Hyper-Personalisation Design
Personalisation at the segment level is table stakes — showing a "developer" a different UI than a "marketer" is not hyper-personalisation. Hyper-personalisation is adapting the experience at the individual level, in real time, based on that specific user's behaviour, preferences, and goals. AI makes this feasible without hand-coding every path.
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Context
Personalisation maturity levels:| Level | What it is | Requires |
|---|---|---|
| Segmentation | Same experience for all users in a cohort | Basic analytics, manual rules |
| Behavioural | Adapts based on what a user has done | Event tracking, rule engine |
| Predictive | Anticipates what a user will want next | ML model, feature engineering |
| Hyper-personal | Adapts to the individual in real time | Embeddings, real-time inference, feedback loops |
More personalisation requires more data. The PM must define data used, user control, and disclosure before designing the logic.
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Step 1 — Define the personalisation goal
Ask: what's being personalised, what user outcome to improve, data available today, data needed, and user awareness/control.
Step 2 — Design the user data model
Explicit data (role, goals, preferences), implicit data (topics of interest, preferred content format, engagement timing), feedback signals (positive/negative/neutral), data freshness rules, and model reset capability.
Step 3 — Design the recommendation logic
Algorithm options: collaborative filtering, content-based filtering, or hybrid. Define ranking factor weights and diversity injection (e.g., 20% of recommendations from outside established interests).
Step 4 — Define the personalisation feedback loop
Real-time signals (thumbs up/down, save, dismiss) and batch signals (dwell time, reading patterns). Transparency labels: "Because you clicked [tag]".
Step 5 — Define personalisation guardrails
Diversity floor, recency floor, transparency requirement, user control (view/edit/reset profile, turn off personalisation), sensitive topic handling, and quarterly equity audit.
Step 6 — Define success metrics
Primary: engagement metric. Secondary: CTR, time to engagement, return rate, diversity score. Guardrail metrics: alert if diversity decreases, negative feedback increases, or opt-out rate increases.