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Guest Post by: Marvin Mathew (Mentee, Session 11, The Product Mentor) [Paired with Mentor, Jordan Bergtraum]. Ruthless prioritization translates to product teams spending time building the right thing at the right time. Each feedbackloop has a minimum of four stages. The feedbackloop process is.
Think of Net Promoter Score (NPS) software as a tool to measure your customers’ feelings about your product, and categorize them based on their level of loyalty (promoters, neutrals, and detractors). The great advantage of these tools is that they streamline the creation, distribution, and analysis of NPS surveys.
Introduction to customer satisfaction surveys Customer satisfaction surveys are vital tools for understanding what customers think, feel, and experience. Surveys provide a range of insights, from quick feedback after a purchase to in-depth assessments of brand loyalty. Don’t worry, we’ve got you.
A custom ChatGPT model that helps accelerate product innovation Watch on YouTube TLDR In this episode, I interview Mike Hyzy, Senior Principal Consultant at Daugherty Business Solutions. He explains how to conduct an AI-powered design sprint that transforms product concepts into clickable prototypes in just hours instead of weeks.
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Without effective UX analytics that goes beyond collecting data, you’re losing valuable customers. Unfortunately, the research backs this up, with a staggering 90% of users reporting that they stopped using an app due to poor performance. It covers key topics, such as: Defining UX analytics.
How product managers can adapt core responsibilities across different organizations and contexts Watch on YouTube TLDR Through his research and practical experience at MasterCard, Nishant Parikh identified 19 key activities that define the role of software product managers.
I was asked to give a ten-minute overview of my continuous discovery framework and then participated in a fireside chat where the host, Cecilie Smedstad , asked me to go deeper in a few areas. Discovery is a team sport. Its not the exclusive domain of product managers. How are we building production-quality software?
In the latest TPG Live session, Driving Impact Through Influence and Experimentation , we dived into strategies product managers can use to expand their influence, foster innovation, and navigate the evolving landscape of product management. Navigating Pushback Resistance to experimentation is a common challenge for product managers.
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How AI captures customer needs that human product managers miss Watch on YouTube TLDR In my recent conversation with Carmel Dibner from Applied Marketing Science, we explored how artificial intelligence is transforming Voice of the Customer (VOC) research for product teams.
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When your company adopts multiple SaaS solutions to drive productivity, you unknowingly create a perfect storm for data fragmentation. Your customer information lives in Salesforce, while your support tickets are in Zendesk, your product usage data in Mixpanel, and your marketing campaigns in HubSpot. Sound familiar?
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“Product thought leaders talk about an ideal way of working. I realize that many product people have never worked in a product trio , don’t have access to customers, aren’t given time to test their ideas, and are working in what Marty Cagan calls “features teams” or “delivery teams.” product outcomes).
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A product manager’s guide to breaking free from reactive problem solving Watch on YouTube TLDR In my recent conversation with Doug Hall, master of turning chaos into clarity, we explored how product managers and innovation leaders can break free from reactive problem-solving and create more value through proactive innovation.
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Guest Post by: Carlos Ruiz (Mentee, Session 11, The Product Mentor) [Paired with Mentor, Nis Frome]. Firstly, Jeff as a new umbrella brand for all the new services will be providing to our customers; Secondly, a new business line called Beauty Jeff was opening the very first venue in Argentina. Very task and feature- oriented.
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Listen to the audio version of this article: [link] What is ProductDiscovery? Productdiscovery is the process of “figuring out a solution to a problem we’ve been asked to solve,” writes Marty Cagan. [1] The solutions, finally, are the products or product capabilities that help solve the customer needs.
How Do You Stay True to Your Product Vision While Adapting to Market Realities? Below is a preview of key insights. Customer feedback is overwhelming , making it hard to separate signal from noise. Strategies for Maintaining Product Vision Without Losing Adaptability Use a structured prioritization framework (e.g.,
How an AI-powered fashion startup achieved product-market fit Watch on YouTube TLDR In this episode, we’re joined by Anya Cheng, former product leader at Meta, eBay, McDonald’s, and Target, and current founder of the AI-powered fashion startup Taelor. ” The problem?
How product managers can use AI to get more actionable insights from qualitative data Today we are talking about using qualitative data to drive our work in product and consequently improve sales. Before founding Viable, he held senior leadership roles in engineering, technology, and product.
Speaker: Miles Robinson, Agile and Management Consultant, Motivational Speaker
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The larger and more complex your company is, the more challenging it can be to introduce continuous discovery. Sandrine Veillet ’s Product in Practice story perfectly exemplifies this. Sandrine Veillet ’s Product in Practice story perfectly exemplifies this. Do you have a Product in Practice story you’d like to share?
What happens when you build a product or service around what you think potential customers want, only for them to buy something else? But worse than that, it leads to lower revenue, failed products, and plummeting customer loyalty. The solution seems obvious: improve your customer research process. The short answer: yes.
Speaker: Richard Cheng, Associate Product Manager, Mark43
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As a product manager, you probably know specific ways to gather data to inform your product decisions, like the ever-popular A/B test. What about the times when it doesn't make sense to A/B test, because you have too small a sample size? Tim will discuss the line between being data-informed versus data-driven.
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