Your Looker Studio dashboard appears flawless. The scorecards are meticulously aligned, the color palette is on-brand, and every chart loads without a visible error.

But what if the numbers themselves—the very insights you depend on for critical business decisions—are fundamentally incorrect?

The most pernicious errors in data visualization are not those that break a dashboard; they are the silent ones that go unnoticed. These subtle mistakes, originating from improper data aggregation, can quietly misdirect your entire strategy, leading to optimization of the wrong campaigns, miscalculation of profitability, and the pursuit of phantom trends.

This article examines the most common and deceptive aggregation mistakes within Looker Studio.

Utilizing a practical marketing analytics example, it will expose how easily these errors can manifest, from the seemingly simple act of averaging a Click-Through Rate (CTR) to the complex pitfalls inherent in time-series comparisons. You will learn not only to identify these flaws but also the fundamental principles required to construct dashboards that are not merely aesthetically pleasing, but analytically bulletproof and trustworthy.

The Core Problem of Incorrect Data Aggregation

Averaging an Average: The Foundational Error

The fundamental rule to avoid the most common aggregation error is this: Always perform calculations on aggregated base metrics, not on other calculated metrics. For any ratio like CTR, Cost Per Click (CPC), or Cost Per Acquisition (CPA), the correct formula is always a ratio of sums, not an average of ratios.

The Right Way ✅: CTR=∑Impressions∑Clicks
The Wrong Way ❌: CTR=AVG(CTR per row)

To illustrate this in a practical context, consider a scenario where two Google Sheets containing performance and conversion data have been blended. At a granular, ad-group level, the data is structured as follows:

date	campaign id	adgroup id	clicks	impressions	cost	# conv A
Sep 7	C1	AG1	10	100	$20	2
Sep 7	C1	AG2	100	10,000	$50	5

At this ad group level, the individual metrics are:

AG1 CTR: 10/100=10%
AG2 CTR: 100/10,000=1%

If you try to calculate the campaign’s CTR by averaging these two figures, you get (10%+1%)/2=5.5%. This is wildly incorrect because it fails to weight the CTR by the massive difference in impression volume.

To get the true campaign CTR, you must sum the base metrics first and then calculate the ratio:

Total Clicks: 10+100=110
Total Impressions: 100+10,000=10,100
Correct Campaign CTR: 110/10,100=1.09%

Relying on the flawed calculation would lead to the erroneous conclusion that the campaign’s performance is nearly five times better than its actual efficacy. This single, common mistake is the root cause of most inaccurate data presented in dashboards.

More Scenarios: The Subtleties of Flawed Calculations

Beyond the incorrect averaging of ratios, several other silent errors can corrupt the integrity of your data analysis.

The Time Comparison Trap

Comparing performance over time often requires blending a data source with itself. The mistake is using a simple join key like campaign id. If a campaign has multiple rows of data in each period, this creates a Cartesian product, duplicating your metrics and wildly inflating totals. A simple “month-over-month” view can suddenly make you think you’ve spent twice as much as you actually have.

The Unique Count (`COUNT_DISTINCT`) Fallacy

You cannot find the total number of unique campaigns that ran in a week by summing the daily unique counts. A campaign that ran on Monday and Tuesday would be counted twice.

The Misleading Weighted Average

Many platforms provide pre-calculated metrics like “Average Position.” If you try to find a campaign’s average position by simply averaging the avg_position of its ad groups, you’ll get it wrong. An ad group with 10,000 impressions should have far more influence than one with 100.

Common Aggregation Errors in Looker Studio

Understanding Aggregation Errors

Aggregation errors in Looker Studio typically arise from a logical conflict between how data is structured and how the platform is instructed to compute metrics. These are not system bugs but rather user-generated logical flaws. The platform’s engine processes calculations based on the dimensions and metrics present in a given chart, and if a formula attempts to mix pre-aggregated and non-aggregated data without proper context, it will either fail or produce a misleading result.

Example: The “Unable to Aggregate Ratio Metrics” Error

A frequent and explicit error message users encounter is “Unable to re-aggregate metrics.” This typically occurs when a calculated field created at the data source level (which operates on a row-by-row basis) is used in a chart that requires a different level of aggregation. For instance, if you create a CTR field in the data source with the formula Clicks / Impressions, Looker Studio calculates this for each row. When you then add this field to a scorecard for the entire campaign and set its aggregation to AVG, you are instructing the system to average the pre-calculated row-level CTRs—the exact fallacy described earlier. The error message is Looker Studio’s way of indicating that it cannot logically or accurately “re-aggregate” a metric that is already an aggregation (a ratio).

Strategic Implementation of Calculated Fields

This aggregation trap can appear in two places within Looker Studio. Understanding the correct context for defining calculations is critical to maintaining data accuracy.

Calculated Fields at the Data Source Level

This is the appropriate location for defining reusable fields that perform row-level operations. However, one should exclusively define additive or non-aggregated metrics here, such as cleaning dimensional data, casting data types, or creating custom groupings with CASE statements.

It is a critical error to create a ratio field like CTR = Clicks / Impressions at the data source level. If this field is subsequently dragged into a chart and its aggregation is set to AVG, it will inevitably commit the foundational error of averaging an average.

Calculated Fields within a Chart

This is the correct place to define ratio metrics like CTR, CPC, and CPA. When you create a calculated field directly in a scorecard or table, the formula is applied after Looker Studio aggregates the base metrics according to the dimensions in your chart.

Best Practice: In any chart, define your ratios using the SUM() function for both the numerator and the denominator. This protocol ensures the calculation is always a correctly weighted ratio of sums, reflecting the true performance at the chart’s level of detail.

CTR Formula in Chart: SUM(Clicks) / SUM(Impressions)
CPA Formula in Chart: SUM(Cost) / SUM(# conversion A)

Practical Solutions and Troubleshooting for Data Integrity

Addressing Common Aggregation Issues

Fixing the Time Comparison Trap:

To prevent metric inflation from Cartesian products in self-blends, use a more specific, composite join key that guarantees a one-to-one match. A synthetic key combining campaign_id and date (e.g., CONCAT(campaign_id, “-“, date)) is a robust solution. For more complex analyses, it is advisable to perform time-comparison logic in a dedicated data warehousing tool like BigQuery or even within Google Sheets before the data is ingested by Looker Studio.

Achieving Accurate Unique Counts:

Never perform a SUM operation on a column that already contains a de-duplicated aggregation (COUNT_DISTINCT). To obtain an accurate unique count over any period, the COUNT_DISTINCT function must be applied directly to the raw, non-aggregated dimension field for the entire desired date range within the chart itself.

Calculating a True Weighted Average:

A true weighted average requires access to the underlying components. The correct formula is SUM(Weight * Value) / SUM(Weight), such as SUM(Impressions * Avg. Position) / SUM(Impressions). If the data source only provides the pre-calculated average and not the component parts, it is often impossible to calculate a correct weighted average in Looker Studio. The critical step is recognizing this limitation and avoiding the use of the simple AVG() function, which will be inherently misleading.

Best Practices for Data Hygiene

Division by Zero:

A campaign with associated costs but zero conversions will cause a CPA calculation to fail. To prevent charts from breaking, wrap division formulas in a CASE statement or utilize the SAFE_DIVIDE function to handle these scenarios gracefully.

Join Mismatches:

When blending data sources, if an ad group has spend data but lacks corresponding conversion data, a default inner join might exclude it entirely. This hides underperforming spend. Ensure the blend’s join configuration (e.g., Left Outer Join) is set to preserve all relevant rows from the primary data source.

Accidental COUNT vs. SUM:

Using COUNT on a numeric field like # conversions will only tally the number of rows containing data, not the total value of conversions within those rows. This can drastically skew performance metrics. Always use SUM for additive numeric fields.

Key Takeaways and Expert Insights

The Golden Rule of Ratios:

Never average a pre-calculated ratio. Consistently calculate metrics like CTR, CPC, or CPA using the formula SUM(Numerator) / SUM(Denominator) directly within the chart component.

Raw Data is Paramount:

Whenever feasible, connect Looker Studio to the most granular data available. Avoid importing datasets that contain pre-calculated averages, as this restricts you to flawed, high-level aggregations and removes analytical flexibility.

Never Sum a Unique Count:

The total number of unique items over a period cannot be determined by adding daily or weekly unique counts. The COUNT_DISTINCT function must always be applied to the raw dimension across the full, desired date range.

Scrutinize Self-Blends:

When creating time-period comparisons by blending a data source with itself, employ highly specific, composite join keys (e.g., id & date) to prevent row duplication and subsequent metric inflation.

Handle Zeros Gracefully:

Proactively prevent charts from breaking due to division-by-zero errors. Wrap division calculations in a CASE statement or use the SAFE_DIVIDE function to manage instances where the denominator is zero.

Question Every “Average”:

If a metric in your data source includes “average” in its name (e.g., avg_position), applying a simple AVG aggregation to it in Looker Studio is almost certainly incorrect, as it fails to produce a proper weighted average.

Community Consensus on Data Preparation:

Across analytics forums and expert communities as well as reddit, a recurring theme is the critical need for upstream data validation. Practitioners emphasize that Looker Studio excels as a visualization layer, not an Extract, Transform, Load (ETL) tool. The consensus advises performing complex transformations—such as time-series analysis or creating intricate join keys—in a more suitable environment like BigQuery or a database before the data reaches the dashboard. This ensures the data entering Looker Studio is clean, structured, and primed for reliable visualization.

Conclusion: Build Dashboards You Can Trust

In the world of data, trust is the most valuable asset. A dashboard that provides incorrect information is worse than no dashboard at all, as it gives the illusion of certainty while leading you down the wrong path. The greatest threat to your data’s integrity isn’t a technical glitch but a logical flaw in aggregation that hides in plain sight.

By internalizing the golden rule—always calculate ratios from aggregated base metrics—you shift your mindset from merely displaying numbers to rigorously defending their accuracy. The journey to data mastery involves questioning every calculation, scrutinizing every blend, and understanding the true shape of your data before you visualize it. Now is the time to go back to your dashboards, not just as a user, but as a detective. Audit your calculations, challenge your averages, and build a foundation of data integrity that empowers you to make decisions with true confidence.

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