CASE DESCRIPTION
The client is an online store selling and delivering high-quality cheeses produced by well-known European brands.
TARGET AUDIENCE:
B2C: people placing small orders for personal consumption, amateur cooks and confectioners, people buying gift certificates or gift baskets.
B2B: store owners and resellers.
STAGE 1. SEARCH ADVERTISING
We collected keywords related to “buy cheese” and categorized them into separate ad groups. We set up a search advertising campaign targeted at the entire territory of Ukraine, with higher bids for locations close to the chain’s stores. We set Leads as the marketing goal of the campaign and selected Maximum clicks as the bidding strategy.
Results after the first 30 days:
The average cost per conversion was UAH 51.70, which equals the cost of 100 g of artisan cheese Gorgonzola Dolce with blue mold
STAGE 2. CLASSIC SHOPPING CAMPAIGN
First, we created a Standard Shopping campaign with a daily budget of UAH 250, aimed at the system’s learning to receive conversions. We allocated a month for testing.
In parallel, we worked on connecting and implementing eCommerce tracking for the site.
Metrics after the first 30 days:
We received 92 conversions, including 46 transactions on the site, 218 orders made via the shopping cart, 28 direct calls from the ads, and 8 filled callback forms. The average cost per conversion was UAH 46.27.
STAGE 3. PERFORMANCE SHOPPING CAMPAIGN
Since we received more than 100 effective conversions during the testing month of the Standard Shopping campaign, we decided to take the next step — the creation of a Smart Shopping campaign.
This tool combines the capabilities of Standard Shopping campaigns with remarketing campaigns on Display Network. They’re easier to manage and they help increase conversion value and expand your reach with Google’s machine learning. Ads are generated based on the product feed and objects added by us. Then the system tests ad versions with different combinations of text and images, showing the most relevant of them on Google networks, including search results pages and Display Network, YouTube and Gmail.
In Performance Shopping, the system automatically assigns bids and selects ad placements to drive the most valuable conversions within a given budget. Therefore, we needed to set the appropriate budget and choose a strategy. We chose “Maximum conversion value”. This strategy is aimed at generating income, exactly what every online store wants. We didn’t limit the return on investment (ROI) strategy to give the system room for optimization.
We created one campaign and didn’t divide it into categories to provide Google with more data for optimization. Within the campaign, we categorized products into groups based on product categories.
Performance Shopping campaign metrics after the two first months:
Keeping to the same daily budget of UAH 250, we managed to get 208 conversions during two months, including 23 direct transactions, 48 direct calls from the ads, and 16 filled callback forms. The cost per conversion was lowered to UAH 32.82.
STAGE 4. SEGMENTATION OF SHOPPING CAMPAIGNS
We analyzed goods that generated transactions and direct sales. Over 50% of them were small purchases that actually consumed most of the traffic and budget.
As an experiment, we decided to segment products in the feed by cost. In the Merchant Center, we set the parameters of the feed rules, dividing it into three price categories: top products, all cheeses, and all categories excluding products from other campaigns:
On the client’s approval, we created three separate shopping campaigns, segmenting products into groups based on the price. This allowed us to identify the most effective products and remove non-converting and non-profitable categories from shopping ads.
We allocated the budgets depending on the category price and profitability of product groups:
Results after the first month of the experiment:
With UAH 41,357 spent on advertising, the client earned UAH 101,835. The cost per conversion was lowered to UAH 14.84 (with the initial cost per conversion of UAH 46).
OPTIMIZATION TACTICS THAT AFFECTED THE RESULTS MOST
REPLACING THE AUTOMATIC FEED WITH A CLASSIC ONE
An automatic feed is, of course, convenient, but the lack of options and opportunities for optimization restricts the advertising specialists’ level of control. The client provided access to their admin panel on prom.ua, which allowed us to set up the necessary fields and create a traditional feed taking into account all the opportunities for attribute optimization. As a result, the number of transactions increased by 46% and the income grew by 86% compared to the previous month.
We optimized the new feed once it was created, adding additional attributes to the mandatory ones to describe the products as accurately as possible, and also made changes to the attributes important for the relevance of future traffic.
The [title] and [description] attributes are the most important attributes that have a significant impact on the performance of feed-based Google Ads campaigns since, in the absence of keywords, the system uses information from these two attributes to make a decision about showing the product to a certain user.
The effectiveness of advertising campaigns was significantly boosted by the client’s promotional offers with significant discounts, which gave users an additional incentive to make a purchase. As a result, the conversion rate grew by 25-30%.
REGULAR UPDATING OF THE RANGE OF GOODS IN THE ONLINE STORE
Here, close communication between our team and the client played a significant role. As soon as certain products or categories from the new arrivals section demonstrated the potential to increase sales, we offered the client to add these products to separate groups and actively promote them with increased budgets.
CONCLUSION
Working with different products is always interesting and experimental. For online stores, it’s important to set up and run feed-based shopping campaigns, connect and configure eCommerce, and constantly work on optimization and scaling. Segmenting products in the feed by category and optimizing feed attributes helps improve results, reduce customer acquisition cost, and increase revenue.
