Customer Journey Research: What Most Brands Get Wrong

Customer experience research is the process of gathering evidence about how real customers move through the buying process, from first awareness to repeat purchase, by combining qualitative and quantitative methods to surface the gaps between what brands assume and what customers actually experience. Done well, it replaces internal opinion with external reality. Done poorly, it produces a map that flatters the business and misleads the team.

Most brands do it poorly. Not because they lack the tools, but because they go in looking for confirmation rather than contradiction.

Why Most experience Research Produces Maps Nobody Uses

I have sat in more experience mapping workshops than I care to count. The format is usually the same: a facilitator with sticky notes, a room full of people from marketing, product, and customer service, and a whiteboard that ends up covered in colour-coded assumptions. The output gets turned into a slide deck. The slide deck gets presented to leadership. Leadership nods. The map goes into a shared drive and is never opened again.

The problem is not the format. The problem is that most experience mapping exercises are built on internal opinion dressed up as customer insight. Nobody has actually talked to customers in a structured way. Nobody has looked at behavioural data to see where people drop off. The team has simply agreed on what they think the experience looks like, which is almost always a version of the experience the business was designed to deliver, not the one customers are actually experiencing.

This matters because the gap between the intended experience and the actual experience is where revenue leaks. It is where customers quietly disengage, switch to a competitor, or complete a purchase once and never return. You cannot close a gap you have not found, and you cannot find it by asking people in the same building what they think customers experience.

If you want a broader grounding in how customer experience connects to commercial outcomes, the Customer Experience hub covers the full picture, from measurement frameworks to retention strategy.

What Good Customer experience Research Actually Requires

Good experience research is not one method. It is a combination of methods, each answering a different type of question, and the skill is knowing which combination fits the business problem you are trying to solve.

Qualitative methods, interviews, diary studies, contextual observation, tell you why customers behave the way they do. They surface the emotional texture of an experience, the frustrations that never make it into a survey, the workarounds customers have invented because the official process does not work for them. They are slow and expensive to do properly, which is why most brands skip them or compress them into a handful of calls that are too short to reveal anything useful.

Quantitative methods, behavioural analytics, session recordings, funnel analysis, tell you what customers actually do at scale. They are precise about behaviour and largely silent on motivation. A funnel report can tell you that 60% of customers abandon at the payment screen. It cannot tell you whether that is because the form is confusing, the delivery options are wrong, or the customer simply went to compare prices elsewhere and forgot to come back.

The combination is what produces actionable insight. Qualitative without quantitative is anecdote. Quantitative without qualitative is a number without an explanation. Most brands default to one or the other based on budget and speed, and then wonder why their experience optimisation work does not move the commercial metrics.

When I was running a performance agency and we started doing proper experience research for clients, the thing that consistently surprised people was how early in the process customers were making decisions. Brands assumed the decision happened at the point of purchase. The data kept showing it happened two or three touchpoints earlier, often in a channel the brand was underinvesting in. That kind of finding does not come from a workshop. It comes from combining search behaviour data with qualitative interviews about how people research a category.

The Methodological Traps That Produce Misleading Results

I am not reflexively sceptical of research. I have seen it genuinely change how businesses allocate resources and make decisions. But I have also seen enough bad research presented with confidence to know that the methodology deserves as much scrutiny as the findings.

The first trap is sample selection. experience research that draws only on existing customers tells you about the experience of people who completed it successfully. It tells you almost nothing about the people who started and stopped, or who never engaged at all. If you want to understand why people do not convert, you need to find and talk to them, which is harder and more expensive than surveying your existing customer base. Most brands do not bother.

The second trap is self-reported behaviour. When you ask customers what they did or how they felt at a particular stage, you are asking them to reconstruct a memory, often imperfectly, and to articulate motivations they may not have been consciously aware of at the time. Self-reported data is useful, but it needs to be triangulated against behavioural evidence. When I have seen the two diverge significantly, the behavioural data is almost always closer to the truth.

The third trap is leading questions. Survey design in experience research is an area where confirmation bias shows up most visibly. Questions that assume a positive experience, or that frame options in ways that nudge respondents toward particular answers, produce data that tells you what you wanted to hear. I have reviewed research briefs where the questions were written by the same team that was going to be evaluated by the findings. That is not research. That is theatre with a sample size.

Tools like heatmaps and session recordings help cut through some of this by showing actual behaviour rather than reported behaviour. They are not perfect, but they are harder to game than a survey.

Where to Focus the Research: The Stages That Actually Matter

Not every stage of the customer experience deserves equal research attention. The stages where the most value is created or destroyed are usually the ones that are hardest to observe, which means they tend to be under-researched.

The pre-awareness stage is one of them. Before a customer knows your brand exists, they are forming category beliefs, developing preferences, and building mental availability for certain types of solutions. Most experience research starts at the awareness stage, which means it misses the context in which awareness happens. Understanding how customers think about a category before they encounter your brand is often more valuable than understanding what they think of your brand once they have found it.

The consideration stage is where most brands focus their research, and rightly so, but the question they tend to ask is the wrong one. They ask “why did you choose us?” rather than “what almost stopped you?” The barriers to conversion are usually more instructive than the drivers, because the drivers are often things you already know and are already doing. The barriers are where the friction lives.

The post-purchase stage is the most neglected, and it is the one with the highest commercial stakes. What happens after a customer buys determines whether they buy again, whether they refer others, and whether they become the kind of customer who defends your brand when someone criticises it. End-to-end experience thinking treats the post-purchase experience as part of the experience rather than an afterthought, which is the correct framing.

When I was working on a turnaround for a client in a high-consideration category, the post-purchase research revealed that customers felt completely abandoned in the 48 hours after they signed a contract. The brand had invested heavily in the sales experience and almost nothing in the onboarding experience. Fixing that single gap reduced early churn by a meaningful margin without any increase in acquisition spend. The research cost a fraction of what the brand was spending on paid media to replace the customers it was losing.

How to Design Research That Produces Findings You Can Act On

The most common reason experience research fails to influence decisions is not that the findings are wrong. It is that the findings are not connected to a specific business question. Research designed to “understand the customer experience” produces outputs that are interesting but not decisive. Research designed to answer “why are 40% of customers not making a second purchase within 90 days?” produces outputs that point directly at a commercial problem with a commercial solution.

Start with the business question, not the methodology. What decision will this research inform? What would you do differently if the findings pointed one way versus another? If you cannot answer those questions before the research starts, the research is not ready to start.

Design the methodology around the question. If you are trying to understand why customers drop off at a specific stage, you need a combination of funnel data to confirm where the drop-off happens and qualitative interviews with people who dropped off to understand why. If you are trying to understand how customers evaluate options in a competitive category, you need research that captures the consideration process in real time, not retrospectively. Diary studies and omnichannel tracking are useful here because they capture behaviour as it happens rather than asking customers to remember it afterwards.

Build in a mechanism for challenging the findings. The most valuable thing a research debrief can do is surface findings that contradict internal assumptions. That only happens if the room is set up to welcome contradiction rather than explain it away. I have seen too many debrief sessions where uncomfortable findings were rationalised out of existence by people who had a stake in the current strategy being correct. The research is only useful if it is allowed to be inconvenient.

There are also emerging approaches worth considering. AI-assisted experience analysis is developing quickly and can help process large volumes of qualitative data at a speed that was previously impractical. It does not replace good research design or human judgement, but it can accelerate the synthesis phase significantly.

Turning Research Into Decisions, Not Decks

The output of experience research should be a set of prioritised actions, not a map. The map is a tool for getting to the actions. It is not the destination.

Prioritisation should be based on two dimensions: the size of the commercial impact and the feasibility of addressing the issue. A friction point that affects 5% of customers and requires a complete technology rebuild is not the place to start. A friction point that affects 40% of customers and can be addressed with a change to the onboarding email sequence is where you begin.

One thing I have found useful is treating experience research as a recurring programme rather than a one-off project. Customer behaviour changes. Competitive context changes. The experience that was accurate 18 months ago may not reflect current reality. Brands that run continuous listening programmes, combining periodic qualitative work with ongoing behavioural monitoring, tend to stay closer to what their customers are actually experiencing. Social listening and feedback channels can be part of that ongoing monitoring, though they need to be treated as signals rather than representative data.

The optimisation work that follows experience research is where the commercial value is realised. Research without optimisation is an intellectual exercise. Optimisation without research is guesswork. The two are meant to be connected, and the quality of the connection determines the quality of the outcome.

If you want to see how experience research fits into a broader approach to customer experience strategy, the Customer Experience hub covers the frameworks, metrics, and thinking that connect research to commercial results.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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