The Emerging Trends of CX in 2024

Published on Jan 12, 2024

The customer experience (CX) landscape constantly evolves, and businesses must stay ahead of the curve to remain competitive. As we look towards the future, we must understand the emerging trends shaping the CX landscape in 2024.

In this article, we’ll explore the top customer experience trends that are expected to dominate the healthcare, ed-tech, and insurance industries in 2024.

The Importance of CX in the Healthcare Industry

The healthcare industry is no exception to the growing importance of customer experience (CX). Providing a positive and personalized CX is crucial in this industry, as it directly impacts patient satisfaction, loyalty, and overall healthcare outcomes.

One of the key trends in CX for the healthcare industry in 2024 is the shift towards patient-centric care. Healthcare providers recognize the need to focus on patient’s needs and preferences rather than adopt a one-size-fits-all approach.

Personalization

Personalization and customization will significantly affect the healthcare industry’s CX strategy. Patients will expect tailored healthcare experiences that address their specific needs and preferences. This could include personalized treatment plans, customized communication channels, and individualized care coordination.

UK-based healthcare company Babylon Health provides personalized care through its subscription-based mobile app. It leverages features such as 24/7 access to virtual consultations with doctors, AI-powered symptom checking, and customized health plans to boost user engagement.

Advanced-Data Analytics

Another vital aspect of CX in the healthcare industry is data and analytics. Healthcare providers can gain valuable insights into patient behaviors, preferences, and health outcomes by leveraging patient data. This data can then be used to improve care delivery, personalize treatment plans, and identify potential health risks.

Several healthcare companies leverage integrations with wearables and IoT devices to provide remote patient monitoring services. With a large amount of data available for each patient, doctors can gain better insights, positively influencing their treatment plans.

CX Trends in the Insurance Industry

The insurance industry has traditionally needed to adopt new technologies faster and adapt to changing customer expectations. However, with the rise of insurtech companies and increasing competition, insurance companies focus on improving the customer experience.

Personalized Policies

Similar to the healthcare industry, personalization will be a key trend in the insurance industry in 2024. Customers will expect insurance policies tailored to their specific needs and lifestyle.

This could include usage-based insurance, where premiums are based on actual usage rather than general risk factors, or personalized coverage options based on individual needs and preferences.

Insurtech firms such as Lemonade, Acko, and Ditto are at the forefront of personalized insurance services with tailored coverage and payment plans to match the needs of evolving users.

Embracing Digital Channels

With the rise of digital natives and the increasing use of technology in everyday life, customers now expect a seamless digital experience from their insurance providers. In 2024, insurance companies must embrace digital channels to meet these expectations.

This could include offering online policy management, digital claims processing, and chatbots for customer service. Insurance companies can improve customer satisfaction and retention by providing a convenient and efficient digital experience.

In India, IRDAI has pushed for the adoption or integration of ABHA by Insurance companies. With a focus on reducing data silos and streamlining processes for the end customer, several insurance companies are adopting the same into their digital systems.

Winds of Change with CX in EdTech

The tech industry is experiencing rapid growth and transformation, and customer experience (CX) is crucial to its success. Here are some key points about the importance of CX in the ed-tech industry

Improving student engagement

CX is essential in the ed-tech industry as it directly impacts student engagement. Ed-tech companies must provide a user-friendly and intuitive platform that encourages students to participate actively in their learning journey. By offering personalized learning experiences, interactive content, and seamless navigation, edtech platforms can enhance student engagement and motivation.

For example, Indian ed-tech firm Takshila Learning provides its students the option to learn through 3D simulations in online classes, gamification to drive motivation in completing quizzes, tests, and surveys, and AI-powered learning assistants, which provide tips, relevant resources, and query resolutions to the students.

Following trends from the past year, many use cases have been built through extended reality technologies such as AR and VR, which promote remote learning. You can find more information in our industry report.

Driving Accessibility and Inclusivity

In the ed-tech industry, CX also focuses on making education accessible to all. By leveraging technology, ed-tech companies can provide learning opportunities to students facing physical, geographical, or socio-economic barriers.

This includes offering multi-language support, closed captioning, and assistive technologies to ensure all students can access and benefit from educational resources.

The Indian government’s initiative Sunbird, built as a digital initiative for learning, provides essential tools for new-age tech firms. The open-source, configurable, and modular digital infrastructure is designed for massive-scale implementation. It has several modules, such as Bhashini, allowing real-time translation into multiple regional languages in India.

Discover how we successfully helped India’s leading online education provider implement Sunbird into their platform.

The Future of CX in 2024

The customer experience landscape constantly evolves, and businesses must adapt to stay ahead of the competition. By embracing emerging trends and leveraging technology, companies in the automotive and insurance industries can provide a more personalized, efficient, and convenient experience for their customers.

In 2024, we expect to see a greater focus on personalization, digital transformation, and the use of technologies such as AI and IoT. By staying ahead of these trends, companies can improve customer satisfaction, retention, and their bottom line.

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

The Emerging Trends of CX in 2024