What are the top methods for prioritizing ideas in customer-centric product development?

This is a great method, however the trick is correctly defining what value means. Value can have a different meaning based on things such as strategic objectives. As an example if your objective is to increase trial to paid conversion, you may focus on different items than if your objective is to decrease churn. Another item to consider when scoring the value is how to define low and high value and being able to differentiate between expected and materialized value.

Many very relevant models to map, score & prioritize development with highest customer attractiveness, the challenge lays in the way assumptions are made, how well is captured the voice of your customers and how much are you involving them, is your development really providing a "Unique Selling Point" meaning you have a clear picture on competition product benefits and misses.

🔍 Navigating the sea of ideas in customer-centric product development is an art! Like a compass, I'd start by deploying the Value vs Effort Matrix to find ideas that promise excellent value with minimal effort. Then, I'd use the RICE Scoring Model as my sextant, measuring ideas by their reach and impact. The Kano Model is like a spyglass, revealing customer delights in ideas. MoSCoW Method acts as my anchor, categorizing ideas for steady progress. Finally, I'd listen to the winds of user feedback, fine-tuning my course toward success. ⚖️🚀

I would recommend the following top methods for prioritizing ideas: 1. Customer Feedback and Data Analysis: Use data from customer feedback, surveys, and usage metrics to understand what customers value most. 2. Value vs. Effort Matrix: Plot ideas based on the value they provide to the customer against the effort required to implement them. This helps in identifying high-impact, low-effort features. 3. Vision Alignment: Align ideas with the strategic vision and overall business goals. In my experience, ensuring that each feature or idea contributes to the broader business objectives is vital product success. 4. Stakeholder Input: Incorporate input from diverse stakeholders, including marketing, sales, customer support, and engineering.

Impact vs. Effort Analysis: Evaluate each idea based on its potential impact on customer satisfaction, revenue, or other key performance indicators (KPIs), as well as the effort required for implementation. Prioritize ideas that have a high impact and require relatively less effort to implement.

Frameworks are really important for your PM entry level and mid level interviews. However when it comes to work it's the agility of the PM, hold on the business, rapport with stakeholders, vision around the product, systems in place (sometimes you have to create them), how you create a relationship with actual data - all these things matter and during all this the frameworks do work like a foundational structure in the back of your PM playbook

This method is of course good, but not enough. From my experience I can say, feeling is important for product manager, especially when we talk about customer needs. When there are many ideas, they can be measured in different ways, but product manager must have feeling, understand that the most successful is hidden somewhere in these ideas, figures can be hint together with feeling and feeling comes from experience.

In my experience, MoScoW has driven the most value when it came to 'customer-centric' development. However, it is also important to have the justifications in place while using this method and those come from historical data. So in come methods like Value vs Effort and Kano as a precursor. With a mix, you are on solid grounds to deliver tangible value.

The Value vs. Effort Matrix is a cornerstone in project prioritization, yet I’ve found it crucial to integrate a ‘stakeholder impact’ dimension to capture the full spectrum of value. In my experience, this addition aligns projects more closely with strategic objectives. However, it’s important to remain flexible, as over-reliance on any matrix can lead to oversight of dynamic market shifts. I advocate for periodic reviews of prioritization criteria to ensure they evolve with business needs. Curious to hear how others might have adapted this framework to their unique environments.

RICE is a very effective method of prioritizing but i have seen my fair share of shabby implementations of it without though put into the nuances of the product and the features being built. So the first step before using rice to prioritize is if the problem you are solving for cares about all elements of RICE (reach, impact and confidence, and effort) it might be beneficial to consider adding weights if any of these elements are more important to you than others. Now don't use arbitrary methods to while scoring weather use a standardized scale like for reach >80% can be 5, 60-80% can be 4, 40-60% can be 3 and so on. Similarly score it all very scientifically and consistently for all features/ epics/ stories and then calculate RIC/E score.

RICE is a definitely one of the best framework available to prioritize the ideas for customer centric products. Since it it a combination of data driven quantitative metrics like Reach, effort and mixed metrics (data driven + estimated) like impact and confidence it serves as a better measure for prioritizing ideas. There are no universal weights for R, I, C, E in RICE framework, these weights of RICE vary from product to product or sometimes even company to company (offering similar products) depending on their product strategy. When combining RICE with organization strategy one can add additional layer of weights on top of the prioritised ideas and arrive at an optimistic priority list that aligns with the organisation goals.

This model decreasing risk of ideas and as a result we have ideas starting from low risk to high, lower one has higher score and the opposite higher has low score, seems decision can be made easily, but we understand, low risk has low profit and high risk has high profit. First one is safe development and another is fast development.

Imagine you're a tech wizard deciding between two features: Personalized Onboarding Tutorial (Idea A): Touches all new users, leaves them delighted. Based on solid user feedback and testing. Requires a moderate amount of effort. AI-Driven Content Recommendation (Idea B): Targets a specific audience for personalized content. Promises significant impact but uncertain outcomes. Demands a high investment in development and AI training. Result: Idea A takes the spotlight with a smoother blend of impact, reach, confidence, and effort, making it a priority without diving into the numerical complexities.

If we are to consider the input parameters of this model as intuitive, it is better to use other qualitative models such as MoSCoW. Otherwise, to use this method, we need a detailed data foundation to calculate the input parameters correctly.

RICE is the most effective way for Prioritisation because it is transparent and data backed. While building any customer centric product the natural emotion is to solve all the customer problems at once. However, it is not possible to do so. This is where RICE comes into play as it gives a clear idea to solve first the problem that caters to most customers, and requires less time to do so. This data backed precedence ensures timely delivery of product keeping customers in play and satisfied.

RICE is one of the best methods available out there. Be wary however of the common understanding and definition of what numerical values in RICE scores mean for different stakeholders. We all work and interact differently with numbers. In my projects we often go for the Fibonacci scale when estimating and scoring priorities, with the number growing with every new uncertainty you add. However it's not intuitive and we only use that with appropriate training and alignment on how it's applied.

RICE is a robust framework, enhancing decision-making by quantifying the intangible. I’ve found it especially effective when coupled with market trends analysis to adjust the ‘Confidence’ factor, ensuring real-time relevancy. However, the model could be refined by factoring in the opportunity cost, which adds depth to the ‘Effort’ assessment. Adaptability is key; hence, recalibrating the model periodically to fit the evolving business landscape is beneficial. Keen to learn if others have integrated additional metrics to further tailor the RICE model’s effectiveness.

In building edtech products for children, achieving the right balance between educational value and fun is crucial. The Kano model provides a structured approach to prioritize features, especially important in preventing teams from overwhelming with ideas. Categorized into basic, performance, excitement, indifferent, and reverse, this model helps teams focus on essential features while avoiding potential drawbacks. In the context of children's products, particularly in edtech, where maintaining excitement is vital, the Kano model proves to be a strategic tool. By prioritizing basic, performance, and excitement features and avoiding or eliminating indifferent or reverse ones, teams can create engaging & enriching experiences for young users.

In app development, the Kano model advises focusing on basics like a smooth UI, attention-grabbers like fast search, and delightful surprises such as AI-powered personal assistants. Avoid or address features users don't care about (indifferent) or dislike (reverse), like intrusive ads. It's a guide for crafting user-centric apps.

The Kano Model excels at identifying features that delight customers, but it’s essential to consider the evolving nature of consumer expectations. What begins as an excitement feature can quickly become a basic one as market standards change. Therefore, continuous customer feedback is pivotal. Moreover, the model should be dynamically adapted to anticipate shifts in customer sentiment, ensuring that today’s innovations do not become tomorrow’s disappointments. It would be interesting to explore how others maintain this adaptability in fast-paced industries.

In my branding journey, the Kano Model has been a guiding star. It's not just about features, but navigating customer emotions. Basics are the bedrock of satisfaction; performance features are where we shine. But the real magic? Excitement features - those unexpected delights turning customers into advocates. Learning to sidestep indifferent and reverse features is crucial. Using Kano isn't about piling on features, it's crafting experiences that resonate emotionally.

Kano model is very useful when you need to think what is the need of the hour in your product from the user POV. For my case (matchmaking app), we mostly end up focusing on ease of use, navigations & amount of information.

The MoSCoW method serves as a practical guide for urgent project triage, but it should be complemented with a feedback loop to reassess priorities over time. While categorization is initially clear-cut, the ‘Should have’ and ‘Could have’ features often need reevaluation in response to changing user needs or market conditions. This dynamic prioritization ensures that the product evolves without straying from its core objectives. I’m interested in how others incorporate ongoing market feedback into the MoSCoW framework to stay agile and user-focused.

MoSCoW in my experience lends itself well to the question - “customer centric”. One of the biggest problems in building a product is when you go to market, customers say “that’s not what we want” or worse “it’s the same as XYZ”. I argue use MoSCoW to create an initial blueprint and then refine based on further layers such as available budgets etc

MoSCoW is usually the second layer in prioritisation. You need to have a solid footing in the reasoning of why different features and enhancements are added to any of the buckets. And that reasoning comes from now data driven prioritisation methods like RICE or Kano or value v effort.

Imagine the MoSCoW Method as the stern but fair sheriff in the idea town. It sorts requests into categories like "must-have for happy customers" ("Musts"), "nice-to-have for smooth sailing" ("Shoulds"), and "fun extras if time allows" ("Coulds"). MoSCoW understands the human language of need. It protects core functionalities, prioritizes customer satisfaction, and lets teams dream big without losing sight of reality. It's the voice of reason, ensuring resources are channeled towards building a product that truly hits the target.

Having navigated the intricate world of marketing and brand development, I've come to appreciate the MoSCoW method as a lighthouse guiding our project journeys. It's about discerning the 'Must haves' - those critical elements without which our brand story falls apart. Then there are the 'Should haves', pivotal in meeting customer expectations. 'Could haves' offer that extra flair, but it's the wisdom to know when they tip into the 'Won't haves' - those overcomplications that dilute our core message. This method isn't just about categorizing; it's about strategic focus, ensuring every decision aligns with our ultimate vision and purpose.

Moscow gives a simple structure for selecting features and ensuring clarity on critical elements. It works best when one incorporates adaptive planning, allowing for modifications as priorities shift, and identifies essential requirements early on, reducing project risks. It definitely helps to control scope creep when team members get diverted to other tasks.

In a recent project, we prioritized ideas by integrating A/B testing results directly with user feedback. This approach provided both quantitative and qualitative insights, allowing us to validate hypotheses with actual user behavior. It was a game-changer in aligning our product development with genuine user needs and preferences.

Customer Feedback and Surveys: Collect feedback from your customers through surveys, interviews, or user testing sessions. Analyze their input to identify common pain points, needs, and desires. Prioritize ideas that address these customer insights and align with their priorities.

User feedback is crucial in product management, as it tracks changing needs and preferences. A pertinent example is Tesla's Cybertruck. Initially met with skepticism due to its unconventional design, changing attitudes towards electric vehicles and innovative design have seen a shift in consumer interest. This shift underscores the importance of continuously gathering and adapting to user feedback, reflecting evolving market dynamics.

Mature products have various ways of identifying problem areas, such as ad-hoc feedback mechanisms and telemetry that is turned into insights. Selecting the right focus area can also come from analyzing this quantitative data and finding the areas with the highest dissatisfaction, as reported by users. This is a good way of turning customers that are about to churn into real promoters. In B2B environments, I have also found it helpful to engage the dissatisfied clients, inform them that their dissatisfaction has been noted and invite them to interviews to dig deeper into the problem. This action on its own will guarantee an increase in their satisfaction.

User feedback and testing are the detectives in the precinct of prioritizing customer-centric product ideas.  They gather insights through interviews, surveys, and experiments, sniffing out the features that matter most to users. By measuring pain points, engagement, and potential impact, they build a case for each idea, making way for the most valuable suspects to rise to the top. In short, they're the evidence that turns gut feelings into data-driven decisions, ensuring your product evolves with, not against, your customers.  No wonder AI can't quite crack their code - it's the human touch that makes all the difference!

This is also one of the most powerful methods. Before doing that a thorough brand persona and user persona to be created ( canva will help in doing this ) and then the user research to be conducted thoroughly that complements that brand persona. The feedback received from the user research along with the assumptions created to be evaluated the usability, desirability as well as viability . These ideas will greatly increase the efficiency of any app or website and the insights will help in new customer acquisition and also retaining existing customers pools. The way the book my show has utilised its user feedback into actionable measures on their apps is phenomenal.

This is most crucial for any major project. Feedback should be taken once the design prototype is ready & a floor testing should be done. There you understand that what are the points that you might have missed. Feedback is important from someone who is not involved in the project, which can provide a different perspective. User testing should be done once the product prototype is ready, you would get answer to questions like - What they liked? What could have been better & What is missing?

I find that when a product has a clear vision, strategy, and success metrics, prioritization becomes easy. So if you're struggling to prioritize and your teams are disagreeing on what is most important, it's a sign you need to realign to the overall goal for our customer and our business.

In my experience where the business model is D2C, prioritization of tasks is a series of steps, where the first step is creating an idea bank from all the stakeholders, next is dropping out those which are not required, the next step is dividing all the ideas into different categories such as monetization, engagement, acquisition & so on, the next step is labeling the impact & effort. Then picking those tasks where the objective is practical & achievable. While mentioning the efforts you need to discuss with all the teams (design, dev, qa) & plan accordingly.

My work in aquaculture is driven by a commitment to eco-friendly practices. Guided by environmental guidelines, I focus on protecting natural habitats and efficiently using water resources. The adoption of smart water systems has notably improved our seafood quality and operational efficiency.

Another framework to consider when prioritizing features is the opportunity scorecard. This is a customer needs-centric approach. If one can visualize a feature, or set of features that help a customer perform a certain job, the feature can be scored based on how important it is to the customer and how currently available features satisfy the customer's needs in performing the same "job". Anthony Ulwick, describes this approach in his book, What Customers Want. It's a great framework to reduce risk and have validation that the features being prioritized are going to delight the customer. I find this framework important in the innovation process in identifying new features after a comprehensive journey-mapping exercise is completed.

Most important factor to consider is what are we trying to improve in short term and long term, we always have certain metric to improve in short term and we are doing changes in Product to improve that metric basis our understanding of customer needs/wants. In the long term we are trying to make these sprints become a marathon. Secondly we must be careful in what will be negative effects of whatever change we are making often in trying to unlock a new user segment we might end up alienating current fans, sometimes in trying to add more features makes it more complex for novice users, sometimes change leads to more operations burden on tech or operations teams (often we are blinded by such things)

I'm not sure if this list truly makes sense - I do get how AI thinks it will be good clickbait. In my view, the specific method you choose—be it Moscow, Kano, or the classic Value*Effort—holds less significance; each has its own merits. I believe that the real determinant of a high-quality customer-centric backlog lies in the fifth method: User testing & feedback. The excellence of your customer research, encompassing both quantitative and qualitative aspects, and how effectively that knowledge permeates the team. Instead of overly fixating on the prioritization method, invest your time in engaging with users, disseminating that knowledge, and assembling a multidisciplinary team with a customer-centricmindset for the prioritization process.

Before we prioritise ideas, we should ensure that we have a set process to capture them as well. The biggest learning in my experience till now, where ideas are unable to get prioritised effectively is because they get created randomly in silos. As we record them in a place and let them accumulate, it gets better to visit them regularly and plan which ones to pick up, instead of evaluating an idea individually as we have it.

Effective prioritization is crucial in steering projects toward success in product development. Each of these frameworks brings a unique perspective to prioritizing product features and fostering a customer-centric and strategic approach to development. You can use them all at different times, but you may not even have the opportunity to do so. The important thing is to analyze the existing data and decide which is the best option in your context.

In real-world scenarios, many theoretical frameworks are challenging to follow or simply don't work. A prudent way to analyze customer-centric product development with prioritization is finding themes of value that work for most businesses: • Customer Satisfaction: How much CSAT/NPS it can improve • Bottom line: How much profit (positive contribution margin) it can add • Efficiency: How much time/cost it can save • Top line: How much revenue it can generate Call it the CBET framework or not, but a practical approach to building products works more efficiently and is easy to follow, making it adoptable by organizations of any size and industry.

In customer-centric product development, prioritizing ideas involves several methods: 1. Customer Feedback Analysis: Review and prioritize ideas based on customer feedback and pain points gathered through surveys, interviews, or support channels. 2. Impact-Effort Matrix: Plotting ideas on a matrix based on their potential impact and implementation effort helps prioritize those with high impact and manageable effort. 3. Value vs. Complexity: Prioritize ideas by balancing their perceived value to customers against the complexity or resources needed for implementation. 4. MoSCoW Method: Categorize ideas into Must-Have, Should-Have, Could-Have, and Won't-Have to distinguish essential features from those that can be deferred.