Staten Island Hunger Task Force is a community resource dedicated to ending hunger on Staten Island. They are an asset for people looking for pantries on Staten Island as well as pantries looking for community and government resources.
Staten Island Hunger Task Force felt their site was complicated and a hassle to maintain. Web and behavior analytics tools were used to assess the mobile site. Our research focused on the user's ability to find pantries on the site. While the site provides a wealth of information for users, we found three areas to improve the discoverability of pantries on the site.
We began our work by speaking with our client representative, Susan Fowler, chair of Staten Island Hunger Task Force. During this call we were able to gain deeper insight into the organization's mission and a clear picture of their current pain points. While the organization is geared toward two user groups - people who are looking for pantries and pantries that are looking for community and government resources - we found Staten Island Hunger Task Force to be most concerned with how people are using the tools available on the site to locate pantries. As a team we set out to:
“The website is kind of complicated…[pantry finding resources are] hard to keep up to date…is there some other better way to help people find food?” -Susan Fowler, Staten Island Hunger Task Force Chair
To get to the bottom of our research focus we asked ourselves 4 research questions to help guide the data we would be collecting and analyzing.
Using Google Analytics and Hotjar we were able to dig into our research questions. These behavioral analytics tools allowed us to collect and analyze data based on the actions our users take on the site.
We collected web traffic data and visual reports. This was an ideal method to use given the short window we had for research.
Web analytics does not tell us about our users motivations, or “why” a user took the actions they took. That is where we as researchers come in to interpret this data.
Looking out for trends as well as unusual behavior patterns we analyzed the data gathered from google analytics and hotjar.
Google Analytics
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Hot Jar
Visual Reports
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After analyzing and comparing the Google Analytics and HotJar data, we found unusual, as well as expected patterns of user behavior. While Staten Island Hunger Task Force provides a wealth of information on their site, we found that user’s have a hard time navigating to and absorbing that information. We identified three key findings that shed insight on our original research questions.
The site's homepage does not appear to users as a homepage.
Users appear to be confused about where to locate pantry information.
Finding relevant details on the extensive pantries list page is difficult for users.
When landing on the site's homepage, users were oddly attempting to navigate back to the homepage. They were able to accomplish this by using the hamburger menu or by tapping on the organization's logo on the top left of the screen. This can be seen within google analytics behavior flow.
We felt this was occurring because the look of the homepage does not fit the user's mental model of how a homepage should appear. The hero section is blank, lacking meaningful information for users. The option “Join Mailing List” is too high in the information hierarchy, giving users an option to opt in to something they lack context for.
How to improve these issues:
We found users did not settle on a single pantry finding resource, rather they bounced around to a variety of pantry finding resources. For example, a popular pattern was jumping between the homepage and calendar zip codes, then to the pantry list, and back to the calendar.
This could also be seen on our HotJar recordings.
We attributed this behavior to vague page names. We also felt that the bouncing between multiple pages was a discoverability issue surrounding pantry finding resources. The pantry finding resources live in multiple places on the site, some of which can be accessed through the navigation menu, others from the homepage. There is no single location on the site where all pantry finding resources can be accessed from.
To improve these issues we recommend:
Finding relevant details on the extensive pantries list page is tasking for users. 75% of users only read the top ⅓ of the alphabetical pantry list page. The majority of users only scrolled to the “E” section of the list, with the remaining pantries being viewed by 60% or fewer users. We felt the poor visual hierarchy of the long panty list resulted in users experiencing cognitive overload and giving up on their search for a pantry.
Users also attempted to tap on unclickable information such as pantry hours and phone numbers, as well as hyperlinks within the list. While there is a wealth of information on the site, it’s inaccessible to its users.
To improve these issues we recommend:
"What you’re saying makes a lot of sense"- Susan Fowler, chair of Staten Island Hunger Task Force
We presented our findings to our client via Zoom. They found the insights interesting and felt our data made a lot of sense. Our client was mostly concerned about development, specifically she wanted to know if it were possible to have a mobile site that was visually different from a desktop site. Our team suggested she double check with the site’s developer, but we felt confident it was an option.
Our biggest hurdle with this project was the small data set from HotJar. If given a second opportunity I would have liked to extend the HotJar data collection by 1-2 weeks before analyzing. The visual reports are very informative when combined with Google Analytics and I felt the data could have been even more impactful if there was a larger set of users for HotJar to collect data from.
My next step recommendations for the Staten Island Hunger Task Force are to develop and run A/B tests. The site is not overly developed and I feel could benefit from running A/B tests based on our recommendations. Once the site has been updated I would suggest answering some of the “Why” questions that inevitably pop up during web analytics analysis by running usability studies directly with users.
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