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Atif brings valuable insights from a recent PDMA executive workshop where leaders discussed their real-world challenges with strategic decision making and innovation strategy. In this episode, he shares some insights from that workshop and his experience in product leadership.
This definition is a mouthful, so I like to visualize it. I’m going to walk through this visual quickly, and then Cecilie and I are going to dive into this in more depth. Using the Opportunity Solution Tree to Guide Discovery The visual at the center of this is called an opportunity solution tree.
How Companies Are Funding User Research in 2025: Insights from the User Interviews Research Budget Report User Interviews 2025 Research Budget Report is a crucial resource for understanding how modern organizations are approaching the financial side of user research. Only 17% experienced reductions.
From Raw Data to Clarity — Cleaning, Sorting, and Synthesising Insights Part 4 (of 5) of the UX Research Playbook series Synthesising qualitative data is similar to reaping the harvest after the diligent effort poured into research — it’s the step where hard work blossoms into meaningful insights. Mural , Miro , etc.)
Every unexpected obstacle, whether professional or personal, tests our capacity to adapt. While adaptability might come naturally to some, others may need tools and guidance to strengthen it. The challenge lies in not just predicting the unexpected but also in reacting to it effectively.
The metaphor continues: You lay the foundation (the backend and data architecture). Teams organize features into an information architecture, then bundle them into menus, apply usability fixes, and run some user testing to improve comprehension. They want service. You install the fixtures (pages, flows, user controls).
A report by Arize AI found that 281 Fortune 500 companies view AI as a business risk, a 473% increase from the previous year. Be Open to Adjacent Roles : Starting in CRM account management gave me invaluable customer insights to share with the product team. to offer 24/7 coaching services.
Picture this: A room full of product leaders huddled around market data like detectives at a crime scene, learning to spot the subtle clues that spell opportunity. Just as a master chef knows instinctively when a dish needs a pinch of salt, great product leaders cultivate an intuition that bridges data and action. The secret?
No matter what your product or service offering is, the landing page of your SaaS website is most likely the customer’s first point of contact with your brand. With a time frame so small, it is no wonder that simple design and layout are found to be much more appealing to users as compared to visually complex designs.
How can you balance functionality with aesthetics, ensuring your app is both intuitive and visually appealing? Benefits of responsive e-learning appdesign Firstly, why do you need a functional design in the app where people study focusing on educational materials, not visuals? Examples include Moodle and Blackboard.
Try this exercise: Take your current roadmap and ask "Why?" When presenting to executives, Maya used data to back up her points, leveraging her analytical strengths and making an impact. As AI reshapes product management, view it as a tool for acceleration rather than a threat. three times for each initiative.
It has helped in gaining alignment on complex topics with senior leaders at Meta (Sheryl Sandberg, Chris Cox, and Andrew Bosworth), paving the way for key launches like Facebook digital well-being tools , privacy protections for youth , and Quest referrals. Let’s go through the steps and action items.
Today’s newsletter gives you the tools to influence, adapt, and lead from wherever you sit. Treat them like a customer, seek to understand their motivations Use data + external insights to introduce small, strategic shifts Alignment: What if your leader keeps changing their mind? Try this interactive exercise.
Use the “competitor test” to validate bold names. Lexicon is behind iconic names such as Sonos, Microsoft’s Azure, Windsurf, Vercel, Impossible Foods, BlackBerry, Intel’s Pentium, Apple’s PowerBook, and Swiffer. Don’t let domain availability drive your naming decision.
Below, we discuss how to prepare for and ace data engineering interviews. 🧠 About this guide : Written by Thang Tran , a senior data engineer (ex-Amazon, Meta, and Apple) and Exponent interview coach. Reviewed by Deeptaanshu Kumar, a VP of data engineering (ex-Capital One, Freddie Mac).
These are some of the most common data analyst interview questions. ✅ Verified : Celine Liu , Uber's former Global Analytics Lead, wrote this guide. Celine has conducted 100+ interviews across analytics, operations, and strategic roles. Technical Round: Can include asynchronous SQL tests or live coding challenges.
When developing products, customer insight is vital to understanding the critical question: where are we going? Insights can help us better to understand our product and how it fits into the everyday lives of users — users who live in an age of abundance, where every product competes for a minute of attention. Size Up Your Sources.
A regular cadence of assumption testing helps product teams quickly determine which ideas will work and which ones won’t. And sadly, most product teams don’t do any assumption testing at all. In this article, I’ll cover assumption testing from beginning to end, including: Why should product teams test their assumptions?
You reach out to all department heads and request to get data for your analysis: Marketing team for data on campaign conversion, customer support for data on call volumes, account team for data on customers feedback, product data for usage, etc. What is a product operations dashboard?
Lisa began her career in data science and spent four years as a data scientist at Airship , a marketing and messaging automation company. During her tenure as a data scientist, Lisa built two predictive products. Lisa says, “We led with ‘What can we predict?’ Tweet This. Tweet This.
What is data-driven analytics in SaaS? How to conduct user data analysis? What are the best analyticstools for the job? TL;DR Data-driven analytics describes the process of collecting, analyzing , and interpreting customer data to help organizations make better-informed product and strategic business decisions.
The idea is to treat the strategy as a prototype that will undergo many iterations and testing. Some fundamental principles I found helpful when starting this exercise, Keep it short and sweet so that you can quickly memorize the core message. Testing the Proto-Strategy. Pillars to a Proto-Strategy. What are we betting on?
“We are a data-driven company”. And, while the logic behind a data-driven approach is undeniable, too often the expectations that come with it aren’t met. And, while the logic behind a data-driven approach is undeniable, too often the expectations that come with it aren’t met. Improper Testing.
The article here focusses on business criterion to use to better evaluate if a predictive model is ready for production and the associated risk when the predictions are wrong. Optimized for better outcomes Today, businesses regularly use predictiveanalytics to optimize their business and achieve better business outcomes.
However, not everyone on the team can take part in every interview or every assumption test. Everybody should help surface assumptions and contribute to assumption test design. When we get results back from an assumption test , we tend to iterate on our ideas based on what we learn. Tweet This. Everybody should contribute ideas.
For today’s Tools of the Trade , we caught up with Yury Oleynik , the VP of Product Management at HiveMQ , who has managed to make continuous interviewing a reality on his team. Yury shares a few of the tactics his team tried to automate recruiting before they came across the tool they’re currently using, Orbital.
And this is why it can be helpful to consider the tools that will best support you in building your continuous discovery practice. Let’s be clear: The tools alone won’t automatically make you better at discovery. Since there are countless ways of approaching this, we’re kicking off a new series, Tools of the Trade. Tweet This.
Data analysis is integral to a product manager’s job – it’s what helps them build impactful products. This article dives deep into data analysis for product managers. User data analysis helps: Provide direction for product development , allowing for effective resource allocation. What is data-driven product management?
Instead, we must go back to the age-old mantra that a picture is worth a thousand words and provide a visual representation of what the future could look like if we are successful. Design: Customer Discovery Insights. Whenever you are surprised it means you've collected a valuable insight. Design: Product Roadmap.
Discover how to enhance user experiences by leveraging quantitative research, usability testing, and A/B testing to make informed, data-driven design decisions that lead to measurable results. These tools enable you to collect feedback and use that data to refine your product’s design and functionality.
Let’s face it: qualitative data analysis is vital to understanding why users act in a particular way and how they feel about your product in a way that quantitative product analytics can’t. This article will teach you how to analyze qualitative data to inform product development and improve the product experience.
And finally, Tali was so convinced of the power of opportunity solution trees that she started leading workshops at product events to teach others how to use this tool. I myself am a visual thinker, and a big believer in external representations as a tool to create common ground (as Barbara Tversky is quoted in Teresa’s book).”
Have you noticed recently an increase in the usage of the terms ‘data-driven’, ‘data-informed’, and ‘data-inspired’ around your office? What does data-inspired actually mean and how is it different from being data-informed? Data-driven, data-informed, and data-inspired describe how data should be used.
Chris shared the story of how he introduced the opportunity solution tree to several teams at his company and a few of the iterations that helped make the tree an indispensable tool at SuperAwesome. He works at SuperAwesome, a UK-based company that develops tools and services to make the internet safer for kids. Tweet This.
Ada and I both had the privilege of working at two data-driven companies, LinkedIn and SurveyMonkey , led by two analytically rigorous leaders, Jeff Weiner , and the late Dave Goldberg. Those experiences shaped the way that we both now think about building an effective data-driven product culture. Why metrics reviews matter.
Interviewing customers , building opportunity solution trees , running assumption tests —these are all activities that take your attention away from delivery. Then I can start doing some of those things like story mapping a solution, generating assumptions, and starting to think about, can I question or test some of those assumptions?
TL;DR A business intelligence (BI) analyst is a data specialist who helps businesses translate raw data into actionable insights. According to Glassdoor data, the estimated total pay for a Business Intelligence Analyst in the United States is $134,912 per year, with a base salary of $99,503 and additional pay of $35,409.
3:18] You made a move from being a senior product manager in the medical industry at a company creating surgical implants to being the senior director of product management for an IT services company specializing in web hosting. At Newfold Digital, we acquire a lot of companies that specialize in a product or service.
To help hiring managers and recruiters, like myself, decide whether or not to interview you, it can be a great exercise to treat your resume like a professional product. Data-Driven. As a hiring manager, I want to hire people focused on gathering good data, s o we can craft good hypotheses about what to build next.
Whether you're an aspiring entrepreneur, a seasoned product manager, a UX designer , or simply curious about the process, this guide will walk you through the essential steps, best practices, and tools you need to create successful products. Test your product prototype and note usability or UX design improvements.
Measuring design and utilizing data are essential steps towards creating a sustainable product. Let me show you how to use data to improve your product. The ideas were tested and researched in a pleasant way. The last round of user tests showed the prototype was seamless and easy to use. The design was fabulous.
A nice photo may provide us with some degree of insight into what someone once did, or their personality at that specific moment or period in time. However, it doesn’t give us any real valuable insight into their present situation, their character or personality, whether or not they’ve changed, or how their future will pan out.
But when we think of rapid prototyping and usability testing as one and the same, we tend to underestimate the power of prototyping. When we conflate rapid prototyping and usability testing, we tend to underestimate the power of prototyping. We’ve discussed the idea of visualizing our ideas before. – Tweet This.
User experience (UX) Write down your hypotheses in each layer then test the product with customers to see where you’re at with product-market fit. Adding that attribute adds predictive power to your model. When I do this exercise, people get so fired up. A lot of it is not data-driven. Target customer 2.
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