Results for “win-rate”

Parse RFP/RFI/RFQ documents, score fit per requirement with verifiable proof points, generate win-themes, estimate winrate, and produce a bid/no-bid recommendation using the Shipley method.

Conversion rate optimization for marketing pages and lead-capture forms. Use when the user wants to improve conversions on a homepage, landing page, pricing page, feature page, blog CTA, contact form, demo form, or campaign page. For product onboarding use user-onboarding; for lifecycle email use marketing-automation; for pricing and paywalls use pricing-strategy; for A/B testing use ab-test-setup.

Turn scattered closed deals into a win/loss program that changes what you ship and what you say. Picks who to interview, gives you the question guide, codes themes across deals so a pattern beats a single anecdote, and turns the pattern into a specific messaging or product change. Built for B2B GTM, product, and RevOps leaders, customizable to your motion and your CRM. Trigger on "start a win-loss program", "why are we losing deals", "run win-loss interviews", "what patterns are in our closed-lost", "who should I interview", or any win/loss or closed-deal analysis.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Build a quarterly bookings forecast with commit, best-case, and pipe-only tiers, project cohort-level NRR/GRR to surface leaky cohorts, and score per-stage funnel confidence using coefficient-of-variation analysis.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Turn "we should run some ads" into a paid-ads plan you can hand to a buyer. Audiences, the offer per audience, the creative angles, the budget split, the metric that calls it a win, and a kill rule so you stop feeding losers. Built for B2B marketing teams, customizable to your channels and your funnel. Trigger on "build a paid ads plan", "how should we split budget", "what should the ads say", "what's our target CPL", "when do we kill a campaign", or any paid-media planning question.

Audits and optimizes conversion points across the funnel, applying behavioral science to produce prioritized, testable hypotheses.

Re-onboard a churned customer with the same care they got the first time. Reads why they left and whether they left the door open, picks the ones worth winning back, and builds a warm re-onboarding sequence, not a "we miss you" discount blast. Built for any post-sale or growth team, customizable to your product and win-back window. Trigger on "win back churned customers", "re-engage a lost account", "build a win-back sequence", "which churned customers should we chase", "boomerang play", "re-onboard a customer who left", or any win-back question.

The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.