Article

Issue #139 | Funnels Are Your New Growth Lever

Advertising Analytics Data Funnels Growth Marketing Testing

October 27, 2025

I hope you’re all enjoying the last weekend before November! We’re officially less than a month from the start of the BFCM week – and (at least where I am), the leaves have turned, the fall chill is in the air and we’re neck-deep in holiday + 2026 planning.

The big theme I’ve been hearing from virtually everyone is growth – every brand wants more sales, more leads, more subscribers, more revenue. Those conversations got me thinking: where is that growth coming from?

For the last twenty years, virtually all marketing (and especially digital marketing) has focused on one core objective: growth. Grow sales. Grow leads. Grow customers. Grow subscriptions.

And, for virtually every brand, the overwhelming majority of that growth has come via more traffic – simply because global conversion rates have consistently hovered between 2% – 3% for most of that period (though there are obvious year-to-year and industry-to-industry fluctuations). Think about that for a minute: despite brands investing tens (if not hundreds) of billions of dollars in new websites, conversion rate optimization and third-party software like heatmaps, A/B testing platforms and the like, and despite agencies + freelancers having access to previously unfathomable amounts of data, global conversion rates have barely budged.

Growth – at least from a marketing perspective – is a straightforward equation:

(Traffic x conversion rate) – losses (churn, returns, etc.) = growth

Well, conversion rates are flat. Losses are flat-to-increasing for most brands/industries. That leaves more traffic as the most viable avenue to growth.

And for most of the last decade, that model was effective enough to avoid major scrutiny.

There are thousands of reasons why. Google sent a ton of organic traffic to the open web (something that has certainly declined). CPMs were cheap. Cookies were plentiful. Attribution was simple. As long as you could spend enough to offset the (relatively minor) increases in traffic costs, you’d continue to grow.

But the media landscape in 2025 looks nothing like it did in 2015. In the last decade, trillions (yes, trillions, with a “T”) of media dollars have flowed into digital ad platforms (most of it from traditional media, like TV, radio, newspapers, billboards, out-of-home, etc.). The truly remarkable thing is that ad revenue growth has eclipsed attention growth on most platforms – and since ad markets are markets, that’s resulted in substantial cost increases (higher supply + way higher demand = higher prices).

Couple that economic reality with declining attention spans and wildly more precise and sophisticated algorithms, and the end state is obvious: the cost of buying attention has never been higher and the margin for inefficiency has never been thinner.

All of this leads me to an uncomfortable truth: increasing traffic can not be the central pillar of a growth strategy going forward.

Why?

I believe most brands don’t have a traffic problem; they have a conversion architecture problem.

When Optimization Works Against You

Here’s the irony of those ultra-sophisticated, “AI-powered” machine learning algorithms: they’ve made ad platforms too good at their jobs.

There are two reasons for this: (1) ad platforms have long known they deliver substantial surplus value to advertisers; their problem was that – prior to the ML/AI revolution – they never had a reliable method to extract it; (2) those same platforms often lacked visibility into follow-on actions taken by traffic directed to your website.

Well, those two limitations no longer exist.

Today, if you tell Meta you want clicks, you’ll get clicks. Tell Google you want traffic, and you’ll get traffic. Tell TikTok you want views, it’ll serve your ad up to people who are extraordinarily likely to view it.

But here’s the catch: each of those platforms will optimize for exactly what you asked for — and nothing more. In fact, I can go one step further: if you’re optimizing for views or traffic, the algorithm is very likely optimizing against conversion.

That seems like an insane statement, but if you think about it logically, it’s not. I call it the “Order of Fill” problem:

The gist is this:

An ad platform has an audience of (say) 100M people. It can predict with astonishing precision (thanks to trillions and trillions of data points across billions and billions of interactions) the behavioral response (ignore, view, click, show interest, convert) each audience member is likely to have in response to each ads/offers/brands (after all, machine learning is, at its core, pattern recognition at unfathomable scale). The platform also knows (thanks to campaign optimization settings) what each advertiser wants (views, clicks, interest signals/micro-conversions, conversions).

Assuming the machine is perfectly rational (as most are), the most logical path forward for the machine is to maximize delivery for a brand with a given objective to the audience members with the highest probability of only having that response AND only that response. Thus, if you optimize for clicks, the platform’s incentive is to wildly over-index your ad delivery to people who are squarely in the red circle above (“People who will click”). The platform has virtually no incentive to show your ads to people it believes will fall into the blue (“people who will click AND add to cart”) or yellow (“people who will click AND add to cart AND buy) – because doing so would provide you – the advertiser – with substantial surplus value. Instead, the platform is going to give you exactly what you asked for (traffic), while reserving those higher-value users (blue + yellow circles) for advertisers who ask for either adds to cart (blue) or sales/leads (yellow).

Why? Because that’s how you squash surplus value.

Unless you’re optimizing for conversions, Meta doesn’t care whether your traffic converts. Google doesn’t care whether the clicks it sends result in a qualified lead. TikTok doesn’t give a damn if the user who watched your video ever becomes a customer.

You asked for traffic. The platform delivered traffic. What happens next – if anything – isn’t their problem. And in that chasm – between what you measure and what matters – billions of dollars of marketing efficiency vanish.

The problem isn’t that platforms underperform. It’s that they’re performing perfectly on the wrong objective.

We’ve built entire acquisition systems optimized for signals that don’t correlate to business outcomes. Clicks, impressions, sessions, engagement – all purport to be proxies for growth, all the while the correlation between each one and actual growth becomes weaker.

The result is predictable:

Platforms reward cheap, shallow engagement
Ad delivery algorithms chase low-friction conversions
Marketers celebrate metrics that don’t move their organizations forward

It’s the equivalent of hiring a world-class chef, asking them to make “something good,” and then being surprised when you get toast, a salad or microwaved soup. The system did what you told it to do. It just wasn’t designed to create what you actually wanted (namely, a delicious culinary experience).

This is the conversion architecture problem. Most marketers simply do not think about the implications of it.

The first implication: always ask platforms for what you really want, not what you think will lead to it (i.e., if you want sales, optimize for sales).

The second implication: everything you do from a growth perspective should be focused on maximizing the value of the traffic you’re getting from each platform, simply because IF you’re optimizing for the thing you want from a paid media perspective, you should assume that the traffic you’re getting has the highest probability of converting.

Funnels = Growth Infrastructure

Over the last few years, the term “funnel” has been thrown around so frequently and so casually that it’s lost any semblance of meaning. For most brands, it’s (at best) a static diagram in a slide deck: a tidy, idealized visualization of the process by which leads become clients or shoppers become customers.

But a funnel isn’t a PowerPoint slide. It’s the architecture of your revenue engine.

Funnels are the connective tissue that join media, product, UX and analytics. It’s the system that determines how efficiently attention is converted into economic value for your organization/brand.

Done well, a funnel answers the uncomfortable questions most marketers don’t dare ask:

What platforms are supplying traffic with the highest potential?
Where are we losing the attention of qualified traffic?
Which parts of the experience create friction or confusion?
How can we better structure our offer / experience to increase yield?
Which optimizations deliver disproportionate return on investment?
Where should we invest more of our dollars? Where should we invest less?

When managed strategically, a funnel is far more than a marketing gimmick; it’s an operational framework for efficiency. It shows how well the business converts potential energy (traffic) into kinetic energy (revenue).

That’s why funnel thinking is so powerful: it unlocks a second lever for growth (going from simply increasing traffic to increasing both traffic AND efficiency). The impact that can unlock is magical – increasing traffic by 10%, conversion rate by 25% and AOV by 12% results in a ROAS increase of 54%. For most businesses, that’s the difference between break-even economics and scale-until-you-can’t economics.

Ultimately, the goal of marketing should not be to simply drive more traffic; it should be to increase expected value per visitor (or revenue/contribution margin per session) alongside total value generated (since optimizing for a rate alone tends to result in shrinking absolute volume).

Diagnosing the Real Leaks

Everything to this point has been super theoretical (and theory does have a place) – but I’m a firm believer that theory without application makes you fun at trivia night, not more effective at your job/in your business.

So, let’s get tactical.

The first step to actually applying this thinking to your business is to map your primary funnel.

For ecommerce, it might look something like:
Landing Page → PDP→ Add to Cart → Checkout → Purchase → Post-Purchase Sequence

For B2B + SaaS:
Ad Click → Landing Page → Form Submission → Demo → Opportunity → Closed Won → Onboarding → Renewal

For a DTC subscription:
Ad → Offer Page → Trial Signup → Onboarding → Renewal

Every business has a funnel. In fact, most are operating multiple funnels simultaneously – either across divisions, offerings, product lines or whatever else. The existence of funnels is universal; it’s just in the nuance where things break.

When you map the funnel and visualize stage-to-stage conversion rates, you reveal a hidden narrative. A sharp drop between stages isn’t just a number; behind that number is a truth about your business and a story about misalignment, friction or lost trust.

That’s the bad news. The good news is that once you shift to thinking about the system – about the funnel – you begin to uncover the hidden efficiency leaks that are hindering your growth and costing you money.

We group leaks into three stages:

Top-funnel leaks usually stem from relevance problems. You’re acquiring the wrong audience or overpromising in creative.
Mid-funnel leaks reflect cognitive friction. Your visitors are interested but confused, hesitant or overwhelmed.
Bottom-funnel leaks tend to be executional failures – think: slow load times, hidden fees, out-of-stock products, purchase/conversion anxiety or pricing problems.

In each case, the quantitative data tells you where drop-offs happen – a massive decline from Add To Cart to Purchase likely indicates a bottom-funnel leak; 90% of your visitors leaving before even clicking into the form could suggest a top-funnel problem. But to get to the root cause – the why – behind the leak, you’ll need some qualitative tools such as heatmaps, recordings and/or user tests.

The final element is categorization. Over the years, we’ve conducted thousands (probably more) of tests. One of the random things I’ve done with AI is run a cluster analysis on those tests (I mean, if Sam Altman is paying….might as well use the compute). What that analysis revealed is that (virtually) every test can be classified into one of the following eleven categories:

Brand: Instilling trust, credibility and/or the appeal of the overall brand + experience
Discovery: Making it easier for users to find the product/service that is most aligned to their needs/desires
Alignment: Better aligning the post-click experience with the ad content/creative/messaging
Appeal: Enhancing how individual products/services/offers are positioned or messaged to increase appeal among your target audience
Validation: Bolstering credibility + trust via third-party credibility and validation (awards, testimonials, reviews, etc.)
Risk Reduction: reducing the perceived risk associated with taking a next step via first-party actions (i.e. money-back guarantee, free trial, etc.)
Product Detail: Surfacing the right level of detail (ingredients, specs, use-cases) that reduce uncertainty
Price & Value: Improving perception of value, clarity of price, removing ambiguity around cost vs benefit
Usability: Reducing UX friction by simplifying forms, clarifying flows, improving interaction quality, avoiding redundancy, etc.
Quantity: Increasing order size, cart size, purchase frequency (or multiple item purchase behavior)
Scarcity: Creating urgency or limited-availability cues that accelerate decision-making.

When you overlay these eight purposes onto your funnel stages, two things happen:

First, you gain strategic clarity. Instead of “we need to test checkout flows” – the question becomes: “Is our biggest leak the result of a Usability issue or a Price/Value issue?” or “Are lower checkout rates a result of poor product discovery earlier in the experience?”

Second, this provides the framework to build a test portfolio with intentional breadth. Rather than endless one-off button color changes, aligning leakage to root cause allows you to move from one-off tests to strategic improvements to your entire experience. Once you’ve validated that (as an example) lower checkout rates on a subscription offering can be addressed via validation, when you observe the same pattern on a standard product, you now have a better starting point for future inquiry.

Say you map the funnel and find a significant drop-off from PDP to Add to Cart. Rather than throwing everything at it, the above framework gives you the tools to classify the leak:

Are users leaving because they don’t trust the site or brand (Brand)?
Are they struggling to find the right product or variant (Discovery)?
Are they unclear on the specs or benefits (Product Detail)?
Do they not trust the claims we’re making (Validation)?
Or do they perceive the price as too high relative to value (Price & Value)?

Based on the quantitative + qualitative data available, identify the category with the highest probable impact, design the test accordingly (e.g., addition of a quiz or sizing sheet = Product Detail; improving price transparency = Price & Value), then measure the stage-specific lift.

By doing so, you shift from random experimentation to an intentional, root-cause-driven optimization architecture that continually contributes to your organizational knowledge AND de-risks future tests (since you will slowly build up a library of issues, interventions + results that is specific to your business/brand and audience). There’s obviously no guarantee that what worked on one funnel will work on another, but the probability is certainly higher.

Transforming Funnel Insights into Positive Outcomes

Once you’ve defined your funnel, the next step is operationalizing it. Funnel optimization isn’t about random, ad hoc A/B testing or chasing new design trends; it’s about building a closed-loop system that drives predictable, compounding improvements. Your overarching goal should be to make each funnel your organization operates progressively more effective and efficient at creating your desired outcome over time.

That sounds nice in theory, but can be wildly difficult in practice. Here’s exactly how we do it, step-by-step:

1. Prioritize What You’re Going To Fix

The #1 mistake most brands make is chasing one of two things: (i) HiPPOs and (ii) cosmetic wins (if you’re unfamiliar – Highest Paid Person’s Opinion). It is always tempting to do, and it almost always ends in failure or frustration (usually both).

If you want to avoid that misery, you need a method to move from personal preferences to objective analysis. To do that, begin by scoring each opportunity on three variables:

Drop-off magnitude (where you’re losing the most users)
Revenue potential (how valuable that stage is)
Implementation effort (how fast you can test)

Then, combine drop-off magnitude + revenue potential into a single score: “Impact”

Finally, create a 2×2 matrix, with “Impact” on the x-axis (low impact on the left, high impact on the right) and implementation effort inverted on the y-axis (low effort at the top, high effort at the bottom).

The upper-right quadrant (high impact, low effort) is the immediate goldmine of opportunity; the lower-left quadrant is the graveyard (seriously, don’t go there – especially after dark). The ones on the lower right (high impact, high effort) are your 10x tests; the ones in the upper left (low impact, low effort) are your 10% tests. Don’t you love it when two frameworks fit together?

2. Form Hypotheses, Not Hunches

I’m willing to bet you never thought middle school science fair principles would be applicable to marketing – but here we are. Every test you run, every optimization you suggest, should begin with a clearly defined hypothesis:

Example #1: “If we simplify our checkout flow from three steps to two, we’ll reduce abandonment by 12%.”
Example #2: “If we completely re-engineer our funnel via this offer structure, we’ll be able to acquire a previously-uncaptured segment of the market that could drive $1M in revenue over the next 12 months.”
Example #3: “If we make pricing transparent earlier, we’ll increase lead-to-demo conversion by 15%.”

There are two things worth noting: (1) not every test needs to be incremental (in fact, #2 above is an example of a 10x test); and (2) every test is designed to validate an assumption, not chase hope or trends. Combined, this blends precision and specificity with the 10% or 10x Philosophy.

The intent behind this section is to force rigor and discipline into what has (historically) been chaos. We clarify what is being changed, the expected outcome of the change and how the result of the test will be validated.

Where things can get REALLY cool is when you add behavioral psychology into the equation – such that you’re asking questions like: (1) why should this change work? (2) what assumptions about our product, offer, framing, proof or experience are we attempting to validate? (3) what cognitive or behavioral bias can we leverage to reduce leakage or improve efficiency?

3. Test Intentionally, Not Sporadically

Let’s start with a controversial statement: the objective of funnel optimization isn’t simply to improve efficiency or fix leaks; it’s to build institutional intelligence. That is how you go from whack-a-mole testing to systematically improving organizational performance.

Doing that starts with a simple question: what did we actually learn from this test?

When you answer that question AND record it, every experiment begins to build a living database of insights: what worked, for whom, under what conditions and why. Over time, that database becomes more valuable than any single result – it becomes an oracle of sorts, capable of telling you what happened in the past and how that insight can help you in the future.

If you know that each funnel where you’ve identified trust-related leakage in the upper funnel, social proof has alleviated the problem – well, what do you think has the highest probability of solving what you believe to be a trust-related problem in this upper funnel?

4. Measure Stage-Specific & Full-Funnel Impact

The second mistake most organizations make is narrow assessment: they run a test intended to move one specific metric (such as lead form completion rate), and assess the relative success or failure of that test based solely on that metric.

The problem with that is funnels are – by their very nature – interconnected. An intervention that improves one stage at the expense of a subsequent one might (paradoxically) harm the business more than it helps.

For example, a 5% increase in “Add to Cart” means nothing if “Checkout Initiation” declines 10%. That’s not progress; that’s displacement. You’ve made clicking easier for some segment of the audience, but (for whatever reason) those people are now not buying.

I often see this in B2B + B2C lead gen: a CRO firm will come in and recommend all sorts of changes to the forms/demo infrastructure – shortening the forms, removing qualification stages, removing qualifying messaging (i.e. for organizations over $1,000,000 in revenue) – all in the name of increasing form conversion rates.

Guess what? Those things tend to work. When we implement them, the conversion rate on the form increases. But…that improvement comes at the expense of lead quality. Instead of the sales team getting 100 leads per month, 80% of which are valid, the team now gets 200 – only 35% of which are legit. The net-net is a double-whammy of bad: the sales team is getting fewer valid leads (bad!) AND they need to do MORE work to surface the valid leads (increasing the probability that an otherwise valid lead will be ignored or missed, since the team is sorting through double the previous total AND the leads are less qualified). The real kick in the nether regions? The company paid a boatload of money to the CRO agency, and it’ll be months before they realize they spent more money to make less.

The solution is a combination of stage-specific analytics: measure every meaningful micro-interaction (hover, scroll, dwell time, field focus, drop-off) and analyze not just whether a user advanced, but how they advanced AND full-funnel analytics: assess the entire funnel pre and post-intervention, to ensure that an improvement at one stage did not come at the cost of another.

This combination – stage-specific and full-funnel – is the insurance policy that protects your brand against tests that displace an issue vs. those that resolve it.

5. Monitor Funnel Health Continuously

Here’s the truth virtually no marketer wants to hear: every funnel degrades.

Every. Single. One.

Incredible funnels today are copied ad infinitum. Exceptional experiences become routine as more brands use (or copy) them. Standards for UI/UX improve. Successful optimizations revert to the mean as user behavior adapts.

Mean reversion is like death: it comes for us all (graveyards, death, decay….can you tell I’m writing this in late October?)

Yet most organizations still treat funnel optimization like a finite project, a thing you “finish.”

That’s like optimizing a Google Ads campaign once, then expecting peak performance forever. It’s insane. Incoherent. Absolutely bonkers.

Your funnel, like your brand + your audience, is a living organism. It learns. It reacts. It adapts. It evolves. And, if you fail to care for it, it will die.

The solution is relatively straightforward: create a structured process to maintain + improve each funnel:

Map: Re-validate every step and event
Measure: Quantify drop-offs and completion ratios
Diagnose: Use behavioral data to find new friction
Prioritize: Rank fixes and tests by leverage
Iterate: Launch, learn, and document insights

It’s not glamorous work, but when it is done consistently well, the outcome is remarkable: an anti-fragile revenue engine that gets stronger, more efficient and more effective as time goes on.

5. From Optimization to Intelligence

At the end of the day, optimization treats symptoms. Intelligence cures diseases.

The future of growth isn’t about testing more buttons or rewriting more copy; it’s about institutionalizing curiosity. It’s about creating a brand that learns faster than its competitors can spend.

When experimentation becomes part of your organizational DNA, each initiative or campaign becomes smarter than the last. Each funnel leaks less because every test, every insight, every small win from any funnel is systematically reinvested. The insights you uncovered last year are no longer confined to some random report whose only purpose is to take up inbox space; they’re actively used to improve your business going forward.

Applying Funnel Thinking Across Business Models

The beauty of funnels is that they are universal – they apply across verticals, industries and business models, from ecommerce to enterprise SaaS to trendy DTC. Every brand has a funnel (whether or not they know it).

For DTC & Ecommerce:
Micro-conversions (quiz usage, size-chart views, variant selections) tend to be leading indicators of buyer intent. Map them, measure them then use those data points to power retargeting and/or on-site personalization.

Be sure to connect funnel improvements directly to contribution margin, not raw conversion rate. High-converting, low-margin SKUs are either distractions (if the margin can’t be improved) OR massive areas of opportunity (if you can create more favorable unit economics).

For Lead Gen (B2B + B2C):
The single-biggest issue for these businesses is the (wrong) belief that the funnel stops at the lead form. The reality is that it extends through demo attendance, opportunity creation, closed/won AND onboarding/usage.

Here, optimization is about activation friction: how fast and clearly a prospect experiences the value you can create. What form that takes will vary (how quickly can you set up a free trial OR give a case evaluation? How fast can you share the potential savings from new home windows, or how much better mom or dad’s life will be in the community?), but the principle is the same –
That “time-to-value” metric (not lead form/demo form conversion rate) tends to be the best predictor of growth.

Every improvement/optimization in this space must be measured relative to

In SaaS & Subscription:
Virtually every SaaS product has some level of seasonality or cyclical-ness – whether those are tied to budgeting seasons (often true for CRMs or ESPs) or customer seasons (i.e. eComm SaaS does NOT buy in November/December). Create funnels that align to both customers + key triggers/moments.

The other major difference in SaaS is retention; while it’s important for every industry, it is mission critical in SaaS (especially when payback periods are often 3-6+ months). The best thing you can do for your funnel is to map the drop-off between “post-conversion” stages (i.e. Free trial to paid plan; paid plan to renewal; renewal to upsell/expansion). These are often ignored by most marketers, but even small gains in retention + upsell will produce more organizational value than larger gains in free trials.

Optimize The System, Not The Symptom

Across every business model, the governing principle is the same: optimize the system, not the symptom. Funnels create shared visibility across marketing, sales, UX and product. They replace departmental KPIs with one universal metric: revenue efficiency per funnel.

There’s nothing more powerful as a business owner or executive than knowing the expected value of a user in each funnel. That knowledge allows me to better allocate my dollars, forecast growth + prioritize other initiatives (such as improving unit economics vs. scaling production).

In an environment where acquisition costs are on a non-stop up-and-to-the-right bender and attribution has never been less janky, funnel health is the last true source of media efficiency.

Every incremental improvement in conversion multiplies the return on investment of every paid impression. A 10% increase in funnel efficiency doesn’t just mean 10% more revenue; it can mean a 20%, 30% or even more improvement in EBITDA/contribution margin.

Funnels bridge the gap between creative excellence and financial performance. They make marketing accountable not just for awareness, but for impact.

More and more, organizations we work with are treating funnel management as a cross-functional discipline, sitting between marketing, product, customer success/support, sales, data and finance. Honestly, I think this is a fantastic, much-needed change – marketing can’t do it alone. When everyone is focused on the same data, the organization can optimize for what actually matters: marginal improvements across each stage of each funnel.

The second reason I love this?

It’s a tactic acknowledgement that most brands don’t lose because their ads are bad or their paid media sucks; they lose because their conversion architecture is inefficient.

The Funnel Audit

For leaders, funnel management isn’t an analytics task — it’s a strategic discipline.

Once a quarter, every growth organization should run a funnel audit:

Map all major funnel stages from attention to revenue
Validate data integrity and event accuracy
Identify the steepest drop-offs
Diagnose friction points qualitatively + qualitatively
Categorize each drop-off’s expected root cause
Plot each drop-off on the 2×2 impact vs. effort matrix
Prioritize those in the upper-right quadrant, then either lower-right or upper-left
Implement

This process isn’t about finding fault or creating problems; it’s about uncovering the issues that are siphoning revenue (leads, customers, whatever) from your organization, then systematically transforming those things from weaknesses into strengths. In the process of doing that, you’ll realize a second benefit: funnel-focused optimization makes everything else you do from a marketing perspective more effective.

Organic traffic benefits from better funnels. Email converts at a higher rate when visitors are directed to a clean, clear, hyper-relevant funnel. Your traditional media will perform better when the people it attracts are provided with a better online experience.

Just as a rising tide lifts all boats, a better funnel lifts all channels.

The New Growth Moat

The reality of digital advertising today is that every company – from the Fortune 500 to the mom-and-pop shop down the road – can buy reach. Google, Meta, TikTok, et al have made online advertising accessible to (quite literally) everyone. Anyone can get more traffic.

The flip side to that is the traffic you acquire has never been more expensive. Platforms are trying (and very often, succeeding) in squashing surplus value. This makes funnel understanding + optimization a competitive advantage that separates brands that grow efficiently from those that simply spend more.

Despite claims to the contrary, funnels are not relics of a time gone by. They are not spreadsheet diagrams that have no use in the real world. They’re simply visualizations of how your organization converts attention into revenue.

If you don’t understand how that happens and (more importantly) where it’s going wrong (or wrong for some segment of your audience), you’ll be hard-pressed to achieve your growth goals.

My belief is that growth tomorrow will come more from improved funnel performance, and less from increased traffic/budget/spend. Ad platforms are going to continue to become more expensive. They will continue to squash surplus value (at least, for most brands). That means the brands that can squeeze additional efficiency out of existing traffic will find themselves at a massive competitive advantage over time.

This week’s issue is sponsored by Optmyzr.

Funnels don’t (usually) collapse like the Francis Scott Key Bridge; they decline quietly – progressively lower engagement. Declining conversion rates. Messaging that no longer resonates with the core audience. Out-of-stock products. Broken links. Little, quiet conversion killers.

The impact of any one of those things, at a given moment in time, is often quite small (maybe even negligible) – but the impact of multiple of them, over time, is often wildly significant. The problem is that most marketers are pulled in a thousand directions, with to-do lists a mile long. Checking and re-checking every funnel every day isn’t something any of us have the time to do.

Fortunately, Optmyzr has developed the ultimate funnel early-warning system, combining its Landing Page Tools with the power of the Rule Engine.

Here’s just a few examples of how this can be magical:

The Ad Testing Tool automatically tells you when you have loser ads, as well as when a given ad should be modified due to low performance.

Optmyzr’s URL Checker continuously scans every link tied to your ads, keywords, sitelinks, and Performance Max asset groups. It spots the silent performance killers – the 404s, broken redirects, “Out of Stock” notices – and can automatically pause those ad groups/asset groups before they waste more money.

The Rule Engine can take this further – allowing you to create custom alerts and automation rules based on your definitions of funnel health.

Flag landing pages with bounce rates above 70%
Pause ad groups if revenue per click dips below threshold
Get instant Slack or email alerts when product pages show out of stock
Trigger budget reallocations from weak to strong funnels
Adjust targets based on funnel performance

All of this allows you to spend less time (and invest less mental effort) into checking the basics and more time into improving your funnels via structured testing + optimization.

If you’re curious about all the cool stuff Optmyzr can do to help you improve your funnel performance.

Try it free for 14 days here.

That’s all for this week!

Cheers,

Sam

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