Alyssa Zeisler is the the research and development chief and manager of editorial tools at The Wall Street Journal. In that role, she oversees the newspaper’s emerging technologies and computational approaches to improving the reader experience.
For example, earlier this year her team launched a new “Flexicle” article feature. The experimental feature is designed to make The Journal’s coverage more accessible and a step toward personalization. It includes a button in articles that says “Learn More” if the reader wants more information about the article’s topic and then “Close” when the reader is finished.
She joined the Journal in April 2019. Zeisler was previously employed at Barron’s as a managing editor, where she established and led an editorial development team.
Zeisler also helped developed and launch Demos Finance, the financial services research arm of Demos UK. She obtained her Master of Science from London Business School.
Zeisler spoke with Talking Biz News via email about her job and her career. What follows is an edited transcript.
What prompted you to get into business journalism back in 2012?
I studied economics and interned in private wealth management but was more interested in the presentation of financial news and information than in picking stocks! So, I looked to business journalism.
Initially, the greatest challenge I identified was a lack of consumer focus in newsrooms (the core product). Always one to run towards a challenge, I became a founding member of newsroom audience teams at the Financial Times and Barron’s, which evangelized an audience and data informed approach. As engagement toolkits became established, I began to use audience data to build new experiences and editorial products like newsletters, podcasts, and tools.
I became increasingly interested in going deeper and using data, research and emerging technologies to build products, which led me to R&D. Now with this expanded team, I’m excited to infuse this thinking into newsroom tools–and through our tools, our culture. Not just to adapt to but to actually get ahead of industry changes and cultivate an environment where we can adjust rapidly, with the ultimate goal of enabling the company to think big and continuously evolve.
What was it like using data to help the editorial side?
I’ve always been fortunate to have collaborators on projects who believe in data and how data can be used. The biggest challenge I’ve faced is getting the right data to make informed decisions, because so much data in media has traditionally been focused on advertising and not on membership. Creating new metrics, databases, etc. have been some of the most impactful ways I’ve been able to use data within a newsroom.
When you joined Barron’s in January 2018, your title was managing editor of audience. What did that mean?
It meant a lot of different things to a lot of different people. To me, it meant developing and optimizing the formats, products and systems to expand Barron’s reach, deepen engagement with subscribers and grow the impact of our journalism — in short, creating a digital newsroom. I was able to build and lead a team of digital editors, multimedia producers, and reporters to build and optimize our CMS, website, and editorial products. It meant creative problem solving, connecting strategy and operations, and keeping the trains running on time.
Within a year, Barron’s had record subscription and audience numbers. How did that happen?
There was no silver bullet, but many different strategies and teams coming together. In the newsroom, we analyzed the supply and demand of our content and updated our editorial to meet readers’ needs. We also experimented with different formats, fostered new distribution approaches and partnerships. At the same time, our product team made a lot of technical improvements to our website which made a big difference to discovery and retention.
As a team, we also collaborated closely with the Dow Jones membership team — creating new welcome journeys for subscribers, providing editorial perspective on content promotion, new packages, and more. Similarly, partnering with our customer research team meant we could create and introduce new qualitative metrics and surveys, like customer-satisfaction, enabling us to see and address nascent signals before they became challenges.
You joined the Journal a year ago as research and development editor. What did that job entail?
In many ways, it was acting as an internal strategy consultant — focusing on the development and deployment of R&D projects and the operations and processes of the team itself. The project I initially joined to work on was to partner with Charles Forelle and WSJ’s markets team on incorporating emerging technologies into their coverage. Keep your eyes on WSJ DXS for more on that soon.
As R&D chief and senior product manager for editorial tools, what do you hope to accomplish?
I believe that the systems we use in the newsroom have to work for us so that we can work for readers. The nature of news — how it is made, collected, and more — is changing and our processes, systems and tools need to support this. I hope to create efficient processes and tools, enabling our reporters to focus on telling the right stories in the right way, while simultaneously pushing the limits of our reporting.
That’s the theory, at least. On a practical level, this means connecting our different tools to create a product ecosystem. It will also mean decommissioning tools that aren’t in use, and creating new ones to fill editorial needs. The team will still undertake computational journalism and partner with reporters and editors on stories, creating repeatable and scalable systems that move certain capabilities from being possible by only a few people to potentially being used by the entire newsroom through an interface.
How do you get engineers and data scientists to understand what journalism is trying to accomplish?
Outside of technical skills, emotional intelligence is one of the most important traits I hire for. It is absolutely necessary to understand one another’s perspectives and communicate effectively. Hiring the right people, providing the necessary context, and creating opportunities for direct interaction is one of the best ways I’ve found to foster a shared understanding.
It’s also worth noting it goes both ways. Not just our technical teams understanding journalism, but our journalists understanding how to work with our engineers.
What’s the most important thing to understand about user experience?
That you must consider both the *what* and the *why*.
One of my go-to examples to articulate this… Someone is reading a story and clicks a link. Is this good or bad?
Generally, about half the people I ask will say good and the other half bad. But the answer is that we just don’t know. The article could have been exactly what that person wanted to read, and we surfaced another relevant article they want to read next. Or, the article was not relevant and they clicked away to find something different. Without the *why* we don’t know how to respond to the *what*.
How can publishing content be improved using technology?
We’re just scratching the surface of what is possible. I’m actually paraphrasing for Paul Cheung here who spoke about artificial intelligence specifically, but I believe is true of technology generally. There are three primary ways to support newsrooms:
- Reduce variable costs (faster and more efficient publishing)
- Augment reporting capabilities (investigations and surfacing relevant data)
- Reach and engage (distribution tools and new experiences)
Of course, these three buckets are all connected. How do we turn unstructured data into structured data? How do we link that to faster insight generation? How do we optimize an article’s format, or tailor it to the individual? They flow into one another.
Anything else that I didn’t ask about your job?
The last thing I would want to note is the importance of collaborating and working with academia. Two of our engineers recently left Dow Jones to pursue academia — because academic institutions are at the forefront of what we are seeing, especially in machine learning. Almost all of my projects, especially on the R&D side, begin with a literature and competitive review, to make sure we’re not trying to reinvent the wheel or using practices that are outdated.