Part 3| Iterate: A step-by-step guide to using UX research for local news product development.

After you’ve explored ideas and evaluated your research results, these are good ways to sharpen your product idea before launch.

Editor’s Note: This is part 3 in a series of posts outlining the Lenfest Lab’s step-by-step approach to UX research through the lens of our next project with The Philadelphia Inquirer about scaling reliable hyperlocal coverage.

Don’t Forget! Steps 1 & 2: Explore and Evaluate

Before you can start the evaluation phase of your user experience research, you first need to explore lots of concepts, browse your competition and then process all of that information together as a team. These are important steps to take in order to make sure you’re building something people will eventually want and that you’re not building something that already exists. You can read about how we approached those steps in our posts about exploring and evaluating.

Step 3: Iterate — How?

So you’ve already explored ideas, evaluated the competition and synthesized what you learned. Now you’re ready to make small iterations on your idea as you prep to launch it. During the iteration phase of our research we were curious to find out which information was the most important for people to see in a neighborhood newsletter and we used an exercise called card sorting to find out.

To do this we took all the elements included in our prototype so far and made a simple wireframe for each. This is how they looked:

Then we found 9 participants to walk through the short card sorting exercise. The goal was to find out what people were most interested in seeing and how they would prioritize information based on importance to them.

The exercise was broken into two parts. The first part included asking people to pick the elements they would be interested in seeing in a newsletter. Once they did that, the second step was for them to rank the elements from top to bottom, with the top being the most important information and bottom as the least important.

With the first two participant I noticed that the number of elements they selected was significantly different. I had a hunch it might be linked to their overall interest in neighborhood news, so I decided to add a question about how actively they seek out neighborhood news to the end of the exercise. We used those responses to give additional context to the results.

The results of the card sorting exercise influenced the order that elements will appear in the first version of newsletter and also which elements will be included.

The analysis ended up breaking down into three parts: we looked at what the majority of participants said, we looked at what active news seekers vs. less active news seekers said and we also noted any surprising insights that sparked additional ideas for how to build a useful newsletter.

General Results (driven by the majority)

We decided to chart the general results on a graph, showing the elements people found the most interesting and also the most urgent, meaning they wanted to see them at the top of the newsletter. The chart below will guide us as we continue to prototype and test the newsletter. We’ll use the chart loosely though since the results are directional, and based on conversations with less than ten people.

Habit-based Results (driven by news-seeking activity)

Active news seeker: 15+ elements

“It depends” news seeker: 8–15 elements

  • Depends on:
  • their topic of interests
  • how relevant is the information to daily life
  • how much time they have

Non-news seeker: Less than 8 elements

Individual Results / Surprises (driven by nuanced or surprising insights)

Insights about responses based on Lifestage

If people like the neighborhood and are interested in staying or buying property, they’re more interested in the real estate modules. If they are not ready to buy a house or don’t want to, they are less interested in those modules.

  • “I’m in the life stage that if I like a neighborhood I would think about living there long term and purchase a house.”
  • “I’m personally not interested in purchasing a property right now so I don’t need that info yet.”
  • “I’m not at the stage which I can afford a house yet, so I won’t be looking at property related information”

Insights about responses based on Behavior

People are not active on social media and don’t like to share things publicly

  • “I personally wouldn’t upload photos because I would rather know about what’s going on, rather than be a part of the conversation.”
  • “I’m not engaged on social so I wouldn’t want to upload any photos.”
  • “Share your story is a section I would never use because of privacy issues, I would not publish anything”
  • “I don’t like to share things publicly so I wouldn’t need the share your story feature”
  • “Don’t want my photo or info to be posted for neighbor profile”
  • “I don’t use Twitter”
  • “I don’t use Twitter or Facebook”
  • “I’m not a big Instagram user but have photos about people, the neighborhood, and local business is nice”

People’s shopping mindset and routine:

  • “I don’t personally shop locally.”
  • “Local business offers will help me save money, so I like that.”

People are attracted more by visual:

  • “I like neighborhood stats because I’m more of a visual person and I learn more from looking at things rather than reading.”
  • “Instagram photos are more visual and photo format attracts me more than other formats.”

Card sorting results

After the card sorting exercise we were able to select and rearrange the newsletter elements based on people’s interests. The early results are below. The elements at the top of the newsletter wireframe are the ones that people had the highest interest in.

Meanwhile the elements that were less interesting are below. We’re saving them for potential future usage.

Our next steps are to move into the visual designs stage (applying colors, styles, etc.) to these wireframes and continue our testing. We’ll share the details in future posts as we continue to work through all the steps of our user experience research process. Thanks for reading and good luck with your own research projects!


The Lenfest Local Lab is a multidisciplinary product and user experience innovation team located in Philadelphia supported by The Lenfest Institute for Journalism.

The Lenfest Institute for Journalism is a non-profit organization whose mission is to develop and support sustainable business models for great local journalism. The Institute was founded in 2016 by entrepreneur H.F. (Gerry) Lenfest with the goal of helping transform the news industry in the digital age to ensure high-quality local journalism remains a cornerstone of democracy.

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