Practical insights on revenue strategy, growth systems and commercial operations— written by our advisory team and featured in leading business publications.
As AI search transforms the discovery landscape, leadership teams face a critical question: how much should we invest in traditional SEO when the rules are changing? We examine the shift from Google dominance to AI-powered search, provide a framework for calculating ROI on search investments, and outline strategies for navigating this transition period.
New service offering leverages proprietary research into Google's after-ranking algorithms to deliver sustained traffic growth. Early client results show 30-47% traffic increases with improved ranking stability.
Why the most common SaaS metric is often misleading and what boards should measure instead to understand true customer acquisition efficiency.
The search landscape is undergoing its most significant transformation since Google's PageRank algorithm revolutionized web discovery in the late 1990s. As AI-powered search engines from OpenAI, Perplexity, and others gain market share, leadership teams face a critical strategic question: How much should we invest in traditional SEO when the fundamental mechanics of search are changing?
This isn't a theoretical concern. Our advisory work with growth-stage companies and public market operators reveals a common pattern: SEO investments that delivered predictable returns 18 months ago now face uncertain payback periods. Meanwhile, competitive intensity in organic search has never been higher, with enterprise players deploying AI-generated content at scale and programmatic SEO tactics flooding SERPs with low-quality results.
Let's establish the baseline: traditional SEO still matters—but its value is declining on a predictable curve, and the competitive dynamics have fundamentally shifted.
Despite the rise of AI search, Google still commands approximately 90% of global search traffic. For most commercial queries—particularly those with transactional intent—users still default to Google. The infrastructure of the web remains built around crawlable content, structured data, and link authority. These fundamentals won't disappear overnight.
More importantly, the skills and systems required for effective SEO—content strategy, technical optimization, authority building—translate directly to success in AI-powered discovery. The companies winning in traditional search today are building the content moats that will surface in AI-generated answers tomorrow.
The challenge isn't that SEO doesn't work—it's that the cost of competitive positioning has increased dramatically while expected returns face compression. Three factors drive this:
Case Example: A fintech comparison site we advised saw organic traffic grow 47% year-over-year through aggressive content investment—but conversion rates from organic search declined 18% as Google's own comparison tools captured more zero-click searches. The net revenue impact was positive but below projected ROI.
Understanding the transition timeline is critical for capital allocation decisions. Based on adoption curves from previous platform shifts (mobile, voice search) and current market indicators, we project the following:
This timeline assumes no major regulatory intervention or technical disruption. The actual pace may accelerate if AI search quality improves faster than expected, or decelerate if privacy concerns or misinformation issues slow adoption.
The strategic challenge is determining optimal investment levels when facing declining but still-positive returns. Here's the framework we use with advisory clients:
Where:
The critical variable is the Retention Factor—how long will your SEO investments continue generating returns? We recommend applying a declining value curve:
For example, if you historically assumed a 3-year value retention on SEO investments:
Use this model to evaluate specific SEO initiatives:
Example Calculation:
A SaaS company considers investing $200K in content and technical SEO to capture rankings in a competitive vertical. Expected outcomes:
Calculation:
Annual Revenue = 50,000 × 2% × $5,000 = $5M
Contribution = $5M × 70% = $3.5M
Total Value = $3.5M × 2.7 years = $9.45M
ROI = $9.45M / $200K = 47x return
Even with declining retention factors, this remains a compelling investment. However, by 2028, the same initiative would project only a 15x return—still positive, but requiring reassessment against alternative growth channels.
Continue investing in SEO for core branded terms, high-intent commercial queries, and existing ranking positions. The cost of maintaining rankings is far lower than the cost of recapturing them, and these positions retain value even as AI search grows.
Move away from pure keyword optimization toward building genuine expertise and authority. AI systems prioritize credible sources—invest in expert content, original research, and thought leadership that positions your brand as a primary source.
Optimize content to surface in both traditional search and AI-generated responses. This means:
Reduce dependency on any single discovery mechanism. Invest in owned channels (email, community), earned media, and emerging platforms. The companies that weather platform transitions best are those with diversified traffic sources.
Build flexibility into your growth budget to shift resources as the transition accelerates. Set quarterly review points to reassess SEO ROI against alternative channels and adjust investment levels accordingly.
The shift from traditional search to AI-powered discovery is inevitable, but it's not immediate. Leadership teams that navigate this transition successfully will:
The companies that treat this as a binary choice—all-in on traditional SEO or complete abandonment—will underperform. The winning strategy is disciplined, data-driven portfolio management: extract maximum value from traditional search while systematically building for the AI-native future.
Need help modeling SEO ROI for your business or developing a transition strategy? Skymaker Advisory works with leadership teams to build rigorous growth frameworks and navigate platform transitions. Contact us to discuss your situation.
San Francisco, CA – January 15, 2025 — Skymaker Advisory today announced the launch of "Viral Phenomenon," a specialized traffic service designed to help digital publishers, e-commerce platforms, and content-driven businesses achieve sustained organic traffic growth through advanced understanding of Google's post-ranking algorithms.
The new service offering is built on proprietary research detailed in Skymaker's recently published white paper, "Viral Phenomenon: Google's Twiddler Algorithm," which provides the first comprehensive analysis of how Google dynamically adjusts search rankings after initial placement.
While most SEO practitioners focus on achieving initial rankings, Skymaker's research revealed that Google's Twiddler algorithm—a post-ranking adjustment system—is responsible for significant ranking volatility that can erode traffic gains within weeks of achieving top positions.
Skymaker's research revealed a consistent pattern: sites would achieve strong rankings through traditional SEO, only to see positions decay 20-40% within 30-60 days. The issue wasn't the initial optimization—it was understanding and counteracting the temporal decay factors built into Google's ranking maintenance systems.
Key Research Findings:
The new service combines technical optimization, content strategy, and ongoing engagement engineering to maintain and amplify ranking positions after initial achievement. Key components include:
Systematic monitoring and optimization of the engagement signals that Twiddler evaluates, including click-through patterns, dwell time, and return visitor behavior. The service implements specific interventions designed to signal ongoing content relevance and authority.
Strategic content refresh and expansion protocols that maintain algorithmic perception of freshness without diluting topical authority. This includes targeted updates to high-value pages and systematic internal linking optimization.
Ongoing link acquisition and brand signal amplification designed to counteract natural authority decay. Unlike traditional link building, these programs focus on maintaining the authority signals that Twiddler specifically evaluates.
Proprietary traffic generation strategies that create the engagement patterns Google's algorithms interpret as quality signals, helping to stabilize and improve rankings over time rather than allowing natural decay.
The service has been in beta testing with select clients since Q3 2024, delivering measurable improvements in both traffic stability and growth:
Case Study: Digital Publishing Portfolio
A media company with dormant assets saw traffic recovery of +30% across their portfolio within 4 months, with ranking positions showing 85% stability compared to 45% stability in control groups using traditional SEO maintenance.
Case Study: Fintech Comparison Platform
A financial services comparison site achieved +47% traffic growth with total ranking keywords increasing by 220%. The platform now maintains category leadership in 8 of 12 target verticals, with significantly reduced ranking volatility.
Case Study: iGaming Multi-Brand Operator
A gaming operator saw direct brand searches increase +156% and brand-driven revenue grow +34%, with organic traffic velocity improving +89% through systematic engagement optimization.
Viral Phenomenon is delivered through Skymaker's established operating model: a compact partner team supported by a vetted network of specialized vendors including traffic specialists, content strategists, and technical SEO operators.
The service isn't a software product or a cookie-cutter playbook. It's a managed service that requires deep understanding of each client's competitive landscape, content architecture, and business model—essentially reverse-engineering Google's quality signals and building sustainable systems to maintain them.
The Viral Phenomenon service is available immediately for qualified clients. Typical engagements run 4-6 months with ongoing optimization available for clients seeking sustained traffic growth.
Pricing is structured around traffic and revenue outcomes rather than hourly fees, with investment levels ranging from $50K-$200K depending on portfolio size and competitive intensity.
The foundational research paper, "Viral Phenomenon: Google's Twiddler Algorithm," is available for download to qualified professionals. The 45-page report includes:
Access to the white paper is available through Skymaker's research portal at skymaker.advisory/research.
Skymaker Advisory is an independent growth and revenue advisory firm serving leadership teams across software, commerce, financial services, and media. The firm specializes in surfacing agile growth methodologies from best-in-class specialists and deploying them at enterprise scale to compound commercial results.
Skymaker's team has advised on growth strategies for venture-backed challengers and global incumbents including Google, Nokia, LeoVegas, and numerous private equity-backed portfolio companies.
Media Contact:
Skymaker Advisory
[email protected]
+1 (415) 555-0100
Service Inquiries:
To discuss Viral Phenomenon service engagement or access the Twiddler research white paper, contact Skymaker's advisory team at skymaker.advisory/contact.
Forward-Looking Statements: This press release contains forward-looking statements regarding service performance and client outcomes. Actual results may vary based on competitive dynamics, algorithm changes, and client-specific factors. Past performance does not guarantee future results.
Every board deck includes it. Every investor asks about it. CAC payback period has become the default metric for evaluating customer acquisition efficiency in SaaS businesses. The logic seems sound: measure how long it takes to recover what you spent acquiring a customer, and you have a clean indicator of capital efficiency.
But here's the uncomfortable truth: CAC payback is often the wrong metric to optimize for, and boards that fixate on it are frequently making worse capital allocation decisions than if they ignored it entirely.
The problem isn't that the math is wrong. The problem is that the metric answers a question nobody should be asking.
The standard formula is straightforward: take your fully loaded customer acquisition cost, divide it by monthly recurring revenue per customer multiplied by gross margin, and you get the number of months to recover your acquisition investment.
A 12-month payback sounds better than 18 months. A 6-month payback sounds better than 12. The implicit assumption is that faster payback equals better business performance. But this assumption breaks down the moment you examine what actually drives business value.
Two companies can have identical 12-month CAC payback periods while having radically different business quality. Company A retains customers for 24 months on average. Company B retains them for 60 months. Both recover their acquisition cost in 12 months, but Company B generates 2.5x more lifetime value from the same initial investment.
CAC payback tells you nothing about this difference. It measures speed of capital recovery but ignores the total return on that capital. This is like evaluating real estate investments based solely on how quickly you recoup your down payment, without considering whether the property appreciates or generates rental income for decades.
Consider a company that could reduce CAC payback from 15 months to 10 months by cutting onboarding support and customer success resources. The payback metric improves dramatically. But if those cuts increase churn from 3% to 5% monthly, the company just destroyed enormous value while making their board metric look better.
This happens constantly. Teams optimize for faster payback by reducing acquisition costs or accelerating early revenue, often at the expense of customer lifetime value. The metric rewards short-term capital efficiency while obscuring long-term value destruction.
Different acquisition channels naturally have different payback profiles. Inbound content marketing typically has longer payback periods than paid search, because you're investing in assets that compound over time rather than buying immediate conversions. But the customers acquired through content often have higher retention and expansion rates.
A company optimizing for CAC payback will systematically underinvest in channels with longer payback but superior unit economics, while overinvesting in channels that recover capital quickly but deliver lower lifetime value. This is precisely backwards.
The right question isn't "how quickly do we recover acquisition costs?" The right question is "what return do we generate on acquisition investment over the customer lifetime?"
The lifetime value to customer acquisition cost ratio is a better starting point, but most companies calculate it incorrectly. The common mistake is using average customer lifetime value across the entire base, which includes customers acquired years ago under different conditions.
Instead, calculate cohort-based LTV:CAC. Track the actual lifetime value of customers acquired in specific time periods, and compare that to the actual CAC for those cohorts. This reveals whether your unit economics are improving or deteriorating, which aggregate metrics obscure.
A healthy SaaS business should target 3:1 or better on recent cohorts. But the ratio matters less than the trend. A company moving from 2.5:1 to 3.5:1 is building a compounding advantage. A company stuck at 4:1 for three years has likely stopped improving their acquisition efficiency or retention mechanics.
If you must use a payback-style metric, adjust it for the full investment horizon. Instead of measuring months to recover CAC, measure the internal rate of return on customer acquisition investment over the expected customer lifetime.
This accounts for both the speed of capital recovery and the total return generated. A 12-month payback that generates 2x return over 24 months is worse than an 18-month payback that generates 5x return over 60 months, even though the first looks better on the standard metric.
Here's a metric that actually drives better decisions: measure the cumulative contribution margin generated after payback is achieved. This tells you how much profit the business extracts from each customer after recovering the acquisition investment.
Two companies with 12-month payback periods might look identical. But if Company A generates $5,000 in post-payback contribution margin per customer while Company B generates $15,000, you're looking at fundamentally different businesses. Company B has 3x more capital to reinvest in growth or return to shareholders.
This metric naturally incorporates retention, expansion, and gross margin improvement over time. It rewards companies that build durable customer relationships rather than optimizing for quick capital recovery.
The deeper problem with CAC payback isn't just that it's incomplete. It's that metric fixation creates organizational dysfunction. When leadership teams optimize for a single number, they inevitably find ways to game it, often without realizing they're doing so.
Sales teams start discounting aggressively to accelerate early revenue and improve payback metrics, even though it reduces lifetime value. Marketing shifts budget to high-intent channels that convert quickly but don't build sustainable acquisition engines. Customer success teams focus on expansion in the first year rather than building the foundation for long-term retention.
None of these behaviors are malicious. They're rational responses to the incentive structure created by the metric. But they systematically degrade business quality while making the board deck look better.
Instead of asking "what's your CAC payback period?" boards should ask:
These questions force management teams to think about total return on acquisition investment rather than speed of capital recovery. They surface the trade-offs between short-term efficiency and long-term value creation. And they make it much harder to game the metrics without actually improving the business.
CAC payback isn't useless. It's a reasonable secondary metric for tracking capital efficiency trends over time. But it should never be the primary measure of acquisition effectiveness, and it should never be used in isolation.
The companies that build durable competitive advantages in customer acquisition do so by optimizing for lifetime value, not payback speed. They invest in channels and strategies that compound over time, even when those investments have longer payback periods. They build retention and expansion mechanics that maximize the return on every acquisition dollar, rather than minimizing the time to recover it.
If your board is fixated on CAC payback, the problem isn't the metric itself. The problem is that you're answering the wrong question. The right question is whether you're generating attractive returns on customer acquisition investment over the full customer lifetime. Everything else is just accounting.
Need help building better measurement systems for customer acquisition efficiency? Skymaker Advisory works with leadership teams to design metrics frameworks that actually drive better capital allocation decisions. Contact us to discuss your measurement challenges.