Search Campaigns Yield 264% Higher ROAS with Velocidi Audiences
Velocidi helped Figleaves deploy first-party audiences capturing high-value, and high-intent customers in their Search campaigns. As a result, Figleaves increased revenue from search by 12.4% while achieving a 264% higher return on ad spend (ROAS) than their other search activities.
Download the case study to learn how they did it, and what this means for their search strategy going forward.
First-party Audiences Outperform Market-Leading Retargeting Vendor
MO, an online fashion brand, was using a market-leading retargeting vendor to run campaigns and was unsatisfied with the limited level of control allowed to them, despite good ROI. By giving MO access to their own first-party audience intelligence, Velocidi helped MO take complete control over campaign management and creative, and improved campaign ROI by 530%.
How Figleaves drove 48% of the revenue using only 22% of the ad spend
Discover how the Velocidi optimized audiences generated 48% of the revenue and 45% of conversions coming from the Display and Facebook retargeting campaigns using only about one fifth (22%) of the ad spend across both channels.
How Barkyn Doubled Retargeting Conversions with Velocidi
As a brand that lives and breathes personalization, Barkyn wanted their retargeting strategy to go beyond the basic assumption of, “You visited our site, therefore you must be a potential customer.” Read the Velocidi case study to learn how we helped them segment their retargeting campaigns according to buyer intent, and deliver relevant messaging to those customers who were most likely to convert.
Identifying Your Most Likely Buyers
A machine learning use case based on client results. In this use case, we describe how the Velocidi CDP’s proprietary machine-learning predictive models helped a busy marketing team develop more efficient and effective marketing campaigns by:
- Predicting buyer intent
- Autonomously identifying visitors who are most likely to buy in the next seven days.
- Enabling their team to deliver tailored marketing messages to visitors based on whether they are more or less likely to make a purchase.
CUSTOMER SUCCESS STORY
6.9x ROAS for Figleaves
Velocidi’s proprietary machine-learning model for predicting buyer intent is used by brands to create more efficient retargeting audiences. Figleaves, a multi-national apparel brand, tested their machine-learning audiences on their Google display campaigns to learn exactly how much money they were wasting on non-converting visitors.
CUSTOMER SUCCESS STORY
Steady Growth on Half the Ad Spend
Velocidi’s proprietary machine- learning model for predicting buyer intent is used by brands to create more efficient retargeting audiences. PROF is an online shoe store that addressed the challenge of increasing customer acquisition costs by A/B testing their existing retargeting audiences against machine-learning audiences created by Velocidi.
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