Best practices for leveraging artificial intelligence and machine learning in 2023

in many ways, this year will be remembered as the year when artificial intelligence (AI) and machine learning (ML) finally broke the hype and delivered consumer-centric products that wowed millions. Generative AI such as DALL·E and ChatGPT has revealed what many already knew. AI and ML will transform the way we connect and communicate, especially online.

This has significant implications, especially for start-ups looking to quickly find ways to optimize and enhance customer engagement following a global pandemic that has changed the way consumers buy products.

Startups must innovate to remain competitive as they navigate a uniquely disruptive season that includes inflationary pressures, changing economic uncertainty, and other factors. AI and ML may finally be able to make it happen.

Hyper-personalization is at the forefront of these efforts. According to a McKinsey & Company analysis, 71% of consumers expect brands to deliver personalized experiences, and three-quarters are frustrated when they don’t. For example, today only about half of retailers say they have digital tools to deliver engaging customer experiences.

As the industry moves forward, consumer innovators will be able to integrate AI and ML tools to engage customers at scale, placing greater emphasis on personalized experiences and connections.

most important data

Hyper-personalization is based on customer data, a ubiquitous resource in today’s digital-first world. Excessive or useless customer data can clog your content pipeline, but the right information can power hyper-personalization at scale. This includes providing key insights on:

• purchasing behavior. Once brands understand shoppers’ buying behavior, they can provide repetitive content that builds on previous interactions to drive sales.
• buyer’s intentions. Buyer intent only loosely correlates with buying patterns, but this metric can provide context to customer trends and expectations.