Autobaza Case Study: How to Reduce Advertising Costs and Increase ROMI by 772%

The process of changing a logo and renaming a project is always stressful for a team of internet marketers. Afterall, customers tend to look for familiar store names and designs when shopping online. So, the first thing to do after updating the content is to inform as many target users as possible. Otherwise, they may see a new store name and leave the site.

In this blog post, we will show you how to improve conversion rates after implementing a new logo for an online store, reduce advertising costs, and redirect the rest of the budget to branding.

Results: We decreased advertising costs and increased ROMI by 772%.

The Client

We have been cooperating with the Autobaza online store since 2014. It is the largest network of сar accessories in Ukraine, and works only with proven brands and official suppliers. The Autobaza store chain entered the market in 2001. Since that time, the range of products has considerably increased, and the online store now caters to all the needs of car lovers. Therefore, the company decided to change its name to Autobaza.

The Challenge

Since a user is accustomed to a certain brand visualization, moving to a new logo can’t be "painless". Therefore, we resorted to several kinds of conversion sources before changing the name and logo.

The Solution

We used Google Ads tools to accomplish Autobaza’s goal of increasing sales within the current budget.

Marketers are usually wary of new products from Google Ads — sometimes they have insufficient knowledge and experience, and there might be no suitable projects.

For Autobaza, we decided to test two relatively new tools:

• Shopping campaigns
• Smart Shopping campaigns

Let’s learn more about each of these tools.

1. Shopping campaigns

An ad example (we apologise for screenshots in Russian, the English version of the site was unavailable).

Setup

We have recently published an article on how to set up Shopping campaigns.

First, we added all the products to an advertising campaign. This allowed us to collect statistics for making further decisions on the campaign structure and optimization.

Optimization

1. We singled out the categories of products with the highest sales results and created individual campaigns for them. This helped us to focus only on efficient goods.
   
   As a part of a campaign that includes all products, products with lower demand receive less traffic; therefore, the budget is mostly spent on goods with higher demand.
   
   

2. We added remarketing lists with bids adjustments of 30-50% that target both a wide audience (all site visitors) and overly narrow or specific (product card visitors/buyers) audience segments.
   
   Bids adjustments are different at each stage of the sales funnel. The closer to a purchase a user is, the higher the bid. Furthermore, site visitors who viewed several product cards are more likely to make a purchase than those who just visited the homepage of an online store.

The Results

2. Smart Shopping campaigns

Smart Shopping campaigns include both traditional Shopping campaigns and remarketing campaigns on the Display Network. When showing ads of Smart Shopping campaigns in various networks, the system resorts to feeds, downloaded objects, and Google machine learning. Unlike traditional Shopping campaigns, the product ads will be shown in search results, on the Display Network, on YouTube, and in Gmail.

An ad example:

Requirements:

1. Providing transaction-specific values for each conversion and tracking them.
2. Getting at least 20 conversions over the past 45 days for traditional Shopping campaigns. This is a strong system requirement (official Google Ads Help).
3. Adding a global tag to the site and creating a remarketing list which includes at least 100 users. Dynamic remarketing will show personalized ads to those who have visited the site before.
4. Calculating ROAS (Return on Ad Spend).
   ROAS = profit from an advertising campaign / costs of an advertising campaign.

The Results

We met the tool requirements and obtained the following results for all Shopping campaigns after connecting Smart Shopping:

Testimonials

Yana Merkotan, a leading marketer of the Autobaza project:

The project team

• Oksana Verlan — Team Lead of PM Team;
• Dmitriy Chernous — PM of PM Team;
• Anna Sergeeva — Senior PPC Specialist.