It’s well known among marketers that a customer’s explicit intent, such as browsing a product page, is a strong indicator of future purchase behavior. By layering previous purchasing patterns and pre-defined behavioral segments, marketers are targeting campaigns that are supposed to be relevant and therefore effective.
But while 89% of marketers say they are personalizing experiences and messages, only 5% of consumers say messages and offers are well-timed with their needs. 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 machine learning techniques for campaign targeting create sub-optimal predictions that eliminate potential buyers and damage customer experience. Meanwhile, marketers embracing deep learning techniques, today’s state-of-the-art technology, 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 data science and deep learning differ, let’s compare the two to see how they can determine whether Tim, the customer of an airline, should be included in next month’s campaign promoting business class seats to Mumbai.