Adam & Eve

Deep learning drives new customer purchases and improves campaign effectiveness

Overview Client
Industry DTC Retail
Program Application Desktop & Mobile Display
Product Cognitiv DSP
Key Business Challenge

Replenish Customer Growth

Drive new customer conversions

Improve campaign effectiveness

Client objective

Adam & Eve was searching for a solution that would support their efforts of maintaining their efficient cost-per-order goals while also significantly increasing their volume of new customer orders. They were tasked with ensuring a notable amount of orders would be made from the net new customers identified.

High-Level Results
0 %

of the total prospecting conversions were only identified by Cognitiv

0 %

of the total prospecting conversions were net new customers

0 %

higher order value on average for new customers identified by Cognitiv

The Solution
The Solution

Predictive audiences and contextual targeting efforts led to exceptional customer conversion rates.

A custom deep learning algorithm was trained using Adam & Eve’s first-party data to uncover complex behavioral patterns of existing customers to accurately predict at scale net new prospects highly likely to purchase.

Our technology combined this predicted performance information with historical pricing data to determine exactly how much to bid for each piece of inventory to maintain the client’s cost-per-order goals.

The algorithm autotuned itself at each impression, becoming smarter with each prediction, allowing it to expand qualified reach and uncover net new high value prospects.

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