Stop Guessing What Drives Your Revenue: A Data Overlay Approach

The Attribution Problem

Every founder has a theory. "Content marketing drives our growth." "Our paid ads are working." "Word of mouth is our main channel." These theories are often based on gut feeling, anecdotal evidence, or whatever the last successful campaign was.

The problem is that attribution in a multi-channel world is genuinely hard. A customer might discover you through a blog post, visit your site three times from different sources, sign up after seeing a tweet, and convert to paid after an email sequence. Which channel gets the credit?

Most analytics tools answer this with attribution models: first touch, last touch, linear. But these models are all arbitrary. First touch gives credit to the blog post. Last touch gives credit to the email. Neither tells you the full story.

Why Traditional Attribution Falls Short

Google Analytics attribution models are useful for understanding which touchpoints exist in the customer journey. But they have a fundamental limitation: they operate at the individual session level and require users to be tracked across all touchpoints.

In practice:

• Users block tracking cookies
• People switch between devices
• B2B purchases involve multiple stakeholders
• The decision to buy happens offline, weeks after the last tracked interaction

For a SaaS founder making resource allocation decisions, individual-level attribution is often unreliable. What's more reliable is aggregate correlation: when we do more of X, does revenue go up?

The Overlay Approach

Instead of tracking individual journeys, overlay your channel metrics with revenue on the same time-series chart and look for correlation.

This approach answers a different question. Not "which touchpoint converted this specific customer" but "which channel, at the aggregate level, correlates most strongly with revenue growth?"

The second question is actually more useful for decision-making because it accounts for all the invisible, untracked influence that individual attribution misses.

Setting Up a Revenue Driver Analysis

Step 1: List Your Potential Revenue Drivers

Write down every channel or activity that might influence revenue:

• Organic search traffic (from Google Search Console)
• Paid ad spend (from Google Ads, Meta Ads)
• Email sends or open rates (from your email platform)
• Social media activity (from Twitter/X analytics)
• Content published (blog posts per week)
• Product changes (deploy count from GitHub)
• Support quality (resolution time)

Step 2: Import Each as a Data Source

In TotalKPI, create a data source for each potential driver. Import via CSV for historical data or connect via API for live tracking.

Step 3: Import Your Revenue Data

Add your Stripe MRR or revenue as a data source. This is your anchor metric.

Step 4: Create a Combined View for Each Driver

Overlay each potential driver with revenue one at a time. This gives you a clear correlation coefficient for each pairing without the noise of multiple overlapping lines.

Step 5: Rank by Correlation Strength

After creating all the views, you'll have a clear ranking:

In this hypothetical example, organic traffic is by far the strongest revenue correlator. Paid ad spend has a surprisingly weak correlation. And support resolution time has a negative correlation: longer resolution times precede revenue drops.

Accounting for Time Lag

Revenue doesn't respond to inputs immediately. Content marketing might take 3 weeks to show up in revenue. Email campaigns might convert in 3 days. Paid ads might have a same-day effect (or no lasting effect at all).

When looking at your combined views, shift your attention. If organic traffic spikes in week 1 and revenue spikes in week 3, the visual overlay makes this lag obvious even without sophisticated analysis. The two curves have the same shape, just offset.

Understanding your specific lag for each channel is incredibly valuable for planning. If you know content takes 3 weeks to convert, you can plan your publishing calendar 3 weeks ahead of when you need the revenue impact.

Making the Decision

With correlation data in hand, resource allocation becomes clearer:

• High correlation, controllable: Double down. If organic traffic correlates at 0.82 with revenue and you can publish more content, that's your highest-leverage activity.
• High correlation, not controllable: Monitor closely. If word-of-mouth correlates strongly but you can't directly control it, focus on making your product more remarkable.
• Low correlation, high cost: Cut or reduce. If paid ads have a 0.28 correlation with revenue and cost $5K/month, that budget might be better spent elsewhere.
• Negative correlation: Investigate urgently. If support resolution time negatively correlates with revenue, improving support is directly protective of revenue.

Replace Gut Feeling With Data

The overlay approach won't give you perfect attribution. Nothing will. But it gives you something far better than gut feeling: a ranked list of which channels actually correlate with revenue growth, with timing information.

Start a free trial and overlay your first channel metric with revenue. The correlation might confirm your theory or completely overturn it. Either way, you'll make better decisions.