Are you maximising the use of location data on your website?
Undoubtedly, location data is one of the most important features of many websites and apps. Yet, many aren’t using location data to its full potential.
Undervaluing location data means you’ll be ignoring customer needs, displaying irrelevant results to users and increasing the likelihood of users bouncing.
In this post, we’ll explore five industries where location data is used – as well as companies that have found innovative ways to use location data to deliver great user experiences. Following their best practices means you can meet your revenue and user growth targets.
1. Job boards
Having an office location that a candidate can easily travel to is an important factor when job hunting. That’s because more often than not, candidates are searching for a specific role in a specific geographic location.
Despite the rise in remote working during the pandemic, many companies are shifting to a hybrid working model. According to one survey, as many as 76% of companies plan to move to a hybrid way of working.
As a result, job boards need to use location and spatial data intelligently to provide relevant search results.
Monster: Providing location-aware, personalised job recommendations
Global job website, Monster.com, uses location data as a feature within its recommendation algorithms.
When a user types in the type of job and the location they’re interested in, the recommendation algorithm can extract the input text that signifies the location. An API is then used to get the lat-long information for both the user’s input location and the location of potential jobs.
Using a combination of the textual and location information, a similarity score can be run to find the best-matched job based on the user’s desired location.
For example, let’s say a user has indicated that they are looking for a specific role located in Boston. There are three different jobs that potentially match, located in Boston, New York and Los Angeles respectively.
Running the similarity score, Monster's recommendation algorithm can determine that the job in Boston is the best match as this exactly matches the user's search. This is followed by New York, which is within the location range. However, LA will have the lowest score as it's the furthest away from the user’s desired location.
In this way, Monster can provide personalised job recommendations, tailored to the user’s search criteria – and ensure a better user experience as a result.
Totaljobs: Allow users to search by commute time
Totaljobs uses location data — and travel time data more specifically — within its Commute Time Filter, a tool that allows users to find suitable jobs based on their commuting preferences.
Commute time is an important factor for job seekers. A study by UK job board, Totaljobs, found that over half of UK workers would quit a job because of their commute.
Users can enter their home postcode, set a maximum commute time and choose their transport preference (the filter offers a choice of public transport, walking, cycling and driving).
The ranking algorithm then finds the most suitable jobs based on these location-based criteria:
In this way, Totaljobs enables users to search for the most suitable jobs based on an important decision-making factor – the ability to easily travel to the workplace.
With Classifieds websites, buyers are often looking for items for sale nearby and will need to meet sellers to inspect and pick up items.
OLX Group: Using location data to improve the customer experience
Serving relevant recommendations based on a user’s location
Global classifieds and online marketplace, OLX Group, uses location data to serve the most relevant results to a user’s search. This includes:
- Suggesting popular results within a user’s area if a new user visits the website and has shared their location
- Serving relevant results to a returning user based on a combination of their location information and past behaviour signals
In both examples, the more relevant results the recommendation algorithm can provide, the more likely users are to return – and the more location data can be used to continue to provide relevant results.
Enabling in-app location sharing
Another way in which OLX enhances the user experience through location data is by [LB5] allowing users to share their location within the OLX app without having to use a third-party social media platform. Similarly, based on a user’s location, the app can be rendered in the language of the region that the user is in – providing a user-friendly tool.
Ensuring fraud prevention
A perhaps unexpected way of using location data is for fraud prevention. Since fraud is typically concentrated in specific locations and committed by a small number of users, content from these locations can be moderated.
3. Food delivery apps
On-demand food delivery apps face the challenge of ensuring that customers find the best match for their search and that restaurants and delivery partners can deliver orders within the maximum delivery time. After all, if it takes too long to deliver an order, there is less chance of a user returning.
One way in which on-demand food delivery apps find the best match for their search is by leveraging location data.
Let's look at two examples below.
DoorDash: Using maps to simplify the pickup experience
Both DoorDash and Uber Eats use location data to provide a map feature to help users who choose to pick up their orders see nearby restaurant options.
The biggest benefit of this is reducing the likelihood of users switching to other map apps to search for nearby food. This is no small feat since as many as 8 out of 10 users have switched to other map apps to search for nearby restaurants.
DoorDash’s map feature interface displays all the restaurants near a user’s location and includes a list of recommendations. This helps users view the different restaurants near to their location:
Uber Eats: Seeing which restaurants are nearby
Similarly, choosing the pickup option in the Uber Eats app allows users to see what restaurants are nearby. Interestingly, users can type in either text or an emoji to perform their search.
4. Property websites
While location is a key factor when looking for a home, more precisely, it is proximity to important locations that home-seekers view as the top priority. For example, 76% of home-movers see access to shops and amenities as important when choosing to move home.
As a result, property websites need to provide users with results that are much more tailored to their search preferences.
Zoopla: Showing travel time proximity to key locations
One way to do this is to enable users to search by travel time to their key locations. This way they can determine how long it will take them to travel from a potential home to a key point of interest, such as work or school.
For example, Zoopla’s travel time search tool lets users search for properties based on the travel time to their most important location:
5. Travel websites
Travel websites must not only help users find the best locations to visit, but they must also help them find the best experiences once they are there.
Airbnb: Helping users find local accommodation and experiences
Airbnb uses location data to help users find both accommodation and experiences within the locations that the user has specified. Its ‘search as I move the map’ feature offers a dynamic discovery-based search experience for users.
It also allows users to find nearby experiences based on the user's current location, which users can also view on a map:
Use location data to build a great UX and drive growth
As we’ve seen, location data powers many of our favourite websites and apps. It helps build a personalised user experience by providing relevant location-based recommendations that improve the user experience and maximise conversions.
At TravelTime, we help you improve your user experience by enabling your users to search by travel time in addition to other search criteria.