Hyper-Personalization: How AI & ML Are Building a New Framework for Ecommerce CX

Hyper-Personalization: How AI & ML Are Building a New Framework for Ecommerce CX

A look at the role of AI and data in elevating e-commerce personalization, enhancing customer experience, and strengthening brand loyalty.

The Gist

Consumer centricity. Embracing hyper-personalization strategies enhances customer experiences and boosts brand loyalty.

AI integration. Implementing AI and ML (machine learning) technologies allows for efficient content creation and targeted marketing efforts.

Data security. Ensuring customer data privacy and protection is crucial for maintaining trust and compliance in the hyper-personalized ecommerce landscape.

With ecommerce sales expected to hit $1.1. trillion this year, companies are looking to set themselves apart from the competition and win consumers’ attention and loyalty.

Brands that will come out on top are those that effectively cater to the “consumer of one” and provide hyper-personalized shopping experiences that feel attuned to individual needs — using artificial intelligence (AI) and machine learning (ML) technology can help enhance these personalization efforts.

This approach focuses on the ability to intake information about an individual who is unknown and then use that information and personal data to get them to perform a certain action — for example, signing up for a loyalty program or purchasing a big-ticket item.

AI and ML algorithms analyze customer data from various sources, including social media, websites and transactional data, to gain insights into buyer behavior and preferences. Through AI/ML-powered predictive analytics, businesses can leverage historical customer data and identify patterns that can be used to make predictions about future behavior.

Lokesh Ohri, consumer consulting leader, EY, formerly Ernst & Young, said technologies like AI and ML are helping to create a highly personalized, ultra-convenient shopping experience today’s consumers expect, while also driving down costs and increasing productivity.

“This will be especially critical for success given the rise in customer acquisition costs, increasing labor costs, and competition,” he said. “Retailers can consider implementing offerings like AI-assisted shopping, virtual try-ons, voice-enabled shopping assistants and more to meet evolving consumer demand.”

Generative AI Aiding Personalization Efforts

Dan Neiweem, co-founder and principal at Avionos, explained newer AI technology, such as generative AI and tools powered by ChatGPT, are aiding in hyper-personalization efforts by allowing marketers to create content necessary to be hyper-personalized at scale.

“It can be used to set a foundational framework for product descriptions and revise those based on the targeted audience,” he noted.

Marketers can create multiple variants of the same content much easier than ever before and use these different assets and product sets to better sell to their audiences.

“It creates better end-user experiences, while also allowing internal teams to be more efficient in the creation of content so they can better target someone with a message that is specific to their industry,” he said.

Josh Thomas, senior vice president of marketing at Madison Logic, cautions however that AI/ML shouldn’t be a reason for marketers to turn their brains off.

“While the technology can be a helpful resource to gain top-level information, it doesn’t replace the deeper insights derived from first and third-party data sources or internal teams,” he said. “Executing a personalized customer experience requires the entire organization.”

Thomas argues solutions that deliver the most effective personalization strategies leverage a mix of qualitative and quantitative insights to inform the most relevant message or offer to the individual, team or overall audience.

Ohri noted the hyper-personalization evolution starts with modernizing back-office systems to achieve agility and scale on demand while shifting the collective company mindset and skill set toward the digital future.

“This requires a solid foundation of data and advanced analytics to anticipate rapidly changing consumer expectations and needs,” he said.

Neiweem explained chief information officers (CIOs), chief data officers (CDOs) and vice presidents (VPs) or heads of data security and data privacy will always be key stakeholders responsible for devising AI/ML-aided customer personalization.

“On one hand, CIOs and CDOs can help support the actual technology implementation and adoption, while data security and privacy leads can handle the data governance of all of the information being collected,” he said.

In addition to those common roles, other stakeholders might be in charge of teaching the AI and ML to respond to customer questions.

You can read the original article written by Nathan Eddy by clicking here.