Results for “funnel-mapping”

Analyze conversion funnel drop-offs and identify biggest leak points with impact-ranked recommendations

Generates a single-file HTML funnel infographic from 3-6 ordered stages with absolute values, showing conversion rates and drop-off at each step. Output is a static 9:16 vertical image with inline CSS, no JavaScript, no external dependencies.

Analyzes a multi-step conversion funnel in Adobe Customer Journey Analytics to find where users drop off and which steps have the worst leakage.

Maps the end-to-end customer experience from awareness to advocacy — touchpoints, emotions, friction points, and improvement opportunities per stage. Use when identifying drop-off points, improving onboarding, or understanding the full user lifecycle. Triggers on: customer journey, user journey, journey map, touchpoints, onboarding experience, funnel analysis, churn points, aha moment, drop-off, user lifecycle.

Use only for developer-facing products; map users, buyers, champions, contributors, and operational stakeholders

Create a holistic experience map showing the full ecosystem of user touchpoints, channels, and relationships across the customer journey.

Maps user flows and journey maps with decision points, error states, and optimization suggestions

This skill should be used when the user asks to "create a challenge funnel", "build a 5-day challenge", "bootcamp funnel", "challenge launch", or mentions challenges, bootcamps, or multi-day engagement funnels. Creates challenge funnels that activate prospects, build community, and convert to core offers.

Use this when a feature or product needs a clear end-to-end user journey across entry, action, system response, and outcome. Trigger on requests to map the full experience before screens or components are finalized.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Maps a codebase into feature-grouped flowcharts, identifies duplicated concerns across features, and proposes a unified architecture with handoff prompts for refactoring.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.