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5 CX trends that will define the Indian Banking Experience in 2023

3 minutes read

Banks initially began as trusted entities that would act as custodians of their customers’ wealth and channel them toward growth opportunities that would aid in growth and prosperity for all. Cut to 2023, that promise alone does not suffice. The modern account holder holds banks to a higher standard and has little to no tolerance for banks with poor customer experience (CX). 

Over the years, CX has gone from being a frill to an essential component that can have a colossal impact on the bottom line for banks. For 2023, incorporating these trends into workflows will help banks pull ahead of their competitors by a significant margin. 

Applied Analytics

Blake Morgan, a notable customer experience futurist noted that 2023 will be a year of reckoning for brands as rising inflation and thrifty spending will up the ante on brands to satisfy their customers. With a trough of data at their disposal, banks are uniquely positioned to apply all of these insights into architecting a bespoke CX that will signal their commitment to their account holders in these trying times.

Automated Onboarding

One of the greatest challenges that India currently faces is the huge gap between the banking experience in urban and rural areas. With close to 70% of the Indian population being concentrated in these areas, it is vital for banks to design a frictionless and delightful customer onboarding experience that will bring these people into the fold. Such a solution would decrease staffing costs in the long run and translate to more customers in urban areas in light of the fact that over a quarter of banking customers would rather not step into a branch at all.

Hyper-Personalization

An eternal feature that has been topping CX trend lists for years, hyper-personalization will continue to pay off in 2023 as few banks have even attempted to get this right. Boston Consulting Group’s (BCG)’s finds on personalization revealed that when done right, it can result in a 10% increase in annual revenue uplifts till the time competitors catch up. 

As open-banking models start to take off and Indian banks become comfortable with experimentation, hyper-personalized services could serve as a key differentiator for both incumbents and upstarts alike. ABN Amro, one of Europe’s leading banks partnered with Subaio, a Danish fintech firm to offer personalized loans. 

Going Phygital

Much has been written about India’s demographic dividend and the implications it has on global economic prospects. With the world’s youngest cohort of millennials and GenZ alike, Indian banks are at the cusp of a massive demographic change that could be the opportunity of a lifetime. By revamping the bank branches to work with smartphones, banks can ‘humanize’ their systems to provide a wholesome, truly immersive banking experience that gives account holders the best of both worlds. JP Morgan Chase’s decision to open a new digital banking unit in the U.K. is an example of combining the trust of an established bank with the conveniences offered by a neobank.

AI goes deeper

As chatbots start to become a staple in customer service workflows, the next step would be to integrate AI across all customer touchpoints to enable better self-service, quicker resolution, and lower servicing costs. For instance, ICICI bank’s virtual assistant is primed to analyze voice queries to deliver instant solutions, point to appropriate areas, or escalate to a support executive if the need arises.

A Year of Reckoning

2023 will be all about resilience, innovation, and focus as central banks all over the world begin to raise interest rates and tighten expenditures in a bid to curb inflation. For banks, the only way to grow through this crisis is to correct technological debt, digitize relentlessly, and build a razor-sharp focus on their customer experience.

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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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