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CX 2021: How will 5G impact the customer experience future

8 minutes read

5G, the most anticipated wireless network technology, is touted to alter the way people go about their daily lives at home and work. Its USP lies in being a lot faster with a capability to handle more connected devices than the existing 4G LTE network. The fastest 5G networks might be at least 10 times faster than 4G LTE, according to wireless industry trade group GSMA. 

5G signals run over new radio frequencies, needing radios and other equipment on cell towers to be updated. A 5G network can be built using three methods depending on the type of assets of the wireless carrier: low-band network that covers a wide area but is only about 20% faster than 4G; high-band network that boasts of superfast speeds but they don’t travel well, especially through hard surfaces; and mid-band network which balances both speed and coverage. 

Industry trade group GSMA estimates that by the year 2025, the number of 5G connections will reach 1.4 billion – 15 percent of the global total. Additionally, global IoT connections will triple to 25 billion by 2025, while global IoT revenue will quadruple to $1.1 trillion, according to this report published by GSMA. 

Image Source: www.speedtest.net 

How will 5G impact customer experience

Image Source: tmforum.org

The increased reliability, performance, and efficiency of the new spectrum will come as a boon while, at the same time, raise the bar for customer expectations. The intertwining of technology with our daily lives could also mean the proliferation of other technologies, including the Internet of Things (IoT), Augmented and Virtual Reality, Big Data, and Cloud Computing. 

Consumers have regularly cited reliability as their biggest gripe with 4G networks. Over 4 out of 10 (43%) consumers say the internet on their mobile device “cuts in and out sometimes/is not always strong,” according to a PwC survey titled, The Promise of 5G: Consumers Are Intrigued, But Will They Pay? 

According to Deloitte, India’s digital economy will exceed USD 1 trillion by 2025 as a result of increased smartphone usage, rapid internet penetration, and the advancement of mobile broadband and data connectivity. 5G, on the other hand, is likely to be the key catalyst of this expansion.

Video options, however, go beyond content consumption unto live support, too. For consumer-facing companies, live video support will open doors to better customer service, a crucial aspect of a good customer experience. A 5,000-person survey done by Oracle found that 75% of its respondents recognize the value and efficiency of voice and video chat. They also look forward to first-call resolutions.

Even for agents providing email support, a quick video explaining steps looks like a more efficient way to give a resolution instead of emails with a step-by-step guide, an aspect that companies can consider for seamless processes. 

The GSM Association, an industry organization representing mobile network operators around the world, says the number of IoT connections worldwide will grow manifold between 2019 and 2025, to over 25 billion.

AR/VR capabilities and 5G

5G’s advent is a likely measure to “revolutionize” tech, especially through AR and VR. The high speed and low latency of 5G might imply that processing power could be moved to the cloud thereby allowing for more widespread use of VR/AR technology.

AR/VR technologies powered by high-speed 5G could help boost interest in newer concepts like virtual stores and the use of AR to experience products in their homes, or makeup on one’s face, and more. The combination of high speed and minimal lag is perfect for both VR and AR, which has a lot in store for the gaming community too. According to Nielsen’s study Augmented Retail: The New Consumer Reality released in 2019, many people are willing to use VR/AR to check out products.

That said, true VR/AR growth from 5G is difficult to predict since it also depends on the pace of customer and brand adoption. Nevertheless, its use in customer experiences will be interesting to watch in the coming years.

Big data processing power and 5G 

AI and big data analytics are currently in use to identify customer patterns in order to personalize CX. 5G’s capabilities are likely to raise the bar on the volume of data companies collect and increase the pace at which AI can process it. 

Faster speeds and lower latency lend themselves to an influx as they prepare for the next wave of automation and AI-backed technologies. Businesses will begin relying on mobile networks more frequently than before while streamlining core operations.

5G latency is expected to be faster than human visual processing, thus making it possible to control devices remotely effectively, in (almost) real-time. 

Insurance and 5G

Image Source: www.capgemini.com 

Insurance agencies rely on network carriers to share data for selling policies. With larger mounds of data widely available through 5G, agencies will be at an advantage to leverage more data without having to host or own it themselves. This means greater efficiency to navigate through data in a simpler manner.

The Internet of Things (IoT) has seemingly benefitted the auto insurance industry the most. The data is easier to generate, which includes the policyholder’s car details, mileage, speed, and overall usage of the car depending on each drive. 

https://www.youtube.com/watch?v=n5wkY3gQYiU

With IoT, a policyholder’s car isn’t the only thing that could help generate data. In case of a home fire, an oven could be used to collect data for requisite claim information. Likewise, a drone could share accurate location data. 

Overall, agriculture, manufacturing, logistics, financial services will all benefit from lower latency, high speeds, thus ensuring an immersive experience for all. 

Dogan Kaleli, CEO at Stere.io, Founder at Nion, wrote in ‘Why 5G is a Major Game-changer for the #Insurance Industry?‘ that 5G along with revolutionary technologies will mark the beginning of the 4th industrial revolution or the flywheel effect.

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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

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?

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