How do you prioritize user stories for mobile app development in a fast-paced environment?

Ruthless prioritization and defining product scope are essential responsibilities for product managers. As the CEO of the product, one must strike a balance between company objectives, customer demands, and competition. To manage these complexities, PMs often employ prioritization techniques such as: 1. MoSCoW Method: Categorizes features into Must-haves, Should-haves, Could-haves, and Won't-haves to prioritize essential versus non-essential requirements. 2. RICE Scoring: Evaluates initiatives based on Reach, Impact, Confidence, and Effort, providing a quantitative approach to prioritization. 3. Kano Model: Distinguishes between basic needs, performance features, and delighters, helping to identify which features will most satisfy users.

To prioritize user stories for mobile app development in a fast-paced environment, use a multi-criteria decision model like Expert Choice. User stories, which are based on assumptions about what users want and need from your app, are ranked using criteria such as business value, user needs, technical feasibility, and risk. Incorporate frameworks like MoSCoW, RICE, Kano, or WSJF to objectively compare and prioritize stories. Ensure that acceptance criteria—specific conditions a user story must meet to be considered done—are clearly defined. This approach allows for transparent decision-making, focusing on the most impactful features.

Our app development prioritization centered on deploying minimum usable feature to pick out user insights and iterate quickly. When introducing enhancements like boosting user ratings, we strategically placed them on highly trafficked pages such as the Home and Order Listing Pages based on user data. Our approach mixed MoSCoW, focusing on user needs, with indirect influences from the RICE to consider feature impact and complexity. Task allocation by Engineering Managers ensured efficient resource utilization, while bi-weekly app releases maintained pace with our team OKR. This balanced strategy empowered us to navigate Meesho's fast-paced environment, consistently delivering user value within tight timelines.

Probably the most time saving and effective framework is the Value vs Complexity framework. Here, for every feature you must determine the expected value that comes out of the feature and also plot it out against the expected effort required to deliver the expected output. To put it simply, anything that has high value & low effort should be given the highest priority whereas anything that has low value and requires high effort must be given the least priority. If you need to analyze multiple features at the same time, you can also use the Value versus Complexity quadrant that is usually helpful while prioritizing your backlog.

Choosing a prioritization framework depends on your project's nuances. MoSCoW offers a broad categorization, while WSJF and RICE are more quantitative. Both WSJF and RICE calculate value-to-effort ratios, generally putting the value to customers in the numerator and dividing it by the effort to deliver, giving you a value/effort measurement. This is a more data-driven approach which can be utilized when you have a clear understanding of your customers' needs and your team's ability to deliver, whereas the less quantitative frameworks work better when there is more ambiguity, such as when you're still trying to find product-market fit.

we craft the acceptance criteria for each user story, ensuring clarity and testability. We divide features into user stories based on complexity, user and business impact, aligning them with our app release timeline, which consider development, testing, regression, and release phases. For example, implementing a size chart on Product Details page involves acceptance criteria such as independent deployment, metrics tracking, testing Our testing and development teams collaborate to formulate comprehensive test cases, encompassing various network conditions(3G, 4G, 5G), API responses(2xx, 3xx, 4xx), and app functionality and UI checks. Once all criteria are met, the feature is ready for end-users, maintaining reliability and scalability.

Begin by using user research with personas, feedback, and analytics to ensure that your stories reflect real user needs. Use data and user feedback to guide decisions, focusing on features that address key user pain points. Continuously run experiments during development to validate the impact of new features and adjust priorities based on what works. Remain agile and responsive to feedback, iterating on your priorities to align with user needs and deliver a compelling app experience.

As a software engineer, I have observed and experienced the challenges that arise in such scenarios. Bridging the gap and understand the product from the eyes of the people who develop it is important to determine the "must have" or highest prioritized features to implement. The quality of acceptance criteria, enabling system tests, and pen testing for bigger features are just many of the considerations to consider after the developers are finished creating a product. Thinking about this holistic software development lifecycle affects time estimates and priorities as well.

TechStream adopted agile methodologies to refine its product priorities in response to changing market demands. Through sprint planning and backlog grooming, they continuously evaluated and adjusted their development focus based on customer feedback and analytics. Regular retrospectives after each sprint allowed for reflection and adjustments. Implementing direct feedback channels and analyzing feature usage data enabled data-driven decision-making. This approach resulted in a product that better met user needs, enhanced customer satisfaction, and allowed TechStream to quickly adapt to new opportunities, demonstrating the effectiveness of regular priority review and refinement in agile environments.

UberEats operates in a highly competitive food delivery market, necessitating quick adaptations to user feedback, technological advancements, and changes in consumer behavior Prioritization Techniques: MoSCoW Method: This method categorizes user stories into four buckets: Must have, Should have, Could have, and Won't have. "Must have" stories might include essential functions like order placement Value vs. Complexity Matrix: UberEats evaluates user stories based on the value they deliver to the user against the complexity or effort required to implement them. High-value, low-effort stories are prioritized Kano Model: UberEats might prioritize a stable and fast app performance (basic need) over introducing a new gamification element