Ecommerce brands don’t fail from lack of data — they fail because their analytics platforms can’t answer one question:
What is actually driving profit?
In 2026, the average ecommerce brand has access to more marketing data than ever before. Google Analytics tracks every click. Shopify reports every transaction. Meta, Google, and TikTok each claim credit for conversions. Yet despite this data abundance, 47% of marketing spend — equivalent to $66+ billion annually — is wasted due to broken attribution and fragmented insights.
The problem isn’t missing data. The problem is that most analytics platforms were built for reporting website behavior, not measuring business profitability. They tell you what happened, but not whether it made you money.
This fundamental disconnect between measurement and profitability is costing ecommerce brands millions in misallocated marketing spend, while drowning teams in dashboards that generate more confusion than clarity.
Reporting Isn’t Intelligence — It’s Just Noise
Walk into any ecommerce marketing meeting, and you’ll see the same pattern: endless dashboards, conflicting metrics, and heated debates about which platform “really” drove that sale.
Dashboards have become a commodity. Every tool promises “data-driven insights,” but what they actually deliver is an overwhelming volume of metrics with no clear connection to business outcomes. Teams spend hours reconciling numbers between platforms, only to make decisions based on incomplete information.
The reality is that brands are drowning in reports while their profit margins steadily shrink. They can tell you their click-through rate down to three decimal places, but they can’t tell you which marketing channels are actually generating profitable customers versus expensive one-time buyers who erode margin.
“If your analytics platform can’t connect spend to profit, it’s not analytics — it’s reporting.”
This distinction matters more than ever in 2026. As customer acquisition costs continue rising and competition intensifies, ecommerce brands can no longer afford to optimize for vanity metrics. They need profit intelligence — the ability to understand not just what’s happening, but what’s actually making the business money.
The Core Problem – Ecommerce Analytics Was Built for Traffic, Not Profit
To understand why most analytics platforms fail ecommerce brands, you need to understand what they were originally designed to measure.
Google Analytics: Built for Websites, Not Businesses
Google Analytics was designed to track website behavior — page views, sessions, bounce rates, and conversion events. It excels at telling you how visitors interact with your site, but it has no concept of profit margins, cost of goods sold, shipping costs, or the dozens of other financial factors that determine whether a sale actually makes you money.
GA can tell you that a campaign drove 1,000 conversions at a 3% conversion rate. What it can’t tell you is whether those conversions came from high-value repeat customers or discount-hunting one-time buyers who will never purchase again. It can’t factor in that 40% of those orders included free shipping promotions that eliminated any margin, or that returns will ultimately wipe out gains from another 20%.
Shopify Analytics: Transactions Without Context
Shopify’s native analytics does track revenue and some basic profitability metrics, but it exists in isolation from your marketing data. It can tell you what sold and for how much, but it can’t accurately connect those sales back to the marketing campaigns that drove them. The attribution models are basic, and there’s no way to measure incrementality or understand which channels are creating new demand versus simply capturing existing intent.
Ad Platform Dashboards: Self-Serving Attribution
Meanwhile, Meta claims credit for conversions that Shopify can’t validate. Google Ads reports ROAS numbers that don’t match your revenue reports. TikTok attributes sales that other platforms also claim. Each advertising platform has every incentive to make their performance look as good as possible, using attribution windows and methodologies that maximize their reported impact.
The result is attribution inflation — where the sum of all platform-reported conversions exceeds your actual sales by 30-50% or more. Brands are making budget allocation decisions based on inflated, conflicting data that bears little resemblance to reality.
The Pain Points Are Universal
This broken analytics foundation creates predictable problems that plague ecommerce brands at scale:
Meta claims conversions Shopify can’t validate. You’re told a campaign drove $50K in revenue, but when you reconcile with actual Shopify data, only $32K can be verified. Which number do you trust? How do you optimize when ground truth is unclear?
CAC is rising while ROAS looks “fine.” Your reported return on ad spend remains steady at 4x, but somehow profitability is declining. The disconnect comes from measuring revenue return without accounting for rising acquisition costs, shrinking margins, and changing customer mix.
Discounts boost revenue but destroy margin. Your analytics show a successful promotional campaign that drove a 40% increase in sales. What they don’t show is that the 25% discount, combined with free shipping and higher return rates, turned profitable customers into loss leaders.
These aren’t edge cases. They’re the daily reality for ecommerce brands trying to scale profitably while relying on analytics tools that weren’t designed for the questions that actually matter.
What Ecommerce Brands Actually Need in 2026
What Should an Ecommerce Analytics Platform Measure?
The answer isn’t more metrics — it’s the right metrics. Ecommerce brands making profitable growth decisions need analytics platforms that measure:
Profit per order — not just revenue, but actual contribution margin after accounting for COGS, fulfillment costs, payment processing fees, discounts, and returns.
True customer acquisition cost — the fully loaded cost of acquiring a customer, including platform fees, creative costs, and overhead, accurately attributed to the channels and campaigns that actually drove the acquisition.
Contribution margin by channel — which marketing channels generate profitable customers versus which ones attract discount-seekers and one-time buyers who erode margins.
Retention-driven revenue — the difference between revenue from new customer acquisition versus revenue from existing customer retention, and the dramatically different economics of each.
Incrementality, not attribution inflation — what marketing activity created new demand versus what simply captured existing intent, cutting through the self-serving claims of individual ad platforms.
This shift from vanity metrics to profit metrics represents a fundamental evolution in how ecommerce analytics platforms need to function. The brands that win in 2026 and beyond won’t be those with the most data — they’ll be those with the clearest understanding of what drives profitable growth.
The Profit Analytics Gap (What Most Platforms Miss)
The disconnect between what analytics platforms measure and what ecommerce leaders need to know creates a dangerous gap. Brands are optimizing for metrics that look good in dashboards but don’t translate to sustainable profitability.
Metrics That Mislead
ROAS without margin context — A 5x ROAS campaign can lose money if it’s targeting products with thin margins or attracting customers who use heavy discounts. Yet most platforms celebrate high ROAS without any connection to actual profitability.
Revenue without cost allocation — Gross revenue is a vanity metric. A $1M revenue month could generate healthy profit or substantial losses depending on the cost structure behind those sales. Most analytics platforms treat all revenue as equal when the reality is dramatically different.
Conversion rate without LTV — A high-converting campaign that attracts one-time buyers is far less valuable than a lower-converting campaign that attracts loyal repeat customers. Yet standard analytics optimize for conversion rate without any consideration of customer lifetime value.
Metrics That Matter
Net profit per campaign — After accounting for all costs — advertising spend, COGS, fulfillment, payment processing, discounts, and returns — how much actual profit did this campaign generate? This is the only metric that matters for business sustainability.
Payback period — How long does it take for a customer to generate enough margin to cover their acquisition cost? Brands with short payback periods can scale aggressively. Those with long payback periods need careful cash flow management. Yet few analytics platforms even calculate this metric.
LTV:CAC ratio — The relationship between customer lifetime value and customer acquisition cost determines the fundamental unit economics of your business. A healthy ratio (typically 3:1 or higher) indicates sustainable growth. A declining ratio signals trouble ahead.
Profit per customer segment — Not all customers are created equal. Some segments generate outsized profits while others consistently lose money. Sophisticated analytics should segment customers by profitability and allow you to target marketing toward high-value segments while reducing spend on unprofitable ones.
The gap between these two sets of metrics — what platforms measure versus what businesses need — explains why so many ecommerce brands struggle to translate analytics into profitable growth. They’re optimizing for the wrong things because their tools can’t measure the right things.
Why “Dashboard Tools” Fail Ecommerce Leaders
The proliferation of analytics dashboards over the past decade has created an illusion of clarity while often making decision-making harder, not easier. Understanding why these tools consistently fail helps explain what ecommerce brands actually need.
Common Dashboard Failures
Data fragmentation — Your customer data lives in Shopify. Your advertising data is scattered across Meta, Google, TikTok, and other platforms. Your email performance is in Klaviyo. Your inventory and COGS data is in your ERP. Most dashboard tools can visualize one or two of these sources, but they can’t create a unified view that connects marketing spend to actual profitability.
Attribution conflicts — When every platform uses different attribution methodologies and lookback windows, you get conflicting stories about performance. Dashboard tools typically just display these conflicts side-by-side, leaving you to manually reconcile the discrepancies and guess at ground truth.
No financial layer — Most analytics dashboards have no concept of profit margins, cost of goods sold, fulfillment costs, or the dozens of other financial factors that determine profitability. They track marketing metrics in isolation from business economics.
No decision layer — Dashboards show you what happened. They don’t tell you what to do about it. You still need to manually interpret the data, identify patterns, develop hypotheses, and make decisions — a time-consuming process that introduces bias and delays action.
No predictive intelligence — Traditional dashboards are backward-looking. They tell you historical performance but provide no forward-looking insights about what’s likely to happen if you continue current strategies versus making specific changes.
The Platform Comparison Reality
| Tool Type | What It Shows | What It Misses |
|---|---|---|
| Google Analytics | Traffic patterns, conversion events, user behavior | Profit margins, true attribution, customer economics |
| Shopify Analytics | Order volume, product performance, basic revenue metrics | Marketing attribution, customer acquisition costs, channel profitability |
| Ad Platform Dashboards | Platform-specific ROI claims, spend and impression data | Incrementality, cross-platform truth, financial outcomes |
| LayerFive Axis | Unified profit intelligence, true attribution, margin-aware performance | None — built specifically to close these gaps |
The limitations of traditional dashboard tools aren’t bugs — they’re fundamental design constraints. These platforms were built to visualize specific data sources, not to provide holistic profit intelligence. Expecting them to answer questions about profitability is like expecting a thermometer to measure distance.
This explains why so many ecommerce teams feel overwhelmed by data while simultaneously lacking confidence in their decisions. They have more dashboards than ever, but less clarity about what’s actually driving profitable growth.
Introducing LayerFive Axis — Built for Profit Intelligence
LayerFive Axis represents a fundamental rethinking of what an ecommerce analytics platform should be. Rather than adding another dashboard to an already crowded landscape, Axis was purpose-built to solve the profit intelligence gap that undermines ecommerce decision-making.
Not Another Analytics Dashboard
LayerFive Axis is not another analytics dashboard. It is a profit intelligence platform built for ecommerce operators, CFOs, and growth teams who need to understand the economic reality behind their marketing performance.
Where traditional analytics tools were designed to track website behavior or report ad platform metrics, Axis was built from the ground up to answer a different question: What marketing activities are generating profitable growth, and how should we allocate resources to maximize profit?
This distinction drives every aspect of how Axis functions, from data integration to attribution methodology to how insights are surfaced and acted upon.
Core Differentiators
Profit-first measurement — Every metric in Axis connects back to profitability. Revenue is always shown with margin context. Customer acquisition costs are fully loaded and accurate. Campaign performance is measured by profit contribution, not vanity metrics.
Unified attribution truth — Axis implements sophisticated multi-touch attribution that reconciles data across all marketing channels, resolving the attribution conflicts that plague traditional tools. Rather than showing you conflicting claims from different platforms, Axis provides a single source of truth about what actually drove each conversion.
Margin-aware performance tracking — Unlike platforms that treat all revenue equally, Axis understands that a $100 sale of a high-margin product is fundamentally different from a $100 sale of a discounted low-margin item. Every performance metric accounts for the actual economics of what was sold.
Executive-grade decision clarity — Axis surfaces insights in ways that enable immediate action. Rather than drowning users in data, it highlights what matters most: which channels are driving profitable growth, which campaigns are underperforming, which customer segments deserve more investment, and where budget should be reallocated.
The result is a platform that finally closes the gap between marketing analytics and business outcomes — connecting every dollar spent to its actual impact on profit.
How LayerFive Axis Connects Marketing Spend to Profit
The power of LayerFive Axis comes from its ability to create a complete, unified view of ecommerce economics — connecting marketing activity to financial outcomes in ways that traditional tools simply cannot.
Unified Ecommerce Data Model
Axis integrates data across your entire ecommerce ecosystem to create a holistic view of business performance:
Sales channels — Shopify, Amazon, retail partnerships, and any other revenue source, all normalized into a single framework that allows true cross-channel analysis.
Marketing platforms — Meta, Google, TikTok, Pinterest, Snapchat, and all other paid and organic channels, with complete spend and performance data synchronized to your actual sales outcomes.
Customer engagement — Email campaigns, SMS marketing, loyalty programs, and other owned channels that influence purchase behavior and drive retention.
Cost structure — COGS, fulfillment and shipping costs, payment processing fees, discount and promotion costs, returns and refunds — the complete financial picture that determines actual profitability.
This unified data model eliminates the fragmentation that undermines decision-making in traditional analytics stacks. Instead of maintaining separate views in different tools and manually reconciling conflicts, everything flows into a single profit intelligence system.
Real Profit Attribution
The most critical innovation in Axis is how it handles attribution — moving beyond the simplistic “who touched the customer” models that create attribution inflation, to answer the more important question: What marketing activity actually created incremental profit?
Traditional attribution asks which platforms a customer interacted with before converting. Axis asks which marketing activities created new demand versus simply capturing existing intent. Which campaigns introduced your brand to new customers versus retargeting people who were already going to buy? Which channels are genuinely incremental versus those that steal credit from other marketing efforts?
This incrementality-focused approach to attribution cuts through the self-serving metrics that plague platform-specific dashboards. It provides a realistic view of what’s actually working, allowing you to confidently shift budget from over-credited channels to undervalued ones that are genuinely driving new business.
The result is attribution you can trust — and business decisions based on profit reality rather than inflated platform claims.
Profit Use Cases Ecommerce Leaders Care About
LayerFive Axis transforms how ecommerce teams approach their most critical decisions. Here are the real-world use cases where profit intelligence creates immediate impact:
Use Case 1: Identifying Campaigns That Scale Profitably
The traditional approach: Look at ROAS, pick campaigns above a target threshold, increase budget on those campaigns.
The Axis approach: Identify campaigns that drive profitable customers with favorable LTV:CAC ratios and short payback periods, understanding not just which campaigns generate revenue but which ones acquire customers who become profitable quickly and remain loyal over time.
Real impact: A mid-market fashion brand discovered that their highest ROAS campaign was actually acquiring one-time discount shoppers with poor retention, while a “underperforming” campaign with lower ROAS was attracting loyal customers with 4x higher lifetime value. They shifted 40% of budget from the high-ROAS campaign to the high-LTV campaign, reducing short-term reported ROAS but increasing actual profitability by 32%.
Use Case 2: Discovering Customer Segments That Actually Retain
Most analytics platforms segment customers by demographics or acquisition channel. Axis segments by profitability and retention behavior, identifying high-value cohorts based on actual economic outcomes.
This allows you to answer questions like: Which acquisition sources bring in customers who make repeat purchases? Which product categories attract loyal buyers versus one-time purchasers? Which customer segments have the best unit economics, and how can we acquire more customers like them?
Real impact: An outdoor gear retailer used Axis to discover that customers acquired through educational content had 60% higher retention rates than those acquired through promotional campaigns, despite lower initial conversion rates. They restructured their entire content strategy around this insight, trading some short-term conversion volume for dramatically better customer lifetime economics.
Use Case 3: Optimizing Channel Mix for Profit
The attribution conflicts between platforms make it nearly impossible to understand true channel performance. One platform says this channel drove X conversions, another says that channel deserves credit, and you’re left guessing about ground truth.
Axis resolves these conflicts with unified attribution that shows accurate, deduplicated performance for each channel. You finally know which channels are genuinely incremental versus which are simply capturing demand created by other marketing efforts.
Real impact: A home goods brand discovered that their Meta retargeting campaigns, which showed excellent ROAS in the Meta dashboard, were primarily capturing demand created by Google and email marketing. By reducing Meta retargeting spend by 60% and reinvesting in the channels actually creating awareness, they maintained the same revenue with 25% lower total marketing spend.
Use Case 4: Forecasting Profit, Not Just Sales
Traditional forecasting projects revenue based on historical trends. Axis forecasts contribution margin by accounting for changes in product mix, channel efficiency, customer behavior, and cost structure.
This forward-looking profit intelligence allows you to see problems before they fully materialize. Are payback periods lengthening? Is customer mix shifting toward lower-value segments? Are acquisition costs rising faster than LTV? Axis surfaces these trends early when there’s still time to course-correct.
Real impact: A subscription box company used Axis forecasting to identify that while revenue growth looked healthy, customer acquisition payback periods were lengthening from 4 months to 7 months due to deteriorating retention rates. This early warning allowed them to address retention issues and adjust acquisition spending before cash flow problems emerged, avoiding what would have been a severe profitability crisis.
The Ecommerce Analytics Platform Checklist
If you’re evaluating analytics platforms for your ecommerce business, here’s what you should demand. A true profit intelligence platform must include all of these capabilities:
✅ Profit-based attribution — Every channel, campaign, and customer cohort should be measured by actual profit contribution, not just revenue generation. The platform must account for COGS, fulfillment costs, discounts, returns, and all other factors that impact margin.
✅ Full cost visibility — The analytics system needs access to complete cost data, including cost of goods sold, shipping and fulfillment expenses, payment processing fees, promotional discounts, and return costs. Without this financial layer, you’re just tracking revenue, not profitability.
✅ Cross-channel identity resolution — The platform must be able to recognize the same customer across devices, channels, and sessions, creating a unified customer view that enables accurate attribution and lifetime value measurement.
✅ LTV and retention analytics — Beyond first-purchase metrics, the system should track customer lifetime value, retention rates, repurchase behavior, and cohort performance over time. The difference between acquiring a one-time buyer and a loyal repeat customer is the difference between profit and loss.
✅ Executive-level KPI clarity — The platform should surface insights that enable immediate strategic decisions, not just display data. What’s working? What’s not? Where should budget shift? These answers should be clear, not buried in dashboard complexity.
✅ Predictive decision intelligence — Beyond reporting what happened, the platform should provide forward-looking insights about what’s likely to happen and what actions will drive the best outcomes. Static reporting is no longer sufficient when market conditions change rapidly.
LayerFive Axis delivers all six — and was purpose-built to excel at each one. It’s not an analytics tool that added profit features as an afterthought. It’s a profit intelligence platform that happens to include comprehensive analytics.
Why Profit Analytics Is the Future of Ecommerce Growth
Looking ahead to the rest of 2026 and beyond, several converging trends make profit-first analytics not just valuable but essential for ecommerce survival and growth.
AI Will Automate Reporting
Artificial intelligence is rapidly commoditizing basic analytics and reporting. Within the next 12-24 months, AI agents will be able to generate dashboards, create visualizations, and produce standard reports with minimal human intervention. The ability to track metrics and generate reports will cease to be a meaningful differentiator.
What AI cannot commoditize is strategic judgment — the ability to understand profit dynamics, identify opportunities, and make resource allocation decisions that maximize business value. This is where profit intelligence platforms create lasting advantage.
Brands Will Compete on Decision Speed
As markets become more efficient and competition intensifies, the time between identifying an opportunity and capitalizing on it continues to shrink. Brands that can quickly recognize when a channel is becoming less efficient, when a customer segment is becoming more valuable, or when a competitive dynamic is shifting will consistently outperform those working with delayed or incomplete information.
Profit intelligence enables faster, more confident decision-making because you’re working from a single source of truth rather than reconciling conflicting reports and debating which metrics to trust.
Profit Clarity Will Outperform Traffic Volume
The era of growth-at-any-cost ecommerce is definitively over. In 2026, investors, lenders, and stakeholders demand profitable growth. Brands that can demonstrate clear unit economics, sustainable customer acquisition, and path to profitability will access capital and resources that loss-making competitors cannot.
This shift rewards brands with superior profit intelligence. When everyone has access to similar traffic sources and advertising platforms, competitive advantage comes from better understanding of what drives actual profitability.
The Winners Will Have Better Intelligence, Not Just More Data
The next era of ecommerce winners won’t be those with more dashboards, more metrics, or more data. They’ll be the brands with clearer understanding of their profit drivers — which campaigns to scale, which customer segments to target, which channels to prioritize, and how to allocate limited resources for maximum profitability.
This is the future LayerFive Axis was built for: a world where profit intelligence is the defining competitive advantage in ecommerce growth.
LayerFive Beyond Axis: An Integrated Profit Intelligence Ecosystem
While LayerFive Axis serves as the central profit intelligence hub for ecommerce brands, it’s part of a broader ecosystem of solutions designed to address the complete spectrum of marketing data and attribution challenges.
The LayerFive Product Suite
LayerFive Edge — Visitor intelligence and predictive audience platform that identifies website visitors with 40-60% accuracy (versus the industry standard 5-15%), enabling more effective personalization, retargeting, and customer data enrichment. When you know who’s on your site, you can treat high-value prospects differently than casual browsers.
LayerFive Signals — Advanced attribution and ID resolution system that creates unified customer profiles across devices, channels, and sessions. Signals solves the identity graph problem that undermines accurate attribution, ensuring that the profit intelligence in Axis is built on accurate customer-level data.
LayerFive Navigator — Agentic AI marketing automation platform that doesn’t just report on performance but actively optimizes campaigns, adjusts bids, and reallocates budget based on profit signals. Navigator turns Axis insights into automatic action.
Together, these solutions create a comprehensive platform that addresses every aspect of the modern marketing intelligence challenge — from identifying visitors to attributing conversions to measuring profit to automating optimization. Brands using the full LayerFive ecosystem gain capabilities that simply aren’t possible when using fragmented point solutions.
Ecommerce Brands Don’t Need More Reports — They Need Profit Truth
The fundamental challenge facing ecommerce brands in 2026 isn’t data scarcity. Every brand has access to overwhelming amounts of data from dozens of sources. The challenge is converting that data into actionable profit intelligence.
Traditional analytics platforms were built for a different era — when the primary goal was understanding website behavior and tracking conversions. They’ve been incrementally enhanced over the years, but they remain fundamentally limited by their original design constraints.
The questions that determine ecommerce success today are different questions:
- What is actually driving profitable growth?
- Which marketing investments have positive ROI and which are destroying value?
- Which customer segments should we target and which should we avoid?
- How should we allocate budget across channels to maximize profit?
- What is the true performance of each marketing channel after accounting for attribution inflation?
These questions require a different kind of platform — one built from the ground up to connect marketing spend to business profitability, to provide unified attribution truth rather than conflicting platform claims, and to surface insights that enable confident, profitable decision-making.
The Path Forward
If your current analytics platform can’t definitively answer: “What is driving profitable growth?”
Then it’s time to upgrade from reporting to profit intelligence.
It’s time to stop drowning in dashboards while margins shrink.
It’s time to stop making budget decisions based on inflated, conflicting metrics.
It’s time to demand analytics that actually connect to business outcomes.
It’s time for LayerFive Axis — built for profit intelligence, not just reporting.
Frequently Asked Questions
What is a profit-first ecommerce analytics platform?
A profit-first ecommerce analytics platform measures what actually matters for business sustainability — contribution margin, customer acquisition cost, lifetime value, and profit per channel — rather than focusing solely on vanity metrics like clicks, impressions, and basic revenue. These platforms integrate financial data (COGS, shipping costs, discounts, returns) with marketing performance data to show true profitability, not just top-line revenue. LayerFive Axis pioneered this approach, building analytics specifically for profit intelligence rather than retrofitting financial awareness into traditional website analytics tools.
Why isn’t Google Analytics enough for ecommerce brands?
Google Analytics was designed to track website behavior — page views, sessions, user flows, and conversion events. While valuable for understanding how visitors interact with your site, GA has no concept of profit margins, cost of goods sold, fulfillment expenses, or customer lifetime value. It can tell you a campaign drove 1,000 conversions, but not whether those customers were profitable, whether they’ll return, or what they actually cost to acquire when all expenses are included. For ecommerce brands where margin and unit economics determine success, GA provides visibility into user behavior without the financial context needed for strategic decisions.
How does LayerFive Axis improve ecommerce profitability?
LayerFive Axis improves profitability by connecting marketing spend directly to margin outcomes through unified profit attribution. It eliminates the attribution conflicts between platforms that cause budget misallocation, surfaces which channels truly drive incremental profit rather than simply capturing existing demand, and identifies high-LTV customer segments worth acquiring even at higher initial costs. Brands using Axis typically discover they’re overspending on channels that steal credit from others while underinvesting in genuinely incremental channels — reallocating budget based on these insights commonly improves profitability by 20-40% while maintaining or growing revenue.
Who should use LayerFive Axis?
LayerFive Axis is built for ecommerce brands scaling beyond $5M in annual revenue, where attribution accuracy and margin management become critical to sustainable growth. The platform serves direct-to-consumer brands, omnichannel retailers, marketplace sellers, and subscription commerce companies that need to make data-driven budget allocation decisions. It’s particularly valuable for brands with multiple marketing channels, complex product catalogs with varying margins, or those experiencing the attribution conflicts that come from running integrated campaigns across Meta, Google, TikTok, and other platforms simultaneously.
What’s the difference between ROAS and profit attribution?
ROAS (Return on Ad Spend) measures revenue generated per dollar of advertising spend, but ignores whether that revenue was profitable. A campaign with 5x ROAS might lose money if it’s targeting low-margin products, attracting discount-seeking one-time buyers, or driving sales with expensive promotions. Profit attribution, by contrast, measures actual contribution margin after accounting for COGS, fulfillment costs, discounts, returns, and all other expenses. A campaign might show 3x ROAS but generate 40% contribution margin, making it more valuable than a 6x ROAS campaign that only delivers 15% margin. Optimizing for ROAS without profit context is how brands grow revenue while profitability declines.
Can LayerFive Axis integrate with my existing tools?
Yes, LayerFive Axis integrates with all major ecommerce platforms (Shopify, WooCommerce, BigCommerce, Magento, Amazon), advertising platforms (Meta, Google, TikTok, Pinterest, Snapchat), email and SMS tools (Klaviyo, Sendlane, Attentive), and other data sources through pre-built connectors and APIs. The platform is designed to function as the profit intelligence layer on top of your existing technology stack, unifying data across sources rather than requiring you to replace existing tools. Implementation typically takes 2-4 weeks depending on complexity, with LayerFive’s team handling data integration, attribution model configuration, and custom reporting setup.
Ready to Move From Reporting to Profit Intelligence?
Book a Profit Intelligence Demo — See exactly how LayerFive Axis connects your marketing spend to actual profitability
See Axis Attribution in Action — Watch how unified profit attribution eliminates the conflicts between platform dashboards
Get a Free Profit Leak Audit — Discover where your marketing budget is going to waste due to attribution inflation and margin-blind optimization
About LayerFive
LayerFive is the marketing intelligence platform built for profitable growth. Our suite of solutions — including Axis (profit analytics), Edge (visitor intelligence), Signals (attribution), and Navigator (AI automation) — helps ecommerce brands consolidate fragmented data, measure true marketing performance, and maximize profitability. We’ve helped hundreds of brands eliminate $100K-$300K in redundant tool costs while improving attribution accuracy and marketing ROI.
LayerFive was founded to solve the crisis of wasted marketing spend caused by broken attribution and data silos. In an industry where 47% of marketing budgets are wasted due to poor data quality and attribution conflicts, we believe brands deserve analytics they can actually trust — platforms that measure profit, not just activity.
Contact LayerFive today to learn how profit intelligence can transform your ecommerce growth strategy.
