The Made With Intent blog

CRO is a process, not an event

Do you know how the term “conversion rate” came to be? Like most things, it has an origin.

Bryan and Jeffery Eisenberg coined the term “conversion rate optimisation” in 1998, partly by serendipity, partly through intent.

Back in those days, it was a pre-problem, and so the category didn’t exist. The term “conversion rate” didn’t even exist, let alone optimise something that was non-existent. In fact, the only reason the search term even remotely appeared in Google was because people were searching for currency conversion rate, not website conversion rate.

“We knew conversion rate from sales was an important metric. We were always doing optimization and search optimization was growing as well. Conversion rate was a ranking term so we combined the two. We were writing about this and similar terms for years before anyone else was even talking about it” said Bryan, and so, they chose to rank for this - “conversion rate optimisation”. But, wrongly, with a Z instead of an S. Not using proper Queens English, I see.

Some say creating this term was nothing more than gaming the Google algorithm designed to create a business. But they were, rightly, trying to assimilate a term that described their efforts to grow businesses and support user experiences while doing so. Yet, their intent had an unexpected outcome.

Despite Bryan always citing that “conversion is a process, not an event,” the term conversion rate optimisation has since stuck. It has become popular amongst brands for its outcome and simplicity—too simple perhaps. It’s not just about optimising a single conversion rate, but the process of improving customer experience iteratively, ideally with validation.

CRO is an event, not a process

Yet, that’s not how people see it. Society sees the event, not the process. They see the 2% that converts, not the 98% that doesn’t. If you optimise the conversion rate, you inherently ignore the needs of the majority.

Is there a blame here? Most tend to lie fault with the definition, but I blame the metric. Seeing conversion rate as a metric or a measure of success is why it has failed us.

Fast-forward fifteen years from Bryan and Jeffery’s digital Frankenstein. In 2013, the confusion of optimising a conversion rate was evangelised further. Dan Barker wrote for Smart Insights on why conversion rate is a—quote—“horrible” metric to focus on. Horrible is an intense term, Dan.

The reasons are obvious: it’s aggregated, not controllable, and does not reflect true performance.

That won’t stop the blog posts from peddling conversion rate as an event with the likes of “What is the average conversion rate?” - the answer being 1.4% for all Shopify stores apparently [-]. Or “What is a good conversion rate? Lo and behold, it’s above 3.3%. Such irreverent content that lacks context; trying to aggregate a measure that varys from product, to device, to source, to demographic, to gender, to weather, to pay day to hour per day.

While conversion rate optimisation might be seen as an event, if you asked any conversion rate optimiser, no one will say they optimise conversion rates. They will talk about the process not the measure of success.

This is because today, experts and practitioners know that conversion rate optimisation involves what Bryan and Jeffery designed it to be, unfortunately, warped by the simplicity of society. For years, there have been movements in the CRO industry to reinvent the term. I’ve heard it all, from CX (customer experience), to GO (growth optimisation) to BGO (business growth optimisation). These calls are cries for help and this has been going on at conferences, in blogs, and podcasts for a long time yet never quite landed.

The common denominator? The removal of the term “conversion rate” and the disassociation of the event from the process.

Beyond conversion rates: Fostering genuine customer experience

Conversion rate optimisation fails us because it focuses on a metric—one that is, as Dan described it all those years ago, an aggregated, macro, uncontrollable, retrospective, binary measure of success that can easily be gamed.

Sure, we can increase a conversion rate - we could:

• just reduce all prices by 10% or give free shipping for our first 5 orders. Done.
• stop sending paid traffic to the website so that only those who know about us will visit. Completed.
• only send desktop traffic to the site, never mobile. Obviously.
• prioritise search results for our cheapest items. Next challenge.

See? Easy.

But can you grow something more meaningful? Something related to average order value? Dare I say, to customer satisfaction? To returning? To loyalty? Something that moves away from being a business KPI, a target to work towards, and more towards a genuine reflection of good user experience? This is what conversion rate optimisation should be.

The cobra effect

Pitfalls of conversion rate targeting: Lessons from Goodhart's Law

Instead of what it should be, and what is seen by experts all around the world, conversion rate optimisation’s success has morphed it into something else. It’s moved down the dwindling haunted road from a process to an event to a target.

When a measure becomes a target, it ceases to be a good measure

This is Goodhart’s Law.

It was a term coined in 1975 by none other than Mr. Goodhart suggesting that “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes” [-]. Fun fact: it was later built upon to inform AI development in the Machine Intelligence Research Institute by a certain David Manheim. No relation [-].

This law is everywhere. In politics, if success is measured by approval ratings, it becomes a popularity contest rather than driving meaningful results. In GP patient admissions, success is determined by the number of appointments completed in a day; the quality of patient care might decrease as a result.

Yet there is no better reflection than the anecdotal and questionable validity of the “Cobra Effect” story.

Under British rule, the government was concerned about the number of venomous cobras in Delhi. So, they offered a bounty for every dead cobra, where large numbers of snakes were killed for the reward. Eventually, however, enterprising people began to breed cobras for the income. When the reward program was scrapped, cobra breeders set their now-worthless snakes free, the wild cobra population further increased [-].

Conversion rates are our cobras. Where, instead of cobra-breeders, we have pop-up breeders.

Continuous optimisation towards a single measure - sorry, target - pushing users down a cul-de-sac of “please convert today”. Don’t lie, we’ve all experienced the “10% off now” pop-up shoved in our face as soon as we land on most sites. Inappropriate behaviours caused by inappropriate targets creating this myopic form of digital directness.

In short, how brands measure directly reflects how they sell. And currently, they sell to do nothing but convert.

How do we solve this?

If the problems of conversion rate optimisation, are that it aims towards a target of something that is macro, binary and out of your control, it makes sense to focus on a measure of success that is micro, scaleable, and controllable.

Towards metrics that are micro, yet specific.

The concept of treating every customer the same is silly. It sounds silly. It feels silly. Everything becomes the same because brands treat everyone the same. This manifests itself in how brands currently measure success on websites: using conversion rate.

The metric used to understand success is highly aggregated at a macro level. It’s an average of averages of averages. It’s made up of so many conversion rates (plural) that trying to shift that behemoth metric is akin to pulling a U-turn on the Titanic – it’s going to take a lot more than a boatload of topless coal trimmers.

The result of optimising for an averaged metric is an averaged approach. The result of optimising with an averaged approach is an average result. Homogenisation begets homogenisation.

Much to Goodhart’s law, by optimising metrics that are aggregated, the output, too, becomes aggregated. It is why in ecommerce that we see page template optimisation based on the homepage, the product listing page, followed by the product detailed page, then the basket and finally the checkout.

A true enemy of personalisation.

The king of which, Netflix, announced as of 2024, they will no longer report on its average revenue per member (ARM), believing this is an irrelevant statistic to measure success. [-] They cite it being an irrelevant measure due to its macro view of what it indicates; the clue is in the name average revenue per member, one that holds so much volatility over a period of time

It’s also a measure of success that’s indicative of business performance, not necessarily customer performance. Sorry, averaged business performance, not average customer performance. And so, moving towards something more symbolic of customer satisfaction, like viewing time, is a movement towards a move away from the macro aggregation of a one-way business metric and towards a measure that highlights the specificity of the business model.

Towards metrics that demonstrate scale and momentum

Conversion is not a binary point of success or failure. It is a progression of performance. A momentum.

As a brand, my job is to build customer momentum, persuade, create desire and elicit these emotions to allow you, the user, to take action. It’s not to just go straight for the jugular or to flip a switch.

And yet - why is it measured as such? There’s a reason why the majority of marketing models have a funnel usually in the form of four letters that tenuously spell out a loosely put-together acronym: the AIDAs, REANs, ACCAs all stand up.

Put somewhat more succinctly, the process of buying online is about an upward trajectory not an end outcome. Translated into KPIs, it’s about scalable performance, not a binary result.

Looking at a not-so-similar industry, we can see what changing the success metric did for the world of football (he writes as Germany are battering Scotland in the Euros). Expected Goals (xG) was a stat created by Opta for use in football. A metric that indicates the quality and performance of the game based on a series of attributes fused together. These might include the angle of a shot, possession of play beforehand, the distance of a shot, and; less so who is taking the shot. It “provides an antidote to the disease of randomness which permeates football” (Tippett). Sounds familiar. In other words, it’s a statistic that is better representative of 90 minutes of a game than just what might be a lucky 5-1 win (granted, Germany isn’t lucky in this game). It’s a statistic that helps understand the performance of the game, not the end result.

Like ecommerce - still, unfortunately - football is built on opinion. OK, that’s what makes it fun. But, the data revolution requires more rigour when commercials are at stake.

Online needs to move towards something more indicative of movement, momentum, performance, quality, and play, less so the binary outcome of whether someone did or didn’t do something. This metric should be one that scales as behaviour does.

Towards metrics that are within your control

Conversion rate is not entirely within your control. If you’re selling umbrellas and it’s sunny, guess what will happen to your conversion rate? It’ll get rained on. The adverse, too, if it rains, the sun will ironically shine on sales.

There are far too many uncontrollable factors to determine whether someone will or will not do something online. When you put it like that, it makes it kind of silly that we look at conversion rate as a measure of success for websites now, doesn’t it?

In the 90s, Donald Wheeler wrote about “the key to managing chaos” and spoke directly about casual relationships. Specifically, “You cannot improve by listening to the voice of the customer. You can only improve a process by listening to the voice of the process”[-].

These are fancy terms to suggest that focusing on the result—a demand from the customer or management—is not nearly as impactful as understanding how the process actually works: casual relationships.

Losing weight is a great example. You are in control of the inputs or calories in and calories out. If you understand how those inputs impact the result, your weight loss process, you will lose more weight. Of course, there are plenty of inputs within each primary input—the balance of carbohydrates, protein and fats, water intake, type of calorie—I could go on, but I’ve clearly not lost much weight in over 12 months.

This is a reason why Amazon only measures success on what they can control, and what they input; something talked about in great detail under Working Backwards [-]. The calories in and the calories out of ecommerce—not the weight loss. This might look like (and does in accordance with Amazon’s WBRs - Weekly Business Review):

• An increase in the number of detail pages, while seeming to improve selection, did not produce a rise in sales; the output metric
• the percentage of detail page views where the products were in stock and immediately ready for two-day shipping, which ended up being called Fast Track In Stock, did produce a rise in sales, the output metric

These are written as hypothesis in alignment with their ruthless test and learn culture at Amazon, but the principle remains: Focus on the input, not the output. This will prevent a Goodhart’s Law type of situation.

Towards intent metrics

If the micro beats macro.

If the demonstration of measures that scale beats an outcome that is binary.

If controllable inputs beats uncontrollable outputs.

We need a measure that is reflective of the nuances (micro measures of success) of the performance (the scaleable momentum shifts) of a user (not the business); not just the end outcome.

We took great inspiration from the Germany-Scotland game and created a measure that helps understand the quality of the play as opposed to the quantity of the outcome. A sentence that permeates the world in “quality beats quantity,” which, if you stand behind, will almost certainly have you sitting on the edge of your seat for this one.

Introducing Intent.

We collect 250x different intent signals—the nuanced behaviour of what users do online—and model those together, outputting them in a series of predictive metrics.

Breaking these metrics down into the known metrics with a predictive layer, we find:

• expected conversion (xC) - the likelihood that a user will convert
• expected add to cart (xATB) - the likelihood that a user will add to their cart.

From there, at Made With Intent, we utilise metrics indicative of behaviour that are more brought forward than the macro level.

• expected exit (xE) - the likelihood that a user will exit their session
• expected return(xR) - the likelihood that a user will return to the site.

And finally, we land on a measure that helps understand the core of what selling is all about: nurturing.

• expected progression (xP) - the likelihood that users will progress to the next stage of the buying journey. We call these “buying stages” and identify them as browsing, refining, evaluating, deciding and committing. They are also similar to those 4-letter, 1980s acronyms thought up by genius marketers.

Not to mention in all of these, the benefit of these being expected measures. One of the consistent flaws of conversion rate is that it is a retrospective target. A measure that’s already happened, and so, not something that you can influence there and then. Intent metrics have the added benefit of being predictive. They highlight what a customer might do, not something they’ve just done, meaning you can intervene in real time. Whilst predictive analytics was on the rise - at the peak of expectations in 2018 [-]- their use cases in ecommerce have been limited. The introduction of real-time analytics has brought this somewhat to life, in combination with using first-principle thinking (thank you, Germany vs Scotland).

Conclusion & next steps

When Netflix announced a move away from reporting on subscriber growth to something more indicative of success, they used two words in their shareholder letter that were the most vital of the announcement: “We’ve evolved”.

Ecommerce needs to evolve.

The flaws within conversion rate are evident for all to see. It is an aggregated, macro, uncontrollable, retrospective, binary measure of success that can easily be gamed, where optimising reflects how you will sell. Cue the 10% off popups as soon as someone lands on your site.

The introduction of Intent metrics is a movement towards the opposite. It is a reflection of the performance, not just the result. An appreciation of the progression and momentum build of prospects, not just a binary outcome. A systematic approach to identifying controllable inputs as opposed to reviewing the uncontrollable outputs. Most of all, it is a focus on the predictive, not the retrospective.

The ecommerce landscape has evolved dramatically over the past decade. Technological advancements, shifts in customer behaviour, and global events like the COVID-19 pandemic have accelerated online retail.

An estimated 24 million online stores worldwide compete for attention and spending. Platforms like Shopify and WooCommerce have simplified online retail, empowering entrepreneurs and established brands to establish their digital presence.

But a significant challenge bubbles underneath all this progress: competitive advantage through pricing strategies—a race to the bottom.

In this race, profit margins are sacrificed in favour of cutting prices to stay relevant and keep conversions high. Unfortunate side effects like eroding brand value and conditioning customers to expect discounts as the norm have been accepted at the price we pay to play.  

For many ecommerce brands, this has become a dangerous balancing act: how to drive sales without compromising profitability or brand integrity.

But there are ways to leverage discounting, not as a desperate measure but as a strategic growth lever.

You can enhance customer satisfaction and loyalty by tailoring discounts to customer behaviours while safeguarding profit margins.

In this article, we’ll explore the challenges ecommerce retailers face with traditional discounting strategies, examine segmenting and targeting your audience effectively and discuss the impact of strategic discounting on brand perception and profitability.

Protecting your margin

The problem

• The cost of goods is skyrocketing
• Prices are constantly under pressure due to intense market competition
• Brands feel compelled to sacrifice margins to maintain or grow market share through widespread discounting 

Discounts can give an immediate boost to sales figures, but often don’t contribute to sustainable growth. They merely accelerate revenue to meet short-term goals.

The solution

Implementing a real-time segmentation framework.

Their current state (whether focused, struggling, or abandoning), and most crucially, their intent, tells you when to step in and offer a discount or whether you can safely stay out of your customer’s way and allow them to purchase.

By segmenting your audience based on these stages, you can identify where customers stand in their buying journey, allowing you to make more appropriate offers.

This segmentation can be integrated into on-site discounting strategies and CRM platforms for managing discounts in behavioural campaigns like abandoned cart communications.

As an example, customers showing focused behaviour with a high intent to purchase may not need discounts to convert. In such cases, retailers can opt to hide or reduce discounts, preserving margins and focusing on opportunities for incremental sales. 

By strategically understanding customer intent, ecommerce brands can steer clear of the race-to-the-bottom mindset prevalent in the market.

This approach ensures profitability while still meeting customer needs effectively.

Protecting your brand

The problem

• Online retail competition intensifies
• Retailers increasingly rely on promotions
• Constant and widespread discounting
• Brands are trapped in a cycle of perpetual discounts to outdo previous campaigns

Over-reliance on discounts damages brand perception. Many customers now question the authenticity of pricing and categorise some brands as “discount retailers.”

This shift in customer perception threatens brand reputation, especially for retailers focused on quality and higher price points, threatening their long-term growth.

The solution

Discounting based on intent to purchase.

The issue isn’t discounting itself but how we execute it. Generic and blanket discounting strategies need to be replaced with strategic discounting reserved for those who actually need it.

Pinpoint where customers are in their buying journey precisely—whether they’re engaged, hesitating, or at risk of abandoning their carts—and, crucially, their purchasing intent.

Use real-time behavioural data to offer discounts only to those showing low intent, are struggling, or highly likely to abandon. Strategically deploy discounts to these specific customer segments at the right moments.

This approach reduces discounting noise, protects brand perception, and responsibly uses discounts as a strategic tool for sustainable growth.

By demonstrating a nuanced understanding of customer preferences and needs, you can not only meet immediate sales goals but also build trust and foster loyalty.

Discount smarter, not harder

Whether you’re protecting margins or brand reputation, it’s clear ecommerce is stuck in a cycle of dysfunctional discounting.

Effective discounting isn’t just about slashing prices randomly; it’s about timing and relevance.

You need a game plan that identifies where customers are in their buying journey and whether they’re keen to buy or still window-shopping.

Instead, tap into intent data to figure out who needs that little nudge and when.

Segments for discounting

Here are a few segments you can implement to identify those that might need a helping nudge.

Basket Pauser

• Context: Abandon behaviour
• Summary: These customers have built up a basket and are now showing signs that they’re likely to leave and may return
• Objective: Maintain Intent. What can you do to encourage this behaviour, e.g., save items for later (with email), sign up for payday reminders, etc., or convince these customers that now is the right time to purchase?

Basket Abandoners

• Context: Abandon behaviour and unlikely to return
• Summary: These customers have built up a basket and are now showing signs that they’re likely to leave and not return
• Objective: Maintain Intent. This is likely one of the last chances you have to persuade these customers; think about appropriate messaging that can reduce their likelihood of exit. This is the perfect segment for you to highlight immediate value. It’s extra time, and it’s probably your last-ditch attempt to keep them and persuade them to convert.

Struggling Buyers

• Context: Struggle
• Summary: Customers likely on the checkout who are showing struggle behaviours
• Objective: Convert. They’re struggling and not checking out. Why? Are they checking discounts? Rather than push them through a checkout when you know this journey isn’t typical, how can you allow for the fact that they don’t seem ready to buy? Without cutting the checkout off, of course. Understanding and addressing this hesitancy is key to converting stalled checkouts into successful transactions.

Hesitant Buyers

• Context: Struggling, not in Committing
• Summary: Customers who showed they are ready to convert but are no longer on the checkout and who are showing struggling behaviours
• Objective: Convert. The challenge with this segment is understanding and addressing the underlying anxieties that hold them back. That can be a set of questions you’re not answering effectively enough, concerns about the product, price, and trust in the brand. How can you influence their experience to remove any anxieties causing hesitation and promote motivating factors?

Last Chancers

• Context: Abandon, low expected return
• Summary: Customers who showed signs they wanted to convert but are now abandoning the journey and are unlikely to return
• Objective: Convert. These customers have demonstrated high levels of intent but are unlikely to return and are predicted to exit soon. This is likely one of the last chances you have to persuade these customers; think about appropriate messaging that can be used to reduce their likelihood of exiting. This is the perfect segment for you to highlight immediate value. It’s extra time and probably your last-ditch attempt to keep them and persuade them to convert.

Segments to avoid discounting

Here are some segments that might not need a discount, but may need other nudges instead.

Unengaged Browsers

• Context: Struggle Behaviour with active events in Browsing (min. 10 events)
• Summary: These customers are starting to interact with the website, but they’re not progressing from browsing and actively engaging in product discovery (yet). Unengaged Browsers are characterised by their lack of interaction beyond basic page views.
• Objective: Engaging this segment means understanding why they’re hesitating, piquing interest and encouraging a deeper exploration of your site. Inspire these customers with categories/products to help progress them in their journey. They interact with the website but haven’t moved into a Refining Buying Stage.

Basket Convincers

• Context: On a PDP with a high affinity for the current product but has not yet added to the basket
• Summary: These customers are on a PDP, with a strong affinity for the product they’re looking at and high Add to Basket activity, but they have not yet added any items to the basket
• Objective: Build Intent. How can these customers be convinced to Add to Basket? Consider product-specific anxieties and motivators that can be used to influence them. They need just the right nudge to transition from interest to action. The challenge lies in identifying and addressing the factors that can convert this hesitation into decisive action.

Struggling Evaluators

• Context: Customers who haven’t added a product to the basket, have viewed multiple PDPs and haven’t built an affinity for any products
• Summary: Broad Evaluators are the *tire-kickers*. They are shoppers immersed in the evaluation stage, showing an interest in what your brand offers but without any clear direction or behaviour towards purchase.
• Objective: Build Intent. The objective is to guide deeper engagement with your brand first and then your products, not the other way around. They need help finding the right product. They need ease of comparison but also content to help them understand what product is right for their needs.

Product Persuaders

• Context: Showing struggle behaviours, haven’t added any products to cart, have built product affinity/affinities but are losing momentum
• Summary: Customers who have built a product affinity but have not added any items to the basket and whose journey is showing signs of slowing down / heading in the wrong direction
• Objective: Build Intent. Their behaviour suggests a moment of hesitation or reconsideration. They have potential interest in specific products or categories but also need further persuasion or reassurance. How can you help these customers get back to a product and persuade them to actually add to basket?

Focused Shoppers

• Context: Positive behaviours
• Summary: These shoppers are on the cusp of conversion, are progressing nicely and are making their final decision(s) before moving to checkout
• Objective: Maintain Intent. Keep these customers on track (probably just leave them alone or use subtle enhancements to the journey), and if needed, help move them into checkout. Make things more accessible, and gently persuade them over the finish line.

Ready Returners

• Context: Landed back on site (at least 2nd session) and have a high likelihood of committing
• Summary: Landing page customers who have returned to the site and, after showing high intent to purchase, are likely back to purchase again.
• Objective: Maintain Intent. Their behaviour is a testament to their previous unresolved actions. Having already shown high levels of engagement, the challenge now lies in nudging them over the final hurdle to complete their purchase. Try to understand why this behaviour exists. How can you get these customers back into their product purchase journey without distracting them when they return?

Embracing change for sustainable growth

You don’t have to get trapped in that discounting death spiral - it’s possible to use discounting as a strategic tool.  

Understanding customer intent allows you to be hyper-targeted with your discounting, protecting your margin and elevating your brand reputation.  

Dig deeper into our intent-based segmentation framework here, or get into some execution strategies for making discounting work harder.

Here’s to shaping the future of discounting—one smart discount at a time.

Pop-ups. Seen as the noisy, uninvited guests of the digital world by customers and a tool for list growth and revenue generation by ecommerce teams.

In the race for conversion, many ecommerce sites bombard visitors with pop-ups from the get-go, hoping sheer volume will lead to clicks and sales.

This “more is more” attitude often results in visitors reflexively closing pop-ups in irritation and possibly abandoning the site altogether. 

Pop-ups can boost engagement and conversion rates when used effectively, but they often miss the mark.

Key takeaways

Pop-up success relies on careful timing

1. Data shows pop-ups are most useful at evaluation stages. Our LLM data shows that pop-ups in the sample were most effective during evaluation for the loyalty campaigns being run.
2. Pop-ups shown too early get ignored. When triggered too early, such as during browsing stages, pop-ups we closed without action taken.
3. Contextual nudges can maximise conversions. Nudges can significantly boost conversions when they’re used in conjunction with intent.

Let’s dive in.

The pop-up pitfall

Ignoring Intent

Many ecommerce teams focus on showing pop-ups to a broad audience early in the customer’s journey.

This rush for conversions often leads to visitors automatically closing pop-ups without reading them.

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Pop-ups should be paired with an understanding of customer intent, only displayed for segments of your audience that need the additional nudge.

To avoid the use of pointless pop-ups, we must provide them to visitors when they’re most receptive.

By leveraging intent data, we can pinpoint the exact stage and intent level where visitors are most likely to engage with a pop-up.

Loyalty program pop-up

The early bird gets... ignored?

Analysing the data for a series of loyalty programs, we tracked the display of pop-ups offering points for spending more.

Despite the attractive offer, 98.5% of visitors ignored the pop-ups because it appeared too early—during the browsing stage.

However, when shown during the evaluating stage, visitors were 71% more likely to engage, highlighting the importance of timing and intent. 

Though the number of visitors at the evaluation stage varies, analysing this data helps find the best triggers and timings for pop-ups.

These pop-ups are crucial in influencing the buying journey, so it’s essential to show them at the right moments to effectively engage visitors on the site. 

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The role of nudges

Conversion optimisation

Nudges, like pop-ups, are key for guiding visitors toward buying by providing important information.

Visitors who see too many pop-ups too early can be overwhelmed and driven away. Instead, spreading personalised information throughout their journey can make a big difference.

Case in point

Digging into the data, an experiment with nudges around product/service USPs based on intent levels saw conversion rate jump by 20% (US) & 44% (UK). 

Aligning pop-ups and nudges with the visitors’ buying stage and intent boosted engagement and conversion rates.

Instead of showing pop-ups to all users right away, a more targeted strategy delivers better results.

Actionable tips

Implementing an intent-based pop-up strategy

1. Analyse Intent: Use analytics tools to gauge your visitors’ intent level and buying stage when interacting with your pop-ups and nudges.
2. Tailor Pop-Ups: Customise pop-ups to appear at the most appropriate journey stage, reflecting their intent.
3. Test and Optimise: Continuously test and refine the timing and content of your pop-ups to maximise engagement.

Recommended tools and resources

• Google Analytics 4 (GA4): Track visitor behaviour and intent to refine your pop-up strategy.
• Google Tag Manager (GTM): Set up tracking for your current pop-ups and include intent data as parameters, allowing you to analyse the data in GA4.

Pop-ups can be a useful tool to leverage engagement and interception strategies.

When timed right and aligned with intent, they can enhance the buyer journey and boost conversions.

By respecting the buyer journey and offering value at the perfect moments, pop-ups can transition from a source of frustration into tools for success. 

MANCHESTER, 02/05/24 - Made With Intent have raised £1.5m to bring their segmentation platform and their vision for more appropriate eCommerce to market. Led by Mercuri, with Portfolio Ventures and previous investors Haatch following, the seed funding will support the fully remote team of 15 with their product, marketing and partnership efforts.

The idea came after the founder, David Mannheim, recognised a fundamental flaw in online retail strategies. After 15 years of optimising conversion rates in eCommerce, he realised the industry’s fixation on conversion metrics was the very thing holding it back.

“The current measures of success are the problem. Metrics like conversion rate, and therefore the actions retailers take to improve them, are short-term, retrospective and aggregated,” David explains. “This creates a race to the bottom. A numbers game that forgets how people really buy.”

Made With Intent looks to change this by giving retailers two things—a more human, segmented perspective of their website performance and a predictive targeting mechanism that lets other marketing tech respond to customer needs in real time. The company reports this new intent-based approach creates a 9.4% average revenue uplift compared to generic optimisation.

“eCommerce is often guilty of trying to convert all customers at all times,” David states. “It focuses on the minority who are ready to buy, at the expense of those who are not. Our product helps retailers be appropriate for every customer. To progress both in-market and future buyers.”

The Beta launched in September has already helped customers like Ernest Jones, Bensons for Beds and Rapha align how they sell with actual buying behaviours. Nik Fletcher, Head of Digital Experience at Rapha, describes the product as “the closest we can get to understanding subconscious visitor signals, like we can in a physical store.”

The platform works by collecting data through an easy to implement script, modelling 250+ signals from online shoppers and returning predictive intent metrics in real time. Visitors are then automatically segmented based on their journey, momentum and how likely they are to buy, exit or return in the future.

This data and the targeting of segments are handled in platform, but the tool is designed to be complementary. With integrations to 40+ marketing tools, from ad networks to experience tools and CRMs, eCommerce teams can use Made With Intent to deliver more appropriate shopping experiences or reengagement tactics with the tools they already use.

“Made With Intent has embraced first-principle thinking and a decade of insights to shake up the vast eCommerce market,” comments Alan Hudson, Founding General Partner at Mercuri. “It empowers online commerce, making it more personal and focused on the quality of prospective customers. The product roadmap excites us and, importantly, those using it.”

“As a VC investor we look at a vast number of potential investments each year, however we only invest in less than a dozen. Made With Intent's strategy is similar for its customers - to focus on the quality of the prospect, not the number of prospects."

With an existing user base in the UK, Denmark, Germany and the USA, the company aims to reach 100 global customers within two years. David Mannheim adds, "Made With Intent is about more than a platform. It's about a movement to create a more personal, human eCommerce. This investment brings us closer to fulfilling our mission."

For media or product enquiries please contact Daniel Gripton, VP Marketing, on LinkedIn.