"Scaling Paid: What Needs to Be True Before You Spend More" - A Pulse Commentary

In the run up to the Pulse eCommerce Summit on the 13th - 14th May 2026, we are launching a series of commentary pieces on topics that will be a focus point at the conference, led by members of the Vervaunt team. Here is our commentary piece on scaling paid channels, with input from Bethan Rainford.

Most eCommerce brands reach a point where they want paid media to do more. The growth targets are set, the board expects acceleration, and the instinct is to increase spend. But scaling paid channels is not the same as spending more. There is a meaningful difference between a brand that is ready to scale and one that has simply decided it wants to, and that gap is where most of the problems occur.

The brands that scale paid effectively tend to share a few things in common: they understand what their unit economics can support, they have the creative infrastructure to feed the algorithms, they measure performance at a business level rather than a platform level, and they have internal alignment between marketing ambition and financial reality. The brands that struggle are often missing one or more of those foundations, and no additional spend can compensate for that.

This article covers what we are seeing work for brands scaling paid investment, what tends to break, and what needs to be in place before committing additional capital.

Understanding what you are actually scaling: the audit before the plan

When a brand approaches us looking to scale paid, the first step is not building a media plan. It is understanding their current setup: which channels are active, how budget is allocated across them, what metrics they are using to evaluate performance, and what they are actually trying to achieve.

That last point matters more than it might seem. "We want to scale" can mean very different things depending on the brand. For some, it means new customer growth at volume. For others, it means improving profitability on existing spend. For a few, it means both, which requires careful sequencing because the two objectives often pull in different directions.

What we find consistently is that brands looking to scale are often still using measurement frameworks that were appropriate for a smaller, more bottom-funnel-focused operation. They are evaluating channels in isolation, looking at in-platform ROAS as their primary success metric, and making investment decisions based on data that does not reflect how customers actually move through the purchase journey. Before building any scaling plan, there is usually an education piece required: moving from channel-level ROAS to lifetime value models, from in-platform attribution to incrementality thinking, and from siloed channel views to a holistic understanding of how paid media contributes to business performance.

This is not about being more sophisticated for the sake of it. It is about ensuring the measurement framework can support the decisions that scaling requires. If you are going to invest significantly more in upper-funnel activity, you need a way to evaluate whether that investment is working, and that it does not depend on last-click attribution showing immediate returns.

The CFO conversation: why finance alignment has become essential

One of the most notable shifts over the last few years is how frequently we find ourselves in conversations with CFOs and finance teams. The performance media budget line has become one of the largest items on the P&L for many eCommerce brands, and finance teams are understandably paying closer attention to what that investment delivers.

A few years ago, the agency relationship rarely extended beyond the marketing or eCommerce team. Now, getting finance comfortable with the paid strategy is often essential to scaling. The brands that scale successfully almost always have alignment between marketing and finance on how acquisition investment gets evaluated. The brands that struggle often have a marketing team that understands why upper-funnel investment is necessary and a finance team that sees it as wasteful spending with poor returns.

How to build that alignment:

The most effective approach we have seen is pulling in broader business data and modelling it alongside paid performance. That means integrating with Shopify data (or whatever eCommerce platform the brand is using) and building a model that shows first-order profit, cost of goods sold, returns impact and then lifetime value. When you can show a CFO that a specific product category has a high lifetime value and a strong repeat rate, and that the current acquisition investment in that category is below what the economics can support, the conversation shifts.

Rather than justifying why you are spending money on ads that do not show an immediate return, you are showing where the business has room to invest more aggressively because the long-term economics support it. Finance teams respond well to this because it is grounded in business data they recognise, not platform metrics they find abstract.

The key concept here is allowable CAC, the maximum you can spend to acquire a customer while maintaining acceptable economics. But allowable CAC is not a single number. It varies by product, by category, by stock level and by margin. A high-margin product with strong stock depth can support a higher acquisition cost than a low-margin product that is running low. The brands scaling well are building dynamic CAC models that reflect this variation and feeding that data into their campaign structures.

CAC at scale: why the number changes when you grow

The relationship between customer acquisition cost and growth strategy is more nuanced than most brands account for. In maintenance mode, CAC is relatively stable because you are primarily capturing existing demand from customers who already have some awareness of the brand. When you shift into growth mode, you are reaching further into colder audiences, and the cost of acquiring those customers is naturally higher.

This is not a sign that something is wrong. It is the expected economics of growth. The question is whether the lifetime value of those customers justifies the higher upfront cost.

How the brands we work with structure this:

Rather than applying a flat CAC target across all campaigns, we set different allowable acquisition costs based on multiple factors. If a product has low stock and low margin, the campaign runs against a lower CAC threshold. If it has high stock and high margin, we are willing to spend more. For brands actively scaling, we model the breakeven CAC and then assess whether unprofitability on the first sale is acceptable given the lifetime value that follows.

This is where the product-level data becomes critical. If customers who purchase a particular product have a 45% repeat rate and a lifetime value significantly above the category average, that product should be the focus of acquisition campaigns, even if the first-order economics look tight. The return comes later, and the strategy needs to be built around that timeline rather than expecting immediate payback.

Feeding this data dynamically into campaign structures, particularly on the Google side, allows budget to shift automatically toward the opportunities where the economics are strongest. It also means the scaling strategy is grounded in what the business can actually support rather than arbitrary targets that do not account for product-level variation.

Creative as the scaling lever: why volume and diversity matter more than ever

Creative has always been important in paid media, but the way platforms use creative has changed fundamentally over the last few years, and most brands have not fully adapted to what that means for a scaling strategy.

The shift started with privacy changes like iOS 14, which degraded the detailed audience targeting that platforms previously offered. Where you could once build highly specific audience segments, the platforms moved toward broader targeting and leaned more heavily on creative signals to determine who sees what. Last year, Meta introduced Andromeda, an algorithm update that explicitly uses creative diversity as the mechanism for reaching new audience segments. If you do not have a large enough volume of creatively diverse assets, the algorithm is limited in the audience pools it can access. Your existing audience keeps seeing the same creative, and you are effectively capping your own reach.

What this means in practice:

We use a creative matrix framework to help brands map out the diversity they need. On one axis, you have customer profiles representing the different types of people who might buy the product. On the other, you have messaging angles: quality, heritage, sustainability, innovation, price value, or whatever is relevant to the brand. Each intersection of profile and angle represents a creative territory that can unlock a different audience segment.

For a brand like Spray Way, which makes outdoor equipment, one creative territory might target extreme adventure enthusiasts with a message about product quality. Another might target casual hikers with a message about sustainability. Another might target gift buyers with a message about heritage. Each of these combinations reaches a different audience that the algorithm cannot access if you are running the same three creative assets on rotation.

The volume required to execute this has increased significantly, and many brands struggle to maintain the production pace. This is where the tension with AI-generated creative becomes relevant.

The AI creative question:

For the premium and luxury brands we work with, there is a real tension between the volume the algorithm demands and the brand integrity these businesses are built on. AI can handle the basics effectively: resizing assets, adjusting copy variations, and minor visual adjustments. Where we see potential is in establishing agreed templates and visual frameworks, then using AI to iterate within those guardrails. But fully AI-generated creative, particularly anything involving AI-generated models or imagery that is clearly synthetic, carries reputational risk that most premium brands are not willing to accept.

The audience is not ready for fully AI-driven creative and consumers still respond strongly to authenticity. Brands that have pushed too far in this direction have faced backlash. The practical advice is to use AI to increase the efficiency of creative production without replacing the human judgment and brand sensibility that make the creative effective. Diversify messaging and visual direction with AI assistance, but keep the core creative decisions human.

Influencer content: the shift from audience access to creative production

The role of influencer content in a scaling strategy has evolved beyond what most brands realise. Historically, brands used influencers primarily to access their audience. You paid for reach, for the influencer to put your product in front of their followers, and the value was distribution.

That value still exists, but the more significant shift is that influencer content has become one of the most effective creative formats for paid media. Influencer content tends to generate stronger engagement than brand-produced content because it carries an inherent authenticity that studio-shot product imagery does not. It helps manage rising CPMs by cutting through the noise of the feed. And the content itself often outperforms brand creative when used in paid campaigns because the influencer understands their audience's preferences, the hooks that work, the formats they engage with, and the cultural references that resonate.

Where this fits in a scaling strategy:

Creator-led content is particularly effective in the consideration phase of the funnel, where customers need social proof and context beyond what a brand ad provides. Partnership ads and whitelisting, where the brand puts paid spend behind the influencer's content rather than their own page, have been especially effective for brands entering new markets or trying to reach new audiences.

The strategic point here is that influencer partnerships should be evaluated as a creative source as much as a distribution channel. The brands getting the most value are giving creators latitude to present the product in their own voice rather than scripting them into rigid brand guidelines. That means accepting some loss of control in exchange for content that actually performs, which is a trade-off that becomes easier to make when you can see the performance data supporting it.

What needs to be true outside of paid: the operational foundations

Scaling paid media is a growth strategy, but it fails if the rest of the business is not ready for the volume it generates. There are several operational foundations that need to be in place before increased spend makes sense, and they sit outside the marketing function entirely.

Localisation and site experience:

One of the most common patterns we see is brands scaling spend into new markets without ensuring the site experience is set up for those customers. If the site is not localised, if the payment options do not match what customers in that market expect, if the checkout flow creates friction, then increased spend simply amplifies a poor experience. The result is wasted budget and lower conversion rates that make the market look unprofitable when the issue is actually operational.

Before scaling into any market, the basics need to be confirmed: is the content localised, are the right payment methods available, and does the user journey align with how customers in that market expect to shop? These are not marketing questions, but the marketing investment depends on the answers. 

Retention investment:

There is a pattern where brands scaling acquisition invest everything in getting new customers through the door and nothing in keeping them. When we audit accounts, we regularly find that there is no meaningful paid investment going toward retention. We would typically recommend allocating around 5% of paid budget to retention activity, whether that is promoting new collections to existing customers, supporting loyalty programme engagement, or simply staying present between purchases.

The economics of scaling depend on customers repeating. If you acquire them and they never come back, the lifetime value model that justified the acquisition cost does not hold. Retention is not a separate initiative from scaling; it is an integral part of the scaling strategy.

Brand as the differentiator:

In a market where the barrier to entry for new brands keeps falling, brand is increasingly the primary differentiator. This becomes more pronounced as agentic shopping and AI-driven product discovery evolve. If a customer searches through an AI interface and is presented with three products at different price points, a price-sensitive buyer with no existing brand relationship will default to the cheapest option. The brands that have invested in building recognition, trust, and community are the ones that can command consideration beyond price.

This is why brand-building activity through paid media is not a luxury for scaling brands but a necessity. It creates the conditions under which acquisition spend works efficiently, because customers arriving through paid channels already have some awareness and affinity, rather than encountering the brand cold.

Measurement that holds up: avoiding the false confidence of platform metrics

The measurement question becomes more important as spend increases, because the consequences of measuring incorrectly scale with the budget. A brand spending modestly on paid advertising can absorb some measurement imprecision. A brand investing heavily in growth cannot afford to discover six months later that the metrics it was optimising toward did not reflect actual business performance.

Where false confidence comes from:

The most common pattern is brands reporting strong in-platform ROAS across Google and Meta, while top-line business metrics tell a different story. Both platforms claim credit for conversions that overlap significantly, returning customers get counted as acquisitions, and the reported performance creates a picture that looks healthier than reality. New customer growth is flat, but nobody notices because the platform dashboards are green.

This is not a new problem, but it intensifies at scale because the investment decisions built on those metrics become larger. If you are allocating significant budget based on platform ROAS and that metric is overstating the incremental contribution of the spend, you are systematically misallocating capital.

What measurement for scaling should look like:

Marketing efficiency ratio, total revenue divided by total media spend, is a blunt metric but a useful one. It tells you whether overall investment is returning at an acceptable rate regardless of which platform claims credit. Beyond that, new customer acquisition volume and cost should be tracked separately from blended performance, and incrementality testing through geo-lift studies or holdout experiments should be part of the measurement infrastructure for any brand scaling spend significantly.

We are also seeing more brands adopt tools like Triple Whale and Adinoma to build a more complete picture of how paid activity connects to business outcomes. The value of these tools is not just the data they surface but the narrative and storytelling they enable around that data, which matters when communicating performance to finance and leadership.

What to reassess before scaling paid investment

If scaling paid channels is part of your plan over the next 12 to 36 months, these are the areas worth addressing before committing additional capital:

On readiness and foundations:

• Audit your current measurement framework. Are you evaluating performance on platform ROAS or business-level metrics? If the former, the measurement infrastructure needs to evolve before scaling decisions can be made with confidence.

• Assess whether you have finance alignment on how acquisition investment will be evaluated. If the CFO is expecting immediate ROAS on upper-funnel spend, that conversation needs to happen before the budget increases.

• Confirm that the operational foundations in your target markets are in place: localised site experience, appropriate payment methods, and a user journey that matches local expectations.

On unit economics:

• Model your allowable CAC by product, category and margin level rather than applying a flat target across all campaigns.

• Build a lifetime value model segmented by acquisition source and first product purchased. Understand which products drive the strongest repeat behaviour and weight acquisition strategy accordingly.

• Determine whether you can accept first-order unprofitability on specific products where the lifetime value justifies it, and get internal alignment on that decision.

On creative and content:

• Assess whether your creative volume and diversity are sufficient for current algorithm requirements. If you are running fewer than ten distinct creative approaches, the algorithm is likely constrained in the audiences it can reach.

• Evaluate whether influencer content is being used as both a distribution channel and a creative source. If partnerships are structured purely for reach, there is likely untapped value in the content itself.

• Define your position on AI-generated creative: where can it accelerate production within brand guidelines, and where does it create unacceptable risk?

On retention:

• Audit what happens between the first and second purchase. Is there a retention strategy that gives customers a reason to return, or does the relationship end after the first transaction?

• Assess whether paid budget includes a retention allocation. If acquisition is funded but retention is not, the lifetime value model that justifies the acquisition investment may not hold.

• Evaluate your loyalty and community programmes. In a market where brand differentiation is increasingly the primary competitive advantage, these programmes are part of the scaling strategy, not separate from it.

On measurement:

• Review whether you are at risk of false confidence from overlapping platform attribution. Are Google and Meta both claiming credit for the same conversions while new customer growth remains flat?

• Assess whether incrementality testing is part of your measurement infrastructure. Geo-lift studies, holdout experiments, and media mix modelling provide the evidence base that platform metrics alone cannot.

• Establish clear reporting that connects paid performance to business outcomes, and ensure that reporting reaches finance and leadership in a format they can evaluate.

Many of the themes explored here will be unpacked in far more depth at the Pulse eCommerce Summit on the 13th and 14th May 2026. Across two days, we’ll bring together senior eCommerce leaders, operators and specialists to share real-world experiences, practical frameworks and honest lessons from scaling brands internationally in a far more complex global landscape. If international growth is on your roadmap for 2026 and beyond, register now to secure your place.