How to track conversion “Maturity” in Google Ads

No BigQuery.
No scripts.
No complicated pipelines.

Just Google Sheets + Looker Studio.

Step 1 — Connect Looker Studio to your Google Ads account

1. Open Looker Studio Blank Report

2. Select Google Ads as the source

3. Choose your ad account and click Connect

That’s it.

Step 2 — Create a simple data table in Looker Studio

1. Click Insert → Table

2. In the right settings panel:

Dimensions

• Day

Metrics

• Cost

• Conversions

That’s your base table — it shows spend and conversions by click date for each campaign.

Step 3 — Add a date range control (this is what powers the experiment)

1. Click Insert → Date range control

2. Place it anywhere in the report

3. Set the range to Yesterday (because today, you evaluate results for yesterday)

From now on, each day you’ll be looking at a new “snapshot” of the same click dates.

This is where the experiment starts.

Step 4 — Export the table to Google Sheets

Click the table → More options (⋮) → Export → Google Sheets

This sends your daily snapshot directly into a Sheet where you’ll store each day’s data.

Don’t overwrite anything.
Each export is a new snapshot in time.
Add a column in google sheet called Recording Day. Add manually the recording day (you start on Feb 05 in my example). Format columns Day and Recording Day to YYYY-MM-DD, by assigning a column, then Format → Number → Custom Date and Time → 1930 - 08 - 05 as example

This is how your table looks:

Step 5 — Take the second snapshot (tomorrow)

Wait until tomorrow at roughly the same time.

In the Date range control, now select the last two days
(In our case: Feb 4 – Feb 5).

Then export the table to Google Sheets again.

Now your Sheet will contain two versions of the same click dates — yesterday’s view and today’s updated view.

This is where you’ll start seeing numbers change. Copy all rows into your first Google Sheet. Table now looks something like:

Step 6 — Repeat daily and let the data mature

Each day, at roughly the same time:

Set the Date range from your first day
(in my case, February 4) up to yesterday.

Export the table again.
Copy the new rows into your main Google Sheet — don’t delete anything.

Do this every day.

After about a week, your Sheet will start looking like a timeline of how each click date keeps updating — multiple snapshots for the same days, each one slightly higher than before.

This is the “conversion maturity” in action. Your table will look something like:

Step 7 — Add “Days since click” column

Now we want to know how many days passed between:

the click day and the recording day.

Add a new column called Days since.

In the first row, use:

=Recording Day - Day

(In our example, it’s simply D2 - A2.)

Then copy the formula down for all rows.

Now each row shows how “old” the data is.

Step 8 — The table alone tells you nothing (yet)

Right now, you only have a table.

It’s full of valuable data —
but on its own, it’s almost impossible to interpret.

You can’t see patterns.
You can’t feel how performance evolves.
You can’t draw real conclusions.

To understand what’s really happening, you must visualize it.

Step 9 — Visualize how conversions mature over time

Now comes the part where everything finally makes sense.

In Looker Studio:

Open a blank page
Choose Google Sheets as the source and pick your document, connect
Again, add Time-range control
Click “add field” in the right bar
Name of the field: CPA
Formula: SUM(Cost)/SUM(Conversions)

• Click Insert → Line chart
• Set:
- Dimension: Days since
- Metric: CPA (Cost ÷ Conversions)
- Date range dimension: Day
- Sort: Days Since (Ascending)

This creates a maturity curve:

What you’ll see immediately:

At first, CPA is high. Because this uses SUM(Cost) / SUM(Conversions), CPA is calculated at each “Days since” stage — not per individual row.

Then it drops fast
Then it slows down
Eventually it flattens

Newer days are still unstable.
Older days are already “finished”.

This is the delayed attribution effect in real life.

Step 10: Visualize conversion build-up

Add one more chart to make it even clearer.

Insert Pivot table.

Row dimension: Day
Column Dimension: Days Since
Metric: Conversions
Date Range Dimension: Day
Row Sorting: Day (Ascending)
Column Sorting: Days Since (Ascending)

Now you literally see how conversions keep growing after the click.

Some days jump fast.
Some take longer.
All eventually slow down.

This explains perfectly why CPA keeps changing.

What you’ve just built

Without scripts.
Without databases.
Without complex tracking.

You now have:

• A view of attribution delay
• A way to know when data is “finished”
• A way to stop reacting to incomplete days

This is conversion maturity.

How does this change decisions immediately

Instead of:

“Yesterday CPA is bad — pause.”

You start thinking:

“Yesterday is only Day 1 — it’s not real yet.”

Instead of panic optimization, you get:

calm, predictable scaling.

Final takeaway

Google Ads isn’t lying to you.

It’s just showing unfinished data.

Once you track how performance matures over time, the chaos disappears — and suddenly:

bad days make sense,
good days are trusted,
and scaling becomes much smoother.

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