Traditional data science targeting methods create an audience of potential buyers by layering a customer’s explicit intent like product page views and recent purchases on to predefined customer segments.
But while 89% of marketers say they are personalizing experiences and messages in this way, only 5% of consumers say messages and offers are well-timed with their needs. So if explicit intent is connected to future purchase behavior, then what’s driving a wedge between marketers and their customers?
The infographic below shows how traditional data science techniques for campaign targeting ultimately rely on gut feel and create sub-optimal predictions that eliminate potential buyers from audiences and damage customer experience.
Meanwhile, marketers embracing state-of-the-art deep learning techniques are leveraging their first-party data to increase the effectiveness of their targeting and planning strategy. Deep learning finds the tiny clues among millions of data points, reaching future buyers with outstanding accuracy.
To understand how traditional data science and deep learning differ, let’s compare the two and see how they can determine whether Pat, the customer of a large retailer, should be included in a November campaign promoting FIFA 20.