation modes. It can encompass public transportation, private vehicle usage, pedestrian traffic, and more. It also encompasses shuttles, scooters, and rideshare. 

Gig Mobility Data

Gig mobility data is more specific and indicates that the data is related to the gig economy segment of mobility, such as rideshare and delivery services (including prepared food, groceries, and parcels). This would be particularly relevant to gig drivers and companies interested in understanding patterns, demand, and operational efficiencies within the gig sector.

Transportation Analytics

Transportation analytics is the interpretation of the related data. As Harvard Business School Online explains, “Data analytics refers to the process and practice of analyzing data [in this case, gig mobility data] to answer questions, extract insights, and identify trends.”

For instance, rap star Snoop Dogg appeared at the Ball Arena in Denver, Colorado, this past July 2023. An analysis of gig mobility data surrounding that event can reveal

  • where concert attendees came from (the geographic reach of a rap performer in the Denver metropolitan area) 
  • what route they took (how will traffic be affected?)
  • how far in advance of the event they arrived (what’s the potential impact on restaurants in the area pre-event?)
  • how long attendees remained afterward (rideshare companies can use this input to determine the time and length of surge periods)

In each case, data from different times and different places tells a different story, of interest to a different entity.

Gig mobility data refers to the individual points of information. Transportation analytics refers to interpreting that information to develop a story and its potential effect on a business. 

What can gig mobility data show us?

Gig mobility data can help us see the overall picture

Image displaying a heat map of San Francisco and its surrounding suberbs. Heavily populated areas turn almost solid gold with activity from gig mobility data.

The image above is a bird’s-eye view of San Francisco and the surrounding suburbs. Red and gold heat map points indicate rideshare pickup locations. Heavily populated areas turn almost solid gold with activity, centralized in the San Francisco city area on the left of the image, with the next most active area across the bridge to the right (east) in Oakland. 

This map highlights other sites that may be less popular but still show significant activity. If a new entrant wants to enter this market but is still determining where they want to focus, this gig mobility data information can help answer that question. 

Incumbents can also use this data to identify where their competitors’ are active. By understanding where competitor activity happens, rideshare companies can position their businesses to capitalize on their unique advantages in ways that competitors can’t, leading to more profitable market share. Maybe rural markets are underserved, presenting an opportunity. 

Gig mobility data can help us see the activity of different gig platforms

This image shows how Gridwise Analytics can overlay gig mobility data from two companies, as we have done here again with San Francisco, providing a view of rideshare market domination. 

Gridwise Analytics can overlay gig mobility data from two companies, as we have done here again with San Francisco, providing a view of rideshare market domination. 

The orange points represent areas of activity for Company A, while the blue points represent activity for Company B. If you were a new industry entrant, you could see where your competitors are focused.

Gig mobility data can identify points of activity

An image showing how companies visualize Gridwise gig mobility data by adding a three-dimensional element to represent more trips occurring in the same area.

Gridwise can help companies visualize this data by adding a three-dimensional element to represent more trips occurring in the same area. Both clusters of columns (in the lower center-right and the far center-right), represent rides at airports, showing the popularity of those locations for rideshare drivers. 

Different colored points represent the different rideshare services. This type of transportation analysis can also be done for grocery and food delivery services, showing clusters of restaurants and even individual establishments that generate substantial orders. These merchant insights are key in developing strategies based on ground truth data. 

How does gig mobility data identify and leverage hotspots?

Residential development

Construction is near completion on a hypothetical new community of 350 homes in a suburb 30 miles from a major metropolitan area. Rideshare companies want to know the kind of traffic the community will generate at key times, both from commuters and for trips to regional airports, among other destinations. 

In advance of that community opening, analytics companies can locate similar communities in the area (similar income and demographics), procure transportation data analytics for commuter rideshare, and predict what the new community will generate. Based on this information, rideshare companies can determine driver incentive programs to service this community, such as surge pricing and other promotions. 

Origin and destination data can optimize route efficiency, reducing fuel costs and travel time. Grocery delivery platforms will also be interested in the information and what it means for the demand for their services. 

Retail establishments, like supermarkets and restaurants, and services like health clubs can also procure gig mobility data to determine the feasibility of new locations to serve the community.

New entertainment and sports venues

A recent AP article predicts that by 2028, Major League Baseball will likely add two new franchises, meaning new teams in new cities. Cities running for the new franchises include Charlotte, NC; Nashville, TN; Portland, OR; and Montreal. According to AP, Salt Lake City, UT, and Austin, TX, have also expressed an interest. The Tampa Bay Rays are finalizing plans for a new ballpark in St. Petersburg, FL, and the Athletics are leaving Oakland, CA, to head for Las Vegas. 

This means those cities and the rideshare companies that serve them will seek gig mobility data to predict the volume of passengers on game days. One tactic will be to examine game day transportation data analytics from stadiums in similar cities. At a minimum, the gig mobility data will again help rideshare companies determine how many new drivers they need to attract to their platforms and what kinds of promotions they will need to draw those drivers out. 

These facilities will no doubt host other events, including concerts and other sporting events. Member companies in the transportation network will be interested in gig mobility data so they know how to serve attendees best. 

Gig mobility data has value for the investment community and hedge fund managers

Investment companies, hedge fund managers, and venture capitalists have also found opportunities to use gig mobility data and transportation data analytics to solidify their investment strategies. The increasing integration of technology and data analytics has opened new avenues for investors to gain insights into various industries, and the mobility sector is no exception. Here are just a few examples.

  • Opportunities in the gig industry. First, it was rideshare, then food and grocery delivery, followed by parcel delivery. Airbnb and Vrbo rent out vacation homes, and Outdoorsy helps RV owners justify the expense of their rigs by renting them out when they’re not in use. All these industries and their investors will benefit from gig mobility data. 
  • Infrastructure investments. Investment firms often seek opportunities related to infrastructure development. Transportation analytics can identify locations where there is a need for improved transportation infrastructure. Cities can use gig mobility data and transportation analytics to structure bond offerings to finance infrastructure projects. Cities are also now considering broadband as part of the infrastructure, which will put more focus on this sector. 
  • Regulatory impact. Regulations and policy decisions heavily influence the gig driver industry. Gig mobility data and predictive analytics can help investors determine how proposed regulations may impact companies in this sector. Anticipating these regulatory shifts allows investors to evaluate investment opportunities better. Industry principals can use predictive analytics to mount campaigns that ensure legislators consider all options and ramifications of potential regulations. 
  • Insurance. Automobile insurance companies base their rates on the zip code of where the insured lives. Gig mobility data can contribute to risk management strategies by providing a more accurate view of risks and challenges in a zip-code area. This includes traffic congestion, supply chain disruptions, and environmental considerations. 
  • Impact of technological advances. The mobility sector is undergoing rapid technological advancements, including the development of autonomous vehicles, electric transportation, and smart city solutions. Investment companies and hedge fund managers can use gig mobility data for predictive analytics, evaluating investment opportunities in companies involved in this technology. Companies in the autonomous vehicle and electric vehicle charging industry have used Gridwise gig mobility data to make decisions that have had considerable financial impact. 
  • Economic indicators. Changes in gig mobility data often reflect broader economic trends, providing investors with early signals about economic downturns or upswings. A decrease in regional commuting activity could indicate layoffs, while an increase in vacation travel could indicate economic growth.

Gig mobility data does not exist in a vacuum

Much of the decision-making regarding transportation and related projects relies on online research, observations, and unscientific studies. Often this information presents a conflicting picture of what’s happening. 

Gig mobility data and transportation analytics provide a more definitive answer. Gridwise Analytics has encountered several companies that were stymied by conflicting perspectives. Once they added gig mobility data from Gridwise, the picture became clearer.  

“We always knew we needed TNC data, but before Gridwise Analytics, there were only limited sources out there,” said one company official. 

Gig mobility data from Gridwise Analytics 

Gig mobility data from Gridwise Analytics offers unique insights for companies involved in and serving the transportation infrastructure, showing how people and goods move from one location to another. The granular data shows where trips originate, where they end, and the major travel corridors. Gridwise Analytics is the who, what, where, when, why, and how of gig mobility data.

Data teams can sort this information to reflect daily, monthly, and annual patterns. Gridwise data comes in a clean format, with little or no need to manipulate it to fit into the model you might be using. Models can be quickly updated with new information, too. 

For a demonstration of how Gridwise Analytics vehicle trip data can sharpen your transportation analytics, contact us below. 

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