Sales Training Course: 5-Techniques for a Human-Centric Sales Process

Sales Training Course: 5-Techniques for a Human-Centric Sales Process 

As more and more organisations undergo digital transformation and leverage technology to automate many of their processes, the old sales playbook is no longer relevant.

The human-centric approach to sales focuses on moving away from being sales-oriented to being helpful. Human-centric sales create real and resilient relationships that anchor a sustainable revenue stream.

Human-centric selling prioritises the customers and empathises with their needs. It adapts to the unique circumstances of the customer through two-way dialogue, aligning to their timeline and working to solve their problems.

The Salience of Human-Centric Sales

The importance of human-centric sales stems from the fact that focusing on customers helps build sustainable relationships. Human-centricity in sales is built by exhorting your sales team to put themselves in the shoes of the customers in all pre and post-sales activities.

To build a culture of human-centric sales, the sales team needs to change the way it thinks about sales. While sales teams undeniably need to meet their KRAs to maintain the revenue stream, their actions should be directed toward helping people. In sales training courses that preach a human-centric approach, salespeople are told to prioritise long-term success over short-term gains.

Techniques for Human-Centric Sales

Adopting a human-centric sales approach has the potential to deliver a significant impact on the business and customer experience. The techniques to foster a human-centric approach to sales emphasised in corporate learning programs include:

Foster Empathy

Instinctively, while hiring salespeople, sales leaders hire people with the right ‘sales’ skill set. Stereotypes of what skills lead to higher sales performance prevent sales leaders from building a human-centric sales team.

To build a human-centric sales team, it is necessary to hire people for their ability to be empathetic. Empathetic people put the needs of the customers ahead of their own. To foster empathy in the team, the salespeople could intern in customer support for a short duration. This would help them understand the challenges that customers face. It would also help the team build kinship with customers and with team members within the organisation. Another technique to build empathy could be to simulate situations that customers face. When team members are put in situations that customers face, it helps them understand the customer’s perspective and fosters empathy.

Look to Add Value

Another human-centric approach to sales, recommended during corporate training and development programs, involves asking customers insightful questions to uncover customer needs. This is true for salespeople selling clothes or an enterprise SaaS tool. In the human-centric approach, salespeople are not motivated to only make the sale, but by the value, they can deliver to the client. Such sales involve professionals striving to understand if they and their product/service are the right fit for the client. Human-centric salespeople look to add value rather than forcing their vision to make the sale.

Empower your Customers

The human-centric approach to sales can be distinguished from the traditional sales approach in that the customers feel no pressure from the sales conversation. This approach prioritises the customers’ needs. Salespeople engage with customers to help solve their problems rather than merely push their product/service. Pushing a product just to make the sale puts the post-sales team in a sticky situation as they now need to fulfill promises that they did not make. Human-centric salespeople will walk away from the deal if they realise that they cannot empower the customer.

Align with Customer Values

People buying goods and services often look for sellers whose values mirror their own. Buyers look beyond their immediate interests when furthering the values that they believe in and advocate. Corporate sales training should instruct salespeople to initiate a sales conversation to talk about the firm’s culture, its focus on accountability, and the ethical implications of the sale. The human-centric approach requires that the business mirrors the values and voice of the clients.

Focus on Superior Customer Experience

Human-centric selling requires that the pre and post-sales customer experience be seamless. This requires integrating sales with every aspect of the business’s operations – from product development to marketing to customer support. Instead of looking to see what more business you can extract from the customer, look to see how you can serve the customer more. Superior customer experience brings more revenue via referrals and word of mouth – your customers start marketing for you.

Final Thoughts

A human-centric sales process helps lay the foundation for robust relationships that go beyond just being transactional and instead drive sustainable growth. That is why it is important for businesses to provide corporate sales training to their sales staff that focuses on developing a human-centric sales process.

Explore Machine Learning: Here’s How to Find Your Way Through the Data Science Maze

Explore Machine Learning: Here’s How to Find Your Way Through the Data Science Maze

The term ‘Machine Learning’ was coined in 1959 by then-IBM computer scientist Arthur Samuel while designing a computer algorithm for the classic game of Checkers. Today, this term is immensely popular owing to the technology’s wide application across industries.

But what is Machine Learning or ML? It is a computational method that is used to obtain artificial intelligence by making a machine learn how to solve problems on its own rather than requiring explicit programming software. 

Machine Learning is widely used in the field of data science as it helps find the way through tons of data instantly and accurately! But how? By using statistical methods and algorithms to train computers so that they can accurately classify data sets and make reliable predictions to uncover key data insights.

Does all this sound interesting to you? Do you aspire to use advanced Machine Learning technologies to solve real-life problems and arrive at data-driven solutions? If yes, then you should check out our data science courses which are equipped with not only data mining techniques but also machine learning tools along with Python, SQL, and Tableau.

Machine Learning Concepts Which Every Data Scientist Must Know About

Data science learners must be able to develop a solid foundation and specialise in machine learning with Python for data-driven decision-making. Ultimately, you want to assist organisations to make smart decisions for growth and offer insightful data analysis.

Following are some of the key Machine Learning tools which you must know about if you are aspiring for a data science career:

  • Clustering

CLustering is the simplest unsupervised ML method that lets the algorithm define the output for mining data. The most famous clustering method is ‘K-Means’ under which the letter ‘K’ refers to the number of clusters into which the miner wants to divide the unlabelled data. 

The clustering method is used for drawing analysis in varied fields such as for creating customer segments for different marketing techniques as well as for identifying earthquake-prone areas.

  • Neural Networks

If you are interested in the Deep Learning subset of ML, then you must know Neural Networks in and out. Neural Network is a network of algorithms that identify patterns or relationships among different data points in a set in a way similar to the working of a human brain. 

It is widely used for making forecasts and improving decision-making in fields like stock market trading, medical diagnosis, etc. You can learn more about neural networks in our data science online training programs.

  • Regression

Regression is one of the fundamental supervised ML techniques which help data scientists in creating predictive models by defining a relationship between dependent and independent variables. 

There are various types of regression models, however, broadly they can be classified into three groups: Simple Linear Regression Model (SLRM), Multiple Linear Regression Model (MLRM), and Logistic Regression.

  • Natural Language Processing (NLP)

Natural Language Processing (NLP) forms the basis of Machine Learning as it trains machines to learn the language of humans. You can find some of the everyday applications of NLP in voice-controlled applications like Apple’s Siri, Google Assistant, Amazon’s Alexa, etc. NLP is also found in execution in the fields of text summarization and sentiment analysis. 

  • Ensemble Methods

The concept of the Ensemble Method is quite similar to that of assembling. For instance, if you are not happy with all the car options available in the market and wish to come up with a car design, you can assemble your favorite car parts of different cars and design a car of your choice. 

Similarly, if as a data scientist, you are not convinced with the results of different predictive models, you can combine all of them to arrive at better predictions.

  • Transfer Learning

Transfer Learning is one of the efficient ML techniques which lets you use parts of previously programmed neural nets to develop a similar model. For instance, if you are a data scientist who has developed a technique to filter different styles of men’s clothing in buckets like shirts, t-shirts, kurtas, etc., you can use parts of transfer learning to develop a mechanism that can be used for categorising women’s clothing in say, dresses, jumpsuits, tops, etc.

Takeaway

Machine Learning has become a crucial part of the data science field today, which has made the process of analysing and predicting data faster and more accurate than before. 

Be it for real-time navigation, or product recommendations, as a data scientist you will always find Machine Learning and Data Science going hand-in-hand. And the future of data science is expected to be even more promising with the advancements in ML techniques and methods.

Thinking of kickstarting your data science career? Contact us through chat support, or just visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.

Our Certificate Program In Data Science And Machine Learning is created by iHUB DivyaSampark at IIT Roorkee and will instruct you on the fundamentals and features of data science and machine learning and give you the skills necessary to put these ideas into practice and apply them to real-world issues.

Financial markets are volatile – Tips for rising in an investment banking career.

Financial markets are volatile – Tips for rising in an investment banking career

The financial sector is one of the most thriving sectors in the economy. Despite so many ups and downs, this sector keeps rising and shining. And, this is perhaps the reason why more and more individuals opt for an investment banking career. But as you know, financial markets are highly volatile, you can’t predict what will happen next. So, to keep up with the changing market trends, you need to keep polishing your skills and abilities even as an investment banker. 

In this article, we are sharing some important tips that will help you grow your career and achieve new heights in the investment banking sector. Keep reading! 

Tips for Growing an Investment Banking Career 

Following are some tips that will help you build a successful investment banking career:

1. Make Sure it is the Right Job for You

Unlike many other careers, investment bankers have a range of jobs to choose from. Note that different investment bankers may come from different backgrounds. So, they can use their transferable skills to join a profession that matches their interests. In the investment banking sector, you can move into trading, financial planning analysis, research, valuation, or structuring posts. For example, if you are good at research and valuation and have a keen interest in investing, then you can grow your career as an investor or a financial advisor. 

Although investment banking is a very high-profile but demanding job, the financial rewards it provides make it worth working hard for. 

Understanding your key skills and interest areas will help you remain focused and earn better opportunities in your IB career. 

2. Don’t Stop Learning 

Since the financial market keeps changing every day, you need to keep polishing your skills and abilities. Even after you get a job, don’t stop working on your skills. Investment banking is a vast field, and it offers many opportunities to become an expert. One good way to learn investment banking is to enrol in the best investment banking courses. In fact, the right IB course is your answer to — how to become an investment banker.

Once you secure the right job and gain some experience, you can consider becoming an expert by earning various investment banking certifications like Chartered Financial Analyst and Certificate in Investment Management. 

3. Do an Investment Banking Internship 

The majority of the top banking institutions and corporations, including J.P Morgan, Goldman Sachs, Barclays, and Morgan Stanley, offer great internship opportunities to individuals. 

For example, JP Morgan offers a 9-week investment banking analyst programme for students, where they can learn important technical skills by working on transactions and deals for the company’s clients. The best part is you can even secure a full-time job depending on your performance. 

However, do your own research and get your facts straight in order to make an informed decision. To do this, you can learn about different opportunities and select the one that matches the best with your interests. 

4. Attend Events and Network 

This is another great tip for a rising career in investment banking. Networking with experienced professionals not only helps you gain knowledge but also helps in earning new opportunities. You can grow your network by meeting new people in your field or contacting them via social networking platforms like LinkedIn. Besides, there are many virtual talks and sessions held online, which can give you a fair idea about the challenges, demands, and working styles involved in investment banking jobs. 

5. Be Passionate 

There is no doubt that investment banking is a highly rewarding career when it comes to salary. You can surely grow your financial health by securing a job in this sector. However, attractive pay should not be the only reason to work in this industry. Hence, it is extremely important to stay interested and passionate about your job. Once you do that, investment banking will become a job you’ll never want to leave. It is one of the most interesting career options that can help you grow professionally as well as personally. 

To Sum Up 

Getting into an investment banking career is a dream of many. All you need is the right road map, guidance, and passion to become a successful investment banker. The tips we have mentioned above will help you raise your career in the field. Moreover, one last piece of advice is when you land your first job, always keep polishing your skills by taking different investment banking courses and certification programs. This will aid your career progression to a great level. 

IIT Machine Learning: Introduction To The Machine Learning World

Machine Learning has been a buzzword in recent years, possibly due to the large quantity of data produced by applications, the rise in processing power, and the development of better algorithms.

Machine Learning is employed everywhere, from automating monotonous operations to providing sophisticated insights; companies in every area are attempting to capitalize on it. You could already be using a gadget that makes use of it. 

Introduction to the machine learning world

You may apply machine learning in prediction systems as well. Supervised learning, reinforcement learning, and unsupervised learning are the three categories of machine learning. The computer is given a set of training data together with the intended output and subsequently learns how to produce the desired output using the training data. This process is known as supervised learning. 

When the computer gets a collection of data but not the intended result, it must learn to recognize the structure in the data on its own through unsupervised learning. Reinforcement learning is where the computer gets a set of data and a reward function, and the computer has to learn to produce the desired output to maximize the reward.

Machine learning is a powerful tool that may apply to many tasks, including predictions, voice recognition, and face and facial expression detection. 

Application of Machine Learning

 Speech recognition is the procedure of converting spoken words into text. Speech dialing, call routing, and appliance control are all examples of voice user interfaces. You may also use it for simple data entering and structuring papers.

One of the popular uses of machine learning is image recognition. It recognizes items, people, places, digital photos, etc. Facebook has an auto friend tagging recommendation option. When we submit a picture with our Facebook friends, we instantly get a tagging recommendation with their names, powered by machine learning’s face identification and recognition algorithm.

Machine learning techniques are currently widely employed in various voice recognition applications. Google Assistant, Siri, Cortana, and Alexa use speech recognition technologies to respond to spoken commands.

Machine learning is a subtype of AI that allows machines to learn from data, improve performance based on previous experiences, and make predictions. 

Learn Machine Learning and AI courses with Imarticus Learning

Become an AI engineer by attending the E & ICT Academy deep learning Artificial Intelligence certificate course. Students will benefit from this IIT Machine Learning Course as they prepare for jobs as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners

  • Students work on 25 real-world projects to get industry experience and prepare for a rewarding career in data science.
  • You may impress employers and exhibit your abilities with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed credential.
  • This machine learning certification will assist students in obtaining lucrative professions in artificial intelligence and machine learning.