Feature Prioritization for Startup Software Projects: MoSCoW, Kano, and Lean MVP Approaches

Introduction

In the fast-paced world of tech startups, success often hinges on delivering a Minimum Viable Product (MVP) that meets both user needs and business goals. However, with limited resources and tight timelines, deciding which features to build first can be challenging. Every feature seems important in a custom software project budget, but trying to build everything at once can lead to bloated products, missed deadlines, or wasted effort. This is where feature prioritization frameworks come into play. By systematically ranking features, startups can focus on the highest-impact functionality and deliver value early.

Proven frameworks like the MoSCoW method, the Kano model, and lean startup principles offer structured approaches to decide what goes into an MVP and what can wait. These techniques help teams separate the “must-haves” from the “nice-to-haves,” ensuring that the first product iteration is both lean and impactful. Seasoned product experts and startup advisors such as Empyreal Infotech, a custom software development firm in Wembley, London, led by CEO Mohit Ramani (also co-founder of top webflow agency Blushush and Ohh My Brand)  frequently leverage these frameworks to guide startups toward high-impact MVP features. This comprehensive guide will dive into MoSCoW, Kano, and lean approaches to feature prioritization, illustrating how each framework works and when to use them, with tips on combining their strengths to maximize your startup’s chances of success in the marketplace. 

Why Prioritization Matters for Startup MVPs

All requirements may feel important in building custom software , but to deliver the greatest and most immediate business benefits early, features must be prioritized. For startups, this is especially critical. New ventures typically operate under constraints of limited budget, small teams, and a ticking clock to prove their product’s value proposition. An MVP, by definition, is a stripped-down version of the product with just enough features to attract early users and validate the concept. The challenge is determining which features are essential for the MVP and which can be deferred. Prioritization ensures that the development team focuses on features that offer maximum value or learning first, rather than getting side tracked by bells and whistles. 

Effective prioritization helps in several ways: 

• Optimal Resource Use: Startups have to make the most of every developer hour and dollar. A prioritization framework helps determine the best use of these limited resources , channeling effort into features that yield the highest return  whether in user satisfaction, revenue potential, or validated learning.
• Faster Time-to-Market: By cutting non-essential functionality, an MVP can be released sooner to start gathering feedback. Releasing a product quickly allows startups to test their idea with real users before committing to full-scale development.
• Alignment with Goals: A clear prioritization method aligns the team and stakeholders on what the product is trying to achieve in its first iteration.It ties feature choices to business goals (e.g. hitting a target number of users or proving a market need), preventing scope creep and keeping everyone focused. 
• Customer-Centric Development: Prioritization forces tough decisions about what customers truly need versus what might be “nice to have.” Frameworks that incorporate customer value (like Kano) ensure the MVP addresses core user expectations and pain points first, which increases the chances of user adoption and satisfaction. 
• Risk Mitigation: Every feature added is an investment and a risk. By prioritizing, startups validate fundamental assumptions early on. They can see if the core features resonate with users or if adjustments are needed  without having sunk costs into secondary features. This lean approach avoids building a full product that might miss the mark.

In summary, feature prioritization is not about denying the importance of certain ideas, it’s about sequencing development to deliver value early and learn quickly. As we explore the frameworks below, remember that the end goal of any prioritization exercise is a focused MVP that provides a solid foundation for future growth while conserving precious startup resources. 

The MoSCoW Method: Must, Should, Could, Won’t 

One of the simplest and most popular prioritization techniques for defining an MVP feature set is the MoSCoW method. MoSCoW is an acronym that stands for four priority categories: Must-have, Should have, Could-have, and Won’t-have (or “Will not have right now”).Each feature or requirement is placed into one of these buckets, which helps teams clearly communicate what is essential for the first release and what is not. 

MoSCoW categorization helps teams divide features into must-haves, should-haves, could-haves, and won’t-haves, ensuring everyone understands what will be included in the MVP and what will be deferred. In the example diagram above, Must-haves are highlighted as non-negotiable items for the first release, while Won’t-haves clearly indicate which ideas are out of scope for now. Such visual prioritization aids in stakeholder alignment and scope control.

Must-have (M): These are mission-critical features without which the product cannot function or would fail to meet its core user needs. In other words, if even one “must-have” feature is missing, the MVP would be considered a failure. Must-haves often represent the core value proposition of your product. For example, if you are building a rideshare app, the ability for users to request a ride and for drivers to accept those requests would undoubtedly be “must-have” features. A good litmus test is to ask: Can the user still accomplish their primary goal without this feature? If the answer is “no,” it’s a must-have. These form the foundation of your MVP  they are the features that absolutely must be implemented for the product to be

viable.(In fact, “MUST” is sometimes said to stand for “Minimum Usable Subset,” highlighting that it must have defined the minimum product you can launch.)

Should-have (S): These features are important and add significant value, but they are not absolutely critical for the first release. A product with all “musts” but missing a particular “should” can still operate, though perhaps less effectively or with a lower user satisfaction. Should-haves are often the second priority; they are the next things to include once the must-haves are secure, or they might be included in the MVP if resources allow. Using the rideshare example, a “should-have” might be a feature like viewing detailed driver profiles or the ability for users to save favorite locations. The app can function without these in version 1, but including them would certainly improve the user experience. If needed, should-have items can be scheduled for a future update without impacting the core functionality.The key difference from a must-have is that not having a should-have in the MVP won’t make the product worthless; it will still work, just with a bit less convenience or polish.

Could-have (C): These are the “nice to have” features  enhancements that are not essential to the core operation of the product. Could-haves have a relatively small impact on the overall outcome if left out.They are often the first to be dropped if the schedule or budget is tight. Could-have features can differentiate a product or make it more fun, but a lean MVP usually excludes them. For our rideshare app, a could-have might be something like a built-in social sharing of your trip or a dark mode for the user interface. Those features might be appreciated by some users, but they won’t make or break the basic utility of the app. In practice, teams will implement could-haves only after musts and shoulds are done and if there is extra time or resources. Keeping track of could-have ideas is still useful (they often populate the future backlog), but acknowledging they are lower priority protects the team from trying to “gold-plate” the MVP with unnecessary extras. 

Won’t-have (W): Sometimes called “Will not have this time,” these are features explicitly decided to be out of scope for the current release.Declaring certain features as Won’t-have is a way of managing scope and stakeholder expectations. It doesn’t mean these features will never be built, but rather that they won’t be considered until after the MVP or in much later phases. For example, support for a completely different user type or a complex analytics dashboard might be put in the Won’t-have category for a startup’s initial launch. By placing features in Won’t-have, the team sends a clear signal: “We acknowledge this idea, but we all agree it’s not a priority for now.” This can prevent scope creep and help the team avoid getting distracted by long-term ideas when trying to nail down the MVP.Some teams even sub-divide the Won’t have list into “not in MVP but later” versus “won’t have ever,” just to differentiate future roadmap candidates from those likely to be dropped entirely.

How MoSCoW Prioritization Guides MVP Development: By categorizing features in this way, MoSCoW provides a structured language for the team to discuss priorities. It forces discipline: the number of Must haves should be kept to only the truly indispensable elements. This method was originally developed for time-boxed agile projects, precisely to ensure teams focus on what’s most important first.For startups, MoSCoW can essentially define the MVP  all the Must-have features collectively are your MVP, the Should-haves might make it in if you have extra capacity, and Could-haves and Won’t-haves will be left for later iterations. In fact, using MoSCoW naturally ensures the team is building a minimum viable product rather than an overstuffed first version.

One of the strengths of MoSCoW is its simplicity. The categories are easy for anyone to understand  including non-technical founders or stakeholders  because they use plain language rather than numeric scores or complex formulas. This makes it a collaborative and transparent prioritization tool. Stakeholders can immediately grasp why a feature is labeled “Must” versus “Could.” It also helps resolve disputes objectively: if someone argues that a feature is critical, MoSCoW forces the question “Does the product fail without it?” If not, it probably isn’t a must-have and belongs in a lower category. In this way, MoSCoW can bring consensus and defuse arguments. Project managers note that MoSCoW is easy to implement and handy for settling disagreements with stakeholders about scope. It ensures the team and clients build what’s absolutely needed first before worrying about extras. 

However, teams must use MoSCoW wisely. A common pitfall is overloading the Must-have category. If half of your feature list ends up as “musts,” you haven’t truly prioritized at all; you’re just calling everything important. Everything cannot be top priority. It’s important to be ruthless and limit the must-haves to the smallest set of features without which the product cannot function. As one product management guide warns, it’s easy to end up with too many must-have features if you’re not strict.To avoid this, some teams establish a rough quota or resource cap for each category (for example, only 60% of the effort can go to Must-haves, 20% to Should-haves, etc.).While the exact numbers can vary, the point is to enforce trade-offs and keep the MVP truly minimal. Additionally, clear criteria should be defined upfront for what qualifies as a Must vs. Should, aligned with the product’s objectives. By agreeing on those criteria early, perhaps in a kickoff meeting with stakeholders, you can prevent every pet feature from being tagged as “must” and keep subjective bias in check.

In summary, the MoSCoW framework offers a straightforward way to trim a product idea down to a viable first release. By classifying features into Must, Should, Could, and Won’t, startup teams can communicate priorities clearly and make tough decisions about scope. This ensures that the initial product focuses on delivering the minimum usable subset needed to start testing the market. Many teams find MoSCoW helpful for maintaining focus and avoiding scope creep.When used diligently, it results in an MVP blueprint for startup that is achievable within constraints and aligned with strategic goals  setting the stage for iterative improvements in later versions. 

The Kano Model: Prioritizing by Customer Satisfaction

Where MoSCoW focuses on internal priorities and business necessities, the Kano model takes an explicitly customer-centric view of feature prioritization. Developed by Japanese researcher Noriaki Kano in the 1980s, the Kano Model is a framework for understanding how different features of a product or service impact customer satisfaction.Product teams use this model to categorize features based on the degree to which they will satisfy (or delight) customers, versus the cost or effort to implement them. The idea is that not all features are equal in the eyes of the user; some features will simply meet basic expectations, some will improve satisfaction in a linear way, and some can truly excite and delight users. By understanding these differences, a startup can prioritize features that maximize customer happiness and loyalty, which is key to gaining traction in a competitive market. At its core, the Kano model evaluates features along two dimensions:

• The presence and degree of implementation of the feature. (Is the feature absent, partially implemented, or fully implemented?)
• The level of customer satisfaction that results from that feature being present or absent. Using these dimensions, Kano defines several categories of features:
• Basic Features (Threshold or Must-Be Quality): These are the fundamental features that  customers simply expect your product to have. If basic features are missing, customers will be dissatisfied; but if they are present and working, customers barely notice them (they take them for granted).In other words, basic features are the “musts” in terms of customer expectations; having them doesn’t especially increase satisfaction, but not having them will definitely cause frustration. For example, in a social networking app, the ability to add friends or post updates might be basic expectations; in a rideshare app, showing the driver’s location on the map and an estimated time of arrival is a basic feature. Users won’t praise your app for doing these things  they assume it will. But if you fail to deliver on a basic need (like a reliable login system or data privacy safeguards), it will trigger immediate dissatisfaction. Thus, basic features often align closely with MoSCoW “Must haves” from a user perspective. The rule with basics is to make sure you have them and they work correctly; going beyond “good enough” on basics usually doesn’t win extra love from users, it just avoids complaints. 
• Performance Features (One-Dimensional Quality): These are features for which “the more, the better” applies  the better you implement them, the more satisfaction they deliver, and if you do a poor job or omit them, satisfaction drops accordingly.There is a roughly linear relationship between how fully the feature is realized and how happy the user is. These are usually the features that customers talk about when comparing products; they are often directly requested by users. For instance, for an e-commerce website, site speed might be a performance factor. A faster site leads to more satisfaction (users love a quick checkout), a slow site frustrates them. In a smartphone, battery life is a performance attribute: more battery life = happier customers. In our rideshare app scenario, accuracy of the pickup time or the price of the ride could be performance factors (cheaper price or more accurate timing typically yields proportionally higher satisfaction). Performance features are important for differentiation. They often tie to competitive benchmarks or marketing points (“50% faster than the competition,” “more storage than other free plans,” etc.). Startups should identify a few performance features that align with their value proposition and ensure the MVP does well on those. Unlike basic features, investing more in performance features tends to pay off in user satisfaction  for example, every reduction in wait time or increase in reliability might win you more user loyalty up to a point.
• Excitement Features (Delighters or Attractive Quality): These are the unexpected, innovative features that can delight customers when they discover them.Excitement features provide a significant boost in satisfaction when present, but their absence does not cause any dissatisfaction because users don’t expect them in the first place.They often address unspoken needs or introduce novel capabilities that users hadn’t imagined. A classic example of a delighter is when early smartphones introduced touch ID fingerprint scanning  it wasn’t something users demanded, but once experienced, it delighted them by making phone unlocking so convenient. In our rideshare app, an excitement feature might be a gamification element (like earning rewards for taking eco-friendly rides) or a highly personalized touch (such as the app automatically playing your favorite music playlist when your ride starts). If you include such a delighter, users might think “Wow, that’s cool!” and it can create positive buzz and loyalty. Importantly, because excitement features aren’t expected, not having them in your MVP won’t make anyone unhappy  which is why MVPs often launch without delighters if time is short. But including even one or two small delighters can set your product apart. For startups, the Kano model encourages brainstorming what could be a delight for your target users: what could we do that would pleasantly surprise them? If there’s a low-cost way to implement a particular delight in the MVP, it might be worth prioritizing after all basics are covered, since it can give you an edge. Dr. Kano originally called these “attractive” features because they have an outsized positive effect on how users perceive your product. In the Kano model, feature categories are visualized on a graph of customer satisfaction (vertical axis) versus feature implementation (horizontal axis). Basic features (threshold) must be present just to reach neutral satisfaction  if absent, they cause dissatisfaction. Performance features create a roughly linear increase in satisfaction as they are improved (more implementation yields more satisfaction). Excitement features (delighters) start at neutral when absent (users don’t expect them) but yield a big jump in delight when fully implemented. This chart illustrates how different feature types influence user satisfaction differently, guiding teams on where to invest their efforts. The three categories above (Basic, Performance, Excitement) correspond to features you likely do want to develop (each plays a role in a successful product). The Kano model also identifies two categories of features you’ll want to keep off of your initial roadmap: 
• Indifferent Features: These are features that customers don’t care about one way or the other; their presence or absence does not meaningfully affect customer satisfaction.An indifferent feature, if built, is essentially wasted effort because it doesn’t drive uptake or enjoyment of the product. For example, imagine an app that offers an overly complex customization of some minor visual detail which users haven’t shown interest in, like choosing the exact shade of the app’s theme color. If users are indifferent to this capability, implementing it won’t make them any happier. Kano analysis helps teams identify such indifferent attributes so they can avoid putting resources into them. In a startup, where resources are scarce, you definitely want to eliminate any feature that doesn’t pull its weight in value. Sometimes teams mistakenly include indifferent features because of internal bias (“wouldn’t it be neat if…”) or because one vocal person advocated for it. Running ideas through a Kano survey or even a quick user test can reveal when a proposed feature elicits a “shrug” from customers. Those should be the first to go. Instead of spending time on indifferent features, focus on basics and performance features that do matter to users. 
• Reverse Features (Dissatisfiers to some): These are features that actually cause dissatisfaction when they are implemented to a high degree, because not all users want them.A reverse feature is something some customers might prefer not to have at all, or there is such a thing as “too much” of it. For example, a highly complex interface with too many options could be a reverse attribute for users who value simplicity  offering every possible customization might annoy those users and they’d be happier with a more streamlined product. Dr. Kano’s research notes that some customers prefer a basic model of a product, and will be upset if it has too many extra frills or if a feature is over-engineered beyond their needs.In a sense, reverse features are the opposite of delighters: instead of surprise and delight, they surprise and frustrate. A common modern example might be excessive notifications in an app. A few alerts can be helpful (performance feature, keeping users informed), but too many become spammy and cause dissatisfaction. For an MVP, reverse features are a clear “no.” If there’s a feature you suspect some users might hate, it’s safer to leave it out initially. You can always add optional features or advanced settings later for power users, but your default MVP should aim to please as many of your target customers as possible, not turn some away. 

Applying the Kano Model in a Startup: In practice, using the Kano model often involves user research to categorize features. Teams might conduct Kano surveys where they ask potential users two key questions for each proposed feature: (1) “How would you feel if this feature were present in the product?” and (2) “How would you feel if this feature were absent or not implemented?” Users typically answer on a scale (e.g. “I like it,” “I expect it,” “I am neutral,” “I can tolerate it being missing,” “I dislike it”). By comparing the pair of answers for each feature, you can classify it into Kano categories. For instance, if a user says they expect the feature when asked about its presence, and say they’d be dissatisfied if it’s absent, that points to a Basic feature. If they say they like it when present, and are neutral if it’s absent, that suggests a Delighter. This method can quantitatively sort features into the five Kano buckets. Of course, a full Kano analysis may be more than a scrappy startup needs or can afford pre-MVP, but the philosophy can be applied in lighter ways. Even a handful of user interviews or leveraging your team’s knowledge of the domain can help guess which features are basic expectations versus exciting extras. By sorting features with Kano, a startup ensures its MVP has the right mix:

– Cover all Basic needs: These should be among your top priorities (likely aligning with MoSCoW Must-haves). You don’t want early users to be turned off because a fundamental feature is missing or poorly implemented. Kano helps by explicitly reminding you what the “expected” baseline is in your product’s category.

– Invest in key Performance features: Decide which attributes your product will compete on or which needs significantly affect user satisfaction. Make those a focus of your MVP as well. It might be tempting to save some improvements for later, but if, say, speed or security or accuracy is critical in your space, your MVP should hit an acceptable level on those performance dimensions to make a good impression.

– Include at least one Excitement feature if feasible: While basics and performance features avoid dissatisfaction and meet needs, a small delight can generate enthusiasm and word-of-mouth. If you’ve brainstormed a clever feature that users aren’t expecting and it’s not too costly to implement, consider slipping it into the MVP to differentiate your product. It could be the thing that makes early adopters say, “Hey, you’ve got to try this app. It does this cool thing!”

One big advantage of the Kano model is that it keeps product discussions grounded in the voice of the customer. It helps prevent scenarios where a team builds something technically impressive that nobody asked for or cares about. As Atlassian’s product guide notes, the Kano model can prevent a team from building features that won’t appeal to customers and instead identify the areas that will genuinely boost customer satisfaction.The outcome is a more customer-driven MVP, a product that has the features people actually need and value, plus maybe a spark of delight.

Of course, Kano is not without challenges. It can be data-intensive to do thoroughly, as mentioned. Also, startups must be careful to balance Kano’s results with practical considerations. Kano doesn’t directly consider development effort or technical risk; it’s solely about customer perception. So, a wise team might pair Kano with an effort analysis. For example, if Kano results suggest that an “exciter” feature would thrill users but it would take 6 months to build, you might decide to postpone it in favor of getting the MVP out sooner with the basics and some performance features. This is where the lean principles come back into play  maximizing learning and speed even if it means deferring a delighter until you have more resources.

Despite the effort involved, even an informal Kano exercise can illuminate things that MoSCoW might not, especially regarding customer delight. For instance, MoSCoW might classify a certain feature as “Could have” from a business perspective, but Kano thinking reveals that it could be a huge satisfaction or delight for users (or vice versa). Having both angles helps startups make more informed decisions about what to build now versus later. 

In summary, the Kano model adds a valuable lens of customer satisfaction to feature prioritization. In building an MVP, using Kano helps ensure you’re not just meeting the bare requirements to get a product out the door, but you’re also laying the groundwork for a product that customers will love. By focusing on must-be qualities, one-dimensional improvements, and a touch of delight  while avoiding the indifferent or off-putting features, startups can craft an MVP experience that satisfies early users and provides a strong base to build on.

Lean Startup Approach: Build Less, Learn More 

In addition to specific frameworks like MoSCoW and Kano, startups should embrace the overarching philosophy of lean startup methodology when prioritizing features. Popularized by Eric Ries, the lean startup approach is all about maximizing learning and value while minimizing waste during product development.When it comes to features, a lean mindset means building only what is necessary to test your assumptions and deliver value to early users, nothing more. It’s essentially the art of doing less but getting more insight in return. 

– At the heart of lean startup is the concept of the Minimum Viable Product (MVP) itself. As we discussed, an MVP is the most pared-down version of your product that can still be released to users for feedback. Eric Ries famously defined the purpose of an MVP as the version of a new product that allows a team to collect the maximum amount of validated learning about customers with the least effort.In other words, the MVP isn’t just a smaller product, it’s a strategy to quickly test your riskiest assumptions and learn what your customers actually want. Each feature you include in an MVP should have a clear purpose: it should either be absolutely required to make the product functional for its core use case, or it should test a specific hypothesis about user behavior or preferences.

– How Lean Principles Influence Feature Priority: When prioritizing features under a lean approach, ask questions like: – “Is this feature necessary to solve the primary problem for our target user?” If not, it might not belong in the MVP. – “What assumption or hypothesis does this feature validate?” If you can’t identify one, the feature might be fluff at this stage. – “Is there a simpler or manual way to achieve the same outcome?” If yes, consider doing that instead of building a fully automated feature (this is the idea of a “concierge MVP” or a “Wizard of Oz” approach, where you fake the automation behind the scenes to test demand). 

– Lean prioritization often leads to simpler implementations or even temporarily manual processes in an MVP. For example, suppose your startup’s product is an AI-driven recommendation engine for shopping. Building a sophisticated AI from scratch is time-consuming. A lean approach might be: for the MVP, have a basic rules-based recommendation or even have staff manually curate recommendations for a small user base. The feature from the user’s perspective (“I get decent recommendations”) is there, but behind the scenes you avoided months of engineering by doing it manually or with a simple heuristic. This lets you test whether users even engage with recommendations and find them valuable, before investing in complex AI. If users don’t care about the recommendations, you’ve saved a ton of effort; if they do care, you now have validation to justify building the advanced feature. 

– Another aspect of lean feature prioritization is focusing on the core user flow. Identify the one or two primary tasks that define your product’s value. Ensure every feature directly supports those tasks. Anything that’s tangential can likely be cut from the MVP. For instance, if you’re creating a task management app, the core flow might be “create a task, assign it to someone, mark it complete.” Features like commenting on tasks or tagging with colors might enhance the experience, but the MVP could prove its value without them; those can come later once you’ve validated that users find creating and completing tasks useful. 

– Lean also emphasizes iterative development. Rather than trying to deliver a “perfect” product out of the gate, you release an MVP quickly and then iterate based on real feedback. This means your feature prioritization is an ongoing process. You might deliberately postpone certain features for the MVP with the plan to add them in MVP 2 or MVP 3 once you gather data. For example, you might say “Our MVP 1 will have only the web version of the app with feature X and Y. We intentionally won’t build the mobile app or feature Z until we see how users respond to the core web product.” This staged approach ties into MoSCoW as well; you might treat the mobile app or feature Z as Won’t-haves for MVP 1 (even if ultimately they are must-haves for the full vision) in order to test the waters. Consider some real-world MVP stories that exemplify lean thinking:

– Airbnb is a famous example: before writing any complex code, the founders simply put up a basic website with a listing for their own apartment, effectively a rudimentary “product,” to see if anyone would rent a room.They manually handled everything else (photos, payments offline, etc.) to validate that people were interested in the concept of staying in strangers’ homes. Only after proving demand did they build out more features. Their prioritization was laser-focused on the core assumption: Will people book a stay in someone’s home? All the MVP needed was a way to showcase a space and let a person express interest. Fancy booking systems, review systems, profiles, those were all secondary and came later.

– Foursquare started as a one-feature app: the ability to “check in” to a location and earn badges/points for doing so.The founders left out a ton of features one might expect in a city guide or social network. There were no detailed venue recommendations or user reviews in the very beginning; it was essentially a game to check in and compete with friends. That lean feature set was enough to validate that users enjoyed broadcasting their location and earning status (a hypothesis about social behavior). Once the concept took off, they expanded the feature set in later versions. But if they had waited to launch until they built a full Yelp-like recommendation engine plus the gamification, they might have taken too long and missed the window of opportunity (or wasted effort if the concept flopped). 

These examples show how lean MVPs often look incomplete compared to the eventual product, but they serve their purpose of learning and de-risking the venture. The features prioritized for those MVPs were only those needed to test the core value proposition.

To apply lean principles to your prioritization: – Embrace the idea of the “Minimum” in MVP: It can be psychologically hard to cut features. Founders often fall in love with their vision of a full-fledged product. Lean methodology gives you permission to launch small. Remind yourself that the MVP is not the end goal, but a stepping stone. It needs to be viable, yes, but not exhaustive. – Plan to iterate: Knowing that you will iterate takes some pressure off the MVP. You can tell stakeholders, “Feature X isn’t in our MVP, but it’s on our roadmap for 6 months out pending the feedback we get.” This helps manage expectations. It also means you should design the architecture in a way that adding those features later is feasible, but you don’t actually build them now. – Use data to re-prioritize: Once your MVP is in users’ hands, gather metrics and feedback. Which features are being used heavily? Which promised features are users asking about that you didn’t include? Are there unexpected use cases emerging? This real-world data is gold for reprioritizing your next development cycle. You might find, for example, that a feature you thought was a Should-have wasn’t missed at all by users; it might remain in the “later” bucket or get dropped entirely. Conversely, users might be hacking a workflow to achieve something you didn’t anticipate, indicating a new feature opportunity that should be prioritized next. The lean approach is to constantly update your understanding of what’s valuable and adjust the roadmap accordingly. 

Lean startup thinking complements frameworks like MoSCoW and Kano by adding this crucial dimension of learning. MoSCoW and Kano help you decide importance and satisfaction at a point in time (usually before launch). Lean ensures that after launch, you keep learning and adjusting those priorities for future iterations. It prevents the team from sticking rigidly to a pre-MVP priority list when evidence from the field suggests a change.

For instance, you might have initially put a sophisticated reporting dashboard as a Won’t-have for MVP (because you assumed basic usage was more important to nail first). But after MVP launch, perhaps you discover that early adopters are desperate for better analytics on their usage  suddenly, that feature jumps to a Must-have for the next release because it’s critical for user retention. A lean mindset allows you to make that pivot in priorities swiftly, whereas a non-lean approach might have left that feature languishing in the backlog due to old assumptions. 

In sum, the lean startup approach to feature prioritization is about being ruthlessly pragmatic initially and then empirical as you go. It asks, “What’s the least we can do to learn the most?” and then encourages continuous re-prioritization based on what you learn. By building less, you can move faster and iterate toward a product that truly matches what users want and will pay for. For a startup, that means a better shot at achieving product-market fit before the runway runs out. 

Choosing the Right Framework (and Combining Them)

With MoSCoW, Kano, and lean principles in our toolkit, a logical question is: which prioritization approach should a startup use? The truth is, these approaches are not mutually exclusive  in fact, they complement each other, each offering a different perspective on what “value” means:

• MoSCoW provides an internal perspective rooted in business or project necessity (it forces a discussion on what the product must include to be viable at all). 
• Kano provides an external, customer-centric perspective (it asks what the user expects and what will delight them, ensuring you don’t forget the end-user’s happiness in your decisions). 
• Lean provides a process perspective (it keeps the team focused on rapid learning and iteration, reminding you not to overbuild and to stay flexible post-launch).

Many successful product teams actually use a blend of these frameworks. For example, you might kick off MVP planning with a MoSCoW exercise to classify features. That gives you a tentative scope defined by Must/Should/Could. Then, overlay a Kano lens: take the features you labeled as Must-haves and verify that these correspond to basic expectations or vital performance needs for customers (if you find a “Must-have” that customers wouldn’t actually care about, you may have mis-prioritized). Likewise, see if any feature you put as a lower priority might be a huge satisfaction or delight  if so, you might consider elevating its priority or finding a way to include a slim version of it. Kano can thus fine-tune your MoSCoW results by injecting the voice of the customer.

Lean comes into play by asking, “Can we cut this even further and still learn what we need?” After using MoSCoW and Kano, you might have a solid list of what should be in the MVP from a value standpoint. Lean thinking might challenge you: do we need all these Must-haves on day one, or can some be phased in after initial user feedback? For instance, MoSCoW might identify 5 must-have features. But maybe you could launch with 4 and add the 5th in an update a month later, if the first 4 are delivering the core experience  especially if that 5th one is complex. Lean would encourage testing if the MVP can stand without it, unless it’s truly critical. In practice, you might still implement all 5 if you’re confident they’re necessary; the point is to consciously make that decision rather than assume all must-haves are non-negotiable no matter what. 

Conversely, lean might also prompt you to include a feature sooner if it’s tied to a major assumption you need to test. For example, if your whole business model hinges on users paying for a premium feature, lean would argue that premium feature (or a proxy for it) should be in the MVP, even if it was initially marked as a “Should-have,” because you need to learn if users will pay. This might override a pure MoSCoW ranking because learning value is also an important kind of value. 

When choosing a framework, consider your situation:

• If you’re very early-stage with no users, start with MoSCoW to get clarity on your own priorities and constraints. It’s straightforward and will give you a rough MVP scope. Use lean principles to scale that scope back to a sensible small first release.
• If you have access to potential users (or are knowledgeable about your target market), incorporate Kano thinking to validate that your Must-haves align with user Must-bes, and identify any potential delighters. Kano is especially helpful if you’re entering a competitive space; it ensures you include features that meet baseline expectations so you’re not laughed out of the market, and maybe one that gives you a “wow” factor.
• If time to market is your top concern (which it often is for startups), lean should be your guiding philosophy. It might mean you consciously decide to drop a feature that, while important, can wait, because you’d rather gather user feedback on the rest. Lean helps you justify to stakeholders why the smaller scope is beneficial (“we’ll learn X, Y, Z earlier and can adapt accordingly”). 

The good news is these frameworks play well together. For example, Atlassian’s own guidance enumerates multiple frameworks and suggests picking based on team needs. Many teams actually try a couple and iterate on their prioritization method itself. You might use a simple numeric scoring for one round, then sanity-check with MoSCoW, etc. 

Ultimately, the goal is to arrive at a prioritized feature set that your team believes in and that is backed by reasoning from different angles (business need, user need, learning need). If MoSCoW, Kano, and lean all point toward including a particular feature, you can be very confident it should be there. If all suggest cutting another, then it’s an easy “not now.” If there’s discrepancy  that’s where discussion and judgment come in, which is fine.

One more tip: re-evaluate priorities regularly. Especially after you launch the MVP, revisit your frameworks. You might even run a new MoSCoW session for the next version, or re-survey users on new features for Kano categorization. Priorities can change as your context changes; perhaps a competitor release makes a certain feature more urgent (a previously “Could-have” becomes a “Must-have” to stay competitive), or user research uncovers a new must-have basis you overlooked. Stay agile in your approach to prioritization itself. 

In practice, successful startups often say they prioritize by a combination of data and intuition. The frameworks we discussed provide a structured way to inject data (or at least structured thinking) into what could otherwise be pure guesswork. They don’t remove the need for intuition and leadership judgment, but they inform it. Use them to surface hidden assumptions, to give everyone a shared language for importance, and to justify decisions to any skeptics with clear rationale. 

Expert Guidance in Prioritization: A Case in Point

While frameworks provide methodology, having experienced guidance can greatly enhance the prioritization process. Startup teams, especially first-time founders, might not have prior experience to draw upon when making tough trade-offs. This is where mentorship or consulting from industry experts becomes invaluable. An expert who has led multiple product launches can spot common pitfalls (like including too many features in an MVP or misjudging what users care about) and provide an objective viewpoint when internal teams struggle to decide. 

For example, Empyreal Infotech  a custom software development company based in Wembley, London  has built a reputation for expertly guiding startups through MVP development. Led by CEO Mohit Ramani, an innovator with deep expertise in mobile app and MVP development (and co-founder of branding ventures Blushush and Ohh My Brand), the Empyreal team emphasizes a strategic approach to feature selection. They help startups ask the right questions: Which features align most with the startup’s core value proposition? What will make the product stand out in the market? What can be iterated upon later once real users are in the loop? By applying frameworks like MoSCoW and Kano in real client projects, they ensure that new products focus on high-impact features that maximize user value from the outset. 

Empyreal Infotech’s global experience in delivering advanced cloud-based platforms and innovative mobile applications means they have insight into what works across different industries and user bases. Mohit Ramani and his team often stress the importance of identifying the minimum feature set that can deliver a coherent, high-quality user experience (echoing the MoSCoW “Must-have” principle) while also finding an angle that differentiates the MVP  sometimes through a small but clever delighter  to capture users’ attention. Their cross-disciplinary background, blending technology with branding strategy and user experience, graphic design, reflects in how they guide feature prioritization: not only should an MVP function well, it should also resonate with users and support a strong narrative around the product. 

Startups that partner with such experienced firms can avoid many early mistakes. For instance, an expert might caution a startup against trying to implement a complex AI-based feature in version 1 (a tempting performance or excitement feature) when a simpler rule-based approach could achieve 80% of the value at a fraction of the cost  allowing the team to launch sooner and start learning. Or they might identify a feature the founders labeled “optional” that is actually a basic expectation in that domain, urging it to be included to avoid user dissatisfaction. Sometimes, insiders can be too close to the idea and miss these points; an external expert brings a fresh, seasoned perspective. 

In essence, expert guidance doesn’t override the frameworks  it works alongside. Think of frameworks as tools and experts as the craftsmen who know how to use those tools most effectively. A seasoned product mentor or a development partner like Empyreal Infotech can help interpret the results of a Kano analysis, facilitate MoSCoW workshops with stakeholders to get buy-in, or inject lean thinking when the team is at risk of overbuilding. They also provide reassurance: someone like Mohit Ramani, who has seen many product journeys, can confidently say “Feature A can wait until version 2” or “Users will expect feature B from day one, trust me,” giving startup teams confidence in their decisions to trim or include certain items. 

For a startup founder, working with an experienced advisor can dramatically accelerate the learning curve of product prioritization. It’s like having a coach. You still run the race, but they can shave off seconds by improving your form. If you have access to such guidance (through investors, industry contacts, or specialized firms), it’s wise to leverage it, especially in the critical MVP planning phase. They can help ensure that when you do launch, you haven’t missed any critical must-haves, and you haven’t wasted effort on a could-have that users end up ignoring.

Conclusion

Feature prioritization is both an art and a science, and it is absolutely vital for startups embarking on custom software for startups projects. By using frameworks like MoSCoW, you ensure your team agrees on what’s truly non-negotiable for your product’s viability. By applying the Kano model, you keep a laser focus on customer satisfaction  making sure your MVP meets basic expectations and perhaps even includes a touch of delight. And by adopting a lean startup mindset, you instill the discipline to build only what is needed now, so you can launch, learn, and iterate faster than the competition. 

The common thread among these approaches is a commitment to delivering value to users and obtaining value in feedback for the team with each feature developed. Instead of being pulled in a dozen directions by feature requests or grand ideas, successful startups identify the few things that matter most and execute on those first. This doesn’t mean the other ideas vanish; they are simply queued for future consideration once the MVP has proven its worth.  

In global markets where user expectations are high and evolving quickly, a startup’s ability to prioritize effectively can make the difference between a product that fizzles and one that finds product-market fit. A well-prioritized MVP delights early adopters (turning them into evangelists) and provides the startup with critical learning  validating assumptions or revealing new opportunities  without exhausting its resources. From there, the team can confidently expand the product, guided by real user input and a solid understanding of what users value. 

As you plan your own product’s journey, remember that prioritization frameworks are guides, not strict formulas. Stay flexible and be willing to adjust as new insights emerge. Engage your team and, if possible, your users in the prioritization process. People support what they help create, and this buy-in will smooth out execution. And don’t shy away from seeking advice; even the most adept teams benefit from real-world experience in applying these principles. Whether through mentors or a partner like Empyreal Infotech that specializes in MVP development, leaning on experience can provide clarity when tough trade-offs arise. 

In the end, great products often start small. By smartly choosing a handful of high-impact features through methods like MoSCoW, Kano, and lean thinking, your startup’s MVP will have the best chance to shine in solving the right problem, for the right audience, with the right functionality. Prioritize what truly matters, and your first release will be a strong foundation to build upon, one focused iteration at a time. Contact Empyreal Infotech today for further information.