A major ecommerce company used a rules-based retargeting model that, while successful, continued to let many potential customers slip by because they don’t meet the specific behaviors marked as rules. As a result, the company’s existing system, which is time- and resource-intensive, was leaving potential revenue on the table. More importantly, the company was using tools that couldn’t give them immediate feedback on their marketing strategies for Facebook ad placements, leaving little room to course-correct if its tactics didn’t pan out.
Using Explorium, the company built a working dynamic model that was ready for production. This new model uses the company’s internally collected datasets and focuses on several key aspects to qualify leads, including:
Explorium end-to-end data science platform then integrates with Facebook to run the right campaigns for the most promising leads.
After deploying their model, our customer saw almost immediate results. After integrating Explorium into their targeting models, the company:
Moreover, the company is now able to predict the most likely shoppers and put users on unique buying journeys that will result in better sales.
More importantly, the company did not have prior knowledge or experience with machine learning models. Explorium offered onboarding and education services for the company’s team and guided them through the transition. This helped our customer immediately start using their data to train new models on Explorium and see results rapidly.
