Vitapur case study
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Vitapur: Increasing Webshop Revenue by 78%

Find out how we exceeded the client's goals by focusing on the entire customer path and campaign optimization based on machine learning.

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Company Background

Vitapur is the largest mattress manufacturer in Slovenia. It has 14 offline stores in Croatia and a webshop with a wide range of home products (pillows, bedclothes, mattresses, kitchenware, etc.)


Our collaboration with Vitapur started in 2019. The client expected to see the growth in revenue from advertising and consequently, an increase in overall webshop revenue compared to the same period last year.

The investment of competitors in key categories is growing each year, and Vitapur already had decent search campaigns so we can say that expectations were set high (as they should always be). Still, challenging projects like this bring out the best in us. Right?


60% growth in revenue and transactions on the search channel.

35% growth in revenue and transactions on the whole webshop.


Our analysis showed that previous search campaigns focused only on attracting customers with high purchase intent while neglecting the overall customer journey and the power of smart bidding.

This was the plan:

Focus on the entire customer path

Show relevant ads through all phases of the customer journey

Use appropriate smart bidding strategy

Allocate the budget based on the attribution analysis

Therefore, our first step was to do thorough keyword research and set up search campaigns that capture the whole customer journey. The second step was to show ads relevant to the user’s search to improve the buying experience. By using Google Ads special functions, we went one step further. We tested things like:

Ad copy based on the device the user is using

Countdown function was used to create a sense of urgency. For example, if the free delivery option ends in 7 hours, the ad stated: “Free delivery ends in 7 hours“. The same ad, 3 hours later stated: “Free delivery ends in 4 hours“.

More than 6,000 search ads were tested during this campaign.

By abandoning Last click attribution model and setting up data-driven attribution as the default model, we gave smart bidding the maximum number of available inputs, which ensured constant bid optimization towards the set goal.

By analyzing results through available attribution models, we found out that some campaigns on the customer path are more important than others. Based on the data we collected over time, we allocated budgets towards campaigns that had a greater impact on the overall result (e.g. we shifted a part of the remarketing budget on a search campaign for mattresses that indirectly brought in more revenue.)

Despite the fact we expanded our search campaigns to gain more volume, the cost per conversion decreased by 24%, and the conversion rate increased by 18%. Additionally, the total number of brand searches increased by 50% compared to the year before.


The results below are compared to the campaign results from the year before.
Transactions and revenue (SEM)
Webshop revenue
Webshop transactions


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