In 1 year revenue increased by 44% for a sushi and pizza delivery site

About the project

After Shopping Cafe "Barbaris" is a well-known brand in Kharkiv and has both cafes in the city's shopping centers and home delivery. When quarantine in March 2020 started, the company has chosen the development priority towards online orders and delivery.

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Period for analysis:

03/01/2020 - 01/31/2021

Goals and objectives:

- Increase the number of orders online;
- Reduce the cost of the order (CPO);
- Increase income.

Results for the year:
  • Decreased CPO by: 49%
  • Revenue growth: 44%
  • Increase in ROAS by: 731%
Project Features:

Before the start of cooperation, the company worked with contextual advertising. We had a large amount of accumulated statistics, which helped us to correctly build hypotheses and set KPIs for the project. The difficulty was that it was the beginning of the 2020 quarantine, when all cafes and restaurants began to advertise delivery. The cost per click in the auction began to rise and it became more difficult to get results at an affordable price.

Analysis of the current state of affairs

1) Lack of 24/7 food delivery

The company accepted orders until 21.45, which set certain limitations. Based on this data

the working hours of advertising campaigns was adjusted according to the delivery schedule.


2) No tracking of phone calls (Call-tracking)

In the delivery niche, some of the orders are made by ordering by phone. To understand

the source of the call it is necessary to implement call tracking or hide numbers.


3) Delivery zones

The company’s territory of the city was divided into 4 delivery zones, with delivery time from 60 to 150 minutes. All this had to be displayed in advertisements. To do this, we used geo-radius with bid corrections.

Map example



Brand strength

Cafe Barbaris has its own base of regular customers and re-attracting such customers is ten times cheaper than attracting new customers. Therefore, one of the main joint tasks was the following: after attracting a new buyer, due to high service and a high-quality product, transfer them to the category of regular customers and re-attract it at the lowest price.

For re-engagement, apart from branded campaigns, RLSA campaigns (search remarketing) were launched with bid corrections for users who had already visited the site. In other words, if the user has already been on our site, but continues to drive in not our brand, but, for example, “order sushi”, we will be shown in one of the first positions at a higher price, knowing that he has already visited our site and more likely to place an order.

How does context work?
Testing Different Delivery Destinations

At the start of work we launched advertising for the main delivery categories in various advertising campaigns; these are sushi delivery, roll delivery, pizza delivery, food delivery.
The main task of this stage was to identify the most cost-effective delivery directions with the subsequent scaling of these categories.

Optimizing and scaling results

After testing various categories of delivery, the main one was singled out, which brought the most income. For optimization and scaling, we used:

1) Smart bidding

We tested different bidding targets, from “Maximum Clicks” to “Maximum Conversion Value”. Based on the test results, the campaign was switched to the “Maximum Conversions” strategy, which showed the best results. Before fully switching to this strategy, all conversions were disabled in the account, and only transactions were left, so that this strategy was optimized to receive more transactions.

2) Setting up automatic rules for increasing and decreasing the budget by day

The automatic rules had two scenarios: – Increasing budgets from Friday to Sunday. This rule was configured based on the analysis of the transaction by days of the week. Users ordered food delivery most from Friday to Sunday. – Budget reduction from Monday to Thursday. During this period, users used home delivery of food the least.


3) Extension of the semantic core

The extension was carried out using two methods: search queries, Google recommendations. Suggested keywords were unloaded from Google recommendations into a table, after which they were viewed and only relevant ones were left.

4) Adding audiences of users who visited the site

Adding users and bidding on them to re-engage them since they are already familiar with the brand



Results analytics

1) Attribution

By default, the Google Ads account is set to last-click attribution. It means that only conversions from the last ad interaction will be counted in the results. Using the Multi-Channel Funnel Conversion Path report, you can see that most of the conversions are from Omni-Channel interactions and ads are not always the last source, which means that more than 50% of all conversions will not be counted for Google Ads. Without considering these conversions, you can make wrong conclusions about the effectiveness of advertising.

Screen with results



2) Buyer behavior

At the start, our strategy was to increase the number of regular customers. The “Shopper Behavior” report is used to make sure that the main cohort of users that brings us income is regular customers.



3) Number of days before conversion
The “Number of days to conversion” report is used to correctly determine the period of remarketing audiences in days .
Screen with results


4) Sales performance
With the help of the “Sales Performance” report we can see which product items are most popular and how much income they bring. We determine the top ten winners and use this information in advertising materials, in text ads and graphic banners, thereby further increasing the demand for them.
Screen with results

Overall Results

During our work we managed to reduce the cost of conversion by 49%.
Due to the reduction in the cost per conversion, the number of conversions increased, since the budget remained almost at the same level, periodically decreasing or increasing.
Due to the first 2 points as well as an increase in the average cost of an order, advertising revenue increased by 44%.
3 charts


For each project it is worth using individual solutions to achieve the KPI of the project. All your decisions should be based on project analisis data.
In the field of food delivery, there are certain days when the demand for the product is greater than on others. It is best tracked based on historical data over a period of more than 3 months. Based on this data, you can raise advertising budgets and set bid corrections to get more traffic, and as a result, orders. Advertising increases brand awareness. Loyal users and buyers should be re-engaged through search and display remarketing campaigns. But do not forget about attracting new users, for this you need to constantly expand the semantic core and launch campaigns for cold audience.

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