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Your A/B testing programme just found a winner. A new discount banner lifted conversion by a healthy margin, the test ran long enough to be statistically significant, and the result is sitting in a dashboard somewhere with a green checkmark next to it. Mission accomplished, right?

For most eCommerce teams, the default answer is the same: roll it out to 100% of visitors.

That decision gets made without a second thought, because it doesn't feel like a decision at all. It feels like the natural conclusion of a successful test. You've gone and validated your hypothesis. And when you've got a winner, there's a paper trail to an uplift.

It isn't. It's a choice, and for most brands it's the wrong one.

A/B testing tells you which experience wins across your traffic as a whole. It doesn't tell you which specific visitor should see that winner, or when. That's a different question, that's answered in an entirely different way. It's a decision that requires you needing to know something about the person you're serving an experience to in the first place.

Let's be clear — A/B testing definitely has a place

A/B testing tools do exactly what they're built to do, and they do it well. They replace opinion with evidence. A test that runs to statistical significance tells you, with confidence, that variant B outperforms variant A on your website or mobile app.

Made With Intent customers continue to use tools like Convert, VWO, and AB Tasty alongside our tool, because testing answers a question nothing else can: does this specific change move the number we care about?

Think about what a test actually proves. It proves that, across hundreds or thousands of visitors, variant B beat variant A. It says nothing about any single visitor in front of you right now. Statistical significance is for large volumes of people not a prediction about the individual standing at your checkout, which is a limitation CRO metrics share more broadly.

The difference between A/B testing tools and Made With Intent

Should 100% of people get your A/B test winner?

Once a test determines a winner, most teams roll that experience out broadly, often to every visitor who fits the original test's targeting. Many testing tools do offer segment targeting: device type, geography, new versus returning.

Some brands go further and layer in basic rules, like showing a winning banner only after 30 seconds on site, or only to visitors on their third page view. These rules feel like personalisation. They're really just a slightly more granular version of the same default: a fixed condition, checked once, applied broadly.

But look closely at how it gets set up. Teams choose segments once, at test design time, and then leave them alone. Even a well-built intent-based segmentation programme needs continuous application, not just at launch, or it drifts back into the same static-rule problem.

A rule that says "show this to returning visitors" doesn't change three minutes later when a returning visitor starts behaving like someone about to abandon their basket, or like someone who was going to buy anyway with or without an incentive.

The real problem is narrower and more difficult to solve. The targeting decision is frozen at the moment the test launches, while the person in front of it keeps changing their mind. (Their intent)

A discount that lifts conversion in aggregate can still be the wrong call for two specific people in your test group. A first-time visitor who's price-sensitive and hesitating needs it. A loyal customer who's already three products deep into checkout, on their fourth order this year, does not.

It's the same tension at the heart of whether discounting is a race to the bottom or a genuine growth lever. There's no problem with offering discounts, but it can be a blunt instrument or entirely inappropriate depending on where your customer is at in their buying journey.

How reading intent allows you to respond immediately to changing demands

A/B testing tools make their decision once, when the page loads or the session starts. Whatever segment a visitor falls into at that moment determines what they see for the rest of that test.

But purchase intent isn't fixed at page load. A visitor can arrive browsing casually, spend four minutes comparing two products, add one to their basket, then hesitate at the price for 30 seconds before starting to type a discount code into Google in another tab. That's not a static segment. That's a person, moving in real time, and most testing infrastructure was never built to react to this situation as it unfolds.

Some testing and CRO tools react to single behavioural triggers: an exit-intent popup fired on mouse movement toward the browser bar, a scroll-based banner, a basic engagement score. We think those are great, and definitely worth using. However, they're single-trigger rules, checked once a condition is met, and not a continuous read on a person as they use your website and apps.

What real-time behavioural data adds

Made With Intent's platform tracks over 900+ behavioural signals through each visitor's session, including scroll speed, click sequences, time spent, and product comparison patterns, updating its prediction of purchase intent continuously rather than once. When intent shifts, the response can shift with it.

None of this means every session tells a dramatic story. Plenty of visitors browse, leave, and never come close to converting in that session, and no amount of real-time signal turns a casual browser into a buyer. The claim isn't that every visitor is secretly ready to buy. It's that the visitors who are ready look different from the ones who aren't, and a rule set once at test launch can't tell the difference between them three minutes into the session.

What real-time intent-based deployment looks like

Picture the same winning discount from earlier. Instead of firing it to every visitor who matches a fixed segment, an intent-based system asks a narrower question: Is this specific visitor showing signals of hesitation or exit risk right now, or are they already moving smoothly toward checkout?

Made With Intent's Intent Scoring analyses those signals continuously and its Timing Engine decides the moment to act, rather than firing on a page-load rule or a fixed timer. A hesitant first-time visitor comparing prices across tabs might see the discount. A returning customer already at the payment page, showing no hesitation signals at all, doesn't need it, and doesn't get it. This is what discounting with intent looks like in practice: the same offer, reserved for the visitors who actually need it to convert.

The winning experience from your test doesn't change. Who receives it, and when, does.

Take a product page test that proved a scarcity message ("only 3 left") increased add-to-basket rate. Fired at everyone, it also lands on a loyal customer who's bought the same product line four times before and knows exactly what they want. To that visitor, it reads as pressure, not information. Deployed by intent, the same message is reserved for the visitors actually showing comparison and hesitation behaviour, the ones it was designed to nudge in the first place.

How an actual company used Made With Intent alongside A/B testing

Appliances Direct, a UK home appliances retailer, ran into a version of this problem. Their discounting was broad and largely untargeted; offers went out early in the visitor journey, without real-time context, regardless of whether a given shopper actually needed the incentive to convert.

Using Made With Intent, they began reserving discounts for visitors showing genuine exit and abandonment signals, rather than firing them to everyone who matched a static segment. They left everyone else to convert at full price, as they were always going to.

In the end, they made a 42% saving in margin previously given away to visitors who would have converted regardless, without a drop in conversions from the visitors who needed the discount to purchase.

But don't take our word for it. Take a look at our Appliances Direct case study and make up your mind yourself.

That figure lines up with a broader pattern. Made With Intent's own Intent Gap research found that 83% of shoppers have used a discount code despite being ready to pay full price.

Why new test winners are rare

That scarcity of new winners isn't unique to Appliances Direct. Research from Microsoft's own experimentation team, drawn from thousands of controlled experiments at Bing and elsewhere, found that only 10 to 20% of test ideas move the metric they were designed to move, and that a genuine breakthrough, the kind of result that reshapes a programme rather than nudging it, shows up in roughly one in 500 experiments. The findings come from Kohavi et al.'s Seven Rules of Thumb for Web Site Experimenters (2014). Most of what a mature testing programme finds after the early wins are used up is small, single-digit-percentage gains, not new step changes.

Testing tells you what works. Intent tells you for whom, right now.

None of this is an argument against testing. It's an argument for finishing the job testing starts.

Your A/B testing programme answers one question well: does this experience outperform the alternative? What it was never built to answer is a second, quieter question that gets decided by default instead of by design: who should see this winning experience, and at what moment in their session?

Testing finds your best experience. Making sure it reaches the right person, at the right moment, without giving away margin to people who didn't need it, is a different problem, and it needs a different kind of data to solve.

If you want to see what your existing winning experiences would look like deployed by real-time intent instead of a blanket rule, book a demo and we'll walk through it against your own traffic.

Made With Intent is an on-site decision engine for eCommerce businesses. It reads people's buying intent in real time and allocates experiences to the visitors most likely to respond to them‍

Optimizely is a digital experience platform. It does web and feature experimentation, rule-based personalisation, content recommendations. It's designed for teams running structured test-and-learn programmes at scale.

This blog post explains where the two platforms complement each other and when you would use both versus just one on its own. But if you're too impatient to read to the end, we've got you:‍

Made With Intent and Optimizely serve different functions. Optimizely handles experimentation and rule-based personalisation.

Made With Intent reads visitor intent in real time and decides who sees an experience and when. The two work really well together:

Optimizely executes what, Made with Intent decides who and when. We serve your Optimizely experiences when it matters (the right time within their session) and to whom, all based on what really matters; their intent.

How Made With Intent is different to Optimizely

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1. Respond to users in real-time with experiences tailored to what their digital body language tells you

Optimizely's strongest targeting comes from rule-based audiences in Web Experimentation and Personalization, boolean logic on geo, device, behavioural events, URL targeting, and page Tags, plus optional machine learning (ML) layers:

• Adaptive Audiences (interest categories inferred from content engagement)
• Content Recommendations (NLP-driven topic affinity per visitor)
• Optimizely Data Platform (ODP) real-time audiences 

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The Stats Engine inside experiments is genuinely best-in-class — sequential testing with always-valid p-values — but the targeting decision still asks the marketer to define which audience rule a visitor fits into.

Made With Intent flips the model. It reads buying intent in real-time from the first pageview. Stage, signals, trends, purchase confidence, abandon risk, shopper mindset, and re-scores every three-five seconds.

Targeting is driven by what's happening now, in real-time, by a human, not by which rule-defined audience or topic interest a visitor has been mapped into. Nor by behaviour that's already happened. This means you can respond to the signals that sit between events and pageviews; what we call moments that matter.

You can read more about How Made With Intent Works, and the moments that matter, by clicking the link.

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2. Segment the right experience to your customers automatically, without fiddling with rule trees manually

Your team currently builds and maintains audiences in Optimizely's Audience Builder, Dynamic Customer Profiles, Adaptive Audiences, and ODP. All with decision rule trees, content-tagging taxonomies, attribute conditions, event conditions.

Even with contextual bandits reallocating traffic within a defined audience, the audience definition itself stays manual: design the rules, QA the segments, redesign as the catalogue and content evolve. These segments are website-attributes, too. Not human attributes, not intent based (the most human of all attributes). That's where personalisation really succeeds.

Made With Intent replaces all of the above with continuous intent prediction and delivery.

Our agent decides who sees what, in real time, across hundreds of intent combinations. You'll focus on strategy, creative and proof, instead of tweaking and analysing rules or segments all the time.

The agent retrains daily, which means the experience compounds toward intent combinations where impact is seen and felt. In an always on state, always learning, always getting better.

Where Optimizely's multi-armed bandits (if used) allocate traffic across the variants of a single experiment to maximise one fixed metric within one defined audience, Made With Intent allocates across hundreds of audiences (intent combinations) — adding the layer of to whom and when an experience should be served, and measuring it against a holdback rather than just exploiting the winner.

Made With Intent and Optimizely: In depth

Here is how the two platforms compare on the things that matter most for eCommerce teams:

Where does Made With Intent integrate with Optimizely?

We've broken this section down into three parts. We want to be honest about where you'll gain functionality by utilising Made With Intent with Optimizely, where we augment it, and things we simply don't do, or Optimizely does better.

How Made With Intent adds new functionality to Optimizely

Understand and act on every visitor (including anonymous) from the first pageview. Optimizely's Personalization is rule-driven. It works once a visitor matches an audience rule. Content Recommendations builds a per-visitor interest profile from content engagement, which strengthens as the session progresses.

Made With Intent's model, which collates 50bn+ monthly events across 150+ retailers, reads continuous intent from pageview one, every few seconds. Anonymous, identified, first-time, returning. The opening moments of a session get the same intent read as the tenth pageview.

Automatically identify and serve the best experiences to people without guessing. Optimizely's contextual multi-armed bandit (CMAB, powered by Opal) is the closest thing in their stack, and it's genuinely good, so it's worth being precise about what it does.

A CMAB picks the best-performing variation for each visitor based on context (device, geo, behavioural history) to maximise one primary metric, within a single experiment.

Three design choices define it: the context attributes are declared up front and can't be added or removed once it starts (even paused); it optimises exclusively to a single primary metric fixed at launch; and it begins with a 100% exploration phase, randomly serving variations until it has gathered enough data before it shifts to exploiting the winner.

It's a smarter way to split traffic across the variations you built for the audience you defined.

But we'd like to go into detail on what that context is.

Device, geo and behavioural history are proxies for a person. They describe who a visitor appears to be, not what they want or how close they are to buying. They're arbitrary website attributes that correlate with conversion only loosely, and a bandit optimising over them is tuning against a weak signal.

Intent — buying stage, momentum, hesitation, purchase confidence — is the proximate driver of what a visitor actually does next. The proxies describe identity; intent describes decision, and decision is what moves the metric. Optimising the allocation over the wrong variable caps how much a CMAB can ever find.

Made With Intent's agentic campaigns work a level up.

Rather than splitting traffic across the variations of one experiment to maximise one metric, it allocates across hundreds of intent combinations.

Diamonds Factory, a Made With Intent customer, ran 560+ on a single abandonment use case, where the 'context' is live, multi-dimensional intent (stage, signal, trend, purchase confidence, abandon risk, mindset) that updates every 3–5 seconds and is discovered by the agent, not enumerated by the team upfront. Learn more about basket abandonment here.

There's no per-experiment exploration tax, because the model is trained across 50bn+ events, 150+ retailers and live from page view one.

And because a bandit is built to shift traffic toward winners, it has no standing no-treatment baseline — Made With Intent keeps a holdback on every experience, so it can answer "did this cause incremental orders," the question a metric-maximising bandit structurally can't.

Causal incrementality on every experience. Optimizely's Stats Engine reports variant lift with always-valid p-values; genuinely strong for variant comparison.

Made With Intent runs a holdback group on every experience by default and reports incremental orders against a no-intervention baseline.

The difference is between "variant A beat variant B" and "this experience caused X orders that wouldn't have happened otherwise."

Where Made With Intent improves Optimizely

These are things Optimizely does that get better with Made With Intent on top:

• Audience Builder and ODP audiences become intent-aware
  Instead of boolean rules on attributes and events, or content-engagement interest categories, Optimizely audiences can take Made With Intent's live intent attributes and target on signals that actually predict conversion. ODP's segment builder picks these up as attribute conditions; Web Experimentation picks them up as Tags.
• Personalization variants get the right routing
  Made With Intent decides which Optimizely-created variant a visitor should see based on their current intent state, replacing rule-based audience routing. The marketer keeps the variant production; our agent handles the allocation.
• Web Experimentation tests get causal lift on top of variant performance. Run Optimizely tests with Stats Engine as you do today. Add Made With Intent's holdback measurement on the experience itself to answer "would users have purchased regardless of any variant?"
• Content Recommendations recommend to live intent, not just topic affinity. Made With Intent lets Content Recommendations reflect what's happening in this session, not just historical topic engagement. Particularly useful for anonymous visitors where you’re not sure what their behaviour is telling you.

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What Made With Intent doesn't do

• Feature flagging and server-side experimentation. Optimizely Feature Experimentation (SDKs in 19+ languages, Edge Workers, Agent microservice) is a category leader. We don’t offer anything here.
• Sequential testing methodology for variant comparison. The Stats Engine's mSPRT-based always-valid p-values are best-in-class for inferring variant winners under continuous monitoring. Made With Intent uses Bayesian A/B with holdback.
• CMS-native content personalisation across the wider DXP. Optimizely Content Cloud, Content Marketing Platform and the broader DXP integration are all things that are not within Made With Intent's scope.
• Made With Intent isn’t going to change your recommendations process Optimizely's NLP-driven content recommendations and ecommerce product recommendations are well-established. Made With Intent adds an intent layer; we don’t replace what’s powering your recommendations process.
• Mobile app personalisation. Optimizely Feature Experimentation has native SDKs for iOS, Android, React Native. Made With Intent is web-first.
• Edge experimentation. Optimizely's Edge Worker integrations (Cloudflare, Akamai, Fastly) sit outside Made With Intent's use case

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How to get started with Optimizely and Made With Intent

1. Use Made With Intent to create intent-ready audiences in Optimizely: Pass Made With Intent's live intent attributes — purchase confidence, abandon risk, buying stage, intent trend — into ODP as attribute conditions, or into Web Experimentation as Tags.
   
   Build audiences like "high purchase confidence + declining intent trend" (needs reassurance), "medium confidence + high abandon risk" (needs timely intervention), "low confidence + active comparison signals" (needs guidance, not a discount).
   
   
2. Execute experiences in Optimizely using those audiences. Use Web Experimentation and Personalization for what they do well, so things like variant production, Stats Engine analysis, content variations across the DXP.
   
   
3. Serve the experience (or a variant) through Made With Intent's decision agent, where the experience is served based on visitor intent rather than rule-defined audiences.
   
   
4. Use Made With Intent to prove the incrementality of "Optimizely + intent". Holdback groups on top of the Optimizely experience answer the CFO question: did this cause incremental orders, or did we just personalise for visitors who would have bought anyway? Particularly valuable for discounting — Made With Intent surfaces which high-intent visitors needed no incentive.

Neve Jewels Group, the luxury jewellery brands Austen Blake and Sacet, moved from universal promotions to intent-level targeting across their basket abandonment strategy.

Rather than applying the same discount to every abandoning visitor, Made With Intent segmented interventions across four intent levels, targeting what Director of Customer Experience Jo Homer described as "the nudge moment."

The result: 13% conversion uplift on basket abandonment, and £2.4m in annual revenue uplift overall, 4.8x the original business case, paid back within a single experience. You can learn more here.

What sort of businesses work best with Made With Intent?

This all depends on the size and structure of your eCommerce operation.

Mid-market retailers (£20m–£100m online revenue)

Made With Intent often leads here. Optimizely's Intelligence Cloud commonly lands at £50–80k+/year for this band, based on G2 reviews and procurement data. Made With Intent's session-based pricing, 30 to 60 minute install, and agentic layer fit eCommerce teams of two to ten people who need to ship and prove things quickly.

If you are running Optimizely for the Stats Engine and feature experimentation and those are load-bearing, keep them. Add Made With Intent for intent targeting and causal measurement of your on-site experiences.

Enterprise retailers with dedicated personalisation teams

Both, with clear role separation. Optimizely for the experimentation surface, Stats Engine credibility, server-side feature flagging, and DXP integration. Made With Intent for on-site intent-driven decisioning and causal measurement.

Try Made With Intent today

There you have it. Made With Intent and Optimizely are designed to complement one-another, not compete with one another. Many of our clients use Optimizely to continue A/B testing, while using Made With Intent to serve experience to customers on a 1:1 basis.

If you're interested in learning more about how Made With Intent works, and how you can use it in your tool stack, book a demo here.

Disclaimer: This comparison is based on publicly available information from Optimizely's documentation, marketing site, and customer reviews as of July 2026. Both products evolve continuously. If anything looks out of date, get in touch.

Editor's note: this article is adapted from a recent Intent Live session with Jack Simkins, Digital Product Manager at Golfbreaks.com. Watch the full recording here.

A test that lifted time on site by 14% still spent its first week looking like a failure. Conversion rate was down. Under a traditional A/B testing programme, Golfbreaks.com would have pulled the experiment.

They didn't. And the reason why says a lot about what happens when a genuinely high-consideration purchase journey meets a testing programme built for one-session eCommerce.

Golfbreaks.com is a golf tour operator based in Windsor, with offices in Copenhagen and Charleston, though the Made with Intent account focuses on the US and UK. It sends golfers on trips ranging from a single night in the UK to a week in Spain or Portugal, across a lot of different golfer segments. It's a lead generation business first. Visitors don't check out online in one sitting, they enquire, then a sales agent works out flights, transfers, accommodation, and course access, and gets them to a booking over the phone, sometimes weeks later.

That's not unusual for travel, where research and comparison typically happen across several separate visits and sites before a decision gets made. Optimising a single-session conversion rate for a purchase that actually plays out over weeks measures the wrong moment entirely.

A booking journey that can't be forced into one session

Jack has spent seven years at Golfbreaks.com, the last couple focused on conversion rate optimisation. "We've got quite a unique scenario whereby we're trying to encourage that inquiry," he said. "Particularly in travel, in the industry in general, it's quite an unusual thing to not be able to book entirely online."

A small portionof trips get booked online. But most go through a sales agent, because a golf trip has too many moving parts (courses, transfers, flights, accommodation, and group logistics) for most visitors to configure and commit to in one sitting.

Colin Spooner, Principal Value Consultant at Made with Intent, put his finger on why that matters: "It's not our traditional eCommerce brand where it's a pure purchase journey. But that almost plays into the hands of intent, where you need to think about that considered purchase and how to get people through the funnel before even thinking about the booking, weeks and months down the line."

Measuring what happens before conversion

"You are what you measure" is a phrase the Golfbreaks.com team has adopted internally. If a new visitor is unlikely to enquire on their first visit, optimising purely for enquiry rate on that visit measures the wrong thing.

So alongside enquiry rate, the team tracks bounce rate and time on site together (a new visitor who bounces immediately clearly hasn't been given a reason to stay), pages viewed per session as a depth-of-exploration signal, and, specifically, movement from low to building intent, the kind of signals behind Made with Intent's content prioritisation and messaging use cases. None of these are vanity metrics here. They're proxies for whether a visitor is progressing through a decision process that runs across several sessions, not on a single visit.

Before building any experience, the team asks these questions to get in their customers shoes:

• What is a brand-new visitor actually trying to work out?
• Who are Golfbreaks.com?
• Can we be trusted?
• Do you have to pay full price up front?
• Can you book online at all?

That last one is really important. Because Golfbreaks.com can't be booked entirely online, setting that expectation early avoids disappointment later in the funnel, right when a visitor is closest to converting. As Colin put it, getting that messaging right up front was "a huge realisation" for how the whole experience needed to be built.

What agentic campaigns change about testing

Golfbreaks.com's testing programme runs on Made with Intent's agentic campaigns. In standard A/B testing, you decide up front which segment sees which variant, based on a hypothesis about who will respond to what. Agentic campaigns invert that: you define the strategy (the moment you're trying to influence, and the goal, whether that's enquiries, conversions, or a secondary metric) and hand the agent your set of tactics. It tests them against real segments and works out which one performs best, for whom, and when, using the same intent signals that power the rest of the platform.

For Jack, a self-described non-developer, the practical benefit was speed. "The tool allows me to get these experiences up much faster," he said. "My concept-to-live process is significantly shorter... it means the agents have got time to learn."

But the deeper change is what gets removed. "No longer am I having to set up those individual segments, or serve experiences to segments that I think will benefit from them," he said. "It's in the hands of the agent to then work out what segments it would benefit from... It's a much wider net." A message built for low-intent visitors might also help a segment already building toward a decision. A manual test is only as good as the human guess behind who it's shown to. An agent testing against a hundred segments simultaneously doesn't have that blind spot.

The "do nothing" variant is a genuine conversion tool

One of the more counter-intuitive parts of Golfbreaks.com's setup is what Jack calls the "do nothing" variant. In standard A/B testing, every visitor sees a control or one of several variants. Agentic campaigns add an option where the visitor sees nothing added or changed at all.

"The do nothing essentially sits within those variants as a copy of the control," Jack explained. "It's a safety net because it prevents us from showing negative experiences to customers that don't need to see it."

"In most experience it's always about adding things onto your site," Colin observed. "Having a version where actually sometimes the best thing is leaving the customer alone to progress, or even suppressing things on site, is a nice alternative to what we've experienced over the last 10, 20 years in experimentation." As the data below shows, it's frequently the top performer, because some visitors don't need an intervention. They're already progressing on their own, or they arrived with enough context that added messaging just gets in the way.

When the agent's early data looks wrong

Here's where the seven-day lesson from the top of this article comes back in. Jack's team built two welcome-visit experiences using the same messaging, one for the homepage, one for a location page such as a product discovery landing point for someone who searched "golf breaks in England."

The homepage experience delivered a 14% uplift in time on site. But conversion rate showed a negative trend for the first seven days. "With traditional AB testing, I would have perhaps turned it off," Jack said. "I would have panicked when I saw negative 14%, and I would have said, this isn't working."

He didn't, because the agent was still learning. That's consistent with what independent testing research shows more broadly: short test windows are more susceptible to random variation, and stopping early on interim results is one of the most common ways a genuinely winning test gets killed before it proves itself. Once the agent had enough data, performance turned around.

Same messaging, two different visitors

The location-page test surfaced something else: identical messaging performed in opposite ways depending on where a visitor arrived. On the homepage, a "how to book guide" message performed best, evidence of a genuinely low-intent visitor who needs some hand-holding.

On the location page, the top performer was a trust-building "number one tour operator" message and the do-nothing variant. Jack's take is that a visitor who searched "golf breaks in England" already has affinity toward the destination, closer to a returning visit mindset than a cold product visit. They don't need the basics explained.

A message about Golfbreaks.com's customisable packages underperformed with brand-new homepage visitors. It's true and important, but it's the wrong message at the wrong moment, the same lesson behind Made with Intent's discounting use case: showing a message before a visitor is ready for it does more harm than good.

The surprise that only showed up in the data

Asked what surprised him most, Jack pointed to something that had been sitting in plain sight. An early "ready to plan?" message aimed at brand-new visitors looked like a reasonable nudge. But in the data, it actually came across as overbearing.

"If you think about a new user landing on the site and asking, are you ready to plan? It's probably a bit overbearing," Jack said. "At the time, when you're setting up those tests, it's like, right, I'm going to use the same messages for the homepage, same message for the location page. They're surely going to work." They didn't, for every segment.

Colin's read: "The amount of times we see customers who have a predefined view of what will work, and it's completely different. That point around being subjective comes to life when you start to see the way the agent starts to make decisions." That tracks with the broader shift in shopper expectations, most consumers now expect a personalised experience and notice sharply when they don't get one, a generic message is no longer neutral, it actively reads as a miss.

When a message doesn't resonate with any segment, the fix is simple. Delete it, let the agent relearn, and add a new tactic later if needed. You don't need to manually re-segment.

Measure the journey, not just the moment

None of this is unique to golf holidays. Any purchase with a real consideration cycle shares the same shape, including B2B software, where roughly 70% of the buying journey typically happens before a buyer contacts a vendor at all. A first-time visitor is rarely the same as a returning, further-along one, and treating them identically wastes the message on the visitor least ready to act on it.

Three things carry over regardless of your industry. You don't need to be a developer to find early wins, a visual editor is enough to start. If your purchase journey has any real consideration cycle in it, top-of-funnel testing should measure more than conversion. And build a tactic sheet of messages at a global level, then let the data show which ones resonate with which visitors, rather than deciding that yourself up front.

If your own funnel has visitors who aren't ready to buy on visit one, the same logic applies. See the intent framework behind it. Or book a demo to see it against your own traffic.

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