Originally published in the MailChimp UX Newsletter.
A popular corollary in design and writing posits that by emphasizing everything, we end up emphasizing nothing. We’ve all seen overzealous designs featuring too many typefaces in myriad weights that compete for our attention, or apps with far too many buttons for any one in particular to stand out as our logical choice. To get our point or intended actions across, focus and simplicity are key.
As a researcher, I’ve learned that only by flipping that corollary on its head does our team uncover meaningful and unique insights. We start with the premise that everything is important, and that every data point tells a story. By listening to each and every story, and then following those stories until they become epics, we achieve a mastery of our data sets and an ability to then focus, prioritize, and emphasize. We’ve radicalized our data, allowing it to dictate direction and scope.
The Journey is the Destination
Like most companies, MailChimp has an annual agenda that guides our work throughout the year. Typically this agenda is brief and amorphous—it’s not so much a directive as it is a few keywords that every team—from engineering to marketing to support—should keep in mind. A typical agenda could be about anything from the competitive landscape to nascent technologies.
This agenda, however, isn’t dogmatic. For instance, if we only look at the competitive landscape, we enter a loop of one-upmanship that fails our customers and our innovative spirit. By focusing on technology, we run the risk of shiny-object syndrome without asking how that technology actually benefits our customers.
At MailChimp, we employ a number of research channels—unsolicited email feedback, surveys, interviews, usability testing, and analytics, to name a few. By purposefully not making assumptions about what our research should uncover, we empty our minds of preconceived ideas and open ourselves to the data. It’s akin to the Grounded Theory method—only the data streams in to us all day, every day, decoupled from any specific project.
Unleash Columbo: Dumb Down to Smarten Up
To radicalize the data is to become subservient to it, no matter the form. By blanketly imposing importance on the data, we open ourselves to anything. In layman’s terms, we’re unleashing our inner Detective Columbo—we embrace befuddlement, we assume that everyone else is an expert and that every sentiment carries weight, and we trust that from this process the patterns and hierarchies will emerge later.
Every day, we receive emails from customers with suggestions and complaints about our MailChimp app. We read every single one of these emails, every single (work) day. We do this because we assume this data is important and that our customers know far more about using our app than we ever will. If we’re building for our customers, who better to learn from?
We take this same approach with our customer interviews. Once our data points us in a direction, like e-commerce, we visit customers to learn more about this topic and how it affects them individually. However, we don’t barge in and ask, “Can you tell me about e-commerce and how it relates to your use of MailChimp?” Instead, we take the long view and ask about typical and atypical days, how products are developed, or what brought them into their particular line of work.
By focusing on the narratives and tangents, we gain the insight that lends context to our original data sets. This is analogous to the Switch methodology espoused by The ReWired Group—customers hire products to perform a task, and our job as researchers is to uncover everything that influenced that choice. When we attribute expert status to our customers, we open ourselves up to empathizing with their situations and rationales.
Last year an email came through our app feedback channel that requested a “Notes” function within our Subscriber Profiles. It was the first and only time we saw this request, but we weren’t going to discount it outright—it was data, and therefore important.
We responded to our customer directly, learned of the use case, and this turned into an app development. The data we received was minimal, but by granting it legitimacy by default, we learned something new and added a feature with potential benefit for all customers.
… and then Wise Up
Of course, following every data point as though each is canon has a logical endpoint. Therefore, we can complete our research mantra by claiming that each data point holds value until proven otherwise. Once we see enough evidence to contradict the data in question, we can move on.
For instance, as an email marketing platform we emphasize mailing list management and offer guidance about building a list. Through our feedback channels, we saw data that told us that unsubscribing from a mailing list is too easy via inadvertent clicks. The evidence mounted over time until it became a pattern and then a quick research project.
Once we looked over the data with a closer eye, however, we could determine that this wasn’t necessarily a data point worthy of further research and development—we examined who provided the data, the frequency, and the rationale. We could empathize with the feedback, but the use cases in question did not add up to a development that would improve our app—it would just appease a few and possibly upset even more. We followed the data and listened to the story until the story reached a conclusion.
Radicalizing the Data
When we research, we’re tasked with reporting our findings and providing supportive evidence. Depending on whether we’re a shop of one or a team of hundreds, we can all find value in a data-first approach to our work. To ensure that I allow the data to guide me, rather than me steering the data, I employ the following mindset:
Everyone is an expert, and everyone is a researcher
The data that is shared with me is credible and valuable, and the opinions surrounding it hold merit, no matter the source.
Every data point is valuable and worthy of collection
I will always collect, store, analyze, and verify. Over time, additional data and surrounding context will either transform the data into a story or discount its veracity. Either way, I didn’t let the data pass me by.
Evidence and empathy, always
By radicalizing our data and allowing it to determine importance, every data point represents continuing education about our products and customers. With this principle firmly in mind, I can follow every lead and know that—even if proven unimportant—my research time was well spent.
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