AI in Customer Experience Mangement

Author
Affiliation

Ashish Kumar

School of Economics, Finance & Marketing
RMIT University

Foundations of AI

The application of Artificial Intelligence (AI) in customer experience management is built upon the fundamental ability of machines to recognize patterns and learn from data.

Pattern Recognition: Human brains excel at recognizing patterns (e.g., face recognition, speech recognition, handwriting recognition.Early AI research aimed to replicate this ability in machines. These systems move from raw data to useful information through a process often rooted in data mining. Core pattern recognition tasks in AI include:

  • Ranking
  • Recommendation
  • Classification

Machine Learning (ML)

Machine Learning is an all-encompassing term and is a subset of AI that focuses on processing the data to look for trends and patterns. ML algorithms1 improve automatically through experience based on data.

Core requirements and applications of ML include:

  • Data is fundamental: ML relies on training data, validation data, and test data.
  • Key Applications: Predictive analytics, Natural Language Processing (NLP), Computer Vision, and Speech Recognition.

Availability of large amount of data (e.g., big data), increased and cheaper computational power, and the development of more sophisticated, open-source algorithms. have driven recent advances in AI and ML.

Generative vs. Agentic AI

AI can be categorized by its primary function:

Feature Generative AI Agentic AI
Focus Creating new content Autonomous decision-making and actions
Technology Large Language Models (LLMs) LLM-powered autonomous systems
Examples ChatGPT, DALL-E, Midjourney Autonomous vehicles, robotic process automation

Use AI in Marketing Mix

AI transforms the traditional marketing mix by providing three levels of intelligence: Mechanical, Thinking, and Feeling AI.

AI Type Function Benefit Example Application
Mechanical AI Automates repetitive and routine tasks Standardization (efficiency & consistency) Automatic payment systems, automating routine pricing tasks
Thinking AI Processes data to arrive at data-driven decisions Personalization (data-driven customization) Dynamic pricing, predicting fashion trends, personalized content creation
Feeling AI Analyzes interactions and human emotions Relationalization (emotional engagement) Training chatbots with brand personality , analyzing and responding to customer emotions in real-time

AI application Across the 4P’s:

  • Product: AI tracks adoption (Mechanical), predicts fashion trends (Thinking), and trains chatbots with brand personality (Feeling).
  • Price: AI automates payment systems (Mechanical), performs dynamic pricing and optimization based on individual customer data (Thinking), and handles AI-powered price negotiation (Feeling).
  • Place (Convenience): AI manages drone delivery (Mechanical), anticipates shipping needs (Thinking), and uses facial recognition for customer identification (Feeling).
  • Promotion (Communication): AI automates targeting and media buying (Mechanical), creates personalized content and identifies influencers (Thinking), and tracks audience emotions to personalize ad messages (Feeling).

AI for Customer Experience

  • Generative AI and Prompt Engineering: Generative AI refers to algorithms that can generate new content, such as text, images, audio, or video, based on patterns learned from existing data. It utilizes deep learning techniques, particularly neural networks, to create content that mimics human creativity. The first step in engaging GenAI is prompting.

  • Prompt Engineering: This is the process of designing and refining prompts to effectively communicate with generative AI models to achieve the desired output quality and relevance.

    • Key Techniques: Prompts should offer Clarity (specific), Context (relevant background), and Constraints (boundaries/guidelines).
    • Types of Prompts:
      • Zero-shot: The model is given a task without any examples.
      • Few-shot: The model is given multiple examples to learn from before generating a response.
        • Chain-of-Thought: The model is guided through a step-by-step reasoning process.
        • Contextual: The model is provided with additional context or background information to inform its response.

Generative AI is used for Automation (e.g., content creation, personalization) and Augmentation (e.g., design, synthetic data generation).

Conversational AI

Conversational AI simulates human conversation using multi-modal inputs (text, images, voice).

  • Chatbots - A Centralized Hub: Chatbots decouple two steps:
    • the Conversation layer handles natural language understanding, context, and flow, and
    • the Application layer executes the backend logic, data retrieval, and functional tasks.

Chatbots act as a transactional operating system, unifying the entire customer journey and eliminating the need for multiple apps or websites. Thus, chatbots are a kind of Centralized Hub for customers. Some of the benefits of chatbots are: Availability (24/7), Cost Efficiency, Scalability, Personalization, Data Collection , and Consistency.

Conversational Commerce refers to using chatbots and messaging apps to facilitate online shopping and customer interactions. This aligns with new consumer behavior where customers prefer conversational interactions over traditional browsing.

  • The Goal: To provide personalized assistance and recommendations at every step, catering to the entire stages of marketing funnels (Awareness, Consideration, Purchase, Retention, Advocacy).
  • Ethical Risk: When chatbots interact directly with people, providers have additional ethical responsibilities. Transparency is required to inform users about the shortcomings and unpredictable behaviors of chatbots.

Agentic AI

Agentic AI systems possess autonomy, decision-making capabilities, and the ability to perform tasks independently.

  • Key Distinction: Agentic AI is given a high-level goal (e.g., “Launch a successful spring sale campaign”) and autonomously creates emergent social behaviors.
  • Vision: Bill Gates conceptualizes the AI agent as a “personal assistant” that will fundamentally change how everyone interacts with computers.
  • Agentic Funnel Transformation:
    • Awareness: Autonomous agents will create, A/B test, and optimize thousands of personalized ad variants.
    • Consideration: “Shopper Agents” will negotiate with “Brand Agents” to find the best fit.
    • Loyalty: Proactive agents will predict churn and autonomously initiate solutions before a problem arises.

Digital Twins

Agentic AI enables the creation of Digital Twins — Virtual replicas of individual customers constructed using publicly available information or proprietary data.

  • These twins allow marketers to simulate consumer behavior at scale, predict market trends and responses to marketing strategies, and test pitches and product ideas.
  • LLMs can simulate consumer responses to marketing stimuli, providing insights into preferences and decision-making processes.

Human-in-the-Loop

While AI excels at processing data, generating predictions at scale, and automating repetitive tasks, it lacks domain knowledge and human judgment.

  • AI cannot yet understand business strategy, recognize cultural nuances, or make ethical judgments.
  • Domain expertise transforms raw technical AI skills into impactful innovations.
  • The most powerful AI systems combine machine intelligence with human domain expertise.

Digital Homogeneity

Marketing in a digital environment is inherently tech-driven, with 15,384 martech products existing in 2025, primarily driven by GenAI. When multiple brands deploy the same technology for their digital marketing strategies, it creates a risk of digital homogenization — what researchers call “algorithmic monoculture” and “outcome homogenization.” The convergence of several contemporary phenomena — brands’ increasing adoption of martech tools, consumers experiencing information saturation while seeking cognitive simplicity — has precipitated a critical juncture wherein disparate brands’ digital marketing strategies are converging toward remarkable similarity, notwithstanding their deliberate differentiation efforts. This paradoxical outcome is conceptualized as “terminal uniqueness,” a phenomenon characterized by the erosion of distinctive brand positioning through the very mechanisms intended to enhance competitive advantage.That’s where the essential role of human creativity comes into play, especially for marketing field.

We noticed in one of the activities that GenAI content exhibits remarkably high cross-platform similarity and consistently lacks authenticity. This remarkable convergence raises serious implications for artificial creativity and originality, leading to digital homogeneity. In creative fields like marketing, the phenomenon of terminal uniqueness becomes seriously potent if brands rely solely on these systems without human intervention. The proliferation of GenAI in content creation gives rise to algorithmic convergence that leads to an undifferentiated mass of marketing noise. Therefore, human creativity — with its unique insights, emotional intelligence, and cultural nuance — remains the essential differentiator that enables organizations to develop distinctive brand voices, establish authentic consumer relationships, and achieve competitive advantage within saturated digital communication environments.

Footnotes

  1. Computer instructions↩︎