When it comes to tracking conversions in platform, Google Ads assigns conversion credit to the day a user clicked on your ad. This means that if a user clicked on your ad three days ago and did not convert until today, the conversion credit would be given to three days ago, not today.

While this day of click conversion attribution is helpful in understanding user behavior and how long it takes the average user to convert on your website, it can be a challenge to understand performance in real-time when your data is lagging by at least a few days, sometimes even weeks.

However, with Google’s “by conv. time” columns in platform, you are now able to evaluate conversions based on the time your conversion actions were completed, as opposed to the dates the ads were clicked. This can help you better understand how many of the purchases you see in your internal sales data on a given day were driven by Google Ads.

Accessing the “By Conv. Time” Columns

To add these columns to your Google Ads dashboard, follow the steps below:

Step 1: In the “Campaigns” tab (or ad groups or keywords, depending on how granular you’d like to evaluate data), select “Columns,” and then “Modify Columns”

Step 2: Under “Conversions,” select “Conversions (by conv. time)”. If you track revenue in the “Conv. value” column, you can add columns for the associated revenue and average order value by conversion time as well.

google ads platform showing conv by conv time column

Gaining Insights From Conversion Data Differences

Let’s take a moment now to look at the differences between the standard “Conversions” column and the “Conversions (by conv. time)” column, using anonymized past 30 day data from an ecommerce advertiser:

table comparing revenue and conversions

While the advertiser in this example has a short purchase window from the day an ad is clicked to the time a user makes a purchase, you can see a clear difference between the two data sets. Based on this data, you can conclude that about 2,500 purchases from the past 30 days were attributed to ad clicks outside of the 30-day window this data was pulled for. The 25,362 number under “day of conversion” should also better match the actual purchase numbers you are seeing in your internal sales data.

Depending on your goals, it may not make sense to suddenly transition reporting on performance using these new columns, but the insights provided by these columns can help you paint a more accurate picture of recent performance within the Google Ads platform, a challenge many advertisers face.

If you use Google Analytics, for instance, you can use these columns to better understand the discrepancy between Google Ads and Google Analytics reporting. Since Google Analytics tracks conversions based on day of conversion and gives conversion credit to the last non-direct click, you can pull your “by conv. time” purchases in Google Ads and compare that to what Google Analytics reports for the same time period. You’ll likely see fewer conversions in Google Analytics due to the nature of its last touch attribution model, so running this comparison can give you an idea of just how much credit is getting pulled away from paid search in Google Analytics reporting.

In addition, this can be another helpful tool for monitoring large sale days in Q4. As we found in research from last year, there is a large number of shoppers who click on ads prior to Black Friday and Cyber Monday but do not purchase until the actual sale days. When this happens, the purchase and revenue numbers you see in Google Ads will not be representative of actual volume on those days; instead, a good portion of the revenue made during Cyber 5 will be attributed to the ad clicks leading up to the sale. With the “by conv. time” columns, you will be able to better assess the actual purchase and revenue volume driven by Google Ads on those specific sale days.

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