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Why PropensityQ?

The real problem with retargeting is not reach. It is misjudged intent.

Most platforms treat all non-converting users the same, pushing them into a single retargeting pool and bidding aggressively without understanding intent. This results in wasted spend, low-quality leads, and inflated cost per acquisition.

The Intelligent Propensity Engine separates signal from noise. By analyzing real behavioral indicators such as form interaction, pricing engagement, and session depth, it predicts the likelihood of a lead submission for every drop-off.

We build this engine to help teams stop guessing and start prioritizing. High-propensity users receive focused follow-up, medium-propensity users are nurtured, and low-propensity traffic is suppressed. The outcome is lower cost per lead, higher lead quality, and ad spend aligned to probability, not volume.

What PropensityQ Solves?

PropensityQ addresses the hidden inefficiencies inside modern performance marketing. It exposes where budget is wasted, intent is ignored, and audiences stop scaling, then replaces guesswork with probability-led action.
Retargeting Waste Is Sky-High

Most retargeting budgets are burned on users who were never close to converting. PropensityQ isolates high-propensity drop-offs and suppresses low-intent traffic, ensuring spend is concentrated where conversion likelihood actually exists.

Intent Signals Are Underutilized

Scroll depth, form interaction, hesitation, and pricing views are captured but rarely acted upon. PropensityQ converts these raw behavioral signals into measurable intent, making them usable for real targeting decisions.

Static Audiences Block Scale

Fixed audiences decay fast and force teams to overbid to maintain volume. PropensityQ continuously refreshes cohorts based on live behavior, allowing scale without sacrificing efficiency or lead quality.

Form Drop-offs are a Black Box

Traditional analytics stop at abandonment. PropensityQ goes deeper by understanding how close a user was to completing the form and why they exited, turning form drop-offs into ranked opportunities instead of dead ends.

How PropensityQ Works?

A probability-driven intelligence pipeline that converts raw behavioral data into actionable lead propensity scores.

PropensityQ operates as a continuous intelligence flow that connects on-site behavior with downstream media activation. Every user session is evaluated for lead intent, scored in real time, and translated into cohorts that marketing teams can immediately act on.

Data Sources Layer
PropensityQ ingests high-resolution behavioral data from your analytics stack. This includes clickstream activity, scroll depth, form interactions, field focus, pricing views, and time spent across key pages. These signals form the raw foundation for understanding lead intent before conversion occurs.
ETL + Feature Store Layer
Raw events are processed, normalized, and transformed into structured lead-intent features. PropensityQ builds feature vectors specific to form behavior, hesitation patterns, and decision signals. These features are stored and continuously updated to reflect evolving user behavior across sessions.
Model Layer
A purpose-built machine learning model evaluates each session against known conversion patterns. Users are mapped into an intent space where the likelihood of a lead-fill event is calculated as a propensity score between zero and one. The model continuously learns from outcomes to improve accuracy over time.
Serving Layer
Propensity scores are converted into actionable cohorts and synced to ad platforms such as Google, Meta, and LinkedIn through secure APIs. High, medium, and low propensity audiences are refreshed in near real time, enabling immediate bid and messaging adjustments based on predicted intent.

Core Capabilities for Growth Teams

Growth fails when intent is ignored. PropensityQ fixes that!
Predictive Lead-Fill Scoring

Every user session is evaluated for its likelihood to result in a lead submission. PropensityQ assigns a probability score based on observed behavior, allowing teams to act on predicted outcomes rather than past conversions alone.

High–Medium–Low Cohort Segmentation

Users are grouped into clear intent-based cohorts instead of broad retargeting pools. This segmentation enables differentiated bidding, messaging, and follow-up strategies aligned to how close each user is to converting.

Real-Time Audience Sync to Ad Platforms

PropensityQ pushes dynamic cohorts directly to ad platforms such as Google, Meta, and LinkedIn. Audiences update continuously, ensuring campaigns always target users based on their most recent intent signals.

Behavioral Signal Intelligence (Micro-Event Mapping)

The engine captures fine-grained interactions, including form focus, hesitation, scroll behavior, and revisit patterns. These micro-events provide deeper insight into intent than page views or sessions alone.

Dynamic Bid Strategy Optimization

Bidding decisions are informed by propensity rather than static rules. High-intent users can be prioritized with aggressive bids, while medium-intent users are nurtured and low-intent users are deprioritized.

Spam and Low-Intent Traffic Suppression

PropensityQ identifies sessions with minimal or misleading intent and excludes them from performance campaigns. This protects budgets from being spent on traffic unlikely to produce genuine leads.

Funnel Stage Intent Categorization

User behavior is mapped to funnel stages such as exploration, evaluation, and commitment. This allows teams to align messaging and spend with where users truly are in their decision journey.

Drop-off Pattern Modelling

Drop-offs are analyzed for recurring friction points and behavioral patterns. This helps teams understand why users exit and which signals consistently precede successful conversions.

Budget Efficiency Insights Dashboard

Growth teams gain visibility into how spend aligns with intent. PropensityQ surfaces insights on where budgets are being wasted, where high-propensity users are underfunded, and how targeting decisions impact cost per lead.

The Propensity Loop

What starts as anonymous activity ends as ranked, activation-ready cohorts that improve with scale, not complexity.

The Propensity Loop continuously converts raw behavioral signals into probability-driven lead intent, updates scores in near real time, and feeds outcomes back into the system so targeting improves with every cycle.

The Propensity Loop

We are Krish

AI-led Digital Experience Agency

Being AI-led agency focused on clients’ growth, we deliver next-generation solutions, leveraging artificial intelligence to in areas like commerce, content and marketing to increase their revenue, global reach and ROI. We serve retailers, manufacturers, distributors, enterprises and conglomerates globally.

  • Global Team
  • Yrs of Experience
  • Revenue Empowered
  • Industries Served
  • Stores/Sites Launched
  • Awards Won

Let’s Predict Customer Intent

Clear signals for confident decisions.


  • Frequently Asked Questions

    What is a Propensity Engine and how does Krish implement it differently?
    A Propensity Engine predicts the likelihood of a user completing a specific action, such as filling a lead form. At Krish, we build PropensityQ using real behavioral signals, custom feature engineering, and outcome-driven models, not generic scoring templates or static rules.
    How does PropensityQ identify high-intent users before conversion?
    PropensityQ analyzes micro-events such as form interaction, scroll depth, pricing engagement, and hesitation patterns. These signals are combined to calculate a lead-fill probability score for every session, even if the user exits without converting.
    Can PropensityQ integrate with existing analytics and ad platforms?
    Yes. PropensityQ is designed to ingest data from common analytics platforms and sync scored audiences to Google, Meta, LinkedIn, and other ad platforms through APIs without disrupting existing marketing workflows.
    How does PropensityQ reduce cost per lead and wasted ad spend?
    By suppressing low-propensity traffic and prioritizing high-intent cohorts, PropensityQ ensures ad budgets are focused on users most likely to convert. This improves bid efficiency, reduces wasted impressions, and increases lead quality.
    Is PropensityQ suitable for different industries and lead types?
    Yes. PropensityQ is industry-agnostic and adaptable across BFSI, education, real estate, automotive, and B2B. The model is trained on intent patterns specific to each use case, ensuring relevance regardless of funnel complexity.

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