5 Continuous Discovery Best Practices from the Atlassian Growth team
Hi, I’m Jet, a product manager on Atlassian’s Growth team! Several months ago, my team started adopting “continuous discovery” best practices, and we’re already seeing amazing results! In this article, I delve into our experiences and share 5 best practices that your team can leverage.
Don't miss the second part of this series where my teammate, Tony Jones, a designer on my team, walks you through how we schedule, structure, and report on customer interviews.
The power of continuous discovery
In the fast-paced world of product development, speed is paramount. Not only is rapid delivery crucial, but so is the ability to learn and adapt quickly. High-performing teams distinguish themselves by their ability to learn rapidly, yet in a world where time is of the essence, striking a balance between swift delivery and thorough discovery poses a challenge.
As product managers, we're always on the lookout for ways to enhance our efficiency and speed. One strategy my team has found particularly effective is embracing continuous discovery, both qualitatively and quantitatively, to ensure we remain customer-centric and deliver impactful solutions iteratively.
Customer obsession is at the core of our approach. We're relentless in understanding customer problems, designing delightful experiences, and iterating based on insights. Several months ago, we introduced continuous discovery practices, and the results have been significant – and I’m excited to share with you the 5 best practices that we followed to achieve these results!
Here they are in short, and I’ll go into more detail on each of these below:
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Qualitative testing → faster decision-making
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Visualize your thinking with Opportunity-Solution Trees
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Customer insights foster better collaboration
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Remain agile, pivoting your plans as necessary
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Give your team a deeper understanding of your customers
Qualitative testing → faster decision-making
Traditional customer research can take weeks of valuable time. For my team, this oftentimes meant we turned to intuition, rather than data, when making decisions. As it turns out, you can also learn a lot through qualitative means. It is fast and cheap to test ideas in a qualitative setting before investing time in shipping an experiment.
Transitioning to weekly customer chats has enriched our understanding. Regular interactions provide valuable real-time feedback, complementing more structured exploratory research. By validating assumptions and gathering insights directly from our customers, we can tailor our offerings to meet their evolving needs and preferences. This iterative feedback loop deepens our engagement with customers and ensures the changes we make will resonate. As a result, we’ve seen a ~10% increase in experiment success rates.
In short, qualitative testing has allowed us to inexpensively test hypotheses before investing resources in experiments, accelerating our decision-making process and boosting confidence in our choices.
Visualize your thinking with Opportunity-Solution Trees
In the past, our team tended to jump into generating ideas and conducting experiments without fully understanding the underlying problem. This made it difficult to iterate on solutions – but embracing continuous discovery practices has made it possible to deeply understand user needs.
The challenge lies in synthesizing insights from various sources and translating them into opportunities, hypotheses, and solutions that align with our business objectives. To do this, we turned to opportunity-solution trees and started visualizing the steps required to improve our products, in a way that was thoughtful but not overly prescriptive. The tree structure allows you to easily follow the steps in order.
With this approach, we could go wide when identifying the problem and creating hypotheses before zooming into a single solution. The visual representation both clarifies our thought process and makes it easier to align with partner teams. Below is an example of how you can structure your opportunity-solution tree.
Customer insights foster better collaboration
We capture the hypotheses from the opportunity-solution tree in Jira Product Discovery and store notes from customer calls and other research in Confluence and Dovetail. Systematically linking customer evidence to ideas in Jira Product Discovery creates a feedback loop that allows you to:
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Continuously evaluate and refine your product roadmap By systematically reviewing and iterating on hypotheses and insights, we ensure that our product strategy remains responsive to evolving customer needs.
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Increase stakeholder visibility. Integrating customer feedback into the development process gives stakeholders greater visibility into user needs and how they connect to planned solutions. This transparency fosters alignment and gives stakeholders greater confidence in decisions, since they can see they are grounded in direct user input.
Overall, this approach not only streamlines the product development process but also fosters a culture of customer-centricity and collaboration, ultimately driving greater value for both customers and the business.
Remain agile, pivoting your plans as necessary
Quarterly planning cycles can be time-consuming and rigid. However, by continuously gathering insights and validating hypotheses, my team has been able to make more informed decisions without needing lengthy planning cycles.
In the past, our projects used to have a detailed timeline, but recently we shifted to high-level quarterly planning where launch dates aren’t specified upfront. Instead, these dates are communicated when engineers start development.
This agile approach empowers us to pivot when necessary and stay ahead of the curve.
Give your team a deeper understanding of your customers
One of the most significant benefits of our continuous discovery work is its capacity to enhance team collaboration and skill development. By involving team members in customer research sessions, we foster a deeper understanding of our customers' needs and motivations and make the team better able to find a solution that customers love. It also supports our ways of working to involve engineers in early-stage problem and solution explorations.
We dive into customer conversations in more detail in the next part of this series.
There’s always room for improvement
While we are happy with the outcomes so far, there is always room for improvement.
Here’s what we are working on to make it even better:
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Consistently mapping insights to ideas in Jira Product Discovery. While we've made strides in documenting insights from customer interactions, we're working to integrate these insights seamlessly into our Jira Product Discovery board. Getting into this habit will make it easier to prioritize and scope ideas.
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Keeping the opportunity-solution tree updated. We're committed to maintaining an accurate and comprehensive opportunity-solution tree with multiple hypotheses to solve a problem. To achieve this, we've set a goal to review and update it monthly, collaborating closely with our triad and partner teams to make sure we’re all aligned.
In conclusion, continuous discovery isn't just a buzzword, but rather a strategic approach that can transform the way product teams operate. By prioritizing customer insights, visualizing our thinking, and fostering a culture of continuous learning, my team has been able to accelerate our product development process and deliver more impactful solutions. So, if you're looking to supercharge your team's performance, I highly recommend embracing our approach to continuous discovery—it's a game-changer!
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Jet
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