Q&AI: Data Sources for Product Decisions
Adapted from https://www.linkedin.com/advice/0/what-best-data-sources-product-development-decisions-etzne
More about our Q&AI series: https://www.productteacher.com/articles/new-series-q-ai
Understanding where to source valuable data for product decisions is crucial for product managers, as it empowers them to make informed choices that drive product success.
Below, we’ll delve into the diverse data sources available to product managers, equipping them with the knowledge to optimize their decision-making processes and enhance their products.
LinkedIn’s question on customer feedback
Customer feedback is one of the most valuable data sources for product development decisions because it tells you what your customers think, feel, and want from your product. You can collect customer feedback from various channels, such as surveys, interviews, reviews, ratings, support tickets, social media, forums, and more. You can use customer feedback to identify customer problems, needs, preferences, expectations, and satisfaction levels. You can also use customer feedback to test your hypotheses, validate your solutions, and iterate on your product features.
Clement, what are your thoughts here?
Clement’s response on customer feedback
Beyond just passively collecting feedback, it's crucial to actively engage in empathetic conversations with your customers.
One-on-one discussions can unveil the underlying context behind their feedback. In my experience, understanding the "why" behind their thoughts and feelings can lead to more nuanced and effective product improvements.
On top of that, I personally believe that using too much structure is counterproductive. We want to give our customers the space to vent, complain, compare, ideate, and more! Putting people into a "standard box" for their responses tends not to be a good way to yield insight.
So, remember, it's not just about data; it's about genuine conversations that reveal the hidden gems of user insights.
LinkedIn’s question on user behavior
User behavior is another important data source for product development decisions because it shows you how your customers use your product, what actions they take, what goals they achieve, and what obstacles they face. You can collect user behavior data from various tools, such as analytics, heatmaps, session recordings, A/B testing, and more. You can use user behavior data to understand customer journeys, patterns, segments, and retention rates. You can also use user behavior data to optimize your user interface, user experience, and user value proposition.
Clement, what are your thoughts here?
Clement’s response on user behavior
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.
LinkedIn’s question on market research
Market research is a useful data source for product development decisions because it helps you understand your target market, your competitors, your industry trends, and your market opportunities. You can collect market research data from various sources, such as reports, studies, articles, podcasts, webinars, events, and more. You can use market research data to define your target market, your market size, your market share, and your market positioning. You can also use market research data to benchmark your product performance, identify your competitive advantages, and discover new market needs.
Clement, what are your thoughts here?
Clement’s response on market research
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.
LinkedIn’s question on product performance
Product performance is a critical data source for product development decisions because it measures the quality, reliability, and efficiency of your product. You can collect product performance data from various metrics, such as uptime, speed, errors, bugs, crashes, and more. You can use product performance data to monitor your product health, identify your product issues, and improve your product stability. You can also use product performance data to set your product goals, track your product progress, and evaluate your product outcomes.
Clement, what are your thoughts here?
Clement’s response on product performance
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!
LinkedIn’s question on team feedback
Team feedback is a helpful data source for product development decisions because it reflects the opinions, insights, and suggestions of your product team members. You can collect team feedback from various methods, such as meetings, workshops, brainstorming sessions, retrospectives, and more. You can use team feedback to foster collaboration, communication, and alignment among your product team members. You can also use team feedback to generate ideas, solve problems, and learn from mistakes.
Clement, what are your thoughts here?
Clement’s response on team feedback
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!
LinkedIn’s question on data quality
Data quality is a key factor for product development decisions because it affects the accuracy, relevance, and timeliness of your data sources. You can ensure data quality by following some best practices, such as defining your data goals, choosing your data sources, collecting your data ethically, cleaning your data regularly, analyzing your data objectively, and presenting your data clearly. You can improve data quality by using some techniques, such as data validation, data integration, data visualization, and data storytelling.
Clement, what are your thoughts here?
Clement’s response on data quality
Beyond the technical aspects of data quality, remember that data quality also hinges on human factors.
After all, people who collect, manage, and interpret data are at the heart of data quality! Creating a culture of data responsibility and accountability within your team is just as important as the technical aspects.
While data validation matters, fostering a data-conscious mindset among your team members is equally essential for top-notch data quality.
Clement’s other thoughts
When it comes to data, remember that quantity doesn't always equal quality! While we have access to a wealth of information, the challenge lies in identifying what truly matters.
Rather than drowning in a sea of data, focus on the critical few that drive actionable insights.
Quality trumps quantity when it comes to making informed product decisions!
