Personalisation With Privacy in Mind
Design personalised marketing and sales messages that customers find useful, not unsettling
A taste of a lesson
We want AI to write emails like 'Still thinking about our loan options? We saw you checked the page twice.' Good idea?
I would advise against that copy. Telling someone you watched them visit a loan page twice exposes your tracking, and financial need is a sensitive area where many readers feel watched and some rules are stricter. It may also reach the wrong person on a shared device. A safer pattern is a helpful message at segment level, such as a guide to comparing loan options, sent to people who opted in to that topic. Check the specifics with your privacy team. Quick test: which of your planned data fields would a customer be surprised you used?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose personalisation data using consent, expectation and minimisation principles
- Decide between segment, behavioural and individual personalisation for a use case
- Set guardrails for AI generated variants: allowed fields, banned inferences, review
- Measure personalisation honestly with random holdout groups and negative signals
- Prepare clear questions for your legal and privacy team before launch
Lesson plan
- 1 Kinds of personalisation Distinguish segment, behavioural and individual personalisation and their costs. Start
- 2 Data, consent and expectation Judge whether a data use is lawful, expected and necessary for the purpose. Start
- 3 Sensitive inferences and creepiness Recognise data uses that harm trust or touch protected categories. Start
- 4 Guardrails for AI variants Constrain AI generation so variants stay accurate, on brand and within policy. Start
- 5 Measuring with holdouts Prove or disprove the value of personalisation with fair comparisons. Start
- 6 Launch review Run a pre launch review covering data, copy, measurement and escalation. Start
Try asking
About this tutor
For marketers, sales leaders and growth teams designing personalised emails, website content, offers and outreach at scale. AI makes it cheap to tailor messages for every person, which raises hard questions: what data you are allowed to use, what customers expect, where helpful becomes creepy, and how to prove personalisation works at all. This tutor teaches a framework for choosing data based on consent and expectation, data minimisation, segment versus individual personalisation, guardrails for AI generated variants, and honest measurement with holdout groups. It covers broad privacy principles across regions without giving legal advice, and always sends you back to your legal and privacy colleagues for decisions.
Reviews
4.3
3 ratingsSample
- Olivia P.Sample
The expectation test is now part of our campaign brief. The holdout lesson made us realise our last personalised campaign had never been compared to anything.
- Arjun N.Sample
Rigorous and fair. It clearly is not legal advice and says so, which I appreciated. I wanted more on consent records in practice.
- Beatriz C.Sample
Good guardrails section. Filling limited slots instead of free generation reduced our review time a lot.
About the teacher
Sales with AI support that keeps the human relationship at the centre
9 tutors 401 lessons taught Sample
I teach salespeople and founders who sell how to use AI for research, preparation and follow up while keeping every conversation personal. I have carried a quota and managed small sales teams, so I know how easy it is to send a thousand bland messages and call it pipeline. My lessons are practical: we research a real prospect, write an...
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