On the occasion of his upcoming speech at CX Summit 2024, Mohamed has had an interiview published in Marketing Week

Link to the article - https://marketingweek.gr/mohamed-zaki-university-of-cambridge-i-diacheir...

What are the current trends in the CX sector?

The current trends in the Customer Experience (CX) sector are as follows:

1.    Data-Driven Design for CX: This trend focuses on leveraging emerging technologies to create consistent customer experiences across digital, physical, and social channels. The emphasis is on designing and delivering unique experiences that meet customer expectations in an experience-driven economy. Companies are realising the importance of viewing their business from the customer's perspective to ensure successful service delivery.
2.    Applying AI to Measure and Manage CX better: Artificial Intelligence (AI) is being increasingly used to manage customer experiences more effectively. With the abundance of data available, companies are moving beyond traditional metrics such as NPS and CSAT to adopt sophisticated data-driven methods for tracking and improving customer experiences.
3.    Building a Customer-Centric Strategy: There's a significant shift from product/service-centricity to customer experience-centricity. Factors like fostering a culture drive this shift centred around CX, converging as providers of customer solutions, achieving customer intimacy, embracing digitalised marketplaces/platform thinking, and promoting knowledge transparency.
4.    Enhancing Customer Engagement Through Digital Technologies: Digital technology's potential is tapped to enhance customer engagement and gain deeper insights into customer behaviours. This involves creating relevant and compelling content, particularly on digital platforms like social media. The challenge lies in understanding the sentiment behind interactions and optimising and personalising content for better engagement.
5.    Personalized Experience: The CX landscape is transforming, with a focus on personalised and empathetic interactions. The shift from traditional call centres to text-based interactions like live chats is prominent. However, there's a growing need for 'conversational care' that combines AI automation with a human touch to manage CX effectively. These channels need to incorporate psychological factors, individual customer knowledge, and contextual elements to personalise conversation styles.
6.    Data-Driven Business Models for Competitive Experience: The modern business landscape sees the rise of data-driven business models. Companies are leveraging the vast amount of data from service interactions to develop new business models focused on the experiences they offer. An example is Amazon's Alexa, which uses AI and voice recognition technology to facilitate interactions and enhance the shopping experience.

Concerning the methods of tracking customer experience, what changes do you see coming in the near future?

Managing and measuring the customer experience is a complex task that requires sophisticated analytical AI tools. Standard methods of tracking customer experience have a significant blind spot as they often fail to capture critical emotional responses and behavioural activities. Consequently, traditional surveys like Net Promoter Score tend to miss essential customer feedback.

CX professionals lack comprehensive AI methods to truly understand their customers' thoughts and feelings about their services, which leads to risky complacency. Many professionals do not monitor customer experience in real-time and rely on simplified, single-metric approaches like Net Promoter Scores and customer satisfaction surveys. While these techniques may be easy to administer and provide presentable numbers for board presentations, they often fail to offer genuine insight into the customer experience. In fact, they can be misleading and create a false sense of security until firms realise, too late, that they have lost many customers. This is because customer experience encompasses multiple dimensions that a single data point cannot capture.

At Cambridge, we have developed a rigorous AI method that has proven to be at least 50% more effective than prevalent industry measures like NPS in predicting customer churn and revenue loss. Our approach enables a deeper understanding of customer decisions throughout the entire customer journey, not just at the point of transaction. Managers can gain a richer view of customers and their interactions with the company by analysing the detailed feedback in free-text comments sections of surveys, social media, reviews, and CRM data. Our machine learning approaches allow for monitoring dimensions such as touchpoints, value creation elements, cognitive responses, discrete emotions, and more, alongside functional performance measures. With this approach, data can be analysed to score customers based on the aspects of the transaction that matter most to them.

These advanced analytical approaches empower CX professionals to develop tailored and responsive services, ultimately managing customer experience better.

Which are the benefits for adopting AI to analyze the customers’ behavior?

The major shifts we can anticipate in the near future in AI to tracking customer behaviour:

1.    AI-Driven Analytical Tools: AI and machine learning are revolutionising how customer data is analysed. These tools can process vast amounts of data, including free-text comments from surveys, social media, reviews, and CRM data, to provide a more comprehensive view of the customer experience.
2.    Real-Time Monitoring: the new emerging data-driven methods will likely focus on real-time tracking of customer experiences. This allows for immediate responses to customer needs and preferences and enables companies to address issues as they arise, rather than after the fact.
3.    Emotion and Behavioral Analysis: New methods are expected to analyse deeper customer emotions and behaviours. This involves analysing not just what customers are saying but also how they are saying it, including their emotional responses and subtle behavioural cues.
4.    Predictive Analytics: Advanced AI methods can predict customer churn and revenue loss more effectively. By understanding patterns and trends in customer data, businesses can anticipate issues before they escalate and take proactive measures.
5.    Personalised Customer Experiences: AI tools enable the creation of more personalised customer experiences. By understanding individual customer preferences and behaviors, companies can tailor their services and communications to meet specific needs.
6.    Employee Training and Engagement: With more detailed insights into what matters to customers, companies can better train their employees to meet these needs, enhancing overall customer engagement and satisfaction.
7.    Problem Identification and Resolution: AI can help in identifying the root causes of problems more accurately and quickly, leading to more effective solutions and improved customer experiences.
8.    Prioritisation of CX Enhancements: By understanding the most critical aspects of the customer experience, businesses can prioritize actions that will have the greatest impact on customer satisfaction and loyalty.

What are the challenges of using AI in CX and how brands can cope with them?

Integrating AI into customer experience (CX) presents several challenges for brands, each requiring careful consideration and strategic responses. These challenges stem from the complex nature of digital technologies and their impact on the customer journey. Understanding and addressing these challenges is crucial for creating exceptional customer experiences. Here are the eight key challenges and how brands can cope with them:

1.    Digital Density and Digital Maturity: This challenge arises in environments with either high digital density or low digital maturity. An example is Microsoft's Chatbot TAY, which faced issues due to its inability to appropriately handle complex digital interactions. Brands can cope by continuously updating and training AI systems to better understand and react to diverse digital environments.
2.    Paradox of Choice: Offering too many options can overwhelm customers. Brands can use AI to personalise recommendations, making choices more manageable and tailored to individual preferences.
3.    Autonomy vs. Interdependence: Integrating autonomous digital platforms without human input can be challenging. Brands should ensure a balance between automated processes and human intervention, maintaining a personal touch in customer interactions.
4.    Regulatory Challenges: With new digital and data-driven business models, navigating regulatory landscapes becomes crucial. Brands must ensure compliance with data protection laws and ethical guidelines in their AI implementations.
5.    Transparency vs. Privacy: The use of technologies handling personal data, as in the case of many apps and services, raises concerns. Brands need to maintain transparency about data usage while ensuring customer privacy and securing personal data.
6.    Standardisation vs. Flexibility: Companies face the challenge of offering consistent experiences while allowing personalisation. The question will be, can we use generative AI capabilities to self-design future services? or Brands should design AI systems that provide a standardised base level of service with options for personalisation.
7.    Avoidance vs. Attraction: Enhancing employee experiences to deliver superior customer service. Brands should use AI to streamline operations, allowing employees to focus more on customer interaction and less on routine tasks.
8.    Capabilities vs. Resources: managing multiple capabilities and resources effectively is key. Brands should leverage AI to optimize resource allocation and enhance their capabilities in responding to customer demands. But the fast pace of the technology could be another big challenge for no digitally born firms.

What is required for brands to personalize an end-to-end customer experience?

To personalise an end-to-end customer experience effectively, brands need to focus on three key CX areas: Attitudes, Capabilities, and Methods. Here's how each element contributes to this process:

Attitudes: Brands must adopt attitudes that prioritise the customer experience in every aspect of their business not only marketing. This involves moving beyond superficial commitments to genuinely embedding customer-centric values in the organisation’s culture. For example, Disney’s approach of treating every customer as a special guest, backed by 'guestology' training for employees, is a prime example. This attitude transforms ordinary interactions into extraordinary/delightful experiences, illustrating the impact of a customer-oriented mindset.
Capabilities: Firms need to develop capabilities of integrated customer journey management that span the entire customer journey (digital, physical, and social). This requires cross-disciplinary skills to enable seamless customer journeys. For example, Amazon and Ocado: Amazon demonstrates this with its exemplary fulfilment capabilities, ensuring reliable and efficient customer experiences during the purchasing process. Similarly, Ocado’s use of robotics and AI in warehouse management contributes to faster and more reliable deliveries, impacting the overall customer experience.
Methods: Mastery of various techniques of CX and AI tools is essential for developing end-to-end customer experience capabilities. This includes utilising new service design methods, data science best practices, and continuous experimentation to enhance CX. For example, the effective utilization of AI and real-time experimentation by ChatGPT, aimed at incrementally enhancing and personalising the chatbot responses and providing the right conversational style that suits the user, exemplifies a strategic approach to optimising customer experience (CX)

By focusing on these three categories, brands can develop small details that lead to significant improvements in customer experience management. Attitudes help in exploring broad-based constructs applicable to CX management, such as cultural mindsets. Capabilities involve resource and skill-based constructs necessary for operationalising customer journeys. Finally, methods encompass specific, detailed elements relevant in conjunction with broader details.

According to Forbes Advisor, marketing content generated by AI enjoys a relatively high degree of consumer approval. Yet, when it comes to more strategic functions, such as upselling or crafting marketing strategies, consumer trust in AI diminishes. Could this be an area where human intuition and strategic thinking are viewed as irreplaceable?

Agree, that the potential of digital technology, including AI, in enhancing customer engagement and understanding customer behaviour is huge. For example, in the context of social media platforms, a primary tool for customer engagement, AI can significantly contribute to identifying content types that drive engagement. Despite the utility of social media metrics like likes, shares, reach, and impressions, these quantitative measures often fail to grasp the sentiment behind interactions.

This limitation is where AI can provide a significant advantage. AI can analyse large volumes of social media data to understand patterns and preferences, helping brands understand why certain posts resonate more with audiences. It can also assist in optimising future content to ensure better engagement. However, the challenges lie in measuring responses accurately, differentiating between various types of content, and understanding the nuanced factors that drive or deter engagement.

The reliance on AI for these analytical tasks does not diminish the value of human insight. On the contrary, it suggests a complementary relationship where AI provides data-driven insights and humans interpret these insights within a broader strategic context. The strategic crafting of marketing strategies and decisions about upselling still require a level of human intuition and understanding that AI currently cannot replicate. This includes understanding complex market dynamics, empathising with customer emotions, and making strategic decisions that align with long-term brand values and goals.

In a few days you will participate in the CX Summit in Athens. Which insights are you going to share with the Greek audience?

I am looking forward to speaking at the upcoming CX Summit in Athens. The insights I plan to share with professionals will focus on the transformative impact of AI in tracking and enhancing customer experience (CX). Traditional methods, particularly single-metric approaches like the Net Promoter Score (NPS), have been the core metrics for understanding customer satisfaction. However, these methods fall short in today's dynamic digital landscape, primarily due to their inability to capture complex emotional responses and subtle nuances in customer feedback.

Additionally, I'll emphasise the importance of integrating various data streams — including attitudes, emotions, and behaviours — into AI-based predictive models. This holistic approach is crucial for businesses looking to enhance their customer experience strategies. By combining structured and unstructured data, AI provides a comprehensive view of customer experience, enabling more nuanced and actionable insights.

The overarching message for the Greek audience will be the need to embrace AI-driven analytics in CX strategies. This approach not only offers a more detailed understanding of customer needs and preferences but also equips businesses with the tools to anticipate and respond to market changes more effectively, ultimately leading to enhanced customer experience.

There are six key benefits of adopting AI for analysing customer feedback:

1.    Uncovering Hidden Insights: AI can reveal crucial insights that are often overlooked in qualitative comments from surveys and call centres. This goes beyond the surface-level data to understand deeper customer sentiments.
2.    Employee Training and Development: By leveraging AI to understand what truly matters to customers, businesses can tailor their employee training programs. This ensures that customer interactions are aligned with customer expectations and preferences.
3.    Identifying Root Causes: AI helps in pinpointing the underlying reasons behind customer dissatisfaction or issues, leading to more effective problem-solving strategies.
4.    Real-Time Response Capability: Capturing customer responses in real time is a game-changer. AI enables businesses to react promptly to customer feedback, enhancing the overall experience and satisfaction.
5.    Predictive Analysis for Sales Trends: AI tools are adept at spotting trends that might indicate a decline in sales, allowing businesses to take proactive measures to avert potential revenue losses.
6.    Prioritising CX Improvements: AI-driven analytics help in prioritising actions that can significantly improve the customer experience, ensuring that resources are allocated effectively.