How does purchase timing prediction work?

Purchase timing prediction analyzes behavioral cadence — visit frequency patterns, time spent on product pages across sessions, comparison shopping intensity, and historical conversion timelines — to predict when a visitor is most likely to complete a purchase.

What causes cart abandonment and how can analytics help?

Cart abandonment is driven by unexpected costs, complex checkout flows, comparison shopping, and decision fatigue. Behavioral analytics detects abandonment intent before it happens by identifying hesitation patterns, enabling real-time interventions like simplified checkout or targeted offers.

Can you predict when a visitor will buy?

Yes. By analyzing behavioral patterns across multiple sessions — increasing product page engagement, narrowing comparison sets, and return visit acceleration — ClickStream's purchase timing model estimates conversion probability windows, enabling precisely timed marketing outreach.

Abandonment Detection and Purchase Timing

What You'll See in the Dashboard

Open the Intelligence tab to find the Abandonment Detection and Purchase Timing cards. Abandonment shows a real-time probability that the current visitor is about to leave without converting — watch it spike as exit signals accumulate. Purchase Timing displays optimal send windows for each visitor based on their historical conversion patterns.

Business Actions: Set up a Rule to fire an exit-intent overlay when Abandonment Detection exceeds 80. Use Purchase Timing scores to schedule retargeting emails at the exact hour each visitor is most likely to convert. Feed abandonment signals into your cart-recovery automation.

Model 12: Abandonment Detection

The abandonment model detects when a user is about to leave without completing their intended action -- whether that is a cart checkout, a form submission, or simply engaging with content. Unlike post-hoc abandonment analysis, ClickStream detects abandonment signals in real time, giving you a window to intervene.

The 8 Abandonment Signals

Signal	Weight	Detection Method
Mouse drift to browser chrome	0.20	Cursor moving toward close/back buttons or address bar
Idle timeout	0.18	No interaction for 30+ seconds on an active page (cart, form)
Tab switch away	0.15	Page visibility hidden while in a conversion flow
Scroll to top on cart/checkout	0.12	Scrolling back to top of checkout page (looking to navigate away)
Form abandonment cues	0.10	Stopping mid-form after completing 30%+ of fields
Price shock indicators	0.08	Viewing total/shipping cost then pausing >10 seconds
Back button readiness	0.09	Browser back button hover or keyboard shortcut preparation
Engagement collapse	0.08	Sharp drop in mouse movement and interaction rate

The 4 Abandonment Stages

Score Range	Stage	User State	Intervention Window
0–25	Engaged	Actively interacting. No abandonment signals.	N/A -- user is engaged.
26–50	Wavering	Slowing down, some hesitation signals.	15–30 seconds. Soft nudge opportunity.
51–75	Disengaging	Clear disengagement pattern. Preparing to leave.	5–15 seconds. Immediate intervention needed.
76–100	Exiting	Active exit behavior. Mouse at browser chrome.	1–5 seconds. Last-chance intervention only.

Exit Intent Formula

ClickStream's exit intent detection goes beyond simple "mouse near top of viewport" heuristics. It uses a composite formula that accounts for velocity, trajectory, and context:

TypeScript

function calculateExitIntent(mouseEvents: MouseEvent[], context: PageContext): number {
  const recent = mouseEvents.slice(-10); // last 10 mouse positions
  if (recent.length < 3) return 0;

  // Calculate upward velocity (toward browser chrome)
  const yVelocity = (recent[0].y - recent[recent.length - 1].y)
    / (recent[recent.length - 1].timestamp - recent[0].timestamp);

  // Position factor: how close to viewport top
  const positionFactor = Math.max(0, 1 - recent[recent.length - 1].y / 100);

  // Trajectory: is cursor heading toward close/back buttons?
  const trajectory = calculateTrajectoryToTarget(recent, context.closeButtonPos);

  // Context factor: higher weight if user has items in cart
  const contextWeight = context.hasCartItems ? 1.5 : 1.0;

  const rawScore = (
    yVelocity * 0.35 +
    positionFactor * 0.30 +
    trajectory * 0.35
  ) * contextWeight;

  return Math.min(100, Math.max(0, rawScore));
}

Timing Pipeline Latency Breakdown

For abandonment detection to work, the entire pipeline from signal detection to intervention display must complete before the user leaves. Here is the latency budget:

Pipeline Stage	p50 Latency	p99 Latency	Budget
Browser event capture	1ms	3ms	5ms
SDK signal processing	2ms	8ms	10ms
Edge worker scoring	5ms	18ms	25ms
Intervention decision	1ms	3ms	5ms
Response to browser	3ms	15ms	20ms
DOM manipulation (show overlay)	4ms	12ms	15ms
Total	16ms	59ms	80ms

The 80ms total budget is critical. Research shows that exit intent users take 200–400ms from "decision to leave" to "browser close." Our pipeline must complete well within that window to display an intervention.

Model 13: Purchase Timing

The purchase timing model predicts how close a user is to making a purchase decision. Unlike the intent score (which measures willingness), purchase timing measures proximity -- how many more interactions until the purchase event occurs.

The 8 Timing Signals

Signal	Weight	Description
Funnel stage progression	0.22	How far through the conversion funnel (browse → product → cart → checkout)
Decision confidence velocity	0.18	Rate of change in decision confidence score (rising = closer)
Cart completeness	0.15	Percentage of typical pre-purchase actions completed
Comparison narrowing	0.12	Convergence of product consideration set
Payment readiness signals	0.10	Scrolling to payment section, clicking payment method selector
Time pressure indicators	0.08	Visiting sale pages, countdown timers, limited stock messages
Visit frequency acceleration	0.08	Increasing visit frequency = decision approaching
Social proof consumption	0.07	Reading reviews then stopping (final validation before purchase)

Proximity Score Ranges

Score Range	Proximity	Estimated Time to Purchase	Optimal Action
0–20	Distant	Multiple sessions away	Content marketing, brand building
21–40	Approaching	1–3 sessions away	Retargeting, email nurture
41–60	Near	This session, but more exploration needed	Social proof, comparison tools
61–80	Imminent	Within the next few minutes	Remove friction, ensure smooth checkout
81–100	Ready now	Within seconds	Clear CTA, one-click purchase if possible

Urgency Classification

ClickStream classifies the type of urgency driving the purchase timing, which informs what kind of messaging is appropriate:

Urgency Type	Detection Signals	Messaging Strategy
Time-limited	Sale page views, countdown timer interaction, promo code entry	Reinforce deadline, show remaining time
Need-driven	Urgent search terms, fast navigation, minimal comparison	Emphasize immediate availability and fast delivery
Research-complete	Long research phase now converging, reviews consumed	Confidence reinforcement, satisfaction guarantee
Externally triggered	Referrer from price-alert email, social recommendation	Validate the referral source, show deal details

Visit Frequency Acceleration

One of the strongest cross-session timing signals is increasing visit frequency. A user who visited once a week for three weeks, then twice in three days, is approaching a decision: