When it comes to UX, are maps truly the only way consumer-facing apps can use location data?
Despite what you may think, the answer is no.
The truth is that innovative companies are using location data in a myriad of exciting ways that go far beyond just displaying data on a map.
These include using location data to combat fraud, organise millions of data points into comprehensible search results and provide users with highly personalised recommendations.
On 5th April 2022, we hosted a live panel discussion, alongside Geoawesomeness, to explore how location data is being used to create great user experiences.
For the event, we brought together a lineup of five speakers to hear their perspectives on the connection between location data and great UX:
- Charlie Davies – CEO, TravelTime
- Shashank Karpadia – Data Science Leader, Monster.com
- Hitesh Kumar – Data Scientist, OLX Group
- Pelayo Arbués – Head of Data Science, idealista
- Jeff Siarto – UX Director, Element 84
Below are five key takeaways from the discussion:
1. Put users at the heart of location search
“Location isn’t static; it’s constantly evolving.”
— Charlie Davies, CEO, TravelTime
Typically, the way search recommendation engines allow us to search for locations is based on government boundaries, a historical legacy that has been kept over time. These include municipalities originally drawn together for tax purposes or postcodes, created to address the complexity of postal deliveries.
However, these boundaries are static and don’t necessarily reflect how we actually think about locations in everyday life. And yet, many search recommendation engines use these rigid parameters as the basis for search.
But what if location search was defined differently?
In the first presentation of the event, TravelTime CEO, Charlie Davies, made the case for why location-based search should start with the individual in mind.
For example, allowing users to search for locations by travel time focuses not on rigid boundaries, but on the user’s transport preferences and where they can realistically travel based on the surrounding transport networks. In this way, location search becomes more personalised, putting the user at the heart of the search.
2. Location data is key to providing personalised job recommendations
“Algorithmically, running job recommendations is more like matching on a dating app than recommending content on Netflix.”
— Shashank Karpadia, Data Science Leader, Monster
Matching candidates to the right jobs on a job board involves using complex machine learning algorithms and, of course, location data.
Shashank Karpadia shared how the company’s data science team tackles the challenge of providing personalised job recommendations, and how the team uses location data to ensure that the right jobs are being served to candidates.
For example, various machine learning algorithms are used, and these include classifying job location type and job quality scoring.
Furthermore, Shashank highlighted three key criteria to consider when building an effective job recommendation system:
- A candidate’s specificity – Candidates are often looking for a specific role, with the occupation, title and job location being important factors
- The dynamic nature of job ads – Jobs are usually available to be recommended for just a short period before the position is no longer available or is unpublished from the job board
- Reciprocity between employers and candidates – A successful match will ultimately depend on the agreement between the employer and candidate
3. Being customer-centric with geolocation
“If we focus on creating better user experiences, business metrics will improve as well.”
— Hitesh Kumar, Senior Data Scientist, OLX Group
Being customer-centric is vital for the growth of any business. OLX Group, a global classifieds and online marketplace, is no exception.
In his presentation, Customer Centricity: Driven through Geolocation, Hitesh Kumar shared several ways in which OLX Group uses location data to serve the most relevant results to users.
For example, imagine a new user visits the website and has shared their location. In this scenario, OLX's recommendation algorithm can suggest popular results within their area.
Another example is serving relevant results to a returning user. Here, both their location information and past behaviour signals can be used within the recommendation process.
For example, OLX’s recommendation algorithm can serve a ‘recommended for you’ option as well as relevant results that are within the user's vicinity. This ensures that the most relevant and personalised results are shown – with a maximum chance of a user converting.
Perhaps the most interesting way in which OLX Group uses location data is through its ‘Location as a feature’ approach. Here, the app offers a location sharing option that enables buyers and sellers can arrange to meet without having to use a different social media platform - and thereby reducing friction for both parties.
4. Using location data to make smart real estate decisions
“Buying or renting a home is a long-term decision, so it’s important to provide users with as much data as they can understand to ensure that they find the best location.”
— Pelayo Arbués, Head of Data Science, idealista
When it comes to buying or renting a home, the adage, “location, location, location” still rings true.
Pelayo Arbués, idealista's Head of Data Science, shared how the company uses location data to help users make more informed decisions when choosing a home.
idealista is a leading real estate website that allows users to find properties to buy or rent in Spain, Italy and Portugal.
As well as its main website, the company also offers its idealista/maps portal. Through the portal, users can explore aggregated data on properties, including house price value and local health services.
Similarly, the company's idealista/energy portal lets users calculate potential savings if they install solar panels in their homes.
All three of idealista's offerings demonstrate the importance of location data in providing users with as much information as possible to enable them to make an informed property decision.
5. Rethinking the “Google Maps Pattern”
“The ‘Google Maps Pattern’ should be a conclusion we arrive at and not where we start from.”
— Jeff Siarto, Director of User Experience, Element 84
When it comes to UX and UI, is it always in the user’s best interest to display geospatial data on a map?
Our final speaker, Jeff Siarto, UX Director at Element 84, sparked an interesting debate on Twitter a few weeks ago by asking this very question:
In his presentation, Jeff coined the term “Google Maps Pattern” to describe the phenomenon of geospatial apps using a similar user interface to Google Maps, whereby a map occupies the majority of an app's UI.
Jeff highlighted this phenomenon in his illustration below:
Of course, there are scenarios in which this approach is a good UI, as Jeff was clear to point out. These include:
- If you’re showing the user a nearby location
- If the user needs to get from A to B or find things along that path (also known as wayfinding)
- If the user is no more than a couple of steps away from being able to complete an action.
However, within many geospatial apps, the map occupies most of the user interface without being used. In these cases, it’s worth considering other alternative approaches.
In one example, Jeff proposed that instead of having users start with a huge map, an app can first start with a search interface that narrows down their options and provides more context. In this scenario, the map becomes an output display.
Another example is using mini-maps, where the map becomes a secondary UI instead of a primary UI – giving users the ability to maximise the map when needed and minimise it when not needed.
Finally, another approach may be to start with a usage-based discovery, where it's first determined what result the user is ultimately looking for. Through this discovery-led search, we can obtain the relevant location data and only then will the output be displayed on a map – avoiding a map-heavy UI from the outset.
Whatever your approach, starting with the end-user in mind is key.
Location data is a vital part of the user experience
Our panel discussion revealed that there are a plethora of ways in which you can use location data to improve the user experience on your website or app.
By putting the user's needs at the heart of your UX strategy, you'll not only optimise the user experience, but you'll also maximise the chances of conversions and help to drive business growth.
At TravelTime, we help you optimise your location search experience by allowing your users to search by time — the most important metric in our day-to-day lives — alongside other criteria,
To learn more about how you can use the TravelTime API, sign up for a free API key or get in touch with our team.
Create travel time polygons and matrices with the TravelTime API
Display more relevant, personalised search results with the TravelTime API