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Happy Sunday, Everyone!
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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.
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When Optimization Works Against You
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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.
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Funnels = Growth Infrastructure
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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).
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Diagnosing the Real Leaks
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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.
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Transforming Funnel Insights into Positive Outcomes
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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
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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 2x2 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
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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
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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
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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
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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.
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Applying Funnel Thinking Across Business Models
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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.
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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.
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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 2x2 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.
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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.
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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.
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That’s all for this week!
Cheers,
Sam
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