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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Employability and the scramble for top Jobs in India 

Looking to hire new talent or searching for a job? Well, some insights several studies and surveys provide about the job scenario in India might interest you.

The Millenial

Indian millennials, aged between 18 and 35 years, according to studies ( wheebox.com/assets/pdf/ISR_Report_2020.pdf ) makes nearly half the Indian workforce and looks likely to remain so for the next decade. This generation of workers are not only working hands but likely consumers as well, strong in their opinions, with access to the internet and social media across urban and rural areas. What they are most ardently looking for are jobs that respect their talent, pay them adequately and improve their employability in the market. 

Employability in India

Employability has remained stagnant for several years now with around 46 per cent candidates job-ready. Of those employed, trends revealed

  • MBA’s in India are now projecting a rate of 54 per cent employability, acquiring the highest paying jobs
  • Employers prefer candidates with work experience, especially 1-5 years. Freshers are least preferred at 15 per cent.
  • The AI industry is showing promise wherein some reports pegged the number of job openings in AI and Machine learning sector at almost 1million in India last year. 
  • Employability for pass-outs of B.Pharma, B.com, BA and Polytechnics showed an increase of around 15% since 2019.
  • Prospective workers from Maharashtra, Tamil Nadu and Uttar Pradesh were found to be most employable
  • While women are as employable as men, women’s participation in the workforce remains at a low 25 per cent vis a vis that of men.

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What employers seek

  • Domain knowledge
  • Adaptability to the work environment
  • Learning ability and agility
  • Positive attitude

What employees seek

  • Majority of Students, around 88 per cent of those surveyed, sought internship opportunities though the supply did not meet demand in most cases
  • Maharashtra, Tamil Nadu and Andhra Pradesh were preferred and most sought after in terms of work opportunity
  • Over 55% students expect the annual salary to be above Rs. 2.6 lacs, a figure which has remained constant for the past few years

Ways to improve employability

Most students or potential candidates, surveys show, seek proper guidance and training and internship opportunities as varied as customer market analysis courses to customer marketing analysis training and courses teaching retail analytics using Python. While most universities lack the wherewithal to skill their outgoing students, students prefer to sign up for short courses online to equip themselves with the requisite knowledge specific to their industry. All this done with a view to increase their employability in a market deeply customer driven.

 

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Credit Risk Modeling: A Comprehensive Guide

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Credit Risk Modeling: A Comprehensive Guide

Credit Risk Modeling is the analysis of the credit risk of a borrower. It helps in understanding the risk, which a lender may face when he offers a credit.

What is Credit Risk?

Credit risk is the risk involved in any kind of loan. In other words, it is the risk that a lender runs when he lends a sum to somebody. It is thus, the risk of not getting back the principal sum or the interests of it on time.
Suppose, a person is lending a sum to his friend, then the credit risk models will help him to assess the probability of timely payments and estimate the total loss in case of defaulters.

Credit Risk Modelling and its Importance

In the fast-paced world of now, a loss cannot be afforded at any cost. Here’s where the Credit Risk Modeling steps in. It primarily benefits the lenders by accurate approximation of the credit risk of a borrower and thereby, cutting the losses short.

Credit Risk Modelling is extensively used by financial institutions around the world to estimate the credit risk of potential borrowers. It helps them in calculating the interest rates of the loans and also deciding on whether they would grant a particular loan or not.

The Changing Models for the Analysis of Credit Risks

With the rapid progress of technology, the traditional models of credit risks are giving way to newer models using R and Python. Moreover, credit risk modeling using the state-of-the-art tools of analytics and Big Data are gaining huge popularity.

Along with the changing technology, the advancing economies and the successive emergence of a range of credit risks have also transformed the credit risk models of the past.

What Affects Credit Risk Modeling?

A lender runs a varying range of risks from disruption of cash flows to a hike in the collection costs, from the loss of interest/interests to losing the whole sum altogether. Thus, Credit Risk Modelling is paramount in importance at this age we are living. Therefore, the process of assessing credit risk should be as exact as feasible.

However, in this process, there are 3 main factors that regulate the risk of the credit of the borrowers. Here they are:

  1. The Probability of Default (PD) – This refers to the possibility of a borrower defaulting a loan and is thus, a significant factor to be considered when modeling credit risks. For the individuals, the PD score is modeled on the debt-income ratio and existing credit score. This score helps in figuring out the interest rates and the amount of down payment.
  2. Loss Given Default (LGD) – The Loss Given Default or LGD is the estimation of the total loss that the lender would incur in case the debt remains unpaid. This is also a critical parameter that you should weigh before lending a sum. For instance, if two different borrowers are borrowing two different sums, the credit risk profiles of the borrower with a large sum would vary greatly to the other, who is borrowing a much smaller sum of money, even though their credit score and debt-income ratio match exactly with each other.
  3. Exposure at Default (EAD) – EAD helps in calculating the total exposure that a lender is subjected to at any given point in time. This is also a significant factor exposing the risk appetite of the lender, which considerably affects the credit risk.

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Endnotes

Though credit risk assessment seems like a tough job to assume the repayment of a particular loan and its defaulters, it is a peerless method which will give you an idea of the losses that you might incur in case of delayed payments or defaulters.

 


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How Customer Data Analytics Can Help Drive Business Success?

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How Customer Data Analytics Can Help Drive Business Success?

The customers are the backbone of a successful organization. No longer does one-size-fits-all kind of advertising or price-based competition reap results. Today, if you want a thriving business, customer interaction is the key. Building relationships based on that interaction will get you going.

Nevertheless, this isn’t enough. To survive in this contemporary competitive world, enterprises need data-driven, powerful insights that will help them comprehend their customers’ needs. The world is rapidly developing and so is the technology domain. Tech bigwigs, including Airbnb and Uber, are utilizing the nuanced concept of data analysis to reshape their way of interaction with the customers; so let’s dive down to know how they are putting their customer’s first and leveraging data analytics in a collective manner.

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Segmentation

This step divides customer data into segments, for example, age, location, buying pattern, product usage, etc. It helps in messaging information to particular groups interested in particular activities. Tailor-made marketing strategies are in demand.

Segmentation also helps you decide which group is profitable and which isn’t. This way, you and your organization won’t end up wasting money on sections that are not likely to yield conversions.

Product Development

To stay ahead of the curve, your products need to be customized. This is done by gathering customer data from detailed reports or with the help of A/B testing.  You can also look up to customer feedback. It helps in determining chances for innovation and gauges the efficacy levels of the products.

Companies, such as Amazon and Netflix use data analytics effectively to understand the preferences of customers and craft recommendation list accordingly.

Agility

Instead of finding new customers, the companies are now focusing more on customer retention. In order to do so, the company executives are channelizing resources to keep their existing customers loyal to them. Nevertheless, this is no mean feat. A recent report has found out that two-thirds of the B2B customer base or even more are currently at risk. Hence, customer retention is a better alternative than luring newer customers.  

Innovation

For data-obsessed people, innovation is the lifeblood for their success. However, it has resulted in disrupting several established companies and industries. Use of chatbots, AI and apps has sparked a phenomenal change in the technology landscape.  Autonomous Vehicles are one of the best examples of disruptive technology, which is a brainchild of Tesla, Google and other path-breaking companies.

Insights Turned into Actions

Irrespective of the industry you work at, customer data analytics helps you tap into your customer’s choices and behaviors and predict how that pattern is going to modify in the future. It might aid you in understanding why customers leave giving you enough room to target retention programs at those who are at more risk of leaving.

No wonder, more and more companies are becoming data-centric. Nevertheless, out of all, very few have actually worked out the best way to use the data and hit notes of business success. Remember, insights are only effective when they trigger change!

Are you interested in customer analytics? Want to enroll in a good marketing analytics certification course? DexLab Analytics is here for help! Feel free to drop by their website and send enquiries. The expert team of the institute will be happy to guide you.

 

The blog first appeared in ― www.entrepreneur.com/article/310001

 

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Customer Analytics: A Basic Introduction

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Customer Analytics: A Basic Introduction

Customer Analytics is today’s hottest kid on the block, especially for executives. In simple terms, customer analytics is the process of analyzing and evaluating a flood of data that is being collected every day from every possible and probable customer standpoint. This customer data is then used in building superior predictive models to ascertain who the best customers for the retailer are, where he can find this kind of customer base and the value-potential these customers possess – either in terms of visits or dollars.

Customer data provides valuable and actionable insights that help retailers in executing their future marketing and real estate strategies. Put simply, it basically uses the past to predict the future.

Inadequate Customer Data: The Problem

No wonder, Customer Analytics is indeed a wonderful tool yet it’s not as simple as it sounds. Basically, collecting and determining data is an expensive affair as well as time-consuming. However, it is an absolute necessity. If not this, the retailers won’t be able to realize the potentials of customer analytics to the fullest.

However, most of the retailers, at least 60% of the lot don’t have access to data or they possess unreliable data. Generally speaking, an average company’s data is nearly 55% accurate and 14 months old, which makes the data fundamentally useless.

Faulty data skews customer profiles – resulting in lost opportunities, escalating costs, poor use of analytic solutions, dwindling numbers of customers – effectively costing retailers $700 billion annually.

Interestingly, the companies that have mastered the art of Customer Analytics are 7.4 times more likely to outdo their rivals in terms of sales, 6.5 times more likely to retain existing customers and approximately 19 times more likely to hit above-average profitability.

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Why Use Customer Analytics?

While there are retailers who have just grazed the layers of customer insights, you will find another set of retailers who are successfully utilizing the treasure trove of customer data merging analytics into it and identifying crucial information that leads to streamlining operations, accelerating productivity, personalizing marketing initiatives in accordance to both current and potential customers. This yields better profitability and detects locations where retailers can open new shops and target new customers.

With such intense market competition, retailers need to outnumber their tailing rivals and for that, they have to leverage the power of customer analytics. Instead of being an option, it has now become a necessity. So, say thanks to Customer Analytics, because of it, retailers are in a position to greatly enhance their potentials to target the right customers at the right time in the right place and in the most effective way.

If you are interested in customer marketing analytics courses in Delhi, feel free to reach us at DexLab Analytics. We offer excellent marketing analytics certification courses to the interested candidates at amazing prices! Contact us now.

 

The blog has been sourced from ―  www.buxtonco.com/blog/what-is-customer-analytics

 

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