$448k for flowers
E-commerce store
Google Ads
TOOL
case study
The client is one of the largest brands in the Ukrainian flower market. The key sales tool for the brand has always been a wide network of offline stores. Our task was to digitalize the business and get less than 20% of sales from the online store.
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Name
Phone
website link
ads budget
RESULT
448k$
3,87$
CAC
revenue
9,95
9192
48,7$
AOV
sales
45k $
ROAS
  1. Before setting up advertising campaigns, we analyzed the client's website. We recommended adding sorting of goods in the catalog and filtering by cost, color, and type of flower. The client implemented all the recommendations, which in turn had a positive impact on the UX of the site;
  2. The next step was to set up Google Analytics and, most importantly, e-commerce tracking. It helped to analyze the number of transactions and income for the entire account, including individual campaigns, groups, keywords;
  3. Analyzed advertising campaigns of competitors: keywords, titles, description, unique offers, budget ad spend;
  4. Compiled a list of keywords that competitors use, supplemented it with keywords from the keyword planner, and also wrote the texts of advertisements. Using all this data, search advertising campaigns were set up with a relevant grouping of keywords;
  5. In addition to search ad campaigns, we set up a DSA campaign (dynamic search campaign) to show ads for low-volume keywords that can be missed when collecting keywords manually. All search ads keywords were added to the negative keywords;
  6. Since the brand is popular offline, competitors show ads for our brand. To get our traffic back, we set up a branded keywords campaign ;
  7. Set up a smart shopping campaign - the #1 tool for eCommerce stores. We added all products to one campaign cause the margin and AOV on all products is approximately the same
  8. We set up additional advertising campaigns for a radius around offline stores. When customers who are nearby looking for flowers can come and buy them in the store;
  9. During the first month of work, advertising campaigns received the first statistics. We, in turn, regularly adjusted bids, checked search queries, expanded the list of negative keywords, and added new keywords;
  10. In the second month of work, we started deeper optimization. We founded best-selling SKUs and made separated them into separate shopping campaigns with a Maximum conversion value strategy.
  11. In search advertising campaigns, we regularly disabled keywords with low ROAS, as well as making adjustments for age, gender, and device.


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