Location Intelligence Helps LinkedIn Members Consider Commute When on the Job Hunt


Finding your dream job can take effort, but with the help of some innovative features, LinkedIn has made the job hunt that much easier for its members. Factors around location are oftentimes central to job selection. How far is the office from my home? What will my commute look like? These are common considerations when applying for and accepting a position.

The acquisition of LinkedIn by Microsoft in 2016, opened up new opportunities for LinkedIn members and customers. The "Your Commute" feature, introduced in 2018, provides answers and location intelligence to the job hunt with the help of Bing Maps.

Caleb Johnson, Software Engineer with the LinkedIn team, headed up the development for the "Your Commute" feature as part of a holistic approach to infusing LinkedIn features with more geospatial capabilities.

"Before we got acquired by Microsoft a number of years ago, we had our own way of doing geo across the world," recalls Johnson. "Obviously, it was not the same level in quality as Microsoft and Microsoft has all of these incredible resources. So, part of this original initiative was to improve our geo. We wanted to see if there was something that we can leverage from Microsoft's stack to improve the experience for members."

After gaining insight into the job hunter's mindset through a LinkedIn members survey, a key data point that emerged was that 85% of job seekers are willing to take a cut in pay for a shorter commute. This made clear how important location is to finding the perfect job.

"The commute is very important, especially for people living in big cities," says Johnson. "Just because you're in the same city, it doesn't really mean anything. It could be 5 minutes away or it could be an hour and half away. It really matters a lot."

The "Your Commute" feature helps deliver on LinkedIn's vision to "Create economic opportunity for every member of the global workforce."

"We want to help everybody to advance their careers and provide economic opportunity to them," says Johnson. "The way someone commutes is an especially big consideration for those who take public transportation as it may not cover an area that would otherwise be reachable with a car. Being able to embed that information directly into the job search and into the job recommendations is something that's very important to making sure we can serve everybody."

Collaborating with Bing Maps to build the just-right API

To help deliver on this, the LinkedIn team set out to build the "Your Commute" feature and connected with the Bing Maps team who were in the process of developing several new logistics and transportation APIs and were looking for real-world use cases that they could tailor the APIs to power.

"It was very fortuitous that we wanted to use it, and they wanted to build it. So, we were able to collaborate with them. We had weekly sync ups as they were building out features and functionality," Johnson explained. "For example, this API needs to be faster or this API needs to have this functionality and they would build out the functionality for us. We would then turn around and provide the feedback by using it and identify an opportunity to improve an area, so it was a really great collaboration model."

High-performance polygons

The Bing Maps Isochrone API lights up the key capabilities of the "Your Commute" feature. Built to calculate the area that can be traveled to within a specified distance or time and provide a travel time or distance polygon to visualize the shape of the area on a map, the API can handle multiple travel modes (i.e., driving, walking or public transportation) while factoring in predictive traffic.

Example commute calculation on LinkedIn

1. Example commute calculation on LinkedIn

The "Your Commute" feature also uses the Isochrone API to generate a gradient of isochrones based on the user's commute preference. For example, if a user prefers a 30-minute commute, isochrones for 30, 15, and 10-minute commutes are requested and stored. With its impressive functionality, performance and ability to generate many polygons, it is a great tool for commute conscious job seekers.

Example isochrone based on travel time
2. Example isochrone based on travel time

When Johnson and team first started building out the feature, isochrones for the longer commutes took several minutes to build out, forcing the team to create suboptimal workarounds that allowed enough time for the isochrones to be displayed. For example, users would provide commute preferences and information several pages back from where they would want to use the information, which added multiple pages to experience, risking user drop off.

With the Bing Maps Isochrone API, they were able to improve performance, calculating and presenting both long and short commutes in a matter of seconds. In the blog post from LinkedIn's engineering team about the new feature, the LinkedIn team states that with the optimizations built into the Isochrone API, 2-hour commutes can be generated in less than 10 seconds.

"People don't like to wait on a website," says Johnson. "If they have to wait, then they are going to leave your website. And so, it's very important that we provide a fast experience for members."

"The Bing Maps team worked with us to understand the requirements and build out a great, performant solution," he continued. "So now even for those potentially really long commutes, people are able to see the results very quickly. I think it makes for a very delightful experience," says Johnson.

The importance of location precision

The LinkedIn team put to use another Bing Maps API to help add location intelligence to the experience - Bing Maps Autosuggest API. With Autosuggest API, job posters and recruiters can quickly enter in an exact address for a job prompted with address suggestions as soon as they start typing. Also, customized to allow only addresses and city names for locations, the API helps focus the job search experience in on locations where jobs would be available.

"One of the things that was great about working with the team on Autosuggest is that there's a lot of functionality and power behind it, but we didn't actually want all of that power," says Johnson. "For example, there are not really jobs posted at a neighborhood level. There are jobs at particular addresses or within a city, so we were actually able to work with the team to customize the responses that we got back so it was exactly what we needed. We didn't have to waste a lot of engineering resources or bandwidth on data that we didn't need, so that was a really great experience."

Including a specific address in the job posting is especially helpful for smaller companies and allows members to see the precise location for a job and calculate their commute time, which was a major factor for job seekers when deciding upon a job according to a survey of LinkedIn members. In the instances where there is no exact address provided (e.g., a general requisition for a company or job postings ingested through an API), an address can sometimes be inferred with the help of the Bing Maps Location API, which provides a geocoding service and gets a standardized address using location coordinates (latitude and longitude) that can then be used in job search and job recommendations.

"Having that very precise location of the job is beneficial for members to be able to identify exactly where they will be working," says Johnson. "People apply to a job because it looks interesting and then they'll start to do their investigation. After an interview, they might learn that the job is 45 minutes away, which is too long, so features like this help to remove some of the false signals and saves everybody's time."

As a Microsoft mapping platform that empowers developers and enterprises to build intelligent location-enabled and map-based experiences for the real world, the Bing Maps platform helps power features that can change how we approach tasks that will impact our lives.

Focused on quality first and foremost for members, the LinkedIn team was able to build location intelligence into the job hunting and hiring experience, helping shape how people go about finding the one meant for them.

"When we added some of these features, we found that people didn't necessarily apply a lot more, but they applied differently," says Johnson. "They started preferring jobs that are much closer to them. This signals that we're doing better at finding that person the right job."

"For recruiters or hiring managers, finding the right person is really important," adds Johnson. "If you have to spend hours and hours sifting through people who aren't good fits that's going to be really frustrating for you. So, LinkedIn's "Your Commute" feature helps to increase the efficiency of the entire job hiring marketplace."

For more technical details about LinkedIn's "Your Commute" feature, read the engineering team's blog.

To learn more about the Bing Maps APIs used in the "Your Commute" feature go to: