How artificial intelligence is revolutionizing the customer journey

From Product-Centered to People-Centered

The retail sector has undergone a significant transformation in its approach, shifting from a primary focus on products
to a priority focus on the customer. This change has been driven by a series of factors that reflect
new consumer needs and expectations.

First and foremost, there is a transformation in the way retail conceives and interacts with customers,
placing the consumer’s individual experience and needs at the center of the corporate strategy.
Today’s consumers expect a personalized interaction with the brand. Customers want to be recognized and engaged
according to their interests, demanding immediate responses and attentive support both during and after their purchase.

Marketing solutions are required to become increasingly technologically proficient, offering value-added experiences
to customers and fostering a sense of brand connection.

The Transformation of Retail with Artificial Intelligence

Innovations in artificial intelligence allow companies to gain deeper insights into their customers.
However, AI does not stop there: it also enables more efficient strategic planning and implementation of
customer experience initiatives. This happens quickly, smartly, and on a larger scale,
both in terms of volume and personalized precision.

Many SMEs in the retail sector are leveraging artificial intelligence (AI) and generative AI (Gen AI)
to radically transform the customer experience.

Here are a few examples of how these technologies are changing the retail landscape:

• Customer Centric
• AI systems analyze customer behavior data to offer a personalized approach that extends
  to online browsing, product selection, and promotional offers, significantly enhancing the shopping experience.
• Virtual Assistants and Chatbots
• AI-based virtual assistants and chatbots improve customer support and provide immediate answers to questions,
  delivering a smoother interaction.
• Predictive Processes
• AI is used to analyze purchase trends and large volumes of data. This enables retailers to anticipate demand
  and optimize inventory, improving operational efficiency.
• Visual Recognition
• Gen AI technologies, such as visual recognition, allow customers to search for and find products similar
  to what they desire simply by uploading images, making it easier to discover new items.
• Immersive Shopping Experiences
• Augmented reality (AR) and virtual reality (VR) create more engaging shopping experiences.
  Customers can “try on” products virtually before purchasing, such as glasses, clothing, or furniture.

Artificial Intelligence in the Customer Journey

Artificial intelligence proves to be a valuable ally, particularly in the stages before
the customer begins their journey with the company.

In the initial awareness phase, when the consumer discovers the brand, its products,
and services, the company needs to understand their needs, desires, spending capacity, and purchasing preferences.
In this context, AI can help more accurately outline customer characteristics by grouping them into homogeneous segments
through clustering based on personal and behavioral traits, as well as through the analysis of socio-demographic,
contractual, and transactional data — all the way to their online behavior in e-commerce and on websites.

In the subsequent phase, when the consumer evaluates the offering and potentially moves on to the
purchase decision stage, AI proves effective in maximizing both the conversion rate and
the average purchase value. This is achieved through personalization, for example using recommendation systems
that suggest products aligned with the user’s profile.

Artificial intelligence can also use big data analysis to identify the ideal touchpoints and content
that facilitate the customer’s decisions.

The use of artificial intelligence in the customer journey aims to improve customer engagement and loyalty,
and is capable of detecting factors that may lead to relationship breakdowns and identifying customer sentiment.