The initial state of the project was as follows:

The client set up and ran a sales campaign in Google Shopping for 6 months and implemented the strategy of maximizing clicks at the lowest price. Only the cost of advertising and the number of purchases through the shopping cart were known to our client, but the ROAS remained un-clear. This took place because a lot of goals that were unimportant for the business were set up in Ads Manager: viewing the contact page, put-in-the-shopping-cart actions which hindered the efficiency analysis in the account and were uninformative. It lacked the most important part – the tracking of calls and processed or already paid orders.

The activities on the project were undertaken in several phases:

The first-phase

Our actions to complete the task included:

1. In order to understand the current situation, we set up enhanced e-commerce data track-ing. Our team organized the transfer from the online store and to Google Analytics of data on user shopping behavior patterns as well as information on sold products and transac-tion amounts (Google Analytics Enhanced E-commerce).
2. Our specialists configured the reception and processing of the above mentioned data in Google Analytics.
3. We spent 30 days collecting statistics.

The interface of Google Analytics began to show product sales performance:

And revenue
by sales channels:

A month later, it became obvious that the current return on investment was 462% in sales cam-paigns and 100% in media campaigns — which meant that the store's revenue was approxi-mately equal to the cost of advertising.

Then we handled the account:

1. Our specialists removed the tracking of every conversion and replaced them with e-commerce data with linear attribution.
2. We conducted a detailed analysis of the actual user requests for the last three months (about 30,000), compiled a list of stop words and connected it to the campaign.
3. Our team disabled
   the ads display in the media network due to the low efficiency;
   some product categories that showed low profitability;
   the ads display in several states with unprofitable sales.

These activities provided the automatic bid management algorithm with correct statistical data and this was the key factor of its proper functioning.

The results:

By June, the revenue was 24.5%, advertising costs decreased by 10%, and the ROAS in-creased from 479% in early April to 659% in early June:

We also noticed that the sales numbers of the store depended on external factors and the prod-uct range stability, so we compared two-week or monthly intervals. The chart shows the drop in all indicators on the day of the beginning of the riots in the United States on May 26, 2020 after the death of George Floyd: