Generating reports
To measure post-view transactions, we used AdRiver. This does not mean that only this service is suitable for solving this problem; you can also use others, such as Weborama or Sizmek. It is important here that the service can install a pixel with custom variables and receive raw data from the system itself in conjunction with the identifiers of the statistics counters.
The usual scheme for such a task would be to use Campaign Manager 360 (DCM) in conjunction with Google Analytics and other Google ecosystem tools, but this is not possible now. Therefore, we used AdRiver data, combining it with Google Analytics data.
Implement AdRiver pixel on the client's website.
Set up event transmission - viewing a product card, adding to a cart and purchasing on the website were important to us.
Make sure that everything works correctly at all stages. We philippines mobile database check whether events are transmitted to the pixel - for this, we make test orders to initiate events. You can check the triggering of events in the code viewing console on the site: in the Network section, enter AdRiver and initiate the tested events on the site (adding to cart, purchase). If configured correctly, events with the name addBasket, product, etc. will appear.
Get the necessary data in the required form - in a table format (download), so that we can compare the download with Google Analytics data and correlate the income from GA. For example, it is important that UTM and Client ID tags are transmitted (this is how we correlate the income from GA, since AdRiver cannot see the income from mobile devices, and in our case this is 70% of the traffic).
Combine them with existing indicators on advertising campaigns in end-to-end analytics reports. To do this, you need to add new columns and calculated indicators to the final Power BI report, which are calculated based on the data from the report using specified formulas. For example, we have data on income and expenses, we need to calculate the DRR. We write the DRR formula (Expense / Income x 100), and the DRR is calculated in a separate column based on the corresponding data from the report.
For flexibility in working with reports, we take data from AdRiver in raw form:
ad impression data, including each user's internal AdRiver user_id;
site data that allows you to match the user_id with the Google Analytics client_id.
This allows you to understand whether an ad was shown to a user, and if so, which campaign banner they saw.
For simple reports, you can take the number of post-view transactions from standard AdRiver reports for each campaign. For more complex reports, you will have to work with raw data.
When we have data on expenses and advertising efficiency from Google Analytics, it is much easier to add data on post-view transactions and post-view income from standard AdRiver reports.
If you need to compare how the indicators changed for different sources after launching a social media outreach campaign, you need to process the raw data. You will have to figure out which users were shown the ad and compare the overall indicators and the indicators of the segment that saw the ads. This will give you an idea of how the outreach campaign affected other sources.