Advanced Visualization Types AI Prompts

Design visualizations for funnel analysis and cohort retention. Funnel stages: {{funnel_stages}} Cohort definition: {{cohort_definition}} Metric: {{metric}} (retention rate, revenue, engagement) 1. Funnel visualization options: Standard funnel chart: - Trapezoid shapes decreasing in width at each stage - Width proportional to the count at that stage - Show: absolute count AND conversion rate (%) between each stage - Color: use color to highlight the stage with the biggest drop-off - Limitation: difficult to compare multiple funnels (e.g. mobile vs desktop) Bar-based funnel: - Horizontal bars ranked by stage, sorted top to bottom - Easier to read exact values than the trapezoid format - Add conversion rate labels between bars: '→ 42% converted' - Add a secondary bar showing the 'lost' volume in each stage (grey bar) Funnel comparison (best for multiple segments): - Grouped or overlaid bars for each stage - Each group = one stage; bars within = one segment each - Better for: mobile vs desktop, new vs returning users, A vs B variant Waterfall funnel: - Shows how volume flows from one stage to the next - Each bar shows: volume entering the stage, volume converting (green), volume lost (red) - Good for showing absolute loss at each stage rather than just conversion rate 2. Cohort retention heatmap (standard format): - Rows: cohorts (typically by acquisition month/week) - Columns: periods since acquisition (Period 0, Period 1, Period 2...) - Cell value: retention rate (% of cohort still active in that period) - Color: sequential scale — dark = high retention, light = low retention - Period 0 is always 100% (the baseline) - Reading the diagonal: shows same calendar period across different cohorts (seasonality effect) 3. Retention visualization variants: - Line chart overlay: multiple cohort lines on the same chart — shows which cohorts retain better - Cumulative retention: useful for subscription products (when does the subscriber cancel?) - Retention cliff: annotate the period where the sharpest drop occurs 4. Actionable design: - For funnels: highlight the single biggest drop-off stage in red - For cohort heatmaps: add reference lines at 30-day and 90-day columns - Add a 'benchmark' row to the cohort heatmap showing the company average Return: funnel chart design (type, labels, color coding), cohort heatmap specification, color scale, and actionability annotations.

Design an effective geospatial visualization for this data. Data: {{data_description}} (geographic level: country, region, state, city, zip code, lat/lon) Metric to show: {{metric}} Key question to answer: {{question}} 1. Map type selection: Choropleth map: - Colors geographic regions by a metric value - Best for: showing variation in a metric across well-known geographic boundaries - Danger: choropleth maps are biased toward large areas — large regions dominate perception even if their values are typical - Fix: use a cartogram or pair with a bar chart for small-area analysis Proportional symbol map: - Places circles or shapes at each location, sized by the metric value - Best for: showing absolute counts or totals where geography is context, not the unit - Better than choropleth for showing concentration vs dispersion Dot density map: - Places one dot per N events at the event location - Best for: showing distribution of individual events (crime incidents, store locations) - Reveals clustering that choropleth aggregation hides Flow map: - Arrows showing movement between origins and destinations - Best for: trade flows, migration, commuting patterns - Danger: quickly becomes unreadable with many flows — limit to top 10–20 Heat map (geographic): - Continuous color gradient showing density of events - Best for: high-volume point data where individual dots overlap 2. Choropleth design: - Color scale: sequential for single direction (more → less). Diverging for above/below baseline. - Classification scheme: - Quantile: equal number of areas in each class — good for comparing areas to each other - Natural breaks (Jenks): class breaks at natural data gaps — good for showing clustering - Equal interval: mathematically equal class widths — good for absolute scale comparison - Number of classes: 5–7 classes for most maps - Projection: choose a projection appropriate to the geographic extent - World maps: Robinson or Winkel Tripel (avoid Mercator for choropleth — distorts area) - Country maps: use a projection that preserves area for that country 3. Accessibility for maps: - Color: always pair color with a supplementary encoding (pattern or label) for colorblind users - Tooltip: rich tooltips with exact values on hover - Table alternative: provide a sortable table of the data alongside the map 4. What maps cannot show: - Causation or correlation between geographic proximity and outcomes - Temporal patterns (use small multiples of the same map, or an animated time series) - Non-geographic relationships (use a chart, not a map) Return: map type recommendation with rationale, color scheme specification, classification method, projection, and tooltip design.

Design an effective heatmap for this data. Data: {{data_description}} Rows: {{row_dimension}} Columns: {{column_dimension}} Values: {{value_metric}} 1. When to use a heatmap: - When the combination of two categorical or ordinal dimensions determines an outcome - When there are too many cells for individual bar charts - When the pattern across the full matrix is the insight (not individual values) - Classic uses: hour-of-day × day-of-week, correlation matrix, cohort retention 2. Color scale selection: - Sequential (one direction): light = low, dark = high. Use for: volume, count, positive metrics. - Diverging (two directions from midpoint): use for: correlation (-1 to +1), deviation from target (negative to positive), profit/loss. - Categorical: only if cells represent categories, not values Color scale specifics: - For retention or positive rates: white/light → brand color - For correlation matrices: blue → white → red (standard in statistics) - For profit/loss: red → white → green - Always: make the color scale legend visible with clear breakpoints labeled 3. Ordering rows and columns: - Do NOT use alphabetical order unless that is meaningful - Order by: magnitude (row total descending), natural order (Mon–Sun, Jan–Dec), or hierarchical clustering - Hierarchical clustering: groups similar rows and columns together, revealing pattern blocks 4. Cell annotations: - Add the value in each cell when: precision matters AND the matrix is small (< 100 cells) - For large matrices: use color only, with hover tooltips for exact values - Number format: 1 decimal for percentages; abbreviate large numbers (1.2M) - Text color: use dark text on light cells, light text on dark cells (auto-switch at midpoint) 5. Size and aspect ratio: - Square cells: ideal for correlation matrices where both dimensions are the same concept - Rectangular cells: for matrices where row and column dimensions differ substantially - Target: cells large enough to read the annotation (minimum 30×30px for annotated cells) 6. Marginal summaries: - Add row totals (right side) and column totals (bottom) - Use a lighter shade or a bar chart strip for marginals - This helps interpret relative importance of each row/column Return: color scale specification, ordering recommendation, annotation rules, and marginal summary design.

Design a visualization for network or flow data. Data type: {{data_type}} (customer journey, supply chain, relationship network, conversion funnel, Sankey flow) Nodes: {{nodes}} (entities) Edges: {{edges}} (relationships or flows between entities) Key question: {{question}} 1. Chart type selection: Sankey diagram: - Shows flow volumes between stages or categories - Best for: conversion funnels, budget allocation, material flows - Read: width of each flow proportional to volume - Limit: < 20 nodes; > 20 becomes unreadable without interaction - Tool: Plotly, D3.js, Google Charts Alluvial diagram: - A Sankey variant showing how categories change over time or across dimensions - Best for: before-after category changes, cohort migration - Example: how customers moved between subscription tiers from Q1 to Q4 Network graph: - Nodes and edges showing relationships - Best for: social networks, dependency graphs, knowledge graphs - Layouts: - Force-directed: natural clustering, no hierarchy - Hierarchical: for tree structures (org chart, taxonomy) - Circular: for dense networks where crossing edges are unavoidable - Color nodes by: category, cluster membership, or a metric - Size nodes by: degree centrality, importance, volume - Edge width: proportional to relationship strength Chord diagram: - Circular diagram showing flows between all pairs of groups - Best for: mutual flows (trade between countries, team collaboration) - Harder to read than Sankey — use only when bidirectional flows are both important Arc diagram: - Nodes on a line with arcs above showing connections - Best for: temporal networks where order matters 2. Managing complexity: - > 50 nodes: aggregate less important nodes into 'Other' category - Filter controls: allow users to filter to relevant subgraphs - Highlight on hover: fade all non-connected nodes and edges when hovering - Community detection: use clustering algorithm to group related nodes; color by cluster 3. Performance for large networks: - > 500 nodes: use WebGL-based rendering (Sigma.js, GPU.js) - Static alternative: aggregate to a summary view with interactive drill-down Return: chart type recommendation, layout specification, node and edge encoding, complexity management approach, and tool recommendation.