MailChimp

UX Research

What can others learn from the successes and failures of the way you’ve unlocked the value of big data?: 
The thinking behind our technique—to compile data centrally, for everyone—is that the siloing of data serves no one. For everyone to march in lockstep toward the goal of improving the user experience, unnecessary barriers must be obliterated. When our engineers can access customer feedback, and our researchers can see site traffic logs, we build a mutual understanding of the context in which our colleagues work.
 
The only failure we've come across are data gaps—areas where we "didn't know what we didn't know." The silver lining is that once we identify a gap, we move quickly to fill it. If this gap involves someone who hadn't shared data before, the better—we're able to say, "Your work is vauable, and your data can help us all."
What were your expectations of the value hidden in your data, and how did they influence the design of your solution?: 
At MailChimp, we have no shortage of data. We send emails on behalf of millions of senders to billions of subscribers. Our customers provide feedback to us via email, surveys, and through support chat sessions; and we log social media mentions and interactions. We run usability tests and conduct customer interviews multiple times per week, and we study the competitive and design landscapes regularly.
 
All of these data points were a blessing and a curse: we knew the data was valuable, but it was either siloed, too voluminous to be usable, or too sparse to see how it fit the bigger picture. What we needed was a way of connecting the data, and connecting our efforts. Our ideal scenario was that making all of our data accessible would engender buy-in and contributions across our entire company.
 
 

 

Conversely, how did the design of your solution affect your understanding of the potential value of your data?: 
Connecting our data streams to an easy to to use UI made our collective knowledge truly usable and accessible. By streaming any and all data to a central location, our separate teams could synthesize their specific projects to company-wide knowledge. Other tools we’ve tested lacked easy data inputs, and offered limited to difficult outputs. By adopting Evernote as a storehouse of data, we were able to build on an already robust tool and suit it to our needs.
 
We're able to funnel information both passively (nightly scripts of aggregate analytics and application metrics) and actively (original reports, interview transcripts, and usability test results), all via email. Email is the key for success—sending emails is part and parcel of the normal workday. By eliminating a separate workflow—the need to visit a site, log in, navigate to the right place, and share data—we've reduced friction and made sharing easy.
Describe the aspects of the design of your solution that do the most to expose meaning in data that would otherwise be harder to discern.: 
Keyword search is the killer feature for our company. Because we're working with so much data, it can be difficult to see a high level view of how each team is tackling a single concept. When each team shares all of its work with our database, and each part of our app pushes nightly statistics to the same database, we're able to simply enter a keyword and instantly grok the various touchpoints related to that word or concept.
 
For instance, as he was working on a new iOS interface, Stephen Martin, a designer in our MobileLab team, was curious which stats are most important to customers looking at campaign reports. He did a quick search in Evernote, and stumbled upon a chart from our 2013 survey sent to thousands of customers. He could see a clear ranking of the stats that customers use the most. He used this data to create a poster showing the hierarchy, which helped him make smarter design decisions driven by user research.
How might your solution be extended or adapted to address additional types of data and other questions?: 
Extending our database is always a priority—we look at any new statistics or processes and ask, "Can we automatically share this?" Culturally, there's no excuse for anyone at MailChimp to be uninformed—now that everyone has access to the data, everyone is a researcher, and everyone can contribute to our collective knowledge.
 

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