What separates a brand that plateaus at $30M from one that scales past $100M?

 

It’s not more ad spend. It’s not a better agency. It’s not even a stronger product.

It’s their data health, and whether they’ve done a true, end-to-end data health check.

We’ve worked with hundreds of scaling brands, and the pattern is clear: growth stalls long before the marketing engine, creative team, or product team does. The real constraint is the state of the brand’s data infrastructure solutions, the accuracy, completeness, consistency, and usability of the data used to make decisions.

A brand can’t scale if the data source it relies on is broken, siloed, or incomplete. And for most organizations, this isn’t obvious until the CAC spikes, revenue flattens, and reporting stops aligning with real performance.

To address this, we utilize a five-part data health check framework—a structured process that assesses and improves data integrity, eliminates data loss, and builds the necessary infrastructure for scalability.

Below, you’ll find the exact framework we use during data infrastructure audits and data engineering engagements for brands ready to grow and get actionable insights.

1. Data Collection: The Foundation of Data Health

A complete, accurate, and properly configured tracking setup is non-negotiable for reliable data.
Without it, every report, forecast, and analysis that follows is compromised.

A proper data health check begins with verifying whether your core collection layer is functioning as intended.

What We Assess:

Macro conversions

• Purchases, lead submissions, subscriptions
• Ensuring these events fire consistently and map to the right revenue values

Micro conversions

• Add-to-cart
• Product views
• Checkout steps

These help diagnose customer journey gaps, test UX changes, and understand funnel efficiency.

Client-side pixels (Meta, Google, TikTok, Snapchat)
Ad platforms rely on clean “signal” data to optimize delivery. If these pixels are misconfigured, you’ll see unstable performance, poor audience quality, and inflated CAC.

Cross-domain tracking
If your checkout is on Shopify, Recharge, or a third-party domain, we ensure sessions and conversions stay connected, preventing them from breaking into fragmented visits.

What Goes Wrong Without This:

• Attribution gaps
• Underreported conversions
• Misaligned ROAS
• Decision-making based on inaccurate data

A brand can’t scale on unreliable data. This is why a data collection audit is the first step of any credible data health check.

2. Data Sharing & Integrations: Everything Must Sync

Even when the data is collected correctly, problems arise when systems don’t communicate.
This leads to data quality issues, including inconsistency, duplicated values, lost events, and incomplete profiles.

A healthy data ecosystem requires synchronized systems and data fluency across marketing, analytics, sales, and customer experience.

What We Evaluate During the Data Health Check:

Server-side tracking

CAPI, Enhanced Conversions, server-side GTM

Prevents data loss and protects signal quality as cookies become less reliable.

Offline conversion syncing
If your primary conversions occur offline (e.g., phone, store, sales team), they must be passed to ad platforms automatically, not manually, on a monthly basis.

Product catalog integrity
Live inventory, pricing accuracy, and enriched product attributes, all critical for dynamic ads.

Attribution data feedback
Passing customer value data (LTV, AOV, discount behavior) back to platforms helps improve targeting efficiency.

CRM, CDP, and messaging tools
Platforms like Klaviyo, Attentive, and Salesforce should sync audience and performance data with your analytics stack.

Why It Matters:

Misaligned tools create data loss, unreliable event mapping, and inconsistent metrics.
The goal isn’t more technology; it’s a marketing analytics strategy that is aligned, using high-integrity systems that work together.

3. Customer Analysis: Build a Unified View of Your Customer

Brands plateau when they don’t know who they’re selling to or which customers drive the most long-term value.

A strong data health check includes evaluating whether the brand truly has a unified customer dataset.

What We Assess:

Centralized data warehouse
BigQuery or Snowflake creates a single location where all data is stored, reconciled, and cleaned, a prerequisite for reliable analytics.

Customer ID resolution

 

Connecting:

• sessions
• emails
• purchases
• support tickets
• loyalty data

…into one profile prevents the creation of fragmented, duplicate, or incomplete customer records.

Behavioral & transactional scoring
Frequency, recency, product affinity, purchase patterns, churn risk, these power segmentation and personalization.

Customer service integration
Support history adds context to churn, dissatisfaction, and lifetime value.

Why It Matters:

Without a unified customer view, brands rely on surface-level metrics rather than deep behavioral insight. Growth becomes luck instead of strategy without centralized data.

4. Testing & Measurement: Validate Reality, Don’t Assume It

A brand cannot rely solely on intuition or in-platform numbers. Scaling requires validated, experiment-backed insights.

As part of your data health check, we evaluate whether the brand has the right measurement infrastructure in place to support the overall health of the business.

What We Review:

Media Mix Modeling (MMM)
Media mix modeling is a statistically sound approach to understanding true channel contributions, incremental revenue, and efficient spending levels.

Incrementality & geo testing
Incrementality experiments allocate the budget geographically across matched regions to observe the true lift, rather than relying on attribution models.

Creative performance analysis
This type of marketing reporting is more than just CTRs, hook efficiency, scroll-stop performance, thumbnails, narrative structure.

Conversion Rate Optimization (CRO)
This includes, landing page experiments, UX flows, and checkout behavior, which are all essential for profitable scaling.

Why It Matters:

Brands that don’t test make decisions based on assumptions rather than reality.
Inaccurate conclusions lead to wasted budget and stalled revenue.

5. Business Intelligence: Turn Data Into Strategy

A data health check isn’t complete until your insights are aligned with financial outcomes.

We assess whether your reporting and BI systems empower informed decisions, not just weekly dashboards.

What We Evaluate:

Unit economics dashboards
CAC, LTV, payback period, contribution margin, at the cohort level.

Profitability analysis
Traditional ROAS ignores returns, fulfillment, shipping, and discounts. We surface true profitability.

Cohort retention curves
Understanding when cohorts decay, which products increase loyalty, and where churn accelerates.

Predictive analytics
Forecast LTV, retention, revenue, and churn using historical + behavioral data.

Scenario planning models
“How does revenue shift if…?”

• spend increases
• prices change
• conversion rates move
• CAC spikes
• discounts are adjusted

Why It Matters:

This is where the data process moves from operational to strategic. It’s what transforms marketing into a core driver of growth.

Ready for Your Brand’s Data Health Check?

If your brand is stuck, it’s not your ads, your team, or your product; it’s your data health.

A proper data health check identifies:
✔ attribution gaps
✔ data loss
✔ broken tracking
✔ missing integrations
✔ unreliable reporting
✔ inconsistent metrics
✔ misaligned systems
✔ infrastructure preventing scale

At fusepoint, we’ll audit your full data infrastructure, show you exactly what’s blocking scale and provide actionable recommendations for how to fix it.

Want to see the hidden gaps slowing your growth? Book your data health check with a marketing science company today.