1. Clustering For Customer Segmentation And Discovery
Not all customers are the same. Unsupervised machine learning can help marketers group their audience into dynamic groups and engage them accordingly. For example, a system can analyze billions of consumer interest variables, identifies specific customer’s interests based on their social media activities, then generates a visual report grouping people with similar interests. You then gain insight on which of your customers are die-hard foodies, who follows which series on Netflix, or who among them have similar travel plans.
2. Multi-Arm Contextual Bandits For Content Optimization
A/B tests are effective ways of finding out which content option (email tone, web page layout, visual elements in an ad, article headline, etc.) resonates better with your audience. However, A/B Testing involves a period of “regret” where you lose revenue while using the less optimal option. You have to wait and finish the countdown before learning which option — the final answer — is better. In contrast, bandit tests mitigate regret (opportunity loss) through dynamic optimization where it simultaneously explores and exploits options, gradually and automatically moving towards the better option.
|Regression Models for Price Prediction|
|Social News Analytics & Summarization|
Marketers can leverage ML to extract relevant content from online news articles and other data sources to determine how people view their brand and/or react to their products
Attention mechanisms in deep learning help improve machine translation and empower your marketing assets for the global stage. Translation work for a brand’s entry into a new, linguistically different market used to be a major marketing spend but advances in AI enable machine translation to achieve near human parity. To rationalize costs and speed up the process, many companies opt to just have a human translator review and sign off machine translation output.