In his talk at UX Lx 2013, Aarron Walter shared MailChimp’s struggle with Big Data and how it helps them make smarter design decisions today.
Big Data is increasingly becoming the most repeated term by executives in a single day. While some companies take advantage of the mountains of data, most do not. As the Director of User Experience at MailChimp, Aarron Walter took this premise and applied it to his own experience at the company, which was struggling with an overload of both qualitative and quantitative data. He and his team solved this problem by channeling every piece of information to a single Evernote account, that boosts simple (yet powerful) features to manage, search, and share all of those different kinds of data. Today, this enables them to find issues and behavior trends that confidently inform design decisions.
Experiencing data overload
- At MailChimp, “tons” of data are gathered from user interviews, usability tests, analytics and other reports. All of this information helps in solving small problems, making things quicker and simpler.
- However, things quickly got out of control: data overload.
- The Big Data UX pyramid from the bottom-up: data, information, knowledge and wisdom. But wisdom was rarely reached. There had to be a smarter way to handle the “petabytes” of data.
Dealing with Big Data
- The idea came from the way Aarron Walter managed the feedback he received in his email. He was stuck in “inbox infinity”, with an overwhelming amount of feedback coming in. So, he decided to write a Gmail filter that would forward every feedback mail that he marked with a star to an Evernote account, tags included.
- A way to make user feedback queryable. This allowed him to instantly quantify the amount of people that requested that feature or had encountered that problem. And the best part: he had a direct way to contact those users, their email addresses! Recommendations were now based off real use cases from real customers.
- They started feeding new Evernote notebooks with Google Analytics reports, aggregate app usage data, user interviews, usability test findings, account closing surveys, social feedback, database content, competitive alerts, release notes, cross-intel, etc.
- One great feature from Evernote is the optical character recognition (also know as OCR). You can actually search inside screenshots or photographs for keywords.
- This new strategy allowed them to get smart about their data. It changed the way they work, it broke down silos. Everyone participates in data analysis, everyone is able to access AND act on data insights.
- One important thing is that the data is now IM-able: this helps get people on board faster.
- Open Data and Data-Driven Decision Making at the same time.
How MailChimp is doing smarter design
- Everyone in the company is capable of forwarding an email to a specific notebook with tags that help classify that information.
- To correlate the data with their personas, they also tag the notes with persona names.
- A quick search helps them visualize trends towards features and problems.
- MailChimp’s way of working with Evernote became a case study. Read the “community story” article or watch the video “How MailChimp uses Evernote Business”.
Principles of Big Data UX:
- Easy in, easy out: you have got this data, get it in this common repository quickly, and also out easily.
- Research data accessible from multiple devices.
- Data for everyone and everyone’s data: the team is part of something bigger, it gets everyone involved.
- It is not about petabytes, it is different types of data, different patterns and silos.
- Open data: share data across silos, across teams, across the organization.
- It is not a replacement, it is about changing the way we build strategy. It is about building smarter companies, smarter products, smarter teams.
For more great stuff concerning Big Data UX, emotional design, design personas and other UX topics, you can follow Aarron Walter on Twitter.
As we approach the final stretch of the UX Lx 2013 series, continue to stay tuned, and please share your thoughts about this talk on the comments section below. How do you use data to inform your design decisions? How do you manage UX data overload in your company? What tools are you using to accomplish this?