How to Overlay Metrics from Different Sources on One Chart

Why Overlaying Metrics Matters

Every business tool shows you its own data in isolation. Stripe shows revenue. Google Search Console shows clicks. Your CRM shows leads. But the questions that actually drive growth are cross-source questions: Does my organic traffic drive revenue? Is there a lag between content publishing and signups? Which marketing channel actually converts?

To answer these questions, you need to see metrics from different sources on the same time axis. That's what overlaying means: putting two or more data series on a single chart so the patterns between them become visible.

The Normalization Problem

The biggest challenge with overlaying metrics is scale. Your Stripe MRR might be $45,000 while your Search Console clicks are 150,000. If you plot them on the same Y-axis with raw values, one line will be a flat line at the bottom while the other dominates the chart.

The solution is normalization. TotalKPI automatically normalizes all data sources in a combined view to a 0-100% scale. Each metric's minimum becomes 0% and its maximum becomes 100%. This makes the shapes of the curves directly comparable, regardless of the underlying units.

A spike in traffic that happens at the same time as a spike in revenue becomes immediately visible, even if one is measured in dollars and the other in page views.

Step 1: Add Your First Data Source

Start by adding the metric you care about most. For most SaaS founders, that's revenue.

From a CSV

Export your Stripe data as a CSV with two columns: date and value. In TotalKPI, create a new data source and paste or upload the CSV. The parser auto-detects date formats and currency symbols.

From an API

If you want live data, add an API data source. Enter the endpoint URL, set your authentication headers, and configure a JSONPath expression to extract the value you want. Set a polling interval (every hour, every 6 hours, daily) and TotalKPI will keep the data current automatically.

Step 2: Add a Second Source from a Different Tool

Now add the metric you want to compare against. If your first source was Stripe revenue, try adding Google Search Console clicks or your email subscriber count.

The key is that these sources come from completely different tools. That's the whole point. You're combining data that normally lives in separate dashboards.

Step 3: Create a Combined View

With two or more data sources in your workspace, create a combined view. Select which sources to include. TotalKPI immediately renders them on the same chart with automatic normalization.

Each source gets its own color and you can see both lines moving across the same time axis. Hover over any point to see the exact values for each source at that moment in time.

Reading the Correlation Coefficient

Every combined view automatically calculates the Pearson correlation coefficient between the included metrics. This is a number between -1 and +1:

• +0.7 to +1.0: Strong positive correlation. The metrics move together consistently.
• +0.4 to +0.7: Moderate positive correlation. There's a relationship, but other factors are involved.
• 0 to +0.4: Weak or no correlation. The metrics don't have a meaningful relationship.
• -0.4 to -1.0: Negative correlation. When one goes up, the other tends to go down.

A correlation of 0.85 between your organic traffic and revenue means that historically, when traffic increases, revenue follows closely. That's actionable information for deciding where to invest your marketing budget.

Real Example: Stripe MRR vs Organic Traffic

Imagine you overlay your Stripe MRR with Google Search Console clicks for the past 6 months. The combined view shows:

• Both lines trend upward over the period
• Traffic spikes precede revenue spikes by about 2 weeks
• A dip in traffic in October correlates with a revenue dip in early November
• Pearson correlation: 0.78 (strong positive)

This tells you that organic search is likely a meaningful revenue driver for your business, and that there's a roughly two-week lag between traffic changes and revenue impact. Without the overlay, you'd see "traffic went up" in Search Console and "revenue went up" in Stripe, but the timing relationship would be invisible.

Beyond Two Sources

Combined views aren't limited to two sources. Add your ad spend, your email open rates, your support ticket volume. The more metrics you overlay, the richer the picture becomes. You might discover that support tickets spike two weeks before churn increases, or that email campaigns correlate with revenue more strongly than paid ads.

Try It With Your Own Data

The best way to understand the power of overlaying metrics is to try it with your own numbers. Start a free trial and connect your first two data sources. The correlation might surprise you.

Or explore the live demo to see overlay charts in action with sample SaaS data.