AI Personalisation at Scale

AI personalisation at scale: the architecture that actually works

Every ESP now sells an AI personalisation layer. Most lifecycle programs deploy them and quietly find the lift smaller than the deck claimed. The reason isn't the model — it's the architecture underneath. Here's the data → model → activation stack that decides whether AI personalisation moves revenue or moves nothing.

9 min read

Custom attributes: the data design that decides what your program can do

Custom attributes are infrastructure. Designed well, they enable every future campaign. Designed badly, they become the reason 'can we segment on X?' is a multi-week engineering project instead of a 15-minute one. Here's the design discipline that prevents the mess.

7 min read

Predictive models in lifecycle: churn, propensity, and recommendations without the magic

Predictive models in lifecycle are mostly three things: churn risk, conversion propensity, and product recommendations. Each one earns or loses its place based on how its score actually changes a decision. Here's the operator view of what's worth deploying, what to expect from ESP-native suites, and when to build your own.

9 min read

Generative AI for lifecycle content: where it earns its place and where it embarrasses you

Generative AI inside lifecycle ESPs has moved from novelty to default in 18 months. BrazeAI (formerly Sage AI), Iterable Copy Assist, Klaviyo's subject line generator — they all promise per-message copy at scale. Some uses are genuinely useful. Others are a fast path to brand drift, factual errors, and reputational damage. Here's the line.

8 min read

Send-time optimisation: what it really moves, and what it doesn't

Every ESP markets an STO feature and every vendor deck shows lift. The honest version: STO moves open rate 3–8%, rarely revenue, and only for certain program types. Here's when it's worth turning on.

7 min read

Personalisation that doesn't feel creepy

There's a line between personalisation that earns trust and personalisation that breaks it. It's not where most people think it is — it's about how you signal what you know, not what you know. Here's the line, how programs cross it without noticing, and the patterns that keep you on the right side.

9 min read

Measuring AI personalisation lift honestly

Every vendor case study shows AI personalisation moving the numbers. Most internal post-mortems show the lift evaporating once a proper holdout is in place. The gap between the two is the measurement methodology. Here's the framework for proving — to yourself, your CFO, and the auditor — whether AI personalisation is actually earning its place.

8 min read