Client case study: Introducing Google Maps API

By Hannah McCreesh | 12 November 2021
Client case study: Introducing Google Maps API

Project Brief


We worked with an award-winning growth consultancy to find out what kind of legacy the London Olympics has left in the capital.


The client wanted to find out survey respondents' attitudes towards different attractions in London, including the Olympic Park.


Ultimately, they wanted to discover what kind of people were willing to visit London to see the Olympic Park compared to other popular sporting events, music concerts or festivals.


Of those people, they wanted to discover their demographics, job titles and behaviours to really discover who these people are.

Timescale


We gave the client a quote for the project within an hour and a custom prototype was developed for the client within a week.

How was this project different?


This project was different because in order to fulfil the brief we had to build a custom survey functionality from scratch - Google Maps API integration. 


The client wanted to know how far away each respondent lived from The Olympic Park but they didn’t just want to leave it up to them to estimate - they wanted to know the exact distance from their home and work postcodes.


Therefore our development team worked their magic to be able to classify people who lived or worked within 15 minutes, 30 minutes, or longer away from the park through Google Maps API integration.


The respondent had to enter the postcode of where they lived and/or worked which was linked to Google Maps through our coding. This meant that we could report precise, accurate data to the client down to the minutes and miles of how far each respondent lived from the Olympic Park. 


If the respondent didn’t want to share their exact postcode, we could work out how far they lived by calculating it by their region or London Borough.


This new functionality meant that we were able to report accurate, precise data for the client as opposed to relying on the respondents to give us data that was likely inaccurate.

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