AI promises revolutionary changes in customer engagement and marketing research

Editor’s note: Polat Goktas is Marie-Curie Research Fellow at the School of Computer Science and Ireland’s Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, Dublin, Ireland. Goktas can be reached at Taskin Dirsehan is professor of marketing at the Faculty of Business Administration, Marmara University, Istanbul, Turkey. He is a guest researcher at Erasmus University's School of Social and Behavioral Sciences in the Netherlands. Dirsehan can be reached at  

The customer service and sales sectors are undergoing a transformation, led by advancements in artificial intelligence (AI) applications, particularly in the realm of conversational AI. Pioneering platforms, such as, are settings a new benchmark in how we interact with AI in customer service. The tool, in its beta testing phase at the time of this writing, aims to conduct conversations that closely mimic human interaction. It is engineered to understand, respond to and learn from each customer engagement. This makes for a more personalized and user-focused experience, going beyond the limitations of traditional scripted responses. Such advancements have caught the attention of the business world; over 100,000 companies have already signed up to be part of's beta testing. 

However, while this technology promises to redefine customer service, it presents challenges and considerations, especially relevant to marketing research. These include concerns about data privacy, potential algorithm biases and the customer's perception of AI interactions. Guided by insights from Marketing Science Institute (MSI)'s 2022-2024 Research Priorities, this article explores these dimensions in greater depth, focusing on the transformative impact of conversational AI in customer service through the lens of marketing research.

Before diving in, let’s first understand the topic: The challenge and promise of conversational AI in customer engagement and marketing research.

Since the 1990s, the realm of marketing has seen tremendous growth, focusing increasingly on creating and augmenting sustainable customer relationships. The rise of big data and AI has only accelerated this trend, offering tools that can enhance customer engagement far beyond the service process. Here, conversational AI emerges as a critical game changer. While it offers incredible efficiencies and personalized interactions, the challenge remains: How do we ensure that these AI-driven interactions don't compromise the nuanced and empathetic aspects of human conversation?

Data privacy in the context of advertising research and customer value creation

Originally, conversational AI was confined to simplistic chatbot systems, far from mimicking a human-like interaction. Fast-forward to today, advancements in natural language processing, machine learning and data privacy regulations have evolved these interfaces into complex dialogic systems. These systems can understand not just the human language but also the context, thereby transforming the conversations from being transactional to more intuitive and human-like.

So, how does this evolution connect to advertising research and the creation of customer value? As platforms like ChatGPT contribute significantly to content creation in advertising and social media, there's an emphasis on harnessing the power of big data rather than just personal data. In the nexus of this technological evolution lies the issue of data privacy, and more broadly, data utilization. Drawing inspiration from MSI's research priority, notably the MSI Research Priority 1.1 “Effects of Privacy Regulation on Customer Value Creation,” marketing researchers are faced with a pivotal twofold challenge in the era of conversational AI. On the one hand, they must comprehend how these AI systems store, utilize and protect customer vast data sets, ensuring they are used effectively and ethically for advertising objectives. On the other hand, they must extract valuable insights from this data to enhance customer experiences, while adhering to privacy norms like the General Data Protection Regulation (GDPR). 

As we move through the realm of conversational AI, the twin objectives for marketing researchers are clear: leveraging big data for innovative advertising research while crafting enriched customer experiences, all while staying within the bounds of privacy regulations.

Where data meets strategy: Algorithm biases and firm strategy

AI algorithms are not inherently unbiased. They can exhibit biases that vary from subtle issues related to tone and engagement to ethical matters like gender or racial discrimination. This is especially important to consider when firms change their strategies to adapt to a fast-paced business environment. Not only must the data and analytics adapt, but conversational AI tools must also evolve to align with new data sets, customer preferences and strategic goals.

The evolution of customer service: Understanding the role of conversational AI chatbots in transformationTracing back, conversational AI's journey has evolved from basic chatbots to sophisticated platforms, primarily driven by advances in natural language processing and machine learning. But a question arises: How does this evolution synchronize with a company's dynamic strategy? According to MSI Research Priority 1.3, titled "Analytics Challenges Following Changes in Firm Strategy," marketing researchers should focus on examining how these advanced AI technologies can be adapted when the company's strategic direction changes. This ensures that they continue to meet customer needs efficiently, even as they evolve.

Customer perception: Striking the right tone, enhancing engagement and building empathy

Conversational AI holds the promise of efficiency in customer interactions. Yet, it's not without its flaws. Notably, criticisms have emerged regarding the AI's tone and its way of asking questions, often deemed as lacking in empathy. Drawing from MSI's Research Priority 2.4, “Attention, Engagement, and Customer Experience,” as AI's role in customer service continues to grow, it becomes imperative for marketing researchers to prioritize understanding customer perceptions of these AI-driven interactions. Questions like, “Do customers trust AI interactions?” and “Are they comfortable discussing sensitive topics with an AI?” are essential for researchers to explore in-depth.

Still, there are areas where conversational AI can refine its approach, especially from a marketing research perspective:

  • Tone: AI systems sometimes exhibit a tone that users perceive as off-putting or even rude. This is an area of considerable concern for marketing researchers, especially those aiming to incorporate more empathetic responses into AI systems.
  • Questioning approach: AI's method of inquiring can occasionally come off as more of an inquisition than a friendly query. For marketing researchers, understanding and correcting this issue is crucial, as the approach can influence customer engagement metrics negatively.
  • Engagement: Another challenge is the AI's inability to proactively engage with pressing customer issues. The future of customer service goes beyond mere problem-solving; it is about connecting with customers on an emotional and contextual level.

The technological backbone: Where data and analytics converge

Conversational AI relies on various technological components that work together to create natural-like and intelligent dialogue:

  • Speech-to-text (STT): This technology converts spoken words into written text, which is essential for voice-based interactions with AI.
  • Natural language understanding (NLU): NLU enables the AI system to understand the meaning of human language, whether it is spoken or written. It helps the AI nuances, idioms and complex sentence structures.
  • Text-to-speech (TTS): TTS technology converts the AI's written responses back into spoken words, especially important for voice assistants. Advanced TTS systems ensure the responses sound natural and engaging.
  • Dialogue policy: This component guides the AI's responses based on context, previous dialogue and its built-in capabilities. A well-designed dialogue policy allows the AI to handle interruptions and unexpected user inputs effectively.

AI-driven chatbots depend on a host of technologies like STT, NLU and TTS. The way these components are set up and used can introduce its own set of challenges. Under the umbrella of MSI's research priority in “Section 2: Measurement and Analytics,” marketing researchers are encouraged to rigorously investigate how the setup of these technologies influences customer experience metrics.

Anticipating the future with research insights 

From classical call centers to advanced conversational AI. The role of conversational AI in customer service is a double-edged sword. While it promises efficiency and personalization, concerns about data privacy, algorithmic bias and the loss of human touch in customer interactions remain. Additionally, being transparent about using AI in customer interactions is key to building and maintaining trust.

Marketing researchers have an important role in helping us navigate these tricky waters. They can focus on key areas identified by the MSI Research Priorities, such as data privacy, analytics challenges and the overall customer experience. By doing so, they can guide the development of conversational AI in ways that are both ethical and effective. 

Looking ahead, conversational AI applications have the potential to bring significant improvements to customer service, offering a range of benefits from smarter call routing to more efficient problem-solving as:

  • Intent detection and call routing: Conversational AI can identify the customer's intent and direct them to the appropriate department or resource. It can also gather essential details, saving both the customer and business time.
  • Bookings and reservations: Conversational AI can automate the reservation process, integrating with existing booking systems and offering personalized recommendations.
  • Authentication: Conversational AI can identify and verify customers through natural conversations, thus automating a time-consuming process.
  • Troubleshooting: AI can handle or assist with troubleshooting processes, potentially resolving issues without the need for human intervention.
  • FAQs: AI can automatically answer frequently asked questions, easing the repetitive tasks of human agents.
  • Account management: AI can assist customers in managing their accounts, including changing details or understanding account services.
  • Order management: AI can handle order-related tasks such as placing, tracking and modifying orders.
  • Billing and payment: AI can guide customers through billing and payment processes and identify selling opportunities.
  • Personalized service: AI can provide personalized customer service by accessing customer data and recalling previous interactions.
  • Data and insights: AI automatically records conversation data, providing valuable insights to improve customer service operations.

The dual role of conversational AI and marketing research 

The MSI's 2022-24 research priorities underscore the significance of the challenges. Conversational AI is not just a tool for improving customer service efficiency – it is a lens through which businesses can gain deeper insights into customer preferences, behaviors and perceptions. However, with these opportunities come responsibilities. Marketing researchers must guide the ethical, empathetic and effective use of AI in customer interactions. The road ahead is complex but holds immense potential for those willing to navigate its challenges.’s latest launch demonstrates the transformative power of conversational AI, not only in English but also in multiple languages. This advancement has the potential to transform global customer service, offering more efficient, effective and personalized experiences. However, it also raises important questions:

  • Job displacement: With AI replacing traditional customer service roles, what steps can be taken to minimize potential job loss?
  • Preserving the human touch: How can we ensure that AI maintains the human touch necessary for successful customer interactions?
  • Transparency and trust: Should organizations disclose to customers that they are communicating with an AI to maintain transparency and trust?

Marketing research practitioners have a crucial role in the AI revolution within customer service. They need to:

  • Advise for AI systems that strike a balance between efficiency and the nuanced understanding and empathetic responses essential for successful customer service.
  • Continuously evaluate how AI can be utilized without displacing human roles entirely.
  • Ensure transparency in AI-driven customer interactions to preserve customer trust.

There are valid reasons to remain optimistic, at least in the coming years. However, the future beyond that becomes increasingly difficult to anticipate.

We are living in exciting times. With thoughtful and careful navigation, this technology holds the potential to reshape the customer service landscape as we know it. Together, we will overcome and progress!

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