A customer can be on the right website, looking at the right product, and still be unsure what to do next.
I spend a lot of my working day in that gap.
In education, the decision might involve choosing a study program, preparing for an exam, or taking the next step toward a different career. The website has products, content, pricing, and ways to buy. The person arriving has a goal, a deadline, a budget, and questions we may not have answered yet.
Relevance is how well we connect those two realities.
Across my work in education, healthcare, and consumer products, I keep returning to a practical question:
What does this person need to understand or do next, and how can the experience help?
Healthcare taught me to recognize intent early
During my time in healthcare, I led digital experience and commerce strategy that included an online appointment-booking journey. We moved toward a guided process, using meaningful decisions to shape the steps that followed.
Someone seeking emergency care had different needs from someone arranging a routine appointment. A returning patient brought a different relationship with the organization from someone visiting for the first time. Those differences deserved to influence the experience.
The work involved recognizing intent early, reducing unnecessary questions, and connecting someone with an appropriate appointment. It also made the individual steps easier to measure, so we could see where people hesitated or left.
That experience continues to shape how I think about relevance. Sometimes the most useful thing a digital experience can do is recognize what someone has already told you and avoid making them repeat the work.
In education relevance often begins with product clarity
Education brings its own version of that challenge.
A learner may understand the outcome they want while being uncertain about which product will help them get there. Two packages can look similar until someone understands the differences in instruction, practice, support, schedule, or access.
In our work on guided product selection, questions about preparation stage and learning preferences help make that decision more manageable. The answers give us context we can use to narrow the options.
The quality of that guidance depends on what happens next. A recommendation should make its reasoning understandable. Someone should be able to compare options, change an answer, or take a different path.
There is a meaningful responsibility here. We are helping people evaluate something that may affect their education, professional credentials, or future opportunities. The experience needs to give them enough clarity to make a choice they can understand.
I think about relevance in those practical terms. Can someone see whether a schedule fits their life? Can they understand what is included? Are the differences between products clear enough to matter?
Those questions influence the work long before someone reaches checkout.
Personalization gives us a way to act on context
Personalization has an important role in that work. It gives us ways to adapt an experience using information someone has shared or choices they have already made.
In education, that might mean using a learner’s preparation stage, exam level, timeline, or preferred way of studying to help organize the options. In healthcare, urgency and an existing patient relationship can shape the path to an appointment. In consumer products, the task someone is trying to complete can guide which product details deserve attention.
The practical question is what should change because we have that information. We might bring a relevant schedule forward, narrow a comparison, explain why an option fits, or avoid asking someone to start over.
That is how I think about personalization supporting relevance: the context needs to produce something useful for the person.
A personalized experience still needs to earn its place. A change that adds complexity or interrupts the task can make the journey harder. And an anonymous visitor deserves a clear, useful experience too. Better product explanations and navigation can improve relevance for everyone.
Relevance is the outcome I want to improve. Personalization is one of the tools available to help us get there.
Relevance depends on the work behind the page
A considerable part of my role involves connecting web strategy, UX, content, product, engineering, and analytics.
Relevance shows up in those conversations as decisions about product information, page structure, comparison tools, schedules, pricing, and the next step we make available.
A schedule hidden too far down a page can leave someone uncertain about whether a course will work for them. A comparison table can contain accurate information and still fail to explain the differences that matter. An interruption can take attention away from the decision someone came to make.
These are ordinary website decisions. Together, they determine how useful the experience feels.
The systems behind them matter too. Product details need a dependable source. Content needs to stay current. Teams need clear ownership of changes. An offer described on one page needs to remain consistent as someone moves through the journey.
That is part of why platform and content work matters to me. A shared digital foundation gives teams a better way to deliver and maintain the experience. Its value becomes visible in what a customer can reliably understand and do.
AI raises the value of the visit we earn
AI is changing where parts of the decision happen. People can use an answer or an assistant to understand a category, compare possibilities, or build a shortlist before visiting a website.
For journeys where those answers reduce the need to click, we have fewer opportunities to make the website useful. When someone does arrive, they may be trying to confirm a specific detail, check availability, resolve a concern, or decide whether a recommendation holds up.
I think that raises the standard for relevance on arrival.
Someone who has already narrowed their options needs an easy way to compare the remaining differences. Someone checking whether a course fits their life needs the schedule. Someone verifying a recommendation needs clear evidence and accurate product information.
The website may never receive the conversation that brought them there.
We still need to learn what matters through useful questions and the choices people make on the site, while keeping the information they need easy to find.
That changes how I think about a landing page. It needs to work as an entry point into the decision, wherever that decision began. The quality of the visit we earn becomes an even more important part of the experience.
Experimentation helps us challenge our assumptions
Relevance can sound obvious in a meeting. Everyone can agree that an experience should be more useful.
The difficult part is establishing whether our idea of useful matches the customer’s experience.
A shorter journey may remove avoidable effort. It may also remove information someone needed before feeling comfortable proceeding. More clicks can reflect interest, but they can also reflect difficulty finding an answer.
In the experimentation work I lead, I want the hypothesis to identify the uncertainty or friction we believe we are addressing. Then the measurement needs to tell us whether the change helped.
Conversion matters. So do the steps leading to it, the quality of the resulting action, and any problems the change creates elsewhere in the journey. A commercial improvement deserves a clear account of what was measured and what we can reasonably conclude.
That discipline helps a team learn. It also makes the next decision better informed.
AI increases the importance of clear judgment
AI adds possibilities for responding to context, generating content, and shortening the distance between an idea and something we can evaluate.
It also raises the importance of deciding what the experience should accomplish.
If a recommendation is based on incomplete information, speed will not make it more appropriate. If the underlying product information is inconsistent, a more confident explanation will not make it more dependable.
My experience building with AI has reinforced how much depends on clear direction and inspection. People still need to define the objectives, decide which information is appropriate to use, establish boundaries, and judge whether the result is ready for a customer.
In education, I believe guidance should help someone understand their options and retain control of the decision. That is a useful standard for evaluating both the technology and the experience around it.
Relevance is built through everyday decisions
Across the different roles I’ve held, the tools and business models have changed. The need to connect an organization’s capabilities with a person’s circumstances has remained.
That connection is built through a series of decisions: what we ask, what we explain, what we recommend, what we make easier, and what we choose to measure.
The question I keep coming back to is one I can bring to the work every day:
When someone is ready to decide, have we made the next step clearer, more useful, and easier to trust?
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