After Shopping Cafe "Barbaris" is a well-known brand in Kharkiv, offering both cafes in shopping malls and home delivery services. With the advent of quarantine in March 2020, the company prioritized online ordering and delivery.
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01.03.2020 - 31.01.2021
- Increase the number of online orders;
- Reduce the cost per order (CPO);
- Increase income.
Before collaborating, the company was working with contextual advertising. We had a large amount of accumulated statistics, which helped us formulate accurate hypotheses and set KPIs for the project. The challenge was that this was the period at the beginning of the 2020 quarantine, when all cafes and restaurants began advertising delivery. The cost per click in the auction began to rise, making it more difficult to achieve results at an acceptable price.
1) Lack of 24-hour food delivery
The company accepted orders until 9:45 p.m., which imposed certain restrictions. Based on this data,
The operating hours of advertising campaigns have been adjusted in accordance with the delivery schedule.
2) No call tracking
In the delivery niche, some orders are placed by phone. To understand
call source requires call tracking or number hiding.
3) Delivery zones
The company's city area was divided into four delivery zones, with delivery times ranging from 60 to 150 minutes. All of this needed to be reflected in ads. To achieve this, we used geo-radiuses with bid adjustments.
Barbaris Café has a loyal customer base, and attracting repeat customers is tens of times cheaper than attracting new ones. Therefore, one of the main joint goals was to convert a new customer into a regular customer through excellent service and a high-quality product, and then attract them again at a minimal cost.
To re-engage customers, in addition to branded campaigns, we launched RLSA (search engine remarketing) campaigns with bid adjustments for users who have previously visited the site. This means that if a user has already visited our site but continues to search for something other than our brand, such as "order sushi," we'll show them in a higher position at a higher price, knowing they've already visited our site and are more likely to place an order.
At the start of our work, we launched advertising campaigns targeting key delivery categories, including sushi delivery, roll delivery, pizza delivery, and food delivery.
The main objective of this stage was to identify the most profitable delivery routes, followed by scaling these categories.
After testing various delivery categories, we identified the primary one that generated the most revenue. To optimize and scale, we used:
1) Intelligent bidding
We tested various bidding strategies, from "Maximize Clicks" to "Maximize Conversion Value." Based on the results, we switched the campaign to the "Maximize Conversions" strategy, which performed best. Before fully switching to this strategy, we disabled all conversions in the account and left only transactions enabled, so that this strategy would optimize for achieving a higher number of transactions.
2) Setting up automatic rules for increasing and decreasing the budget by day
The automatic rules had two scenarios:
3) Expansion of the semantic core
The expansion was accomplished using two methods: search queries and Google recommendations. Suggested keywords from Google recommendations were exported into a table, then reviewed and retained only the most relevant ones.
4) Adding audiences of users who visited the site
Adding users and increasing bids on them to attract them again, since they are already familiar with the brand.
1) Attribution
By default, Google Ads is set to last-click attribution. This means that only conversions resulting from the last interaction with an ad are counted. Using the "Multi-Channel Funnel Conversion Path" report, you can see that the majority of conversions occur through multi-channel interactions, and ads aren't always the last source. This means that over 50% of all conversions are not counted for Google Ads. Not including these conversions can lead to incorrect conclusions about ad performance.
2) Buyer behavior
At the start, our strategy was to increase the number of repeat customers. To ensure that repeat customers are the primary cohort of users generating our revenue, we use the "Customer Behavior" report.
3) Number of days until conversion
To correctly determine the remarketing audience period in days, use the "Days to Conversion" report.
4) Sales efficiency
Using the "Sales Performance" report, we can see which product items are the most popular and how much revenue they generate. We identify the top ten and use this information in advertising materials, text ads, and graphic banners, thereby further increasing demand for them.
During our work, we managed to reduce the cost of conversion by 49%.
By reducing the cost per conversion, the number of conversions increased, as the budget remained practically at the same level, periodically decreasing or increasing.
Due to the first two points, as well as the increase in the average order value, advertising revenue increased by 44%.
Each project should be tailored to achieve its KPIs. All decisions should be based on project analytics.
In the food delivery industry, there are certain days when demand for a product is higher than others. This is best tracked using historical data over a period of more than three months. Using this data, you can increase advertising budgets and adjust bids to drive more traffic and, ultimately, more orders. Advertising increases brand awareness. Loyal users and customers should be re-engaged with remarketing campaigns on search and the Display Network. But don't forget about acquiring new users, too, by constantly expanding your semantic core and launching campaigns to target cold audiences.
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Main works:
As a bonus you will receive:
*The price shown is for agency work and does not include the advertising budget. Web analytics systems are configured within the first month of project development.
Main works:
As a bonus you will receive:
*The price shown is for agency work and does not include the advertising budget. Web analytics systems are configured within the first month of project development.
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