Adaptive Intent Graph™

The Problem We Solve

Traditional personalisation treats all 35-year-old males from South Africa are the same. But what if one is researching for his company, another is price-comparing for himself, and a third is gift-shopping for his partner?

Traditional Personalization

Static personas based on demographics

Slow batch processing

One-size-fits-few experiences

Creepy "we know who you are" messaging

Adaptive Intent Graph™

Dynamic intent inference

Real-time experience morphing

Context-aware messaging

Privacy-preserving intelligence

Demographics don't buy. Intentions do.

How it works

Behavioral Signal Collection

Every micro-interaction tells a story about intent. We capture and interpret these signals in real-time.

Signals We Track:

Scroll velocity and patterns

Hover behavior and hesitation

Click sequences and timing

Content consumption depth

Navigation patterns

Device and time context

Privacy First: All processing happens in real-time. No long-term behavioral profiles stored.

Intent Classification Model

Our ML models classify visitor intent into dynamic categories that evolve with each interaction.

Intent Dimensions:

Mission: Browsing vs. Buying vs. Researching

Urgency: Immediate need vs. Future planning

Confidence: Expert vs. Novice vs. Confused

Context: Personal vs. Business vs. Gift

Mindset: Rational vs. Emotional vs. Social

Real-time Reclassification: Intent updates every 3 seconds based on new signals.

Experience Orchestration

Based on detected intent, we dynamically adjust every element of the experience.

What Changes:

Content hierarchy and messaging

Navigation prominence and options

Call-to-action language and placement

Social proof and trust signals

Pricing and offer presentation

Support and help visibility

Change Velocity: Smooth transitions prevent jarring experience shifts.

Feedback Loop Optimization

Every interaction trains the model to better predict and serve future visitors.

Learning Mechanisms:

Conversion event correlation

Abandonment pattern analysis

Satisfaction signal tracking

Cross-session intent evolution

Cohort behavior modeling

Model Improvement: 2-3% accuracy gain monthly through continuous learning.

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ROI Impact Model

Typical Improvements by Metric:

+35-50%

Engagement Rate

+15-25%

Average Order Value

-30-40%

Support Ticket Volume

+20-40%

Conversion Rate

+25-30%

Customer Satisfaction

Deliverables

Intent Taxonomy Development

Custom intent categories for your business Behavioral signal mapping Experience variation planning Success metric definition

Technical Implementation

Edge computing setup ML model deployment Experience orchestration layer Analytics integration

Experience Templates

Intent-based variations Dynamic component library Personalization rules engine Testing framework

Performance Monitoring

Intent accuracy tracking Conversion lift analysis Experience performance metrics Optimization recommendations

Time to Value:

Week 1

Basic intent detection live

Week 2

First experience adaptations

Week 3-4

ML optimization & Full deployment

Week 6

Measurable ROI positive

Schedule a call with us to find out how we can help you.

We’re more than a digital consultancy. We’re your partners in growth, humanizing every interaction and building lasting connections.

Yusuf Abrahams

Experience Director

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Stop Segmenting Who.

Start Predicting Why.

Yusuf Abrahams