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The Importance of Machine Learning for Data Scientists

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3 minutes, 7 seconds read

The concept of Machine Learning, Artificial Intelligence (AI), Big Data has been around for a while. But the ability to apply algorithms and mathematical calculations to big data is gathering momentum only recently.     

In this article we will discuss the importance of Machine Learning and why every Data Scientist must master it.

What is Machine Learning?

Simply put, we’re contributing to Machine Learning through our day to day interactions on the internet. Whether you search your coffee maker on Amazon, “top tips to lose weight” In Google, or “friends” in Facebook you see Machine Learning in action, but you don’t realize it.

It is the Machine Learning technology that lets Google, Amazon, and Facebook search engine offer relevant recommendations to the user.

These companies are able to keep tabs on your day to day activity, search behavior and shopping preference with the help of ML technology.

Machine Learning is also one of the main components of Artificial Intelligence.

Who is a Data Scientist?

Before assessing the importance of Machine Learning for Data Scientists, here’s a brief note on who Data Scientists are. We’ll also discuss how one can become a Data Scientist.

Data Scientists draw meaningful information from a huge volume of data. They identify patterns and help build tools like AI-powered chatbots, CRMs, etc. to automate certain processes in a company.

With a sound knowledge of different Machine Learning techniques and contemporary technologies like Python, SAS, R, and SQL/NoSQL database, Data Scientists perform in-depth statistical analysis.

The role of Data Scientist might sound like that of Data Analyst, but, in fact, they are different.

Difference between a Data Scientist and a Data Analyst

  • Data scientist predicts future based on past patterns. Whereas, a Data Analyst curates meaningful insights from data.
  • Data scientist’s work involves “estimation” (or prediction) unknown facts; while an analyst investigates the known facts.
  • Data Analyst’s job is more geared towards businesses. Data Scientists’ work is integral to innovations and technological advances.

Why Machine Learning is So Important for a Data Scientist?

In a near future, process automation will superimpose most of the human-work in manufacturing. To match human capabilities, devices need to be intelligent and Machine Learning is at the core of AI.

Data Scientists must understand Machine Learning for quality predictions and estimations. This can help machines to take right decisions and smarter actions in real time with zero human intervention.

Machine Learning is transforming how data mining and interpretation work. It has replaced traditional statistical techniques with the more accurate automatic sets of generic methods. 

Hence it is imperative for Data Scientists to acquire skills at Machine Learning.

4 Must Have Skills Required to Become a Machine Learning Expert

To become an expert at Machine Learning every Data Scientists must have the following 4 skills.

  1. Thorough knowledge and expertise in computer fundamentals. For example, computer organization, system architecture and layers, and application software.
  2. Knowledge of probability is very important because Data Scientists’ work involves a lot of estimation. Analyzing statistics is another area that they need to focus on.
  3. Data modeling for analyzing various data objects and how they interact with each other.
  4. Programming skills and a sound knowledge of programming languages like python and R. A quest for learning new database languages like NoSQL apart from traditional SQL and Oracle.

Conclusion

Data is the new oil.

IBM predicts that the global demand for Data Scientists will rise 28% by 2020. Finance, Insurance, Professional services and IT sectors will cover 59% of the Data Science and Analytics job demand.

In the coming future, Machine Learning is going to be one of the best solutions to analyze high volumes of data. Therefore, Data Scientists must acquire an in-depth knowledge of Machine Learning to boost their productivity.   

This article is contributed to Mantra Labs by Jenny Hayat. Jenny is an established blogger and content writer for business, career, education, investment, money-making ideas and more.

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NPS in Insurance Claims: What Insurance Leaders Are Doing Differently

Claims are the moment of truth. Are you turning them into moments of loyalty?

In insurance, your app interface might win you downloads. Your pricing might drive conversions.
But it’s the claims experience that decides whether a customer stays—or leaves for good.

According to a survey by NPS Prism, promoters are 2.3 times more likely to renew their insurance policies than passives or detractors—highlighting the strong link between customer advocacy and retention.

NPS in insurance industry is a strong predictor of customer retention. Many insurers are now prioritizing NPS to improve their claims experience.

So, what are today’s high-NPS insurers doing differently? Spoiler: it’s not just about faster payouts.

We’ve worked with claims teams that had best-in-class automation—but still had low NPS. Why? Because the process felt like a black box.
Customers didn’t know where their claim stood. They weren’t sure what to do next. And when money was at stake, silence created anxiety and dissatisfaction.

Great customer experience (CX) in claims isn’t just about speed—it’s about giving customers a sense of control through clear communication and clarity.

The Traditional Claims Journey

  • Forms → Uploads → Phone calls → Waiting
  • No real-time updates
  • No guidance after claim initiation
  • Paper documents and email ping-pong

The result? Frustrated customers and overwhelmed call centers.

The CX Gap: It’s Not Just Speed—It’s Transparency

Customers don’t always expect instant decisions. What they want:

  • To know what’s happening with their claim
  • To understand what’s expected of them
  • To feel heard and supported during the process

How NPS Leaders Are Winning Loyalty with CX-Driven Claims and High NPS

Image Source: NPS Prism

1. Real-Time Status Updates

Transparency to the customer via mobile app, email, or WhatsApp—keeping them in the loop with clear milestones. 

2. Proactive Nudges

Auto-reminders, such as “upload your medical bill” or “submit police report,” help close matters much faster and avoid back-and-forth.

3. AI-Powered Document Uploads

Single-click scans with OCR + AI pull data instantly—no typing, no errors.

4. In-the-Moment Feedback Loops

Simple post-resolution surveys collect sentiment and alert on issues in real time.

For e.g., Lemonade uses emotional AI to detect customer sentiment during the claims process, enabling empathetic responses that boost satisfaction and trust.

Smart Nudges from Real-Time Journey Tracking

For a leading insurance firm, we mapped the entire in-app user journey—from buying or renewing a policy to initiating a claim or checking discounts. This helped identify exactly where users dropped off. Based on real-time activity, we triggered personalized notifications and offers—driving better engagement and claim completion rates.

Tech Enablement

  • Claims Orchestration Layer: Incorporates legacy systems, third-party tools, and front-end apps for a unified experience.
  • AI & ML Models: For document validation, fraud detection, and claim routing, sentiment analysis is used. Businesses utilizing emotional AI report a 25% increase in customer satisfaction and a 30% decrease in complaints, resulting in more personalized and empathetic interactions.
  • Self-Service Portals: Customers can check their status, update documents, and track payouts—all without making a phone call.

Business Impact

What do insurers gain from investing in CX?

A faster claim is good. But a fair, clear, and human one wins loyalty.

And companies that consistently track and act on CX metrics are better positioned to retain customers and build long-term loyalty.

At Mantra Labs, we help insurers build end-to-end, tech-enabled claims journeys that delight customers and drive operational efficiency.
From intelligent document processing to AI-led nudges, we design for empathy at scale.

Want a faster and more transparent claims experience?

Let’s design it together.
Talk to our insurance transformation team today.

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