Arman Javaherian is former Chief Product Officer at Shortcut, a project management software for software development teams. He began his career at Wells Fargo as a business analyst before transitioning to product management within the company. From there, Arman joined product management at Trulia (acquired by Zillow). After continuing at Zillow for four years, he moved to lead product and design teams at CrunchBase before his latest role at Shortcut.
In our conversation, Arman talks about the importance of setting aside time to help grow and mature product managers on his teams. He also discusses initiatives he’s worked on to foster a connection between leadership and individual contributors, including the manifesto, The Shortcut Way, he wrote for product teams.
I graduated from college in 2004 and started my career as a business analyst at Wells Fargo, where I helped implement payment platforms for their foreign exchange department. They didn’t call it product management back then, but that’s what it was — we were writing requirements, working with engineers and designers, coordinating with business stakeholders, and project managing it to fruition.
A few years later, I moved into a more formal product manager title, working on their consumer-facing online banking platform. That team was more modern in terms of product management lingo and tech-relevant titles were starting to pop up around San Francisco.
We’re starting to see the proliferation of AI-powered tools that can help supercharge specific aspects of a product manager’s responsibilities, and I see that continuing. It’s not a replacement, but an augmentation. For example, tools can use AI to aggregate customer feedback and provide recommendations for feature improvements. AI can inspire better user experiences and designs, help with organizing PRDs, etc.
Product managers, more than any other role in an organization, have to wear multiple hats, and we always wish we had more time to do something — whether that’s discovery, customer interviews, competitive research, or discussing solutions with engineering. AI can really help product managers with menial tasks so they can focus on the real human-oriented work of product discovery that most of them love.
One of my favorite parts of being a product leader is seeing those on my team grow and mature in their roles. I ensure that I discuss growth in 1:1s and understand if and how that individual wants to grow in their career. If they want more leadership opportunities, for example, I’d work with them to devise a plan internally or externally. Likewise, if they were happy as an IC and want to continue in that role, I’d suggest different skills they can build to up-level their IC work.
It all depends on the individual and the spending the time they each need. It’s really important for any leader to step in and make the time. Not only does it provide satisfaction, but showing up like that also motivates those on the team to do more and to get excited about their day-to-day work.
Product managers wear so many hats, and that’s why it’s often difficult to find one who’s worth their weight. There are two critical responsibilities for any product manager: being able to say yes and being able to say no.
They need to be able to say yes because product managers have to be the optimists within a team and organization. The best of them can run into challenges and failures but quickly pivot in a different direction. It takes immense optimism to believe there’s always a better path and great humility to admit failure and move on. They also need to be the cheerleaders for their engineering team and the organization writ large to ensure that people are excited and motivated about the vision the PM has in their head.
Simultaneously, they need to be able to say no to help get things done and move things forward constantly. There’s no shortage of ideas and suggestions coming from business stakeholders, engineers, designers, marketers, sales customers, support, and everyone in and around the organization. The best PMs know how to say no when they feel these ideas or requests are a distraction. It’s hard to say no, but it’s a skill you have to build over time with practice and experience.
Shortcut is a tool for product teams that combines project management, document collaboration, and OKRs in one solution. Teams can create an OKR and layer in projects and individual tickets tied back to those goals and then clearly see how each initiative contributed to or didn’t contribute to each goal. That’s something you can’t find in other tools.
This manifesto, The Shortcut Way, was something we wrote for leaders looking to organize their teams better. We had no shortage of customer feedback and stories about this, and when we interviewed dozens of customers at Shortcut, we saw this firsthand. One problem we saw over and over again with these teams was how leadership was disconnected from their product managers, engineers, and design teams that built those products.
It’s why we evolved Shortcut to be what it is today: a tool that helps connect OKRs and goals directly to individual projects and tickets. This gives everyone in an organization transparency into what projects are driving which goals successfully (or unsuccessfully).
I’m a big believer in user research and how the team needs to be at the same level as product management and design. Too often, organizations layer in user research under either design or product management. And that creates a bias of sorts.
I think the larger product org needs to have its own system of checks-and-balances, so to speak. This way, a product manager who is excited about an idea but is unaware of their own bias can be in the same conversation with a user researcher. They could help direct the product team to a specific data set, whether qualitative or quantitative, that might lead the team in a different direction.
We did that at Shortcut, and it was effective because of the way we talked to these customers. It wasn’t just a product manager or designer going in and figuring things out however they thought was best — it was product, design, and research working together to meet with these teams and understand their journeys, jobs to be done, struggles, etc. We realized that there was a disconnect between what the leadership wanted to do, the vision they had, the goals they were setting, and the actual work that was happening.
Too often, it was because people would spend all this time building OKRs, and then they would just forget about it for the rest of the quarter and come back to it at the beginning of the next quarter. We saw this as an opportunity to layer that aspect into the tool. So, the actual execution and the product development happening in the tool and the tickets that are getting created are tied back to specific KRs and organizational objectives.
I spent six years at Zillow helping build their agent platform, which helped connect the hundreds of thousands of real estate agents with the millions of consumers and home buyers who came to the site.
As of August 17, 2024, home buyers, for the first time, will have to pay their real estate agents out of their own pockets. Previously, the home sellers would pay for the home buyers’ agents. But now, because home buyers have to pay this themselves, it presents a huge opportunity. Millions of home buyers may not want or be able to pay the 2.5 percent these buyers’ agents have traditionally asked for. That’s $25,000 on a million-dollar home, which is a huge amount for many home buyers who are already struggling to come up with a down payment.
It’s why we built Homa — an AI platform that helps home buyers get through the process on their own. The more the home buyer can do with our tool, the less they’ll need to rely on and pay a real estate agent. Homa is specifically trained in real estate knowledge in each state, and it’s completely free to use right now. It helps home buyers come up with offer prices for their homes, recommends an offer strategy, helps walk through all the steps in the home buying process, and more.
One of the reasons we made the tool free is to get as much feedback as possible early on. We want people to try it out and provide their email addresses, and we’ll reach out to every person to see what they’re going through. What is the value they’re getting? We want to dig into the data and understand what questions they are asking the AI.
We had a lot of hypotheses, but it’s a good practice to be humble. Whatever you thought you knew and whatever you thought you were doing, be very open to discarding it if new information comes in.
The trick here is to always ask about their problems but avoid any solutions they share. Naturally, people would try to come up with solutions regardless of who the user is. You have to keep coming back to digging into the problem and understanding what that problem is.
Once you have a better understanding of the problem, you can use design thinking approaches to come up with solutions — whether it’s diverging and then converging or using various different kinds of techniques to get a group of people together to try and come up with those solutions. That can be done later. But the most critical is just really hyper-focusing on the problem when you’re talking to these users.
There are a lot of techniques, including building journey maps or the jobs-to-be-done frameworks or others. It’s critical for product teams to have a dedicated user researcher on staff to help with this step. Not only can that person or that team lead some of the discovery efforts, but they can also review your questions in advance to make sure you’re not being biased with the user and following a train-the-trainer approach. Everyone in an organization can be involved, and that’s a great way to build empathy within it.
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