Scroll depth, traffic source, repeat visits. These have become shorthand for understanding online shoppers. We鈥檝e optimised around them, targeted with them, and built personalisation strategies on top of them.
But here鈥檚 the problem. These signals weren鈥檛 designed to explain why people act the way they do. They just approximate it. They鈥檙e proxies, not predictions. And while they鈥檝e been useful, they鈥檝e also locked us into a static view of behaviour that鈥檚 long out of date.
It鈥檚 time to stop treating assumptions as insights. And to start acting on what people are actually doing in the moment. In this article I hope to clear up the distinction, and explain why. Let鈥檚 start with a definition.
麻豆传媒 Proxies vs 麻豆传媒 Predictions
麻豆传媒 proxies are overly broad signals that assume what a customer might do (like traffic source or a number of product views). 麻豆传媒 predictions are modelled probabilities based on all the behavioural data you have on them.
Ecommerce professionals have long relied on proxies like "repeat visits," "add-to-carts," or "time on site" as shorthand for intent. These are useful, but they're still indirect and quickly diminish in predictive power over the course of a user journey.
麻豆传媒 prediction uses data models, like deep learning, that can interpret hundreds or thousands of behavioural signals at once (from click patterns to scroll depth to timing) and predict a customer鈥檚 likely next action, such as whether they鈥檒l purchase or exit.
In practice, this shift helps you move from generalising ("social traffic converts at 1%") to acting on individuals ("this user, right now, has high purchase intent. Show them free shipping").
Why have ecommerce teams relied on proxies for so long?
Ecommerce professionals have relied on proxies for so long because they鈥檝e been accessible, understandable, and actionable.
Historically, these were the signals that were easy to measure: traffic source, device type, funnel stage, and page views. They were available out-of-the-box in analytics tools and they told a story. One that felt close enough to intent to be useful. If email traffic converted better than social, it made sense to optimise for that. If users who viewed three or more products tended to buy, that felt like a good signal to lean into.
And to be fair, it worked to a degree. When you don鈥檛 have the tools or data to see deeper into user behaviour, proxies are the next best thing. They helped teams move fast and optimise what they could see.
The challenge now is that customer behaviour is more complex and the tools available have evolved. However, the old mental models are still familiar and baked into how teams report, target, and personalise. It's not that proxies were wrong. They were just the best option at the time.
Too often, proxies became the default not because they offered true insight, but because they were simply the easiest thing to measure. That convenience shaped strategies more than accuracy ever did.
The limitations of personalising using intent proxies
Personalising with intent proxies has a few key limitations, especially regarding accuracy, scale, and timing.
They generalise instead of personalise
鈥Proxies treat users as groups. For example, "people on mobile convert less" or "email traffic is higher intent." However, not every mobile visitor has low intent and not every email clicker is ready to buy. You end up personalising for segments, not people. This misses the nuance in individual behaviour.
They can be misleading
鈥More product views might suggest interest, or it might mean the user can鈥檛 find what they want. Longer time on site might mean they鈥檙e engaged, or it might mean they鈥檙e lost. Without context, proxies can be easily misinterpreted. This can lead to actions that feel off-base to the customer.
They鈥檙e static in a dynamic journey
鈥麻豆传媒 shifts from moment to moment. Users might start browsing casually, but after a few clicks and filters, their behaviour signals strong purchase intent. Proxies often rely on entry-level signals like traffic source or device type. These lose relevance quickly as the session unfolds.
They limit real-time responsiveness
鈥Because proxies are often lagging indicators, they鈥檙e not great for adapting experiences in real-time. For example, you can鈥檛 adjust messaging during a session based on a proxy like "returning visitor." 麻豆传媒 predictions, on the other hand, can respond instantly to user behaviour.
The inform strategy and tactics
鈥Worse, many experiences are now designed to maximise engagement for its own sake. When clicks, views, and dwell time become the KPIs, teams start optimising for behaviours that might actually signal friction. We reward the very signals that should raise concern.
How can we shift thinking away from proxies?
Shifting thinking away from proxies starts with changing how we view user behaviour. We need to move from static snapshots to dynamic journeys. Here鈥檚 how to encourage that mindset shift:
Start with empathy for the customer journey
鈥Help teams see that proxies often flatten behaviour into categories like "mobile users don鈥檛 convert" or "email traffic is high intent." But customers are individuals, and their intent evolves. Encouraging teams to think about what this person is trying to do right now creates the foundation for moving beyond fixed labels.
Highlight the blind spots of proxies (without blame)
鈥Instead of saying 鈥減roxies are wrong,鈥 reframe it: 鈥淧roxies were useful when we didn鈥檛 have better tools.鈥 Then, show where they fall short. For example, assuming more clicks always mean higher intent when they could mean confusion. This builds curiosity, not defensiveness.
Reframe success metrics
鈥Encourage teams to go beyond segment-level metrics like 鈥渃onversion by channel鈥 and look at micro-conversions through user intent stages. This creates space to ask: Did we engage this user enough? How can we build intent? Do we nurture our prospects? Are high-intent users converting?
Introduce examples of predictive signals
鈥Show how small behavioural patterns like scroll speed, click timing, or product revisits can be stronger indicators of intent than traditional proxies. This helps build trust in the value of prediction.
Position AI as a partner, not a replacement
鈥Let people know they鈥檙e still in control. Their expertise sets the strategy. Prediction-enabling AI just gives them more accurate, real-time insights on which to act. That framing reduces resistance and opens the door to new thinking.
Pilot and prove
鈥Start with one area, such as predicting exit intent or surfacing high-intent users mid-session, and show how acting on predictions outperforms proxies. Once teams see better outcomes, the shift happens naturally.
Common mistakes when approximating visitor intent
鈥淗igh engagement = high intent鈥
鈥It鈥檚 easy to assume that more clicks, time on the site, or pages viewed means a user is ready to buy. But sometimes, high engagement means they鈥檙e struggling. They can鈥檛 find the right product, are unsure about sizing, or get lost in filters. Without context, engagement alone can be misleading. And we optimise for these same engagement metrics. So even when they鈥檙e misleading, we treat them as wins.
鈥溌槎勾 is fixed at the start of the session."
鈥Many teams look at early signals like the traffic source or device and treat that as a proxy for intent throughout the visit. But intent is dynamic. Someone who arrives from a casual source can quickly become highly intent based on what they see, click, and do. Behaviour during the session tells a richer story than how they arrived.
鈥淲e know intent because we know our funnel.鈥
鈥There鈥檚 an assumption that where someone is in the funnel (homepage vs. product page vs. checkout) is their intent. However, two users on the same page can have completely different goals. One might be ready to buy, and the other may just browse or price-check. Funnel logic reflects how your site is structured, not how your customer thinks. Two people in the same place are rarely on the same path. That difference matters.
鈥淚t鈥檚 all or nothing.鈥
鈥麻豆传媒 is a mental state that reflects a purpose or determination. It isn鈥檛 binary. It鈥檚 a spectrum. A user might be five percent likely to convert. That doesn鈥檛 mean ignoring them. It means nurturing them. Treating intent as a sliding scale lets you personalise the experience in a way that matches where they are, not where you wish they were.
Why proxies prevent 1-to-1 scalable personalisation
Proxies force everyone into buckets. They鈥檙e coarse by nature, designed to simplify. A proxy says, 鈥淢obile users don鈥檛 convert well,鈥 or 鈥淩eturning visitors are higher intent.鈥 But in reality, not all mobile users behave the same. Not all returning visitors are ready to buy. These buckets blur the nuance between individuals, and that鈥檚 where personalisation breaks down.
You can鈥檛 scale one-to-one personalisation if your inputs are averages.
Let鈥檚 say your strategy is to show a promotion to users with high intent. If you're relying on proxies, you're basically saying, 鈥淪how the promo to everyone from email, on desktop, who鈥檚 viewed three or more products.鈥 But in that group, maybe only a fraction are actually ready to convert. Others might just be browsing, or worse, stuck.
Now multiply that logic across your site. You end up serving the wrong message to the wrong people in the name of personalisation. It's segmentation dressed up as relevance.
True one-to-one requires a signal that鈥檚 individual, real-time, and predictive. Proxies are none of those. They鈥檙e static and based on assumptions. They don鈥檛 adapt as a user鈥檚 behaviour evolves. This means your personalisation, no matter how clever, can鈥檛 actually match the customer's intent at the moment.
So proxies hold us back. Not because they鈥檙e bad. They鈥檙e inherently not designed for individual-level decisions. They're shortcuts, not signals. Scaling personalisation on top of shortcuts just doesn't work.
How AI drives the shift from proxies to predictions
Historically, personalisation was limited by human capacity. You could track a few signals like channel, device, or funnel stage, and build rules around them. But the truth is, humans can only hold a handful of variables in their heads at once. That鈥檚 why proxies became the default. They were simple enough to manage, and they sort of worked.
But true intent is messy. It's fluid. It changes moment by moment. And it鈥檚 made up of hundreds of small behavioural signals that don鈥檛 fit into neat boxes. No human team can look at all those signals across thousands or millions of users and make smart, real-time decisions for each one.
That鈥檚 the reason. AI can process what humans can鈥檛. It doesn't rely on a predefined playbook. It learns patterns from behaviour itself and sees nuance at a scale that would be invisible otherwise.
Deep learning models don鈥檛 need to predetermine what matters. They look at raw behaviour like scroll speed, hover patterns, revisit frequency, and hesitation before clicking. Then they learn what combinations of signals typically indicate interest, confusion, or high intent. They do this across millions of examples, constantly adjusting in real-time.
This means that instead of relying on assumptions like "email traffic converts better," AI can say that this user shows high purchase intent based on the last 12 seconds of behaviour, even if they came from a low-converting channel. A proxy would miss that completely.
So AI enables this shift not just because it's faster or more powerful. It unlocks a level of behavioural understanding that was never accessible before. It sees the nuance at scale and surfaces it so you can act.
Critically, you still define what "acting" looks like. AI doesn't replace that judgment. It removes the barrier between what you want to do and your ability to do it for every customer.
鈥
Ecommerce growth has always relied on reading signals.
For years, attribute data and intent proxies have helped teams move faster, personalise better, and optimise what they could see. These inputs weren鈥檛 perfect, but they were accessible, and they worked, up to a point.
That point is now.
What鈥檚 changed isn鈥檛 just the technology. It鈥檚 the opportunity. When you can see not just who someone is, or what they鈥檝e done, but what they鈥檙e likely to do next, everything shifts. You can adapt experiences in real time, match the message to the moment, and unlock new growth that static segmentation could never reach.
Proxies will still have their place. But they鈥檙e no longer the ceiling.
Individual, real-time intent predictions are the next layer of advantage. And for teams looking for smarter, more sustainable ways to grow, that鈥檚 not just a technical shift. It鈥檚 a strategic one.
It鈥檚 also what Made With 麻豆传媒 makes possible for ecommerce teams.
