Measuring What Matters: Why Your Ecommerce Data Isn’t Telling the Whole Story

The promise of ecommerce has always been that everything is measurable. Every click, every impression, every add-to-cart is all trackable. And yet, most brands are drowning in data while still flying blind on what’s actually driving growth. 

The problem isn’t a lack of metrics. It’s that the metrics brands rely on often create a false sense of clarity. Dashboards light up with impressive numbers, reports get generated, and teams feel informed. But when it comes to making strategic decisions—where to invest, what to scale, what to cut—the data frequently falls short. 

Understanding why this happens—and what to do about it—is essential for any brand serious about growing their ecommerce business strategically rather than reactively. 

Key Takeaways 

• Siloed channel measurement creates blind spots—you can’t see how platforms influence each other or where customers actually convert 

• Vanity metrics like impressions and even ROAS can be misleading without context about incrementality and customer behavior 

• Last-click attribution systematically undervalues upper-funnel activities and distorts budget allocation 

• Product data quality is the hidden variable; inconsistent data across channels compromises all downstream measurement 

• A connected measurement framework with incrementality testing provides clearer strategic direction than optimizing channels in isolation 

Why Siloed Channel Measurement Creates Blind Spots 

Most brands measure each channel independently. Amazon has its own analytics. Your retail media campaigns on Walmart or Loblaw report through their own platforms. Your DTC site feeds into Google Analytics. Each channel generates its own metrics, its own reports, its own version of success. 

On the surface, this looks comprehensive. But this fragmented view creates significant blind spots—specifically, you can’t see how channels influence each other. 

Consider a common scenario: a customer discovers your product through a retail media ad on one platform, researches it on your website, and ultimately purchases through Amazon. In your siloed reporting, Amazon gets full credit for that sale. The retail media campaign that sparked the journey looks like wasted spend. Your website appears to have a conversion problem when it’s actually functioning as a research tool. 

This isn’t hypothetical—it’s the reality for most multi-channel brands. Customer journeys are messy, non-linear, and increasingly cross-platform. When your measurement approach treats each channel as an island, you’re making optimization decisions based on an incomplete picture. 

The downstream effects compound: 

• Teams optimize for the metrics they can see, which means optimizing each channel in isolation 

• Marketing dollars flow toward channels that appear to perform well on paper, even when those channels are simply capturing demand generated elsewhere 

• Strategic decisions get made based on channel-specific data that doesn’t reflect the full customer journey 

What’s the Difference Between Vanity Metrics and Decision-Ready Metrics? 

Not all metrics are created equal. Some look impressive in reports but don’t actually inform action. Others might seem modest but reveal genuine strategic insight. 

Impressions are the classic example. Millions of impressions sounds impressive until you realize that number tells you almost nothing about whether your message reached the right people or influenced their behavior. Click-through rates suffer from similar limitations—a high CTR on an ad that attracts the wrong audience isn’t a win. 

Even ROAS, which many brands treat as the ultimate measure of advertising effectiveness, can be misleading without context. A campaign with a 5x ROAS looks like a clear winner. But what if that campaign is only capturing existing demand from customers who would have purchased anyway? What if it’s cannibalizing organic traffic? What if the high ROAS is driven by a small audience segment that’s already saturated? 

Common vanity metrics that can mislead: 

• Impressions without reach or frequency context 

• Click-through rates without audience quality assessment 

• ROAS without incrementality consideration 

• Conversion rates that don’t account for traffic source quality 

Decision-ready metrics answer specific questions: 

• What is the incremental revenue generated by this campaign—revenue that wouldn’t have occurred without this specific investment? 

• What is the true cost of acquiring a new customer across all touchpoints, not just the last click? 

• How does customer lifetime value vary by acquisition channel? 

• Which investments are generating new demand versus capturing existing demand? 

The shift from vanity metrics to decision-ready metrics requires asking harder questions. It often means accepting more uncertainty in exchange for more accurate directional insight. A rough estimate of incrementality is more valuable than a precise measurement of something that doesn’t matter. 

How Does Attribution Distort Your Ad Spend Decisions? 

Attribution—the process of assigning credit for conversions across touchpoints—remains one of ecommerce’s most persistent challenges. Most brands still rely on some form of last-click attribution, which gives full credit to the final touchpoint before purchase. 

The problems with this approach are well understood but rarely addressed. Last-click systematically undervalues upper-funnel activities. Brand awareness campaigns, content marketing, social media presence—these investments often spark customer journeys that convert elsewhere. Under last-click attribution, they look like poor performers even when they’re essential to pipeline generation. 

This measurement bias leads to predictable budget distortions. Brands underinvest in awareness because it doesn’t show up in the numbers. They pour money into bottom-funnel tactics that capture existing demand. Over time, the top of the funnel starves, the bottom gets saturated, and overall growth stalls—even though every individual channel appears to be optimized. 

The challenge intensifies when you’re working across walled gardens. Each major platform—Amazon, Google, Meta, retail media networks—has limited visibility into what happens outside its ecosystem. Each naturally tends to claim credit for conversions it influenced. When you add up the attributed conversions across all platforms, the total often exceeds your actual sales by a significant margin. 

Better approaches to attribution include: 

• Multi-touch attribution models that distribute credit more evenly across the customer journey 

• Incrementality testing through controlled experiments that measure true lift from specific campaigns 

• Media mix modeling to understand channel interactions at a portfolio level 

The key is recognizing that attribution data is always an approximation. Treating it as gospel leads to bad decisions. Treating it as directional input for a broader strategic conversation leads to better ones. 

Why Product Data Quality Affects Your Performance Measurement 

Here’s something that rarely gets discussed in conversations about ecommerce measurement: your performance data is only as good as your product data. 

If your product information is inconsistent across channels—different titles, different categorizations, different attributes—then your performance data is compromised before you even start analyzing it. You can’t accurately compare how a product performs across platforms if it’s not consistently represented. You can’t identify category trends if products aren’t categorized consistently. You can’t optimize search performance if your taxonomy doesn’t align with how customers actually search. 

This is the ‘garbage in, garbage out’ problem applied to measurement. Most brands focus their analytical energy on sophisticated reporting tools and dashboards while neglecting the foundational data that feeds those systems. 

The issue amplifies in B2B2C models where brands sell through dealer networks or multiple retail partners. Data quality problems don’t just affect your own reporting—they cascade across every partner who relies on your product information. A taxonomy error at the source multiplies across dozens of downstream systems. 

Addressing data quality isn’t glamorous work. It doesn’t generate the immediate excitement of a new campaign or platform launch. But it’s foundational. Brands that invest in consistent, accurate, well-structured product data across all channels create the conditions for meaningful measurement. Those that don’t are building their analytical house on sand. 

How to Build an Ecommerce Measurement Framework That Actually Works 

Moving from fragmented, vanity-driven measurement to a strategic framework isn’t a one-time project. It’s an ongoing practice that evolves as your business and the ecommerce landscape change. 

1. Define what success actually means for your business 

This sounds obvious, but many brands have never explicitly articulated their measurement priorities beyond ‘grow revenue.’ Are you optimizing for customer acquisition, profitability, market share, lifetime value? Different objectives require different metrics. A brand focused on profitable growth will make different decisions than one focused on market share capture, even when looking at the same data. 

2. Build a connected view across channels

This doesn’t necessarily require a massive technology investment. At minimum, it means establishing common definitions, consistent tracking parameters, and regular cross-channel analysis. The goal is to understand your business as an integrated system rather than a collection of independent channels. 

3. Prioritize metrics by funnel stage

• Top-of-funnel activities should be measured on reach and engagement 

• Mid-funnel metrics should focus on consideration indicators 

• Bottom-funnel measurement should emphasize conversion and efficiency 

Trying to hold awareness campaigns to conversion metrics—or conversion campaigns to reach metrics—leads to poor decisions. 

4. Invest in incrementality testing

Running controlled experiments is the most reliable way to understand the true impact of your marketing investments. Even simple holdout tests—running a campaign in some markets while holding others as a control—provide insight that attribution models alone cannot. 

5. Establish regular cadence of strategic review 

Data is only valuable if it informs decisions. The most sophisticated measurement framework in the world doesn’t help if insights sit in dashboards that nobody acts on. Build the organizational muscle to regularly review performance, question assumptions, and adjust strategy based on what you learn. 

Moving Forward 

The brands that win in ecommerce aren’t necessarily the ones with the biggest budgets or the most sophisticated tools. They’re the ones that understand what their data is actually telling them—and, just as importantly, what it isn’t. 

This requires a shift in mindset. Stop treating measurement as a reporting function and start treating it as a strategic capability. Question the metrics you’ve always relied on. Invest in the foundational data quality that makes meaningful analysis possible. Accept uncertainty where it exists rather than hiding behind false precision. 

The goal isn’t perfect measurement—that doesn’t exist. The goal is measurement that’s good enough to make better decisions than your competitors. In a landscape where most brands are still optimizing in silos based on vanity metrics, that bar is more achievable than you might think. 

If you’re looking to build a more strategic approach to ecommerce measurement—one that connects channels, focuses on metrics that matter, and drives real business outcomes—we’d welcome the conversation.