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Videnov Case Study
Best Performance in Retail
Videnov is a Bulgarian company that has been active in the field of furniture retail for over 30 years, with 44 physical stores in Bulgaria.
In addition, it is expanding beyond Bulgaria to 2 other countries (Romania and Greece) through its eshop. Videnov in numbers.
In Greece it has an eshop, trades Home furnishings and has over 2,000 immediately available products for delivery, with its own privately owned fleet of vehicles.
The goal was to maintain ROAS around 1:31, and also increase revenue and total transactions.
For the period of 01/06/20 to 31/05/21 (12 months), they had a total of 13,995 Transactions, €6,865,409.85 Revenue & 1:31 ROAS in Google Ads.
- Increase in transactions +50%
- Increase in Revenue +30%
- Desired ROAS Google Ads 1:31
- A big challenge was to meet the above goals set by the company, with the priority of keeping ROAS at a minimum of 1:30, despite the increased spend of + 200% from the Google Campaigns.
- Huge competition by successful brands in the field of home furniture which also possessed physical stores.
- Restriction on targeting due to logistics. Limited to Northern Greece and specifically (Thessaloniki, Halkidiki, Drama, Serres, Kavala, Kilkis, Imathia, Pella, Kozani, Kastoria, Pieria, Xanthi, Alexandroupoli, Komotini, Florina and Larissa)
- Absence of physical stores
- Over 2,000 products and 50 categories with various profit margins
The solution we implemented was the utilization of automations & AI in Google Ads through the attribution model, the data analysis through Analytics for better targeting and budget allocation.
- We changed the attribution model from last click to data driven to see the real contribution of campaigns to revenue
- We created a plethora of campaigns utilizing automated bidding strategies
- We created Shopping Ads
- We utilized UTMs
Google Search Ads
- We divided the account into thematic campaigns depending on the category of furniture, as it is configured in the e-shop (Living Room, Kitchen, Bedroom, etc.). In this way we reduced the volume of campaigns to just 14, with 38 ad groups, while also targeting over 60,000 keywords without any overlap.
- We managed this using Google's new best practices at the keyword level, Updated phrase match combined with BMM match types and extensive use of negative lists.
- We used the Recommendation Tool and AAR (Auto Apply Recommendations) to automatically use negative keywords, new audiences and new search terms, after first "training" the recommendation tool to give us relevant suggestions.
- We exclusively used Smart Bidding strategies depending on which step of the buyer's journey the user is. For specific generic searches we used max.clicks and then created RLSA campaigns with Target ROAS for maximizing performance.
Google Shopping
We used a Feed Management Tool. Through this we added custom columns with the margins per brand, always in consultation with Videnov. The goal was to chase the best possible ROAS by segmenting our shopping campaigns based on the margin given per category.
- We used the solution of a feed editing tool of the Feed Editor in order to be able to edit the product feed dynamically without the help of the dev team.
- Initially we added custom label columns with the category name as the condition and inputted the desired ROAS for each product (taking into account the margins that the customer had passed on to us.)
- Subsequently, in the google shopping campaign we created separate ad groups where each one was filtered based on the custom label by including or respectively exclude the products that could meet the goal.
