Product Management Topics
Product leadership, vision articulation, roadmap development, and feature prioritization. Focuses on product strategy and business alignment.
Feature Analysis and Launch Evaluation
Designing and applying evaluation frameworks to measure feature success and inform launch decisions. Topics include defining success metrics, experimentation design and basic A over B testing concepts, setting evaluation timeframes, identifying confounding factors, cohort and funnel analysis, instrumentation requirements, and how to iterate based on results. Candidates should be able to propose metrics, describe trade offs in evaluation design, and explain how launch evaluation influences product prioritization.
Translating Business Problems to Computational Solutions
Techniques for turning an ambiguous business request into concrete, buildable technical work. Covers eliciting requirements from stakeholders (including non-technical ones), distinguishing functional from non-functional requirements, defining measurable success criteria across business, product, and technical layers (e.g., SLAs/SLOs, KPIs, model-level metrics), scoping an MVP versus a full solution, writing user stories and acceptance criteria, and documenting open assumptions and trade-offs for the team that will build the solution. Applies whenever a high-level ask (an executive request, an RFP, a customer need) must be translated into a technical spec, architecture decision, or system requirement.
KPI Trees and North Star Metrics
Learn to build KPI trees that connect a North Star metric (the one metric that represents overall product success) to lower-level operational metrics that your team can influence daily. For example: 'Engage Active Users' = 'Login Rate' × 'Feature Usage Rate.' Each level should be measurable and actionable. The tree helps you understand how different levers drive your north star. Practice building trees for different business models: consumer engagement apps (DAU/engagement), marketplaces (GMV), B2B SaaS (ARR, CAC, LTV).
Ambiguous Product Scenario Navigation
Develop your approach to product scenarios with incomplete information. Practice asking targeted clarifying questions (user context, business goals, constraints, success metrics), sizing the problem, and building a logical approach step-by-step. At Staff level, also articulate how you'd establish decision-making frameworks for the future so similar questions are resolved faster.
Technical Requirements and Specifications
Covers the end to end practice of translating product vision and business goals into clear, actionable technical requirements and specifications that engineering teams can implement. Includes writing product requirement documents and technical specifications with problem statements, success metrics, user and developer personas, API contracts and interfaces, data and schema considerations, functional requirements, and non functional requirements such as performance targets, latency and throughput expectations, scalability goals, reliability targets and service level objectives, security and privacy constraints, backward compatibility, and rollout and migration strategies. Encompasses requirements gathering techniques such as stakeholder identification, discovery conversations, clarifying questions, scoping, constraint identification for budget and timeline, defining measurable acceptance criteria, traceability to business objectives, and documenting assumptions and open questions. Also covers communicating requirements effectively to engineering and cross functional partners, knowing how to be specific without over constraining implementation, iterating requirements as learning emerges, and involving engineers early so they provide technical input and ownership.
Setting Targets & OKRs for Technical Products
Learn to translate high-level business goals into specific, measurable Objectives and Key Results (OKRs). For example: Objective - 'Make our API platform the easiest to integrate in the industry' with Key Results like '80% of new developers can publish their first API call within 15 minutes' and 'Reduce average time-to-first-API-call from 90 minutes to 15 minutes'. Understand how to set targets that are ambitious but achievable, that drive the right behaviors, and that align teams. Be able to discuss how you'd break down OKRs into team-level goals.
Product Decisions and Business Outcomes
This topic examines how product strategy and decisions drive business metrics. Candidates should show how feature prioritization, pricing, positioning, and go to market choices connect to key performance indicators such as acquisition, activation, retention, revenue, and lifetime value. Expect evaluation of frameworks for prioritization, methods for estimating and measuring product return on investment, experiment and rollout strategies, funnel analysis, and how to set measurable success criteria and objectives for product initiatives. Communication with stakeholders and alignment to company goals should also be covered.
Prototyping and Validation Strategy
Focuses on designing and evaluating prototypes to validate product ideas, features, or models before full implementation. Topics include choosing the right prototype fidelity from paper sketches to interactive prototypes, defining clear hypotheses and success criteria, selecting the right users or segments to test with, and deciding what to measure qualitatively and quantitatively. Covers experiment design principles such as control and treatment groups, split testing, sample size and statistical considerations, instrumentation and metrics collection, and using qualitative feedback to iterate. Emphasizes annotating what to test, how to recruit participants, how to interpret results robustly, and how to incorporate findings into subsequent iterations or production plans.
Product Metrics and Key Performance Indicators
Covers designing, implementing, and governing metric frameworks for products. Topics include defining a north star metric that aligns the organization, identifying supporting and diagnostic metrics that drive and explain the north star, and understanding metric types such as engagement, retention, monetization, and quality. Candidates should be able to discuss metric hierarchies, instrumentation and data pipeline considerations, segmentation and cohort analysis, and the use of metrics for experimentation and decision making. Governance topics include ownership, alerting and anomaly detection, preventing metric manipulation, establishing thresholds and statistical rigor, retiring obsolete metrics, and balancing business and product analytics needs across stakeholders.