Machine learning can help spot patterns or changes in customer behavior more swiftly, enabling marketing to respond in real time by adjusting offers.
Use AI/ML to continually refine the respective ideal customer profiles (ICPs).
ICPs created with AI/ML, they can move forward with very little information, perhaps even just a web view, and combine that with information from other sources to score customers or prospects on how similar they are to the ideal profile and use that to determine marketing and sales efforts.
Social media commentary as a way for companies to learn which of their competitors’ customers might be prepared to make a switch, adding that AI- and ML-powered sentiment analysis can analyze the commentary to determine the most dissatisfied customers who would be the best targets for marketing outreach.
Using AI and ML enables marketers to not just scour social media feedback but also information from other digital touchpoints and in-store visits to help determine customers’ emotional attitudes.
AI and ML enables companies to take this approach on a micro-segmentation level, being extremely precise in the types of content, offers, or interactions they offer prospects.
Using machine learning combined with historical customer data, risk scores, and details on timely bill payments to third parties, the bank greatly refined the customers it targeted for a credit offer, generating a 14% success rate, compared to just a 1% success rate for previous campaigns.