If you use Google Ads to obtain traffic for your website, you may have noticed that the conversion data in this tool is not always consistent with those presented in Google Analytics reports. So, does this mean that these services are not properly configured? Not necessarily.
Inaccuracies that we see often result from differences in the way conversions are recorded between Google Ads and Analytics. To make fully informed decisions about ongoing PPC campaigns, it is worth understanding the reason for the discrepancies in the data provided to us.
Overview of attribution models
The basic model that is used as standard is the called Last Click model. As the name suggests, the success of the conversion is attributed to the last source of visits to the website. Therefore, you will often hear that this is not the best approach to measuring conversion. In marketing, this will work well, for example, with brand inquiries. A variation of this model is the last indirect click. This means that the conversion is assigned to the last source, but on the condition that it was not inputted directly. For example, a customer enters a page from a Facebook ad, then a Google Ads ad, then by clicking on natural results, and finally converting after direct entry. In this case, success is attributed to clicking on organic searches. This model is a standard set in Google Analytics. In these two models, the entire conversion is assigned to one source, so for them, the number of conversions is an integer. For other models, this number may be fractional.
If last-click models are considered imperfect, what other options do we have to choose from? One of them is the Time Decay model. In this model, the success of the conversion is spread across all the user’s traffic sources. However, the values assigned to them depend on the time elapsed between the visit from a given source and the conversion itself. The more time has passed the less it matters. The highest value is assigned to the last source before conversion.
Another way of measuring conversion is the Position Based model. It is about the user’s subsequent visits to the site. The sources of the first and last visits receive the greatest value. Each of them is assigned 40% of the merits. Sources of visits between them are assigned 20%. If there are several of them, the share is divided evenly between them, i.e. each of them receives 20% divided by their number. There is also a Linear model where all sources receive the same value. It does not matter the order of time. 100% of success is shared across all sources on the customer journey.
The newest way we can use for the Data-Driven Attribution model. As the only one, it requires a minimum of data collected in a Google Ads account and is unavailable in Google Analytics. Have a minimum of 15,000 clicks on a language and 600 conversions in a 30-day period for use. This is due to the specifics of its operation. This model analyses all conversions and compares them with each other. The keywords/ad groups/campaigns that respond to the customer journey get more value. By using this model and automatic bids, or at least improved CPC, we allow the system to adjust based on the data and increase it when the conversion is useful.
Who does this conversion belong to?
The first differences that can be noticed appear when determining the source of the conversion. Analytics uses the source of the last visit of the user as the source of the conversion – last-click attribution (unless it is a direct input, e.g. by entering the address directly into the browser bar, then the source of the previous visit, if any, is the source of the conversion), while Google Ads, regardless of what the user does between clicking on a paid link and converting, assigns it to the last ad clicked.
Dates, delays, and cookies.
Further inaccuracies appear in the distribution of conversions over time. They arise when the user, after clicking on a Google Ads ad, needs a few more days to make a final purchase decision. In this case, Analytics will determine the conversion date on the day of purchase, while Google Ads will define it as the moment of clicking on the ad.
In both of the discussed tools, there are delays in updating the data. Google Ads updates its data within 24 to 72 hours. Delays in Google Analytics, depending on the volume of traffic, may also be up to two days for traffic over 50,000 visits a day. For this reason, the observed differences in the number of conversions may also result from the different “freshness” of the presented data.
It is worth noting that if the conversion does not take place within a maximum of 30 days from clicking on the ad, Google Ads will not register it at all. On the other hand, Analytics relies on this case on a cookie which expires 6 months after clicking on the ad and going to the website. It follows that if your customer decides to buy in your store after 31 days from clicking on the ad, the number of conversions reported by Analytics will be 1 higher than the number shown in Google Ads.
Who to believe?
It is difficult to generalize and clearly indicate whether it is more correct to base on the results from Google Ads or Analytics. Each of you must answer this question for yourself, considering the specificity of your business and personal perception of the concepts presented (is it easier for you to understand the transaction than conversion?). Remember, however, that the most important from the analytical point of view is to identify and analyze trends, and on their basis improve the results of previously established KPIs. It is worth understanding the differences between the two discussed tools while focusing too much on standardizing the data between them can lead to sterile discussions that do not have a direct impact on our business.