Paid Search Ad Solutions - How to Lift Efficiency, Save Time, Cut Costs and Lift Revenue

How did a nationwide leader in a highly competitive consumer space use data science and machine learning ad solutions to dramatically lift its efficiency - all while saving time, lowering cost-per-acquisition and actually lifting revenue? Read about these innovative ad solutions in the full customer success story.

Paid Search Ad Solutions - How to Lift Efficiency, Save Time, Cut Costs and Lift Revenue

Can modern ad solutions lift efficiency and revenue simultaneously - all while making your life easier? And is this kind of improvement even possible at enterprise scale?

Consider the story of Dermstore - the second-largest beauty e-commerce retailer in the US and a subsidiary of Target - and its search for better ad solutions and better performance. This highly successful operation had already carved out a market-leading position as an exceptionally popular purveyor of beauty and skin care products, but it sought even better performance, particularly in the area of efficiency.

To improve its already-successful operations, the company looked for ad solutions that use next-generation technology to drive significantly higher efficiency and found what it was looking for with a predictive advertising management platform that combines data science to drive incremental performance improvements at the individual keyword level and machine learning to efficiently manage bids.

Dermstore’s search for better ad solutions resulting in lifting efficiency for its nonbrand keywords +35%, operations time savings of 15%, reduced cost-per-acquisition of 21%...and increased revenue of +6%. (Typically, revenue and efficiency, measured by return on ad spend, are at odds with each other, and strengthening one tends to weaken the other).

How did Dermstore use these next-gen ad solutions to drive this significantly improved performance? Get the details on these innovative ad solutions in the full customer success story.

About the Author

Andrew Park

Andrew Park is a content marketing manager at QuanticMind. A UC Berkeley graduate and lifelong Bay Area resident, Andrew has done tours of duty in editorial, PR and marketing, and now works with the QuanticMind team to communicate the importance of data science and machine learning in digital advertising.

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