Tap to begin · 13 screens
Predicting
the Why.
the Why.
A guided walkthrough of the ABSD framework research. Tap the right side to advance. Tap the left edge to go back. Hold to pause.
The problem
73%
of companies stuck at basic segmentation
40%
more revenue from good personalisation
500B
events/day · Netflix · still Layer 1 only
The data exists.
The interpretation doesn't.
The interpretation doesn't.
Core claim
AI systems predict what users will do.
Not why they're doing it.
Not why they're doing it.
That is the gap. It has a structure. It has a name. ABSD. Four layers from the observable surface to the motivational core.
The ABSD framework · four layers
Drive
Why · L4 · longitudinal
State
What users feel · L3 · 70–79%
Behaviour
Patterns over time · L2 · 82%
Action
Observable · where AI sits today · L1 · 96%
Drive generates State → State produces Behaviour → Behaviour manifests as Action. AI must infer upward.
Confidence gradient
Deeper inference.
Lower certainty.
Lower certainty.
Action96%
Behaviour82%
State70–79%
DriveLongitudinal
Four traditions · never previously connected
Self-Determination Theory
Drive layer
Affective Computing
State layer · 70–79%
JITAI Research
Behaviour layer
Learning Analytics
Action layer · 78–84%
Each tradition solves one layer. ABSD is the first framework to connect all four.
Seven signals · already in your data
Hesitation
Pause before action · uncertainty
Thrashing
Rapid switching · overwhelm
Regression
Returns to earlier content · consolidation
Acceleration
Speeding through · boredom
Persistence
Continues after failure · high drive
Avoidance
Early exit · anxiety
Flow
Steady progress · optimal state · don't interrupt
Critical qualification
Same signal.
Different context.
Different meaning.
Different context.
Different meaning.
A 30-second pause before a complex financial trade is prudence. Before a simple form field it is confusion. Three modifiers determine which: domain, user history, and temporal context. Apply all three before acting on any signal.
Case study · CX · Netflix
500 billion events
per day. Layer 1 only.
per day. Layer 1 only.
When you pause a film, Netflix logs the event. It cannot determine whether that pause was anticipation, confusion, or discomfort. Three possible states. One data point. The signals for Layer 3 inference are in the data. The framework to read them is not.
Case study · EdTech · Khan Academy
Mastery tracked.
Not understood.
Not understood.
A student who answers correctly after 45 seconds of hesitation receives the same credit as one who answers in 4 seconds. VanLehn (2011): AI tutors underperform human tutors precisely because they cannot read the psychological state behind the answer. That gap remains accurate today.
The meta-insight
Right now, you are being observed through the framework you are learning about.
The survey captures how you answer - hesitation, pace, answer changes - not just what you say. The closing card reflects your ABSD layer profile back at you. The thesis demonstrated, not just described.
Open research · live now
50 references.
Live survey.
Always open.
Live survey.
Always open.
50
References · 4 traditions
14+
Survey responses
5
Expert interviews
SP Jain EMBA · Applied Research Project. Full paper always readable - no login, no paywall.
Your next step
Does this resonate
with your experience?
with your experience?
Your answers and your behavioural patterns as you answer are both part of the research.
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