A few months ago we wrote about how about how Figleaves, an online apparel brand, saw almost seven times the ROAS from their ‘likely to buy’ retargeting audience compared to their ‘unlikely to buy’ audience. In itself, this was an exciting discovery because it uncovered an opportunity to reorganize their retargeting segments to get the maximum impact.
Fast forward to today, with a few more months of data in the books, and we are excited to share the results from that shift in strategy.
By augmenting their retargeting strategy with machine-learning audiences isolating customers who are most likely to buy, Figleaves generated about 24% more revenue per month on average from retargeting.
How did this improvement happen?
Figleaves already has tried-and-true retargeting strategies driven by personalized messaging. Adding Velocidi audiences to the mix simply boosted their existing practices by adding a timing and frequency dimension that makes their ads relevant to each customer’s intent level as well as their product interests.
As a result, not only is each customer getting served ads for products they want, they are only getting served ads if they intended to buy.
Get the full story
Read our case study with Figleaves to see the final results of this experiment.
