AI personalisation in marketing should make a message more useful, not make a customer feel watched. Start with a context the customer understands: a selected product category, an expressed preference or a stage in an enquiry. The quality of the decision matters more than the number of variations the system can generate.
Choose a customer benefit first
Define the problem personalisation should solve. It might help someone compare suitable options, find relevant instructions or understand the next step after an enquiry. Avoid using personalisation simply because a tool offers it.
A useful test is whether you could explain the choice to the customer in plain language. “You asked about gifting, so here is our packaging guide” is clearer than an unexplained assumption about their private circumstances.
Use appropriate and reliable inputs
Work with information your business is entitled to use and has assessed for the intended purpose. Do not infer sensitive characteristics from browsing patterns or upload customer records into an unapproved tool. Follow your organisation’s privacy and data-handling process.
Check whether the data is current. A previous purchase does not prove an ongoing preference, and an old enquiry may no longer represent an active need. Provide a sensible generic experience when the signal is weak.
Create a small set of meaningful variants
Begin with a few audience needs rather than a unique message for every person. For example, a product guide might distinguish first-time selection, gifting and repeat purchase. Give each version a clear information purpose.
Keep prices, product facts and availability tied to an approved source. AI-generated copy should not invent a discount, delivery promise or product claim in an attempt to sound more relevant.
Review the full experience
Test the message and destination together. A personalised email that opens a generic or contradictory page can create confusion. Check that the same offer and information appear throughout the journey.
Use human review for tone, fairness and factual accuracy. NIST’s risk framework is a useful governance reference, but your team still needs practical ownership of inputs, approvals and error handling.
Measure benefit without overclaiming
Compare the variants with a suitable baseline and record the intended outcome. Useful measures might include successful task completion, relevant enquiries or fewer repeated support questions. A higher click rate alone does not prove a better customer experience.
Monitor complaints, opt-outs and incorrect recommendations as well as positive responses. Stop or revise a variant that creates confusion. The objective is a more helpful journey, not personalisation at any cost.
Primary source
NIST provides a voluntary framework for managing AI risks and a companion generative AI profile. It is a governance reference, not evidence that a particular campaign will perform. Read NIST AI Risk Management Framework.
How The Media Lab can help
The Media Lab can develop message variations and creative concepts around clearly defined audience needs and approved brand information. Explore our AI content production, social media marketing services in Dubai, review our portfolio, or send a brief to our team.
Frequently asked questions
Does personalisation require knowing everything about a customer?
No. A small amount of relevant, appropriately used context may be enough. More data can add risk without adding value.
Can AI write offers automatically?
Only within controlled rules and verified information. It should not invent prices, discounts or promises.
What should happen when the data is uncertain?
Use a useful generic message or ask the customer to choose a preference rather than making an intrusive assumption.
