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Find mental models and ideation strategies for product management (for data) and data-driven decision-making. Explore product thinking in terms of MVPs, defining success metrics, prioritizing what's most important, and thinking about value v/s cost.
Defining the solution within the context of bringing product thinking to data involves a comprehensive understanding of the problem space and leveraging data-driven insights to craft effective solutions.
Transitioning from a problem-focused to a solution-focused mindset within the realm of data-driven decision-making involves a shift in perspective and approach. The problem-focused mindset traditionally revolves around identifying and analyzing problems or challenges within the data landscape. It involves a thorough examination of existing issues, their causes, and their impact on organizational goals or processes. This video explains the concept beautifully.
Embracing a solution-focused mindset entails a shift from solely dwelling on problems to actively seeking solutions. Rather than lingering on the intricacies of problems, this approach emphasizes exploring potential pathways and strategies to address these challenges using available data resources. Leveraging available data resources becomes pivotal in this transition. Instead of merely diagnosing issues, data becomes a strategic asset utilized to formulate actionable solutions. It involves exploring patterns, trends, and insights within the data that can be harnessed to drive innovative solutions.
Solution-focused thinking encourages a proactive approach to problem-solving. It involves anticipating challenges and envisaging potential solutions beforehand, based on data-driven insights. This proactive stance empowers decision-makers to be more prepared and agile in addressing emerging issues. Shifting focus towards solutions fosters collaboration and creativity within teams. It encourages a diverse range of perspectives and ideas in brainstorming and devising innovative solutions backed by data insights.
Embracing a solution-focused mindset often involves an iterative process. Solutions may evolve and refine based on continuous feedback, learning from trial and error, and refining approaches based on evolving data insights.
By adopting a solution-focused mindset, organizations can pivot from mere problem identification towards actively using data-driven insights to generate and implement effective solutions. This approach not only streamlines decision-making but also fosters a culture of innovation and agility, enabling organizations to stay adaptable and responsive in dynamic environments.
Building consensus among executives can be one of the most challenging aspects of a product manager’s job. Part of the solution is to get an upfront agreement around your organization’s strategic goals and be transparent about your prioritization process. It’s important to ensure all parties in your organization are on the same page.
As a product manager who now sits on the other side of the table as an executive, We understand how much thought (and possibly anxiety) goes into road mapping and roadmap presentations.
Road-maps culminate a lot of work, customer conversations, and experience. Product managers' most common mistake in road mapping is assuming they know exactly what to build without building consensus. Of course, you are the customer expert, but leading with the assumption that you alone know the ideal priorities has consequences that might sabotage your well-intended roadmap.
Have informal discussions with executives and other stakeholders before the executive planning meeting. That way, you’ll present a roadmap with the right priorities and align those to its business goals. It will help you to have a seamless review and approval process.
Flexibility is a friend of the essentialist product manager. If you must create a roadmap with delivery dates, keep broad time-frames such as quarterly. Things are always more complicated and take longer than you and your team think. It will help your cause if you educate executives over time to know that you can only estimate fuzzy delivery dates and that priorities will undoubtedly shift.
Another characteristic of an essentialist product manager is the preparation of the roadmap. This preparation comes in many forms, such as reviewing prototypes and concepts with customers early, working with the team to groom features well ahead of the sprint, and well-organized stories ahead of sprint planning meetings. In contrast, a non-essentialist product manager will work on these things at the last minute and hope for the best.
Using a visual roadmap to connect the roadmap to the strategy behind it is helpful. It ties the roadmap initiatives to actual customer value, business goals, and meeting real needs. Executives have a lot on their plates and generally try to stay as high level as possible.
Don’t be afraid to acknowledge that there are unknowns. Communicate your level of certainty for each initiative during the presentation. Of course, the further you go, the less certain things are for everyone. You can mitigate this by creating road-maps that don’t extend as far into the future.
Essentialist-oriented product managers work with their teams to create Key Performance Indicators (KPIs) for setting business goals and measurable outcomes. KPIs are often used for quarterly planning, so they are much shorter time-frames and more measurable than a product vision.
For effective utilization, KPIs must directly align with overarching strategic goals, serving as quantifiable measures that reflect progress and success. Measurability and relevance are imperative—KPIs should be clearly measurable, offering insights specific to the activities being assessed, enabling informed decision-making and strategies for improvement.
These measurable objectives keep employees and stakeholders on the same page. They’re not set in stone. The objectives are reevaluated and adjusted regularly to ensure organizational alignment. KPIs are ambitious direction setters, singularly focused on the company’s ideal destination.
Moreover, actionable insights derived from KPI analysis prompt proactive decision-making, identifying trends and areas needing attention. Continuous monitoring ensures ongoing alignment with objectives, allowing for timely adjustments in strategies. Transparent communication of KPIs across the organization fosters shared understanding and accountability, encouraging collective efforts toward achieving set targets.
KPIs foster a discussion among stakeholders and help us agree on what’s important—this creates alignment. They also serve as inspiring goals for employees, keeping them excited. And, of course, the key results are measurable ways the business or product is improving. This adaptability ensures their continual relevance and effectiveness. Ultimately, incentivizing actions aligned with these metrics cultivates a culture of performance-driven excellence, leveraging data to drive actions that propel the organization toward its defined goals.
A minimum product is an opposite of the “it’s all-important” mentality. But please don’t confuse the term “minimum” with essentialism.
Product teams often misunderstand the concept of an MVP, and entrepreneurs and product teams misinterpret it with unfortunate results. Those that take the concept of “minimum” at face value risk releasing a thin set of features that may get their product to market quickly yet deliver a poor customer experience and ultimately fail.
Thinking in terms of Minimum Viable Product (MVP) involves a strategic approach to product development and data utilization. The concept revolves around creating a basic version of a product that delivers core functionalities, allowing for rapid testing and validation of assumptions. In the context of data-driven initiatives, applying the MVP philosophy means focusing on the essential data components necessary to test hypotheses or validate concepts.
Sure, it reduces a product’s scope to get it into customers’ hands faster. But the MVP must also be a set of features that provides customer value and delight. Important: You need enough customer value/delight that it stands out from alternative solutions. And enough amount that a customer is willing to pay for it (or use it).
“Perfect is the enemy of good”
Your product needs to solve a real problem, and you can often do that with 80% of the features you believe provide value. In my experience, you never want to shoot for 100% of the features because getting to 100% presumes you know the right thing to build in the first place (you don’t).
A great example of this mindset was a team that launched the online meeting software GoToMeeting. The team concluded they could market their MVP with half the features. Yet that wasn’t enough to have a product that stood out from a vast field of online meeting competitors. Their customer discovery interviews teach us that we can differentiate our product with several innovative “features.”
First, they made it the easiest-to-use product on the market. They also introduced what was, at the time, an innovative all-you-can-use monthly subscription model that disrupted the market. MVP begins with customer discovery—deep learning about customer problems. You then define a core set of issues you want to solve.
By embracing an MVP mindset in data utilization, organizations can efficiently allocate resources to develop and launch initial data-driven solutions. This approach emphasizes the rapid deployment of minimum functionalities required to gather feedback and validate the viability of ideas. In essence, it streamlines the data exploration process, prioritizing the most critical data elements needed to derive meaningful insights or validate hypotheses.
Moreover, the MVP strategy encourages an iterative approach to data utilization. Rather than aiming for a comprehensive and elaborate data solution from the outset, it advocates for incremental improvements and iterations based on user feedback and evolving requirements. This iterative cycle allows for constant refinement, enabling organizations to adapt to changing data landscapes and emerging insights efficiently.
Defining success metrics within the realm of data-driven initiatives is pivotal for gauging progress, measuring performance, and aligning efforts with overarching organizational objectives. Success metrics serve as quantifiable indicators that reflect whether data-driven initiatives are effectively contributing to desired outcomes and goals.
Primarily, success metrics need to be aligned with the specific objectives of data-driven endeavors. These metrics should directly reflect the intended impact and align with the broader organizational goals, ensuring that they serve as meaningful benchmarks for evaluating progress and success. For instance, if the goal is to enhance customer satisfaction, success metrics might encompass metrics related to customer retention rates, Net Promoter Scores (NPS), or customer feedback sentiments derived from data analysis.
Moreover, success metrics should be actionable and measurable. They should offer tangible and quantifiable measurements that enable teams to track and assess progress effectively. Metrics that are easily measurable not only provide clarity but also enable teams to make data-backed decisions and adjustments in real-time based on the insights derived from these metrics.
These metrics should be dynamic and adaptable, capable of evolving alongside changing organizational priorities and market dynamics. The flexibility to refine and update these metrics allows for their continual relevance and alignment with shifting business landscapes. By regularly reviewing and updating success metrics, organizations ensure that they remain aligned with evolving objectives, fostering a more responsive and adaptive approach to leveraging data for decision-making and performance evaluation.
Prioritizing is a key part of a product manager’s day. You’re working with stakeholders to plan for the next two quarters. Prioritizing the product backlog. Prioritizing the development backlog. If you have non-essentialist habits, you’ll soon drown in all the decisions to prioritize.
The concepts of essentialism make sense - of course, we want to be working on the most important things. But how do we prioritize these things in the first place? Like many companies, ProductPlan has a vision, mission, strategic goals, and KPIs that guide them. When you’re evaluating an opportunity, these all come into play.
The process of prioritization starts with a clear understanding of organizational objectives. By aligning data-driven endeavors with overarching goals, it becomes easier to identify which initiatives or data-driven strategies directly contribute to achieving these objectives. It involves a critical evaluation of various tasks, initiatives, or data sets, assessing their potential impact on key performance indicators (KPIs) and organizational success.
Further, you can use various frameworks to evaluate what to work on - the methods abound, for example, a matrix that plots urgency with importance. For the products, we can use prioritization frameworks to cut out 80% of the feature requests that ultimately would be distracting and focus on the 20% that will make a big difference to our customers.
Adopting frameworks or methodologies such as the Eisenhower Matrix or Value vs. Complexity Matrix can aid in prioritization. These tools help in categorizing tasks or initiatives based on their importance and urgency, facilitating a more structured approach to decision-making. Ultimately, effective prioritization in data-driven initiatives ensures that efforts are concentrated on the most impactful endeavors, optimizing resource utilization and driving success aligned with organizational objectives.
Essentialist product managers have a mental model for looking at features and opportunities. Typically this model is based on customer value and its relative complexity to implement. Based on conversations with product managers, this is a common approach. Many product managers go through this assessment instinctively every day.
The matrix is simple: The initiatives with the highest value and the lowest effort will be the low-hanging fruit for your roadmap. These are the opportunities in the upper left of the quadrant pictured above (High Value, Low Effort). As an essentialist, you’ll want to include many of these opportunities on your roadmap. The opportunities in the lower right (Low Value, High Effort) you will probably never work on, so don’t spend any effort debating these.
Understanding the value proposition of data initiatives is crucial. It involves assessing the potential benefits, insights, and competitive advantages that data-driven decisions can provide to the organization. This evaluation encompasses the impact on revenue generation, operational efficiencies, customer satisfaction, or strategic positioning within the market landscape. Concurrently, weighing these potential benefits against the costs incurred in data collection, analysis, infrastructure, and personnel provides a comprehensive view of the value derived from these initiatives.
Optimizing the balance between value and cost requires a keen assessment of the return on investment (ROI) of data initiatives. This analysis involves quantifying the tangible and intangible benefits accrued from data-driven decisions against the expenses incurred. By measuring the direct impact on key performance indicators (KPIs), ROI calculation aids in determining the effectiveness of data investments and justifies the allocation of resources towards data-related activities.
Embracing a cost-effective approach doesn't necessarily equate to compromising value. It entails identifying cost-efficient methodologies, leveraging innovative technologies, and streamlining processes without compromising the quality or significance of derived insights. Striking the right balance between maximizing value and minimizing costs ensures that data-driven initiatives remain sustainable, impactful, and aligned with organizational objectives. Ultimately, it's about optimizing the value extracted from data while managing the associated costs efficiently, fostering a culture that prioritizes prudent resource allocation while maximizing the benefits derived from data-driven endeavors.
The Kano model is one way to think minimally about the features you add to your product. With the Kano model, product managers can look at potential features through the lens of the delight a feature provides customers versus the potential investment you make to improve the feature.
In this model, there are some basic features that your product needs to have for you to sell your product in the market. You need to have these “threshold” features, but continuing to invest in them won’t improve customer delight dramatically. Don’t spend much energy here.
Some features (like performance) give you a proportionate increase in customer satisfaction as you invest in them. You can continue to invest some energy and resources over time in this category.
Finally, there are some exciting features that you can invest in that will yield a disproportionate increase in customer delight. If you don’t have these features, customers might not even miss them; but if you include them and continue to invest in them, you will create dramatic customer delight. This might be the one area to test and support to achieve a significant result - the very definition of essentialism.
Delighter features don’t need to be paramount. They can be small, sometimes UI features that make a difference in the customer’s experience. These “small wins” are important for an essentialist product manager. They allow you and your team to celebrate progress and keep the team’s energy higher for the more significant, long-term projects—your customers also benefit.
In the case of ProductPlan, one of their early delighters was a visual drag-and-drop interface for building a roadmap. They spent a lot of time and engineering resources, making that 3-second experience of dragging an item onto the roadmap into delight. Ultimately, they created delight through the ease of use to create, collaborate, and share a roadmap with your team in minutes.
Important: If you cut features back to only the “must-haves,” you have a recipe for a weak product that doesn’t succeed. Like a non-essentialist, you would be scattering your energy in multiple places to achieve a limited result.
Think of these delighter features as the extra touch that surprises and captivates users. In the realm of data-driven initiatives, these could be innovative data visualizations, predictive analytics that offer unexpected insights, or personalized recommendations that anticipate user needs. By prioritizing these delighter features, organizations can elevate their offerings beyond mere functionality, creating a lasting impression and fostering user engagement.
Deep Dive
The Essentialist Product Manager
