At the Marketing Association meetup this week, I asked the room a simple question: have you ever sat down with a customer and asked them directly how they feel about what you are doing with their data?
Nobody had.
That silence matters, because most marketers can explain why they collect customer data. Far fewer can explain, in plain English, what actually happens to it. Where it goes. Who touches it. What decisions it drives. What a customer sees differently as a result.
That is the gap I wanted to explore. Not data governance in the abstract. Not whether the privacy policy technically covers it. The real question is whether a customer would still feel respected if we explained the full data journey clearly.
The numbers show why this matters. 82% of New Zealanders want more say in how their information is collected and used, 64% want to know more about what organisations can do with their personal information, and 66% would consider switching providers over poor privacy or security practices (Office of the Privacy Commissioner). At the same time, customers still want relevance. NZ-reported Qualtrics findings show 58% want brands to tailor experiences, but only 30% believe those tailored experiences are worth the privacy trade-off (IT Brief NZ).
Read those last two numbers together. Customers want better experiences, but they do not automatically trust the mechanisms behind them.
For me, this is the difference between permission and comprehension. Permission asks: did we disclose it, did they tick the box, is it covered in the privacy policy? Comprehension asks something harder: would the customer understand why they received this message, what behaviour triggered it, where their data went, and what else it might be used for?
A customer might understand that they gave you their email address when they signed up to a loyalty programme. They may not understand that their purchases, clicks, app behaviour, website visits, location, offer redemptions, or service interactions are being combined to decide what they see next. They may not realise that their data can move between a CRM, an email platform, a customer data platform, analytics tools, agency partners, advertising platforms, and AI-enabled decisioning tools.
None of that is automatically wrong. In many cases, it creates better experiences. It can reduce irrelevant marketing, suppress messages that are no longer useful, remind customers about products they genuinely need, or make offers more timely. But if the customer would be surprised by the mechanics, we have a trust issue to examine.
That is why I shared a simple framework marketers can use: the 5 M model. Map. Move. Model. Message. Meaning.
The first M is Map. What data are you actually collecting? Not just the obvious fields like name, email, mobile number, or date of birth. Map the behavioural signals too: purchases, browsing, clicks, opens, app events, service calls, loyalty activity, complaints, preferences, inferred interests, eligibility, and engagement history. If you cannot list it clearly, you cannot explain it clearly.
The second M is Move. Where does that data go? This is where the customer’s mental model often breaks down. They may think they gave data to one brand, but that data may flow through multiple systems and partners. It may be used in owned channels such as email, SMS, app, web, and call centre experiences. It may also move into paid media environments through customer match, suppression audiences, lookalike modelling, retargeting, or campaign measurement. The question is not only “is this permitted?” It is “would the customer understand this movement?”
The third M is Model. What decisions are made from the data? This includes segmentation, propensity scores, churn risk, lifecycle stage, product recommendations, next-best action, eligibility, suppression rules, frequency controls, and AI-assisted decisioning. As AI enters more marketing operations, this step becomes even more important. The more data we feed into models, agents, platforms, and decisioning tools, the greater our responsibility to explain what is happening. The obligation to create understanding follows the data wherever it goes.
The fourth M is Message. What does the customer experience as a result? Do they receive an email, an app push, a targeted ad, a personalised offer, a different website experience, a call centre prompt, or no message at all because they have been suppressed? This is the customer-facing outcome. It is also where trust is won or lost, because customers do not experience data governance. They experience relevance, repetition, convenience, creepiness, exclusion, or control.
The fifth M is Meaning. Would the customer understand and accept the explanation? This is the most important step. It asks us to move from internal language to customer language. “We use data to optimise customer engagement” may make sense inside a marketing team. But it tells the customer very little. A plainer version might be: “We use your purchases, clicks, and preferences to decide which offers you see, which messages we suppress, and which ads we show or avoid showing you.”
That version is more uncomfortable because it is more honest.
I think every marketing team should pick one real customer journey and run the 5 M model against it. Choose a loyalty sign-up, an abandoned cart journey, a win-back campaign, a personalised offer, a retargeting journey, or an onboarding programme. Map what is collected. Track where it moves. Identify what decisions are made. Look at what the customer experiences. Then ask what it means from the customer’s point of view.
The opportunity is not to use less data by default. Good data use can make marketing more useful and customer experiences better. But the value exchange needs to be visible enough to be trusted.
That is the customer across the table test. If a customer sat across from you and asked what was really happening with their data, could you explain it plainly? And if you did, would they still feel respected?
If we have never asked, we probably do not know whether we pass.
Written by: Gabby Mclean - Campaign Analytics & Reporting Manager, Woolworths NZ, 25th May 2026