One caution that is worth naming clearly before we close this module.
When you load a client project with detailed information and ask the tool to personalise output, the tool will sometimes extrapolate beyond what the client has explicitly said. It will infer preferences based on patterns in the data. It will suggest things the client has not asked for. That is useful when the inference is sound. It is a risk when the inference is wrong.
The line between personalisation and assumption is your professional judgement. When the tool suggests a recommendation based on what it has inferred from the client’s history, ask yourself: do I know this about the client, or is the tool guessing? If it is a guess, even a plausible one, treat it as a hypothesis to test in the consultation rather than a fact to build on.
A client who said they love wildlife does not necessarily want a walking safari. A couple who travelled with children last time may be travelling without them this time. A client who booked a luxury lodge does not automatically want the most expensive option next time. Preferences evolve. Circumstances change. The tool works from the data in the project. You work from the relationship.
Use the tool’s inferences as prompts for your own thinking, not as substitutes for it. The personalisation is at its best when it reflects what you know and confirms in conversation, not what the tool extrapolates and you accept without checking.
