Of many ways, this year will be remembered as the year artificial intelligence (AI) and machine learning (ML) finally broke the hype, delivering consumer-centric products that wowed millions of people. Generative AI, including DALL E and ChatGPT, manifested what many people already knew: AI and ML will transform the way we connect and communicate, especially online.
This has profound implications, especially for start-ups looking to quickly figure out how to optimize and improve customer engagement in the aftermath of a global pandemic that changed the way consumers buy products.
As startups navigate a uniquely disruptive season that also includes inflationary pressures, changing economic uncertainty, and other factors, they will need to innovate to remain competitive. AI and ML may finally be able to make that a reality.
Hyper-personalization is at the forefront of these efforts. An analysis by McKinsey & Company found that 71 percent of consumers expect brands to provide personalized experiences, and three-quarters are frustrated when they don’t deliver. Currently, for example, only about half of retailers say they have the digital tools to deliver a compelling customer experience.
As the industry advances, consumer-facing innovators can better emphasize personalized experiences and connections by integrating AI and ML tools to engage their customers at scale.
In many ways, this year will be remembered as the year that artificial intelligence (AI) and machine learning (ML) finally broke the hype.
The data that matters most
Hyper-personalization relies on customer data, a ubiquitous resource in today’s digital environment. While excessive or useless customer data can clog content pipelines, the right information can drive hyper-personalization at scale. This includes providing critical information on:
- Shopping behaviour. When brands understand shopper buying behaviors, they can provide iterative content that builds on past interactions to drive sales.
- Buyer’s intention. While buyer intent only loosely correlates with buying patterns, this metric can provide context to customer trends and expectations.
