For a while I was listening to podcasts on my way in to work in the mornings, but then I switched to music, and then had a phase of silence, and was back to craving podcasts again but wasn’t sure what to listen to. I value my time and try to get the most out of every hour and didn’t want to waste my commute on invaluable content. Just when I was having these thoughts, a timely tweet came across my (virtual) desk, linking to a list of relevant recommendations. I decided to start (or, continue… I had listened to a number of episodes in the past) with #4: : UI/UX design and product strategy. popped up on my podcast app. Here are three from what I heard this morning.

1. Segment the data

In this episode, Jane Portman interviewed Sofia Quintero. is the founder and CEO of NomNom, “the easiest way to search, organize and share all your customer feedback in one place.” Quantifying, or otherwise translating qualitative data (user feedback) into action items is a challenging and critical part of iterating your product. NomNom aims to streamline that process to maximize insights, efficiently.

One of the interesting nuggets Quintero threw out there is to segment the data: volume isn’t everything. Just because more participants expressed one pain point than any other does not necessarily mean that you should attack that point first. Kind of counterintuitive at first.

However, when you consider that user-centered design must be balanced with business considerations to make a successful product, it makes a lot of sense. When you segment the data by user cohorts, you may find that it would behoove you to make more strategic decisions about which pain points to address first. For example, if you have a freemium model, what are the paying customers saying? What are the highest-intent-and-yet-not-converting users saying? Segmenting your feedback may shed valuable insight into where your UX is failing most critically and where you should focus resources STAT.

2. Users don’t always know what they want

I had heard this truism before but the example Quintero brought made the concept clearer to me, and stickier than ever before. She gave the example of users asking for an “export to PDF” feature. If you get a significant amount of feedback asking for this feature, does that mean you should run to develop it? Maybe.

Using the Jobs to Be Done approach, however, may lead to a different conclusion. Does the user really want a PDF output? Or does the user want to share the output he or she is getting from your product and can’t think of a better way to do that? Consider that the user is saying “PDF export!” but what they mean is “share functionality!”

Now you can develop an optimal feature for getting that job done, and at the same time, probably satisfy additional users who didn’t articulate that particular pain despite feeling it.

Share functionality that gets the job done

. Include UX writers

OK, this one is mine. It wasn’t explicitly mentioned in the interview but what Quintero did say is that the product teams that NomNom serves don’t only want insights, they also want access to the raw data. They want the quotes, etc. that were collected, and when possible, as much context as possible as well (e.g., who was the user on the phone when the quote was recorded).

My first thought was, “What about UX writers?!” We need that data, too. It is our job to craft the voice of the product and there has been a wave of chatter out there lately about integrating exact user language (as appropriate) into your product copy to create rapport with your users, strengthen your brand, and as an integral part of creating a delightful UX.

I first heard about this concept at a talk by Kinneret Yifrah and have been seeing articles pop up everywhere about it ever since (frequency bias?). I launched my own research project at the company where I work, listening to customer support calls and writing down what users call our features — they’re often very different from what we call them; getting the raw data from a usability research project, basically a massive spreadsheet of quotes from Facebook, tagged by topic; ran word clouds on these quotes; and analyzed all of this user language in other ways as the basis for constructing a voice and tone that resonates with our users because it matches the way they talk.

So thank you UI Breakfast! Keep on keeping on.

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