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THEKINGJEZE

b2b-visualisation

by THEKINGJEZE

0🍴 0📅 Jan 25, 2026

SKILL.md


B2B Market Intelligence Visualisation

Purpose

Design visualisations for B2B buying signals with time-to-insight as the primary imperative. In high-velocity environments, the interface must facilitate comprehension of complex multivariate signals in under two seconds.

  • adhd-interface-design — For cognitive load patterns
  • action-oriented-ux — For lead card and queue designs
  • uk-police-design-system — For colour tokens and typography
  • notification-system — For alert severity and badging

Cognitive Foundations

Pre-Attentive Processing

Visual properties detected in under 200 milliseconds, before conscious attention engages:

AttributeDetection SpeedBest Use Case
Colour intensity/hue~50msStatus, urgency, category
Position on common scale~100msComparison, ranking
Length~100msMagnitude, progress
Size~150msImportance, volume
Orientation/slope~150msTrend direction
Motion/flickerImmediateCritical alerts (use sparingly)

Accuracy Hierarchy

Visual encodings ranked by reliability for accurate value judgement:

  1. Position on common scale — Most accurate (bar charts, dot plots)
  2. Length — Very accurate (bars, bullet graphs)
  3. Angle/slope — Moderate (line charts)
  4. Area — Poor (bubble charts, pie charts)
  5. Colour saturation — Least accurate for precise values

Implication: For comparison tasks, prefer bar charts and dot plots over pie charts and gauges.

Cognitive Load Management

Every pixel must serve a data-communicating purpose. Eliminate:

  • Unnecessary gridlines
  • 3D effects
  • Heavy borders
  • Decorative backgrounds
  • Redundant legends

The 2-Second Rule

A user must answer "Is this account interested?" and "Why?" within two seconds.

2-Second Test Checklist

  1. What is it? — Clear label
  2. Is it good/bad/urgent? — Direction + threshold indicator
  3. What changed? — Delta value
  4. What should I do? — CTA or driver hint visible

If any answer requires a legend, tooltip, or reading axis ticks, move that information to a detail view.


Visualisation Selection by Data Type

User TaskVisualisation TypeRationale
Scan/triage many entitiesSparklines, micro-chartsWord-sized, pattern recognition
Diagnose/explain one entityFull charts with axesPrecision, interaction
Compare valuesBar charts, dot plotsPosition on common scale
Show progress vs targetBullet graphsLength encoding, compact
Indicate status categoryBadges, colour codingPre-attentive, categorical
Show exact valueNumber + deltaPrecision with context

Sparklines vs Full Charts

Use Sparklines When:

  • Task is "up/down/volatile?" not "what was the value on April 12?"
  • Displaying many rows (account list, territory list)
  • Paired with number + delta
  • Time window is consistent

Use Full Charts When:

  • User needs to answer "why did this spike happen?"
  • Axes, annotations, or event markers required
  • Interaction expected (zoom, filter)

Practical Pattern:

  • List view (scan): Number + delta + sparkline + 1 badge
  • Detail view (explain): Full chart with baseline band, annotations

Gauges vs Progress Bars vs Numbers

ElementBest ForEfficiency
NumbersExact values needed quicklyHigh precision
Progress/Bullet barsValue vs target/thresholdsHigh comparison
GaugesCapacity/utilisation metaphorLow (use rarely)

Research shows bullet graphs support more efficient reading than gauge graphs.


Trend Visualisation: Showing Surge at a Glance

Making "Surge" Binary First

For sales scanning, "surge" should read as a state (Yes/No + severity), then users inspect magnitude.

The Baseline Band Pattern

To show "surging relative to normal," add a typical-range band behind the line:

  • Upward breakout → Green highlight, "SURGE" badge
  • Downward breakout → Red highlight, "DECLINE" badge

Band Calculation:

  • Median ± IQR (interquartile range)
  • Mean ± 2σ (standard deviations)
  • Rolling average ± percentage threshold

Period-over-Period Comparisons

Ghost Bar (Fastest in Lists):

  • Solid bar = current period
  • Light/outlined bar = previous period
  • Delta text adjacent

Indexed Line (100 Baseline):

  • Convert each series to index = 100 at start
  • Divergence shows acceleration

Score Visualisation: Explainability

The Opacity Problem

Composite scores are often "black boxes." Teams disregard scores they don't understand.

Principle: Separate the score for triage (one glance) from the reasons for action (one click/expand).

Progressive Disclosure Architecture

Layer 1: Score (Always Visible)

  • Large number + categorical label (Cold/Warm/Hot)
  • Delta from previous period
  • Optional percentile

Layer 2: Top Drivers (Compact)

  • Up to 3 driver chips
  • Each shows direction + contribution magnitude

Layer 3: Full Breakdown (Expanded)

  • Sorted contribution bar list
  • Waterfall chart showing how components sum
  • Top positive and negative factors

Alert Design and Anomaly Highlighting

The Highlight Budget

Only ONE strong highlight per row/card:

  • Badge OR border OR background tint OR icon — not all four

Reserve pre-attentive cues for top-priority signals only.

Severity Coding

SeverityColourIconUse Case
CriticalRed/Orange⚠️Competitor contract signed, Champion left
WarningYellow/AmberUsage dropped 10%, Stalled in stage
InfoBlue/Grayℹ️New CFO hired, Press mention

Motion: Use sparingly. Reserve for states requiring immediate action.

Avoiding Alert Fatigue

Gate alerts by:

  • Persistence: 2-3 consecutive periods above threshold
  • Impact: Score contribution ≥ X points

Throttle display:

  • Show "Top 5 today" by default
  • Provide "view all" expansion

Sparklines and Small Multiples

Sparkline Specifications

  • Aspect Ratio: Wide and short (4:1)
  • Start point: Gray dot (anchor)
  • End point: Coloured dot (current state)
  • High/Low points: Optional markers
  • Smoothing: Slight curve, avoid over-smoothing

Small Multiples

Use when comparing many entities with the same question.

Consistency Requirements:

  • Same time window everywhere
  • Shared Y-scale when magnitude matters
  • Normalised Y-scale when shape matters
  • 1 series ideal, 2 maximum

Coordinated Highlighting: Hover one account → highlight same time region across ALL sparklines.


Mobile-Responsive Data Visualisation

Adaptation Playbook

DesktopMobile
Horizontal bar chartVertical list of bars
Multi-series line chartSingle series + toggle
Data tableCard stack
Dashboard gridVertical scroll of cards
Detailed chartSparkline + tap to expand

Touch-First Interactions

The Scrubber Pattern: Dragging anywhere on chart snaps to nearest data point; readout updates above.


Technical Implementation

Rendering Performance

TechnologyUse Case
SVGInteractive elements, <1000 elements
CanvasHigh-density plots, >1000 elements

Progressive Loading

  1. Render chart skeleton immediately
  2. Load macro trend line first
  3. Progressively enhance with high-resolution data

Summary Principles

  1. Velocity over Static State — Show rate of change, not just current value
  2. Context over Raw Counts — Always provide baseline or comparison
  3. Binary First, Quantitative Second — State (Hot/Cold) before magnitude
  4. Progressive Disclosure — Triage → Detail → Full analysis
  5. Pre-attentive for Priority — Reserve colour/size/motion for what matters
  6. Position over Angle — Bars and dots over pies and gauges
  7. Explain the Score — Show drivers, not just the number
  8. Respect Cognitive Load — Every pixel earns its place
  9. Mobile as Primary — Design for glanceability
  10. 2 Seconds or Less — If it requires legend/tooltip, simplify

Score

Total Score

50/100

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