The Product Leader's Toolkit

The RICE scoring model helps product teams objectively rank features by multiplying Reach, Impact, and Confidence, then dividing by Effort. Product leaders use RICE when they need a data-driven prioritization approach that reduces bias and politics in roadmap planning discussions. Score multiple features side-by-side and instantly see a ranked priority list you can share with stakeholders.

ICE scoring is the fastest prioritization framework available — three simple scores multiplied together give you a ranked backlog in minutes. Product leaders use ICE for rapid triage when speed matters more than precision, such as sprint planning or hack-week idea selection. Ideal for teams that find RICE too heavyweight for their current process maturity.

WSJF (Weighted Shortest Job First) is the prioritization framework used in SAFe and Lean enterprises. It balances business value, time criticality, and risk reduction against job size to identify features that deliver the most value per unit of effort. Product leaders in scaled agile organizations use WSJF to align multiple teams on the highest-value work during PI planning.

MoSCoW is a categorical prioritization method that forces stakeholders to commit to what's truly essential versus nice-to-have. Product leaders use MoSCoW during scope negotiations and release planning when they need clear boundaries between must-ship and can-wait. Particularly effective for cross-functional alignment because the categories are intuitive for non-technical stakeholders.

The weighted scoring model lets you define your own prioritization criteria — strategic alignment, customer impact, revenue potential, technical feasibility — and assign relative weights. Product leaders use this when standard frameworks like RICE feel too rigid and they need scoring that reflects their unique strategic context. Build a repeatable, transparent scoring rubric your entire organization can adopt.

The Kano model classifies features by their relationship to customer satisfaction: must-be features prevent dissatisfaction, performance features scale linearly with investment, and attractive features create delight. Product leaders use Kano to avoid over-investing in table-stakes features and to identify the delighters that differentiate your product. Essential for customer-centric roadmap discussions.

Not sure whether to use RICE, ICE, MoSCoW, WSJF, or Kano? This diagnostic quiz asks about your team size, process maturity, data availability, and decision-making style to recommend the best-fit prioritization framework. Product leaders use this when establishing or upgrading their team prioritization process — it saves weeks of framework evaluation and pilot testing.

Net Promoter Score is the gold standard for measuring customer loyalty and predicting growth. This calculator takes your survey responses, computes your NPS, and benchmarks it against SaaS industry averages by company stage and segment. Product leaders use NPS to track satisfaction trends, set customer health OKRs, and build the case for retention investments.

The Sean Ellis test asks users how they would feel if they could no longer use your product. If 40% or more say "very disappointed," you have product-market fit. Product leaders use this metric to determine whether to double down on growth or pivot back to discovery. This calculator gives you the PMF percentage plus actionable guidance based on where you fall on the spectrum.

Running experiments without statistical rigor wastes engineering resources and leads to false conclusions. This calculator handles both pre-test sample size planning and post-test significance analysis. Product leaders use it to ensure experiments have adequate power before launch and to make confident ship/kill decisions when results come in, reducing the risk of shipping features that don not actually move metrics.

MRR and ARR are the foundational metrics of every SaaS business. This calculator breaks down your recurring revenue into new, expansion, contraction, and churned components so you can see exactly where growth is coming from. Product leaders use MRR decomposition to identify whether growth is driven by acquisition or expansion and to spot early warning signs in contraction trends.

Customer Lifetime Value tells you how much revenue a customer generates over their entire relationship with your product. This calculator computes LTV from your ARPU, gross margin, and monthly churn rate, then benchmarks it against enterprise, mid-market, and SMB SaaS standards. Product leaders use LTV to justify retention investments, set acquisition budgets, and segment customers by value.

Churn is the silent killer of SaaS businesses. This calculator converts between monthly and annual churn rates and benchmarks your performance against SaaS companies at different stages — early-stage, growth, and mature. Product leaders use churn analysis to set retention goals, identify at-risk segments, and quantify the revenue impact of reducing churn by even a single percentage point.

LTV:CAC ratio is the single most important unit economics metric for SaaS businesses. A ratio of 3:1 or higher indicates healthy economics; below that, you are spending too much to acquire customers relative to their value. Product leaders use this metric to evaluate go-to-market efficiency, negotiate marketing budgets, and determine whether the business model is sustainable at scale.

The SaaS Quick Ratio measures the efficiency of your revenue growth by comparing incoming MRR (new + expansion) against outgoing MRR (contraction + churn). A Quick Ratio above 4 indicates highly efficient growth; below 1 means the business is shrinking. Product leaders use this metric to evaluate whether growth is sustainable or if a leaky bucket problem needs urgent attention.

The Rule of 40 states that a healthy SaaS company should have its revenue growth rate plus profit margin equal or exceed 40%. It measures the trade-off between growth and profitability that every scaling company faces. Product leaders use this benchmark in board discussions and strategic planning to evaluate whether the company is balancing investment in growth with path-to-profitability expectations.

TAM, SAM, and SOM quantify your total addressable market, serviceable market, and realistic obtainable market. This calculator supports both top-down (industry data) and bottom-up (customer × ACV) methodologies. Product leaders use market sizing to validate new product bets, prepare investor materials, and set realistic revenue targets that align executive expectations with market reality.

Knowing your burn rate and remaining runway is critical for strategic decision-making at startups and growth-stage companies. This calculator computes net burn from your revenue and expenses to determine how many months before you need additional funding. Product leaders use runway calculations to scope roadmap ambition, time fundraising activities, and make hiring decisions with financial confidence.

Not every feature is worth building. This calculator compares the development cost of a feature against its projected revenue impact to determine ROI. Product leaders use feature ROI analysis in roadmap prioritization to ensure engineering investment flows toward the highest-value opportunities and to justify development resources to finance and executive leadership.

The AARRR (Pirate Metrics) framework maps your full user journey across Acquisition, Activation, Retention, Revenue, and Referral. This calculator helps you measure conversion rates at each stage and identify the biggest drop-off points. Product leaders use funnel analysis to focus team efforts on the stage with the highest leverage — fixing activation is often 10x more impactful than adding new acquisition channels.

Every product team needs a single North Star Metric that captures the core value delivered to customers. This quiz analyzes your product type, revenue model, company stage, value driver, and usage frequency to recommend the best North Star from a database of proven metrics with example companies. Product leaders use this to align their entire organization around one metric that drives both customer value and business growth.

This diagnostic assessment evaluates your product management practice across discovery, prioritization, roadmapping, metrics, strategy, and stakeholder management. Each dimension is scored on a 4-level maturity scale from Emerging to Advanced. Product leaders use this to identify specific capability gaps in their organization, build a PM transformation roadmap, and benchmark progress over time against industry-standard maturity frameworks.

This simulation puts you in the hot seat with realistic AI feature requests that need immediate Ship, Defer, or Kill decisions under time pressure. It tests your ability to evaluate AI features by weighing technical feasibility, user value, ethical risk, and resource requirements simultaneously. Product leaders use this to sharpen AI product judgment and train their teams on the unique decision-making challenges of AI feature development.

Choosing from 100+ PM tools on the market is paralyzing. This diagnostic asks about your role, primary needs, team size, budget, maturity, integrations, and priorities to recommend the best-fit tools from a curated database of 40+ products. Product leaders use this to cut through vendor noise, build their ideal PM tech stack, and make informed tooling decisions that match their team context and budget.

Shipping AI features without ethical review is a reputational and regulatory risk. This scanner evaluates your AI feature across five ethical dimensions — bias, privacy, transparency, safety, and accountability — with weighted scoring and actionable mitigation recommendations. Product leaders use this to embed responsible AI practices into their development process and ensure features pass ethical review before reaching users.

AI product management requires a distinct skill set spanning ML literacy, data strategy, ethical AI, prompt engineering, evaluation methods, and more. This assessment scores you across 8 AI PM competencies and identifies your specific skill gaps with targeted learning recommendations. Product leaders use this to plan their own AI upskilling journey and to design training programs for their PM teams entering the AI space.

Not every problem needs an LLM — and using one when rules suffice wastes money and adds latency. This decision tool asks 8 questions about your use case to recommend whether you should use an LLM, traditional ML model, or rules-based logic. Product leaders use this to make informed build decisions, avoid over-engineering with AI hype, and choose the approach that balances cost, accuracy, and maintainability for each specific feature.

Pricing AI features is notoriously difficult because costs are usage-based and value is hard to quantify. This simulation lets you set pricing for a fictional AI SaaS product and watch 12 months of market response play out based on your decisions. Product leaders use this game to develop intuition for AI pricing models, understand the trade-offs between per-seat and usage-based pricing, and test strategies without real-world consequences.

AI features have unique cost structures — development, inference APIs, and ongoing model costs. This calculator models the full financial picture including time savings, revenue uplift, API costs, and development investment to produce ROI projections and break-even timelines. Product leaders use this to build business cases for AI investments, set realistic expectations with leadership, and compare AI features against non-AI alternatives.

LLM API costs vary dramatically across providers and models, and small differences in token usage multiply into significant monthly expenses at scale. This estimator compares costs across GPT-4o, Claude, Gemini, and open-source models based on your expected usage patterns. Product leaders use this to forecast AI infrastructure costs, choose providers for new features, and negotiate enterprise API agreements with data-driven volume estimates.

The build vs. buy decision for AI is more nuanced than traditional software — with fine-tuning as a powerful middle ground. This analyzer evaluates your specific context across team capability, data assets, differentiation needs, budget, and time constraints to recommend building in-house, fine-tuning an existing model, or buying a vendor solution. Product leaders use this to make strategic AI investment decisions that match their organizational capabilities.

Before investing in AI features, you need to honestly assess whether your organization is ready. This assessment evaluates AI readiness across data infrastructure, team skills, organizational culture, technical capabilities, and governance maturity. Product leaders use this to identify readiness gaps before committing resources, build an AI enablement roadmap, and set realistic expectations with leadership about what is achievable in the near term.

Shipping AI features without proper evaluation is like deploying code without tests. This generator creates structured evaluation scorecards with appropriate metrics, pass/fail thresholds, and minimum sample sizes for your specific AI use case. Product leaders use this to establish quality gates for AI features, define acceptance criteria that engineering and QA teams can execute against, and build repeatable evaluation processes.

The AI design tool landscape changes monthly with new products and features. This picker asks about your design workflow, team size, primary tasks, and integration needs to recommend the best AI-powered design tools from a curated database. Product designers and design leaders use this to evaluate new AI tools without spending weeks on trials, build their AI-augmented design workflow, and stay current with the rapidly evolving tool landscape.

AI features fail when they ignore human-AI interaction principles — unclear confidence levels, no graceful degradation, and poor error recovery destroy user trust. This audit scores your AI feature against guidelines inspired by Microsoft HAX (Human-AI Interaction) research. Product designers use this to identify UX gaps in AI features before launch, ensure designs follow established interaction patterns, and advocate for better AI experiences in product reviews.

Design teams are increasingly expected to design for AI-powered experiences, but most lack the knowledge, processes, and patterns to do it well. This assessment evaluates design team readiness across AI literacy, design patterns, tooling, prototyping capabilities, and cross-functional collaboration. Design leaders use this to build a capability development plan, make informed hiring decisions, and set realistic timelines for AI design initiatives.

Prepare for product management interviews with timed practice questions across strategy, execution, analytical, behavioral, design, and technical categories. Each question comes with a timer and structured evaluation criteria. Product leaders use this to prepare their teams for internal promotions, upskill during career transitions, and develop structured thinking skills that apply directly to daily PM work.

The only resume scorer built specifically for product managers. It evaluates your resume across five PM-specific dimensions — metrics density, impact framing, keyword coverage, structure quality, and seniority alignment — with targeted role weighting for SaaS, Growth, Technical, B2B, and AI PM positions. Unlike generic ATS checkers, it analyzes each bullet point for outcome framing and quantified results, the two factors PM hiring managers care about most.

Weak resume bullets describe tasks; strong PM bullets quantify outcomes. This rewriter analyzes individual bullet points for weak verb usage, missing metrics, task-focused framing, and absent PM context — then shows you exactly how to fix each issue with annotated before/after comparisons. Browse 30+ example transformations organized by PM type for inspiration. All analysis runs client-side with a rules engine, no AI API required.

Product management has fragmented into distinct specializations — Growth PM, Technical PM, Platform PM, AI PM, and more — each requiring different skills and offering different career trajectories. This quiz maps your background, interests, technical comfort, industry preferences, and career goals to recommend the top PM specializations that fit you best. Each recommendation includes a role description, key skills to develop, salary context, and links to relevant IdeaPlan resources to start building expertise.