What are the best ways to use data and evidence to support your arguments in product management?

As a PM, I applied data driven product mindset to leverage the available information to support in end to end aspects of product management. Data driven product culture helps in exploring the market potential, conducting user research, identifying use cases, prioritizing use cases, new feature requirements, analyzing the usage trend, tracking the issues and challenges. The quantitative insight, qualitative insights is also critical by conducting interviews and user testing provide context and depth. This helps in crafting persona based goal by considering all the dimensions to outline specific goals, behaviours and expectations. This will set a good example of customer or user centric mindset for product development and product management.

Without quantitative points, it feels like basing arguments on just opinions. Define the problem statement clearly and more specifically “the WHY” behind that which is backed by concrete data points. And then define the goal that you want to achieve so that mid-way you don’t keep moving the goal post. I remember a time when I proposed to build a new feature to improve our new user retention, we first understood where exactly did the problem was, why was it happening specifically for new users and how will the improved state look like. All of these hypotheses were formed after looking at data from various angles to exactly identify the problem areas. Hence, hypotheses building and validation through data should go hand in hand

Without data, you are just another person with an opinion. Clarity in defining the problem and goal is crucial before diving into data. It's like setting the stage for the story your data will tell. I recall a time when I was proposing a new feature for a mobile app. We started by articulating the need for this feature - how it addressed a specific user pain point and aligned with our overall product strategy. We built an argument backed by data, making it compelling and relevant. It's about connecting the dots between your goal, the problem, and the data that underscores your rationale. Any prioritization, revenue model, or even design process shifts in a different direction as soon as data justify a particular problem.

In product management, leveraging data and evidence is crucial for persuasive arguments. Start by defining the problem and goal to frame your argument. Use diverse data sources, including both quantitative metrics and qualitative feedback, to provide a well-rounded perspective. Visualizing data simplifies complex information, making it more accessible. Comparing and contrasting your data against benchmarks or previous results can highlight your product's impact or improvements. Anticipate potential objections to strengthen your argument's credibility. Addressing these objections head-on shows thorough preparation and deep understanding of your product's context and potential challenges.

Create dashboards which shows the actuals a of a product journey for a quick visibility of the product health. And evidences are important to retrospect and improve the product quality while enhancing it. If you have those two with a valid idea you can propose any argument and increase the standards of delivery.

Leveraging evidence is key to obtaining support for your product strategy. Three effective ways to do this are: 1) Mastering the art of storytelling, using data to craft compelling narratives that illustrate conflicts, resolutions, and the customer as the hero. 2) Balancing quantitative and qualitative data to understand not just what and how, but also why certain patterns emerge, helping to redirect team efforts effectively. 3)Building a robust product intelligence system that connects data from multiple sources, providing insights and opportunities for the product team to make informed decisions. By implementing these strategies, Product Managers can create a data-driven vision and empower their teams to innovate effectively.

Using targeted data really helps in supporting an argument or an approach for something which exists (when proposing a change). For those items which have not been built, surveys & user interviews are powerful since they are indicative of general behaviours. Having said that, in certain scenarios where there is still uncertainty, testing a prototype also helps if feasible.

It is essential to source the data from diverse data sets for building structured and robust arguments. In every stage of product management, begin by collecting user analytics, market trends and feedback from customers to establish foundational understanding. Supplement this with qualitative data, including user feedback, surveys, and customer support interactions. This insights will provide the credibility in the evidence produced to build feature sets. It is always recommended to include industry benchmark and competitive analysis to visualize the product performance. Integrate the diverse data sets to create comprehensive narrative that supports the argument. It is essential to bring technological advancement in supporting evidence.

Arguably the most important part of your pitch: Evidence and supporting data! Focus on these key areas/activities: - For completely new innovations, emphasize user problems and market opportunities while addressing risk analysis and mitigation. - For all other purposes, gather data on market trends, consumer behavior, and competitor analysis (what worked, why, how). - Include case studies or data from pilot projects to demonstrate real-world applications and results.

Using multiple sources and types of data is absolutely necessary to make your arguments more comprehensive and robust I recall a time when we were trying to solve a problem related to one of the game rules we had in our game and initially we were just focused on customer support ticket as the sole data point to look at the depth of the issue. But, it always came below our set benchmark. Then we shifted gears to understand the problem from multiple sources such as - user interviews, survey results and other proxy metrics After looking at those additional data points, we identified that indeed the depth of the problem was really large which initially felt small and hence we built out a comprehensive argument to solve the problem

The data from various sources enhances argument by showcasing how product measures up against industry standards and competitors. Data visualization tools helps in presenting a trend in more interactive way for enabling users to interpret seamlessly. Graphs and charts can simplify complex data, making it more accessible and compelling to stakeholders. This can be in form of a dashboard for visualizing the data which is refreshed at regular frequency. It is always helpful to visualize the data in AI-driven analytics or predictive modelling to demonstrate a commitment to continuous improvement and innovation at work. This is one of the way to support the argument for the product and create a meaningful and compelling narrative.

A few things that I've found consistently helpful: 1. Start with a framework like 'Capability-Advantage-Benefit', or something similar, to capture key development items to organize the value prop succinctly would help translate pitch into tangible benefits. Follow it up with the 'Data-Insight-Action' framework to structure how you present data, the insights derived, and the actionable steps based on these insights. 2. Introduce interactive elements like real-time polls or Q&A sessions to foster interaction. Use storytelling techniques to make your presentation more relatable and engaging. 3. Use anecdotes that senior managers can relate to, like navigating complex stakeholder landscapes or spearheading innovative projects.

It is very critical to make your product standout in comparison with an exemplar product or any alternates available in case of no direct competition. Comparative data enhances your argument by showcasing how product measures up against industry standards and competitors. Establish industry benchmark if not available to set a precedent for any new upcoming competitors. To visualize the comparative data set can be prepared by identifying the common factors between the products and rank them in terms of user impact to create an interactive radar chart. This can also be visualized in simple table with different precise data points to avoid verbosity. The comparative analysis helps in building the product narrative in nuanced way.

In product management, leverage data effectively by comparing and contrasting relevant metrics. For instance, when proposing a feature upgrade, present user engagement data before and after a similar enhancement in the past. Highlighting the positive impact on key performance indicators, like increased user retention or higher conversion rates, strengthens your argument. This method not only supports your recommendations with tangible evidence but also provides a contextual understanding of how proposed changes align with historical performance, making your case more compelling to stakeholders.

Personally I try to gather the right data before I try to improve a product, and compare against public benchmarks or historical company data. These are some use cases where I have collected data for comparison: 1. Increase in engagement & conversion post a feature release on a sign up page 2. Website audits to collect load time data before and after feature releases (compare vs benchmarks) 3. Increase in revenue or engagement on an eCommerce checkout page after product improvements 4. A/B testing to find better conversions on contrasting CTA designs

A data driven product manager always keeps a step-ahead approach for being proactive to keep evidences and product information handy while addressing any potential objections or questions from customers. Begin with empathy and identified persona to think through the scenarios. Sometimes, anecdotal evidences such as success stories or testimonials can humanize the data which helps resonating with decision-makers to anticipate the challenges and objections. Sometimes, it is always helpful to create a real-world examples to bring the data to life, reinforcing the impact of the product.

The most underrated trait of a great Product Manager is their ability to simulate thought processes of other people, and also simulate situations that might present potential objections or roadblocks. Be proactive and think from their shoes! 1. Identify potential objections in advance and prepare clear, concise responses. Show how you've incorporated feedback or learnings from past projects to mitigate similar risks (personally or as seen in market). 2. Employ a 'Pre-mortem Analysis' to anticipate potential failures and develop strategies to address them proactively.

In PM, effectively using data and evidence involves several key strategies. First, clearly define the problem and goal to frame your argument. Utize a variety of data sources, both quantitative and qualitative, for a comprehensive view. Visualizing data simplifies complex information, making it more digestible.