Corporate Travel Management (CTM) Group

Feeling Understood Without Feeling Watched

In nearly two decades in marketing I've seen the full arc— from being unable to target meaningfully, to having access to more data about people than many people were comfortable with. What's become clear is that while people genuinely respond to personalisation, they also want privacy. The line between feeling understood and feeling watched has never been thinner.

The brands navigating this well are making deliberate choices–not using every signal available just because they technically can, and being thoughtful about timing. Serving someone hyper personalised content within seconds of a page visit can start to feel like surveillance. Good personalisation requires strategy and restraint as much as capability.

The second shift is execution. Most frameworks assume an enterprise stack and a large team, but the practical reality for many marketers is a lean team, a mid-market tool set, and an expectation to deliver across multiple audiences and markets. Fewer, better-defined audience cohorts consistently outperform attempting granular personalisation across a wide range of audiences the team can't sustain.

On the privacy side, the foundation is having governance properly embedded in your marketing foundations-cookie consent across key markets, email compliance, and a genuine working relationship with your DPO and legal team. But the harder decisions can come when you have the legal ability to do something but choose not to. There's frequent pressure to reach out directly to people with marketing messages who've interacted with us but never opted in. Technically permissible in some markets—but the experience from the recipient's end is unsolicited outreach from a brand they moved past once. We want our owned audiences to want to hear from us, they are more engaged and result in better outcomes. For those who haven't opted in, there are better ways to build familiarity first before going direct. The question shouldn't be "can we reach these people" but "should we, and what will they think of us when we do."

From Vanity Metrics to Genuine Business Insight

Performance analytics, when used well, reshapes how decisions get made before a campaign launches. We've refocused how we analyse our performance metrics and it's having a much greater influence on decisions. The honest question we keep asking is whether we're looking at the right data-clicks and form fills are easy to measure but they don't tell you whether the campaign actually worked. What matters is what converts to genuine opportunity and ultimately to customer. A campaign that drives strong engagement but attracts poor quality leads has missed the mark regardless of how good the dashboard looks. We track that full journey through Marketo and CRM to maintain visibility across the pipeline. That said, in a long B2B buyer journey you can't ignore engagement metrics entirely- someone might interact with content months before they're ready to buy. The challenge is making sure those surface metrics are informing the picture rather than becoming the whole story.

“Treat AI as a problem solver for specific problems rather than a blanket solution looking for a problem.”

Connecting early engagement signals to conversion outcomes that can be months or even years after capturing the campaign lead has driven some real strategic shifts. When the data showed we were converting strongly in a segment that wasn't our primary target, it prompted a full re-examination of targeting, messaging and content to understand why- and how to shift that conversion toward the audiences we actually wanted to be winning.

The challenge is that consolidating meaningful data can take real time and effort. It's easy to drown in easily obtainable vanity metrics that look good but don't move the business forward. The harder work is knowing what actually matters and being willing to act on what it tells you.

That same discipline of letting data lead also changes how creative work gets done. The challenge isn't protecting creativity from data, it's making sure data is informing decisions before the creative work starts rather than being used to validate instincts after the fact.

In practice that means data is part of the briefing process, not a post-campaign review. Before creative development begins we're looking at what the data is telling us about the audience-their pain points, how they talk about their own problems, intent signals, page and site visit behaviour, existing providers they use and so on. Knowing exactly who you're talking to, what they care about, and where they are in the buying journey means creative energy goes toward solving a specific problem and speaking to the audience where they are, in their voice-rather than producing something that gets lost in a sea of generic messaging people scroll past every day.

The difference between a campaign built on that kind of audience insight and a broad brand campaign is significantyou can often be more genuinely creative, specific and ‘fun’ with an audience you know will respond to the message rather than the generic and safe for everyone.

The Future of Targeting and Content Distribution

Looking ahead, audience targeting will continue to move toward more sophisticated segmentation and intent-based signals. AI is accelerating that, but its most immediate practical impact is in productivity and insightconsolidating data across multiple platforms currently costs marketing teams enormous amounts of time, and AI is making it faster and easier to bring those data points together and act on them.

The more significant shift is in how we think about content distribution and measurement altogether. We've spent years keeping bots out-now we have to learn how to invite them in. We're moving from SEO and website traffic as the primary signals of content performance to ensuring your content surfaces in LLMs when your audience asks relevant questions. That requires thinking about content differentlyfrom how it's structured on the back end to how it reads on the front end. Targeting the ‘bots’ is becoming as important as targeting the person.

My advice is to think about marketing and AI pragmatically. Some of this isn't optional-if your content isn't structured to be found by LLMs, you will be left behind. But knowing where AI tools are actually useful in your specific context matters almost as much. Commit too early to the wrong thing in a MarTech environment and that piece of string can be very hard and costly to unravel. With lean teams and lean budgets, look at where the biggest realistic win is for you. Treat AI as a problem solver for specific problems rather than a blanket solution looking for a problem.