What are the best data sources for product development decisions?

Make sure you increase the awareness on customer side. Most of the times, the customers do not feel that we, as a company, want to listen from them. So, a little promotion saying "We want to hear from you!", will go far away. Also, make sure the customer know what is it in for them. Influence will be superior than just a small monetary price.

Leverage the goldmine of customer feedback as a cornerstone for product development decisions. It's an unfiltered narrative, a compass steering features and improvements. By tapping into this dynamic source, organizations not only enhance their products but forge a customer-centric trajectory, ensuring relevance and resonance in a competitive landscape.

Using a structured approach to analyze customer feedback helps to overcome biases and get objective results. Make informed decisions when prioritizing product features and working on your growth strategy using results from customer feedback. Test early concepts before implementation to validate ideas and customer needs.

I use Jira Product Discovery for systematically gathering and organizing user feedback. ?? Each feedback is tagged and, whenever possible, linked to existing opportunities in Jira product Discovery. ?? This methodical approach transforms our feedback database into a rich, primary data source about our users. ?? Regularly, I dive into this feedback to conduct analyses. It's a strategic exploration to uncover new opportunities and insights. By dissecting patterns, trends, and user sentiments within the feedback, I can identify emerging needs or areas for improvement.

Increase your channels for getting "easy user feedback" by: 1. In app surveys after they've carried a key user journey 2. On-line forums to give feedback/initiate. discussions 3. Whatsapp/telegram communities 4. Host industry networking events 5. Offer users the option to self-book feedback interviews with you

Hotjar is my favorite tool for monitoring user behavior! It allows you to piece together the full story: - Seeing their scroll behavior to tell you what is catching their eye & if you’re important information is being seen - Monitoring their click patterns to see how they are focusing their time on the site - Watching their journey on the site to see what their flow is and areas that their user journey doesn’t follow your intentions for them

Utilize "behavioral segment analysis" to discern user behavior data effectively. Categorize users into power, casual, and non-user groups, akin to quartiles. For instance, in a transaction management product, the search feature's usage may span from the top quartile to the bottom quartile. Leverage this analysis to encourage non-users to become casual users and brainstorm strategies for transitioning casual users into power users. Analyzing user behavior not only informs current interactions but acts as a foresight tool, offering insights into evolving customer needs, and preferences, and guiding future-oriented product development strategies.

User behavior has long been a fantastic way to suss out what users are trying to do, how they're attempting to do it, and where they're getting stuck. Analyzing this data often points to opportunities for UX improvements or even new features.

Specifically, leverage "behavioral segment analysis" for user behavior data! For just about every feature, we can split users into three different groups: power users, casual users, and non-users. These roughly correspond to "top quartile", "median", and "bottom quartile." For example, let's say that you're in charge of a transaction management product, and one of your features is a search feature. You could wind up with a usage distribution like this: * Top quartile: 10 or more searches per week * Median: 2 - 10 searches per week * Bottom quartile: 2 or fewer searches per week This info is gold! We can identify how to nudge our non-users towards becoming casual users, and we can brainstorm ways to turn casual users into power users.

Well, Data is one of the most important things in order to improve the Product & and when we talk about data, User Behavior is top notch what we look at & and what needs to be improved basis that. You can collect data from various tools, like Mixpanel, Amplitude, and Hotjar for UX, in order to understand the User journey Mapping (MoEngage, Clevertap), all the tools are the same what your approach & and what data points you will look at & and how you will define your insights from those data points will be crucial

As a product manager, it's a continuous task to constantly monitor competitors and understand the evolving trends and preferences of customers. However, the key isn't to simply copy; instead, you should use this information to develop a robust value proposition that aligns with your product's mission. In today's market, it's evident when a product is a mere copy of a competitor's, and customers recognize that lack of originality. The focus should be on creativity, market awareness, and maintaining a clear differentiation, as opposed to replicating what already exists.

Market research is a bit of a double-edged sword; be careful with how you use it! While competitive actions and industry benchmarks can be insightful, remember that your specific customer base is not the same as "the market." Too often, I've seen product folks chase "market trends" and completely forget to solve the pains of their current customers & users. When you spend too much time chasing competitors, you lose your differentiation! A better way to think about market research - be divergent! Don't limit your research to your immediate industry. Look beyond your borders, explore diverse markets, and draw inspiration from unexpected places. Sometimes, groundbreaking ideas come from connecting dots that others haven't even noticed.

Tap into market research data from reports, articles, and events to comprehend your target market, industry trends, and competitors. Define your market size, share, and positioning. Benchmark your product performance, identify competitive advantages, and uncover emerging market needs.

Comprehensive market research involves studying market trends, competitors, and industry reports. This data source informs product positioning, identifies market gaps, and helps in strategic decision-making. Analyzing competitors' products and strategies offers valuable benchmarks. Understanding what works well or falls short in competitor products can guide decisions on differentiation and feature prioritization.

Market research plays a pivotal role in product development, providing valuable insights into the target market, industry landscape, and competitive dynamics. By conducting comprehensive market research, product teams can make informed decisions that align with market demand, address customer needs, and position their products for success.

My take on Product Performance Metrics is to highlight that their purpose could be different depending on their criticality. They can serve as North Star Metrics, guiding long-term strategy with a focus on a key quality aspect crucial for success. Alternatively, they can act as quality gates, ensuring products meet set thresholds before release, thus guaranteeing a standard of excellence. It's really important to differentiate between the two but to put the mechanisms in place to measure them both.

Product performance data is like a health check-up for your product. It tells you how well your product is working. You look at things like how often it's up and running without problems (uptime), how fast it is (speed), and any problems like errors, bugs, or crashes. Tools like New Relic or Datadog can help you keep an eye on these things. They're like dashboards that show you the health of your product in real time. They can alert you when something goes wrong, so you can fix it fast. Keeping track of performance helps you set goals and measure progress. For example, if you have a goal to reduce crashes by 50%, you can watch your metrics to see if the changes you make are working.

Quantitative product metrics show you the "what" and the "how much" - that is, it'll give you insights into customer behaviors and usage patterns, but it's not going to tell you the "why." Qualitative insights e.g. customer interviews, moderated user tests, etc. will show you the "why" and the "who" - which kinds of customers do you attract, which kinds do you fail to attract, and why each segment makes the decisions that they make. While I wholeheartedly support using product metrics to inform decisions, I strongly recommend pairing metrics with qualitative insights. In other words, don't fall for the trap of being "data-driven"; data can be dangerous when used in isolation!

Product performance is relative. Systems that work great with less than 200 users can completely fall over when you hit 1,000. We need to have incremental load tests that fully replicate real world behaviors. It's a high class problem when servers overload - and we'd like to minimize that issue.

Product Performance is any another aspect, when we say we want to improve product & make sure your Customer are not facing any issues. There are various metrics * it can varry from Business to business like for OTT Or Video Consumption Platform top notch metrics' to track can be: Buffer Rate, Content Loading Time, crashes, Bugs, Re-Buffering Ratio etc. Most of the time, many consumer centric business have a low NPS because of their poor app Performance

Team feedback is critical, yes, but folks aren't going to give you feedback if they don't feel psychologically safe! As product managers, it's our responsibility to create an environment where team members feel safe and empowered to share their insights. Building trust and psychological safety is the foundation for fruitful team dynamics. On top of that, your teammates should be empowered to make proactive proposals. They shouldn't need to wait for you to "give them the floor" for presenting feedback (both for product ideas and team processes). Encourage people to raise suggestions to you in one-on-one meetings or over email & Slack. Publicly celebrate people who demonstrate the courage to speak up and share their valuable perspectives!

"Team" should include everyone in the company. There's a reason Google dog-foods their products, they get a lot of internal feedback. Making feedback easy, safe (anonymous?), and ubiquitous gets the best results.

In addition to customer and user feedback, internal team insights are crucial for holistic product development. Regularly gather feedback from team members involved in various stages, including design, development, and testing. Team feedback provides a valuable perspective on the feasibility and practicality of proposed features or changes. It fosters collaboration, aligning everyone towards a common goal. By incorporating the collective expertise and experiences of the team, we can enhance decision-making, streamline processes, and ensure a more robust and well-rounded product development strategy.

Customer support data can provide you with insights into the problems that users are experiencing with your product. This data can help you to identify bugs, prioritize fixes, and improve the overall user experience. You can collect customer support data from a variety of sources, such as support tickets, chat logs, app/playstore store ratings.

Harness insights from your product team through methods like meetings and retrospectives. Foster collaboration, communication, and alignment. Team feedback is a valuable resource for generating ideas, problem-solving, and learning from collective experiences.

To ensure data quality you can also consider the following key practices: - Identifying Data Patterns: Look for trends and oddities to understand what's happening. Use tools to find recurring patterns, giving you a better idea of what's going on and helping in decision-making. -Testing Hypotheses: Go beyond descriptive data by actively testing hypotheses aligned with business objectives. This ensures a predictive and explanatory purpose, validating assumptions, refining strategies, and uncovering hidden relationships. - Continuous Monitoring: Regularly check data quality in real-time. Get alerts for anything unusual, so you can fix issues quickly. This prevents problems from affecting your decisions later on.

Data is over-rated. Data only answers questions we previously thought to ask. Conversations bring up insights we never thought to ask about. Data is more a confirmation (or not) of our hypothesis, it rarely exposes "aha" moments. Conversations don't scale - and they don't need to.

Recognize the pivotal role of data quality in accurate decision-making. Ensure data quality by setting clear goals, ethically collecting and cleaning data, and presenting it objectively. Use techniques like data validation, integration, visualization, and storytelling to enhance data quality.

Be it customer feedback or product or platform usage, data gathered from these serves as a critical input to product decisions. High quality data ensures accurate insights that helps with effective decision making and provides a competitive advantage. Data driven product decisions can be tricky. As a famous economist once mentioned “If you torture data long enough, it will confess to anything”. So, Objectivity and unbiased assessment of data is definitely the key.

Establishing clear data collection protocols can help defining specific metrics and ensuring consistency in data gathering methodologies enhances the reliability of the information collected. Employing data validation techniques, such as cross-referencing with multiple sources or running integrity checks should add an extra layer of assurance. It must be helpful to assess the relevance of the data to the specific product context, filtering out noise and focusing on key indicators. Advanced analytics and visualization tools can provide deeper insights, making it easier to identify patterns and outliers. Taking feedback loops with stakeholders ensures ongoing validation and refinement of data quality.

One often overlooked aspect is monitoring technology trends. While it's not a daily occurrence, in my experience a new ground breaking technology that is relevant to your product emerges every five years or so. This technology can either significantly enhance your product's capabilities or, conversely, render some of its features—or even the entire product—obsolete.

For effective product development decisions, it's crucial to gather comprehensive product requirement data. This data shouldn't just come from the top customers; it needs to encompass insights from all stakeholders, including internal users such as QA and external customers across various segments. A holistic approach to data collection ensures a more accurate representation of market needs and user expectations. It's also vital to perform a reality check, assessing the alignment between product requirements and the latest technologies. Are these requirements realistically supported by cutting-edge technologies? This evaluation is key for informed decision-making in product development, fostering innovation, and meeting diverse user needs.

In addition to the data sources for product development decisions mentioned above, here are other valuable inputs that can inform the product development process: - Competitor Analysis: Understanding what competitors are up to, their wins, and areas where they fall short can guide our product strategy. - User Analytics: Going beyond behavior, a deep dive into user analytics, including engagement metrics, helps us identify features that resonate most with our users. - Conducting targeted customer surveys and interviews gives us specific insights into customer needs, preferences, and challenges. - Industry Reports and Studies give us a broader view, keeping us in the loop about trends and emerging technologies.

Ultimately, integrating these diverse data streams in a synergistic manner empowers product managers to navigate the development landscape with clarity and precision.

While all the suggested data sources for product development decisions are valuable, it's crucial to first understand the specific context of your product. In my experience, the type of product, its goals, and the platform it's built on significantly influence which data sources are most relevant. For instance, if I'm working on a mobile app, user behavior data from in-app analytics becomes a key source of insights. Similarly, for a B2B software product, direct customer feedback and market research might hold more weight. It's about asking the right questions: What are we trying to achieve with our product? Who is our target audience? How do they interact with our product?