Context

So our problem was churn right? Well what would normally make you churn when searching for an Airbnb or a hotel or searching for anything honestly? Based on some user behaviour heat maps, I was able to validate that the biggest drop off point was on the searching screen and often times when sessions were ended or became inactive, users normally had an empty search result on their scree. ( see image below ) 

‍

Pain point 1 - non-urban relevance

The original design allowed for real-time searching, but when no matches were found, users received an error message with no clear next steps.

Solution -

Give a clear CTA on the next steps for a user to take. This idea came from the fact that while this site seems like Airbnb for co-working, it is actually an enterprise app and it requires several steps before one can do a typical search that make the user go though decision fatigue. Unlike Airbnb where you can search as soon as you are on the platform, even with an error, users have to do 2Fa, account verification, company log ins, location marking, amenity checking, etc all before they get to this screen

So by the time someone actually gets here, if it turns out they have no results they are reasonably going to leave. That is why I proposed this below solution.

A button that will automatically tell you what filters you can remove from your search to automatically get several more search results.

Context

Now Google Analytics proved the most common page for drop off was the search page, however the heat map told another story. The most common drop off point here was normally after clicking several of the filters in the filter search.

Pain point

Due to techincal restrictions, we can't dynamically update the image on the left while filters are toggled on and off, so users often removed all their options simply by setting the filters like normal.

Solution

Here several solutions were explored again but the one that we settled on was redesigning the modal so that users would be able to adjust the level of importance for each filter.

This way users can select their amenity preferenes, but also indicate how relatively important each filter is. This is helpful as it aids in not only giving users options if we can't find a perfect place for them to work, but it also helps inform our ranking algorithm on what filters people find the most important.

This data, when communicated with stakeholders and property listing agencies, can help co-working spaces know what to invest in to make their space a better co-working enviroment. (i.e. if users commonly chose catering as a "must have", a coworking space may consider investing in a food truck vender or something similar to help them get more customers and rank better in the rankings for our platform ) 

Context

So with Google Analytics, we knew the drop off point was the search page (solution M) and we also built a way to make the search page work better (solution N). The only thing left was to make a map update, just in case the true issue was the map being hard to understand.

Pain point

The heat map showed many users move and hover around the map, even if they didn't click it nearly as much as the filters. How might we make it clear to know what locations are relevant to the user while hovering around the map? 

Solution

We introduced color-coded map markers, which visually indicated filtered-out options, improving the clarity of search results without eliminating potentially relevant spaces.