How does hover intent analysis work?

Hover intent analysis tracks mouse movement patterns over interactive elements like CTAs, product cards, and pricing sections. Prolonged hover time, approach-retreat patterns, and hover-to-click ratios reveal purchase consideration signals that traditional click analytics miss entirely.

What is scroll depth intelligence?

Scroll depth intelligence goes beyond basic scroll percentage by measuring scroll velocity, pause points, reverse scrolling, and content visibility duration. It identifies which page sections capture attention and which get skipped, enabling data-driven content optimization.

Can hover data predict conversions?

Cursor behavior is a meaningful engagement signal. ClickStream's conversion readiness score combines funnel depth, engagement, and frustration signals — computed in real time on every event — to estimate how close a visitor is to converting.

Conversion Readiness, Hover Signals, and Scroll Depth Intelligence

What You'll See in the Dashboard

Open the Intelligence tab to find the Conversion Readiness and Scroll Depth Intelligence cards. Conversion Readiness shows a real-time 0–100 score, recomputed on every event. Scroll Intelligence shows a visual heatmap of where this visitor paused, re-read, and skipped.

Business Actions: Create a Rule to trigger a limited-time offer when Conversion Readiness exceeds 65. Feed Scroll Intelligence into your content team to identify which sections lose readers.

Model 19: Conversion Readiness

The conversion readiness model produces a single number that answers the most important question in real-time analytics: How close is this visitor to converting during this session?

Unlike the intent score (Model 1), which classifies motivation level, conversion readiness is a 0–100 score computed deterministically from weighted behavioral signals on every event — there is no batch retraining cycle, because there is nothing to retrain: the same event stream always produces the same score, in real time at the edge.

The 9 Conversion Signals

The signal names and weights below are illustrative of how weighted behavioral scoring works — the shipped model's exact inputs differ, and model weights are tunable per site.

Signal	Weight	Description
Funnel stage reached	0.22	Deepest conversion funnel stage visited (browse → product → cart → checkout → payment)
Historical conversion rate	0.16	This visitor's personal conversion rate from prior sessions
Session intent score	0.14	Current intent model output (Model 1), providing behavioral context
Time in conversion zone	0.12	Active time spent on checkout, pricing, or payment pages
Cart value momentum	0.10	Whether the cart value is increasing (adding items) or decreasing (removing)
Form completion progress	0.08	Percentage of required checkout/signup form fields completed
Device & channel context	0.07	Conversion rates vary by device type, traffic source, and time of day
Frustration dampening	0.06	Current frustration score inversely affects conversion probability
Social proof exposure	0.05	Whether the visitor has seen reviews, testimonials, or trust badges

Score Freshness

Scores are recomputed synchronously as each event is ingested — the full 26-model scoring pass benchmarks at p95 under 3 ms per event (a CI-enforced benchmark), so conversion readiness is always current as of the visitor's most recent action. There is no recalibration cadence or refresh schedule: real-time scoring is the only mode.

Readiness Bands

Score (0–100)	Tier	What It Means	Action
0–15	Cold	Very unlikely to convert this session	Content nurturing, email capture
16–35	Warming	Some conversion signals present	Social proof, case studies
36–55	Considering	Active evaluation, could go either way	Urgency messaging, limited offers
56–80	Likely	Strong conversion trajectory	Remove friction, streamline checkout
81–100	Imminent	Strongest readiness signals present	Upsell/cross-sell, order bump

How You Could Calibrate Readiness Scores Against Your Own Conversion Data

Note: this is not part of the ClickStream platform. Readiness is a deterministic heuristic score with no training loop. If you want calibrated conversion probabilities, you can fit Platt scaling downstream against your own conversion outcomes:

function calibrateProbability(rawScore: number, params: PlattParams): number {
  // Platt scaling: P(y=1|f) = 1 / (1 + exp(A*f + B))
  const calibrated = 1 / (1 + Math.exp(params.A * rawScore + params.B));
  return Math.round(calibrated * 100);
}

// Example: fit Platt params on your own predictions/outcomes pairs
function fitPlattParams(predictions: number[], outcomes: boolean[]): PlattParams {
  // Gradient descent to minimize log-loss between
  // calibrated predictions and actual outcomes
  let A = -1, B = 0;
  const lr = 0.001;

  for (let epoch = 0; epoch < 1000; epoch++) {
    let gradA = 0, gradB = 0;
    for (let i = 0; i < predictions.length; i++) {
      const p = 1 / (1 + Math.exp(A * predictions[i] + B));
      const y = outcomes[i] ? 1 : 0;
      gradA += (p - y) * predictions[i];
      gradB += (p - y);
    }
    A -= lr * gradA / predictions.length;
    B -= lr * gradB / predictions.length;
  }

  return { A, B };
}

Hover and Cursor Signals

Cursor behavior — dwell, hesitation over CTAs, erratic movement — feeds several of ClickStream's shipped models, including frustration detection, engagement scoring, and stealth-bot detection (mouse entropy). ClickStream does not ship a standalone "hover intent" score; these signals surface through the intent, engagement, and frustration scores in the Signals snapshot.

Model 17: Scroll Depth Intelligence

Scroll depth intelligence goes far beyond "the user scrolled 73% of the page." It analyzes how the visitor scrolled — the rhythm, pauses, re-reads, and velocity changes — to extract rich behavioral signals about content engagement and interest.

The 8 Scroll Intelligence Signals

The signal names and weights below are illustrative of how weighted scroll scoring works — the shipped model's exact inputs differ, and model weights are tunable per site.

Signal	Weight	Description
Maximum depth reached	0.20	Deepest scroll position as a percentage of total page height
Content completion rate	0.18	Percentage of content sections the viewport has paused on (300ms+ per section)
Scroll velocity profile	0.15	Speed pattern: fast-skip vs. slow-read sections mapped to content zones
Pause-to-read ratio	0.14	Time spent paused vs. actively scrolling (high ratio = careful reading)
Re-scroll events	0.12	Scrolling back up to re-read a previously viewed section
Fold interaction	0.08	Whether the visitor scrolled past the initial viewport fold (and how quickly)
Scroll-to-action correlation	0.07	Whether scrolling to a CTA section correlates with interaction
Content zone dwell time	0.06	Time spent in each content zone (hero, body, pricing, footer)

Scroll Patterns and What They Reveal

Five archetypes worth watching for — a useful way to read scroll telemetry, not a fixed ClickStream classification:

Pattern	Scroll Behavior	Interpretation	Action
The Reader	Slow, steady scroll with regular pauses	Carefully consuming content top-to-bottom	Serve related content, newsletter prompt
The Scanner	Fast scroll with brief pauses at headings	Looking for specific information	Improve headings, add table of contents
The Bottom-Liner	Fast scroll to bottom, then slow scroll up	Checking conclusion first, then reading details	Put key info in summary/conclusion
The Bouncer	Scrolls to fold, stops, leaves	Above-the-fold content did not convince them to continue	Optimize hero section, value proposition
The Re-Reader	Multiple scroll-up events to specific sections	Complex content requiring re-reading, or comparison with later content	Add anchored navigation, expandable details

Under the Hood: Scroll Zone Analysis (Illustrative Pseudocode)

interface ScrollZone {
  startPercent: number;
  endPercent: number;
  dwellMs: number;
  scrollVelocity: number;  // px/sec within zone
  revisits: number;
  contentType: 'hero' | 'body' | 'pricing' | 'cta' | 'footer';
}

function analyzeScrollDepth(zones: ScrollZone[]): ScrollIntelligence {
  const maxDepth = Math.max(...zones.map(z => z.endPercent));
  const totalDwell = zones.reduce((sum, z) => sum + z.dwellMs, 0);
  const totalScroll = zones.reduce((sum, z) =>
    sum + Math.abs(z.endPercent - z.startPercent), 0);

  const pauseRatio = totalDwell / (totalDwell + totalScroll);
  const reReadCount = zones.filter(z => z.revisits > 0).length;

  // Content completion: sections with 300ms+ dwell
  const completedSections = zones.filter(z => z.dwellMs >= 300).length;
  const completionRate = completedSections / zones.length;

  return {
    maxDepth,
    completionRate: Math.round(completionRate * 100),
    pauseRatio: Math.round(pauseRatio * 100),
    reReadSections: reReadCount,
    pattern: classifyScrollPattern(zones),
    score: calculateScrollScore(maxDepth, completionRate, pauseRatio, reReadCount)
  };
}

How Readiness, Frustration, and Scroll Interact

These scores provide overlapping but distinct views of purchase readiness:

Combination	Interpretation	Action
High Readiness + Low Frustration + Deep Scroll	Ready to buy: has read everything and is moving smoothly toward checkout	Minimize checkout friction, show trust badges
High Readiness + High Frustration + Shallow Scroll	Wants to buy but something is in the way (skipped product details, hit friction)	Surface reviews, guarantees, key product info; fix the friction point
Low Readiness + Low Frustration + Re-Read Scroll	Deep research phase: comparing options carefully	Comparison tools, side-by-side features
Low Readiness + Low Engagement + Bouncer Scroll	Not engaged: landing page failed to hook them	A/B test hero section, improve value proposition

Conversion readiness tells you how close the visitor is to buying. Frustration tells you what is getting in the way. Scroll intelligence tells you how deeply they have evaluated your offering. Together, they enable truly intelligent real-time personalization.

Configuration & Tuning

Scoring is tunable per site — ClickStream's per-site scoring configuration lets you adjust how models weigh their behavioral inputs:

Per-site model weights: Adjust the relative weight of the behavioral signals feeding each model for your site's traffic patterns.
Scroll zone definitions: Customize content zone boundaries per page template for accurate scroll analysis.
Cross-model triggers: Create compound rules (e.g., "Conversion Readiness > 60 AND frustration rising") for targeted interventions.

Conversion Readiness, Hover Signals, and Scroll Intelligence