Artificial Intelligence (AI) is undergoing a period of intense growth after being stagnant for years. A career path in AI has become a very appealing choice for individuals interested in data science and software engineering, with the demand for AI skilled professionals increasing over these recent years. Here are the top ten programming languages to become an AI developer.

Artificial Intelligence offers businesses and software developers a whole realm of possibilities. AI has brought about a revolution in the technology world, with it still rising and expecting to reach human intelligence.

Required Programming Languages for AI Developers

It is because developers can discover, innovate, and adapt their ability to meet human and organizational needs. Where do you start if you would like to take advantage of this highly in-demand skill?

Top 10 AI Programming languages

Python

Python is an interpreted language, and it is called that because it goes through an interpreter, which turns your code into the language understood by your computer’s processor.

Because of the simplicity and ease of use, Python deserved a chance to be first in the list for Programming languages for AI Developer.

Python’s syntax is very easy and can be learned quickly. This makes it quite simple to implement AI algorithms in it.

Python has been leading in the market with its counterintuitive support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy) that help accelerate AI development.

Top features of Python

• It is easy to use
• Interpreted and cross-platform
• Free and open-source
• Object-oriented
• GUI programming support
• Dynamic memory allocation

Java

Java is a highly versatile, robust, and transparent language supported by several libraries. Ever since it first appeared in 1995, it has seen a huge growth in the market. Java is also very user-friendly, easy-to-debug, and runs through platforms without engaging in additional recompilation.

The Java-code can be run on any Java-supported platform with its Virtual Machine technology. Artificial intelligence does indeed have a lot to do with search algorithms, genetic programming, and artificial neural networks, making it one of the ideal choices for Programming languages for AI Developers.

Top features of Java

• It is simple and easy to use
• It is an object-oriented language.
• Platform independent
• Secured and robust
• Architecture neutral
• Interpreted language
• Multithreaded

Julia

Julia is a high-level, efficient, and dynamic programming language. While it is a general-purpose language and can be used to write any application, many of its features are well-suited for numerical computing that’s required by AI.

The central framework for programming involves a parametric polymorphism and multiple dispatch mechanism. In comparison to the above languages, Julia might not sound like an ideal choice.

As a consequence, a substantial number of libraries or a quickly evolving community do not support it. However, Wrappers like TensorFlow.jl and Mocha provide excellent support for DL.

Top features of Julia

• It is fast and dynamic
• It is reproducible
• It is composable
• It is open-source
• Provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager

LISP

Lisp is one of the oldest and most appropriate languages for AI development. It was introduced by the father of Artificial Intelligence, John McCarthy, in the year 1958.

It has the capacity to process symbolic data successfully. Lisp can be represented as a mathematical notation for computer programs. AI developers frequently turn to Lisp for a series of AI projects that are ML centric.

LISP is renowned for its outstanding prototype capabilities with automated garbage collection and the simple dynamic development of new objects. It has an integrated development cycle for analyzing expressions and recompiling functions or files when the program is still running.

Top features of LISP

• It is machine-independent
• It an iterative design methodology
• It provides high level debugging.
• It is an object-oriented language.
• It is expression-based.
• Provides a complete I/O library.

Scala

Scala comes from the JVM family, much the same as Java. Scala is a relatively new language in the AI domain. Recently several companies and start-ups have incorporated it into their business, allowing it to gain some recognition.

Developers from all around the world like Scala because of the many features that it has to offer. Also,  ScalaNLP, DeepLearning4j, etc., are some of the tools that facilitate the smooth AI developing process with Scala.

It is ideal for projects that need versatility. It merges the advantages of functional and imperative programming models while serving as a strong tool that helps to create highly competitive applications while, at the same time, harnessing the strengths of an OO approach.

Top features of Scala

• Type inference.
• Singleton object.
• Immutability.
• Case classes and Pattern matching.
• Concurrency control.
• String interpolation.
• Higher-order function.

R

R is one of the most powerful languages and environments for statistical analysis and manipulation of data.

In addition to being an open-source and general-purpose language, R includes several packages, including RODBC, Gmodels, Class, and Tm that are also being used in machine learning.

These packages implement machine learning algorithms quite simply. Statistics form the basis of ML, and AI and R are popularly known to revolve around statistics a lot.

R is considered to be similar to the popular statistical applications SAS and SPSS. It is suitable for data analysis, visualization, and general statistics.

However, compared to Python, it is less versatile but also more specialized.

Top features of R

• It is free and open-source
• It is robust and highly extensible
• Effective data handling
• It provides a storage facility
• Integrates with  C/C++, Java, Python, etc.
• It is platform-independent

Haskell

Haskell is a general-purpose, statically typed, purely functional programming language. It was developed in the 1990s with non-strict semantics.

It gained popularity in academic circles but was soon known to be used by tech giants like Facebook and Google. Haskell is being used for research as it supports embedded domain languages, which play a large role in programming language research and AI.

Unlike Java. Haskell is ideal for dealing with abstract mathematics because it enables libraries to construct expressive and efficient AI algorithms.

HLearn, for instance, uses regular algebraic structures such as modules and monoids for expressing and speeding up basic ML algorithms.

Top features of Haskell

• It is a functional language
• Modularity
• Statically typed
• It is easy and cost-effective
• Lazy language

Rust

Rust is a multi-paradigm programming language boasting of being secure, efficient, and safe concurrency.

Rust is syntactically similar to C++ and provides memory safety without using garbage collection. Rust has actually been chosen in Stack Overflow’s annual developer surveys for the last 4 years as the most popular and most loved language that fills the void that can be found in other languages.

The newly open-sourced Verona Project also uses Rust principles, an emerging language that may allow Microsoft to safely maintain legacy C and C# code.

Mozilla Research defines Rust as a “systems programming language that focuses on speed, memory safety, and parallelism.”

Top features of Rust

• Zero cost abstraction.
• Pattern matching.
• Error messages.
• Move semantics.
• Threads without data races.
• Guaranteed memory safety.
• Safe memory space allocation.

Prolog

Prolog is a logic programming language associated with artificial intelligence and computational linguistics. When we talk about programming languages for AI developers, this language stands next to Lisp.

Efficient pattern matching, tree-based data structuring, and automated backtracking are some of this language’s features. These features provide a remarkably strong and versatile structure for programming.

Prolog is commonly used in medical projects as well as the production of AI systems for experts.

Top features of Prolog

• It is a declarative language
• It uses the language of predicate calculus.
• It manages lists and recursion naturally.
• It is a fully object-oriented language.
• Pattern matching and unification
• It supports direct linkage with C/C++.

MATLAB

MATLAB is a proprietary multi-paradigm programming language and numerical computing environment that is introduced by MathWorks.

The use of Matlab is suggested for complex mathematical functions. Matlab offers  AI capabilities like Caffe and TensorFlow. It helps you to incorporate AI into the entire workflow.

In a sense, even without machine learning knowledge and experience, you can work around AI with MATLAB. You can use applications and easily play with various approaches.

Top features of MATLAB

• It is a high-Level language.
• Interactive environment.
• Handling graphics.
• Mathematical functions library.
• Application program interface (API).
• Interfacing with other languages.
• It provides built-in graphics.

To Sum Up

Artificial intelligence is a branch of engineering that ultimately seeks to render intelligent computers and to target the way an intelligent human thinks. There are unique features and advantages of each language.

However, you have to select the perfect language for your AI projects as an AI Engineer and not just follow the herd blindly. It is best to learn about each language individually and then understand if those will work in your favor.

Also, the selection of programming language for AI often depends on a variety of main factors. Consider your business type, whether you are just getting started or already have a setup, how the market looks, who your clients or customers are, what problems you are trying to solve and what your objectives are, etc.

Besides that, many solutions are not dependent on one technology alone. So, keep the experimentation on until you find that ideal language.

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Biotechnology is a branch of science that merges biological concepts with technology for better use and advancements in the field of science, technology, and humanity. Here is biotechnology — the benefits and the risks at a glance.

Biotechnology deploys a range of technologies that use living organisms or a part of them to make all kinds of useful products. These products are as diverse as renewable fuels, drugs, and therapeutics. The products are also as disparate as nutritional compounds, environment-friendly chemicals, and materials, household cleaning products, organs for transplant, and more.

What is Biotechnology?

We can say that biotechnology is the manipulation of living organisms and organic materials in certain different patterns that meet our needs on various platforms. Or simply put, Biotechnology uses living organisms to create useful products.

Biotechnology may have different variations in definitions, but it has a purpose to achieve certain requirements in the field of medicines, agriculture, industries, and more, through manipulations and production.

Let’s check out some of the technical definitions and terminologies of biotechnology as defined by some federations and organizations.

• Defined by the European Federation of Biotechnology – Biotechnology is the application of biochemistry, microbiology and engineering sciences in order to achieve the technological application of the capabilities of microorganisms and cultured tissue cells.
• According to the Biology Industry Organization – Biotechnology is the science of using cellular and biomolecular processes to develop technologies and products.
• According to International Unions of Pure and Applied Chemistry (1981) –  biotechnology is the application of biochemistry, biology, and microbiology, chemical engineering to industrial processes, products and on the environment.

The term Biotechnology is often misunderstood.

The OECD — the Organization of Economic Co-operation and Development —  defines biotechnology as “the application of scientific and engineering principles to the processing of materials by biological agents.”

Some people have believed the biotechnology is limited to the application of molecular biology techniques to identify genes responsible for particular traits to clone, study, characterize and manipulate, but it is not the case.

There are many rapid advances taking place in different fields of biotechnology like medical biotechnology, agricultural biotechnology, and industrial biotechnology.

The four major disciplines in biotechnology.

The four major disciplines in biotechnology are medical, industrial, marine and agricultural processes and each process is represented by a specific color. These colors are: red, white, blue, and green respectively.

The four disciplines are known as Red biotechnology, White biotechnology, Blue biotechnology, and Green biotechnology — as per their roles and functions.

• Green biotechnology is used to improve the production quality of crops. Green biotechnology boosts the economy that includes the plant’s tissue culture, engineering culture, and molecular marker-assisted breeding. Green biotechnology is based on providing agricultural solutions without affecting the environment. As it specializes in improving agricultural processes, it has obtained the obtaining of transgenic plants resistant to terrains, and adverse environmental conditions as well as resistant to diseases and pests.
• Red biotechnology is used by biotechnologists to find healthcare solutions. These solutions include vaccines for many dreadful diseases and viruses that currently plague humanity. Also, much research work is done in gene therapy, testing genetics and in the area of “improvement diagnosis.”Red biotechnology is also used for medical processes like obtaining antibiotics, new drugs, vaccines, and newer forms of molecular diagnosis. It has regenerative therapies and the application of genetic engineering to cure diseases.
• White biotechnology is exclusively applied to improve industrial processes. The white biotech will continue producing services and products for industrial and environmental processes. White biotech uses yeast, molds, bacteria, and enzymes for its research. The main aim of white biotechnology is the development of biodegradable products to earn profits and provide better services.
• Blue biotechnology is also known as marine biotechnology. Blue biotechnology is the application of molecular biological methods to marine and freshwater life. It uses marine organisms, and their derivatives, to increase seafood supply. Blue biotechnology increases safety and controls the proliferation of noxious water-borne organisms, and develops new drugs. Blue biotechnology is responsible for the development of aquaculture, care of marine creatures, water treatment and production of food derived from the sea.

Biotechnology and Artificial Intelligence

Artificial intelligence is considered the next big thing in biotechnology. Biotechnology is not the only area that artificial intelligence has revolutionized. It’s transformed the research and development activities in many crucial areas.

Almost all the leading biopharmaceutical bigwigs globally are strategically implementing AI-driven technologies in their biotech businesses. AI boost’s up the development processes and gains newer insights in research and development outsourcing.

Artificial intelligence (AI) can be described as the ability of a computer or robot-controlled computer to perform tasks that are commonly associated with intelligent creatures. Specifically, a “scientific discipline that involves building computer systems whose behavior can be interpreted intelligently.

The following are a few of the major applications of AI in biotechnology.

• Diagnostics
• Radiotherapy and Radiology
• Personalized Medicine
• Gene Editing
• Electronic Health Record (EHR)
• Medication Management
• Sales Rep Performance
• Target-based and phenotypic drug discovery
• Polypharmacology discovery
• Biomarkers development
• Analyzing research literature, publications, and patents

Clearly, AI can help identify drug targets, find good molecules from data libraries, suggest chemical modifications, identify candidates for repurposing and so on.

Artificial Intelligence is transforming data into drugs by its amazing technological advances. No wonder all the big pharmacy and biotech are betting big time on AI.

How does Biotechnology impact our lives?

Biotechnology is an innovative, dynamic and ever-evolving cutting-edge sector. It plays a significant role in all major areas of human interests and benefits. Biotech touches all areas including economics, environment, and health. The applications of biotechnology have both positive as well as negative impacts for human civilization.

Biotechnologies are fundamentally oriented for improving the efficiency of production techniques in agricultural, industrial and biomedical fields.

Several advanced surveys and statistics across the globe emphatically explain how biotechnology is poised to change our lives for good.

The “higher purposes” of Biotechnology.

In earlier times, biotechnology’s usage was restricted to fundamental processes like making cheese, wine, bread. Today we have the advantage of superior technologies and the motive of urgent requirements to use biotechnology appropriately for many higher purposes of the society.

Biotechnology is controlling a major part of our lives without us realizing its presence in our surroundings and utilities.

The four disciplines of Biotechnology are in control of our lives.

Controlling many areas, such as the food we eat to medicines we consume, the clothes we wear and how they are washed to the fuel we use, biotechnology is everywhere. The four disciplines of biotechnology are making its invaluable presence felt in meeting our daily needs.

Biotechnology is often being regarded more as an art than a science.

Biotech is sometimes not thought of as a science, but it should be. Because of biotechnology’s ability to touch upon people’s lives by accentuating the quality of life, it’s becoming invaluable.

The solutions Biotechnology provides change results for the grand challenges of society. Solutions are found for food security, climate change, energy shortages, healthcare choices, and affordability. We find solutions for industrial processes and medical needs — even solutions in fighting with epidemics.

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The Supply chain has transformed, and companies have not updated the underlying technology for managing them in decades. With Blockchain technology, companies can rebuild their approach to supply chain management at the ecosystem level and go from the island of insight to an integrated global view.

“At its most basic level, the core logic of blockchains means that no piece of inventory can exist in the same place twice.”

-Paul Brody
EY Global Innovation Blockchain Leader

Here are some ideas on how Blockchain is revolutionizing the supply chain industry.

Everyone loves to hate middleman, but it turns out they are really useful. Until the advent of bitcoin and blockchain technology, the only way you could get a large number of entities to agree upon a shared, truthful set of data(such as who owns how much money).

This was for appointing an impartial intermediary to process and account for all transactions. Blockchain makes it possible for ecosystems of business partners to share and agree upon key pieces of information. Facebook’s Libra is also using blockchain technology.

Instead of having a central intermediary, blockchains synchronize all data and transactions across the network and each participant verifies the work and calculations of others. This enormous amount of redundancy and crosschecking is why financial solutions like Bitcoin are so secure and reliable.

Even as they synchronize hundreds of thousands of transactions across thousands of network nodes every week.

“The Core logic of blockchain applied to the supply chain”

The radically new approach to supply chain management.

Apply the same security and redundancy to something like inventory — and substitute supply chain partners for banking nodes. You’ll have the foundation for a radically new approach to supply chain partners for banking nodes. You’ll also have the foundation for a radically new approach to supply chain management.

The use cases for this new way of working are compelling.

At its most basic level, the core logic of blockchain means that no piece of inventory can exist in the same place twice. Move a product from finished goods to in-transit, and that transaction status will update for everyone, everywhere, within minutes.

With Blockchain you have full traceability back to the point of origin.

Before diving into the pool of ” How Blockchain is Revolutionizing the Supply Chain Industry” let’s take a brief look at the definition of Supply Chain and Blockchain.

Blockchain

Most people have heard the phrase, “Blockchain is the greatest invention since the Internet ” or ” Everything will be Blockchain in a few years.”

These phrases tend to leave people more confused then they were previously. Blockchain is a decentralized, distributed database that holds digital records securely. Furthermore, although all records are transparent and accessible to the public, they are not altered, deleted or edited.

All data inserted into the blockchain remains impaired permanently. Each transaction or record inserted in the Blockchain registers a different “Block” on the chain.

Basically, Blockchain provides a method of record-keeping which is highly secure and more efficient for businesses/individuals to work with. BlockChain along with Ai is also helping to shape the future of robotics in various ways.

Robotics Programming Language for robotics uses AI and blockchain together can add efficiency to autonomous cars, or they can help in bitcoin mining.

Supply Chain Management (SCM)

Nowadays we have the luxury of having ready-made, high-quality products right on our doorstep. It’s easy to go to a store, buy a shirt, and not think of where that shirt came from or how it was manufactured. That shirt came from how it was manufactured.

For that shirt or product to make it store shelves it must go through plenty of hands stemming all the way from the provider of the raw materials, to the retailer who is giving you the ready-made product.

The process which links all the parties involved in delivering you the ready-made product is called the Supply Chain.

Logistics is a huge aspect of Supply Chain Management.

Products are shipped, stored in warehouses, and go through customs. The entire process is time consuming, costly, and many times complicated. In addition, since global trade involves dealing with foreign organizations, importers and exporters may have to deal with political outcomes, international law, and high tariffs.

“Through blockchains, companies gain a real-time digital ledger of transactions and movements for all participants in their supply chain network. But don’t let the simplicity of the tool overshadow how transformational it is.”

Paul Brody
EY Global Innovation Blockchain Leader

Blockchain Stepping In

Some of the most urgent issues facing supply chains can be addressed through blockchain technology, as it provides novel ways to record, transmit, and share data.

In essence, a blockchain is a unique database system created and maintained by participants in a decentralized network. It offers a secure and reliable architecture for conveying information and transactions ( e.g. the exchange of data and assets among participants in a supply chain), which can be recorded digitally.

As the distributed ledger is decentralized, each stakeholder maintains a copy, which prevents a single point of failure or data loss. This also means blockchains are highly resistant to altering or tampering. Such accurate and tamper-proof records secure data integrity and can be accessed to make regulatory compliance easier.

Ultimately, blockchain can increase the efficiency and transparency of supply chains and positively impact everything from warehousing to delivery to payment.

All parties involved from beginning to end in the supply chain will be aware as the product is transacted and handled from party to party at all times.

Through the implementation of Blockchain technology in the Supply Chain Industry, products can be tracked and traced throughout their entire process.

Traceable and Immutable Records

Blockchain data is immutable and digital signatures which require to confirm information ownership. If multiple companies work together they can use a blockchain system to record data about the location and ownership of their materials and products.

The data is stored in the blockchain, which offers a full history of all items in the supply chain. Any member of the supply chain can see what is going on as materials move from company to company. These data records cannot be altered and are highly Traceable.

In the event of a defective product, the source of the problem can be identified more quickly, which improves the efficiency of product recalls and disruption resolution between stakeholders in the chain.

Having a transparent and complete inventory of product flow help businesses make better decisions.

It gives stakeholders and customers more confidence in the products’ quality. The improved transparency is also a tool for fighting fraud and counterfeiting.

Cost Savings

Inefficiencies in the supply chain create a lot of waste. This is especially prevalent in industries that have perishable goods, such as the food industry.

The improved tracking and data transparency that blockchain offers can help the business identify. These wasteful inefficiencies so they can implement targeted cost-saving measures.

The use of blockchain can also eliminate fees associated with funds passing into and out of various bank accounts and payment processors. Such fees cut into profit margins, so being able to take them out of the equation is significant.

Interoperability

One of the problems with current supply chain technology is not being able to integrate data across every partner in the process.

In contrast, blockchains are built as distributed systems that maintain a unique and transparent data repository. Each party in the network contributes to adding new data and verifying its integrity.

This means that all parties involved in the network. So one company can easily verify the information being broadcasted by another.

Replacing Electronic Data Interchange Systems

Many companies rely on Electronic Data Interchange (EDI) systems to send information to each other. However, this data goes out in timed batches rather than in real-time.

If a shipment goes missing or changes in pricing, other participants in the supply chain will get this information by involving parties. Also, read how AI will shape the future of the internet.

Transparency

The blockchain is a shared database that ensures honest transparency.

All partners have the responsibility to upload their information and data about the product. A digital collection of accurate data improves accountability and trust between partners. Blockchain technology can show updates to the product in mere minutes.

Everyone involved knows exactly where a product stands at all times. You can see exactly where a product is, how it’s being made, and when it will be delivered all in one place.

Streamlined

All of the logging involved with the digitally done blockchain.

This leads to less administrative work and more consistent and speedy data tracking. With the blockchain, you cut out the middleman and sign on to the blockchain to instantly download information. Everything is in one spot, making communication and operations highly streamlined.

The blockchain is global and scalable. The technology can support worldwide partnerships and communications just as fast as regional partnerships. This makes it the ideal solution for an economy of globalization.

Enhanced Analytics

The blockchain offers complex solutions to analyze the uploading data.

Blockchain can help create forecasts and predictions based on previous data, and it can allow users to pinpoint legs in the supply chain.

These data analytics are proving invaluable to companies who want to minimize supply chain expenditures and grow their businesses.

Customer Satisfaction

The blockchain technology can also use for boosting customer satisfaction. Business owners can use the blockchain database to see where items are in production and shipment to build a delivery timeline for their customers. It also has a social advantage.

A clothing brand with a dedication to fighting sweatshops may give their customers access to the blockchain. Showing them a social consciousness approval form, and a labor union sheet.

A World of Potential

The Wall Street Journal recently posted an article that exclaimed, “after initial tests, 12 of the world’s biggest companies, including Walmart and Nestle, are building a blockchain to remake how the industry tracks food worldwide.”

The blockchain is already revolutionizing the financial world. It’s only a matter of time until it takes over every other industry.

A technology like this is a generational technology, something that will change the way the world works. The blockchain technology will update a 200-year-old system which makes it more reliable, secure, and transparent.

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Everyone is excited about starting their own business. Countless people try their luck every year, but most of the business failed due to a lack of proper planning.

Approximately 45% of startups struggle because they didn’t reach their target market. To run a startup successfully, you need to validate your idea systematically for making your product successful in the market.

There are some essential steps to follow for validating any startup idea –

Market Validation of the Product

Market validation is comprised of surveys and feedback from your target market. It is a process to know about the interest of your target market, whether your product is market fit or not.

Share Idea:

Most of the people make a mistake that they keep their startup idea with themselves. Sharing your idea with the right people and taking their views. it is the initial step of validating your startup idea.

“Tell anyone and everyone your idea without fear they are going to steal it.“                                                                    —Aaron Patzer, founder of mint.

You can take quality advice from those who are already in that business and ask them directly specific questions and follow successful entrepreneurs on LinkedIn who have accurate information about a specific industry. You can collect more quality information and advice if you might get a chance to meet any of them.

Analyze the competition:

The first thing you should always keep in mind is analyzing your competition. you should analyze their business and find some areas they are lacking as you can focus on those areas.

For example, if you are planning to launch an operating system in the market (and surely there is a huge market for this) you know that windows is already dominating the market. But you think, on another hand, Mac and Linux are also doing very well.

So it is hard to find space for your product, and even if you made it in the market, you can’t compete with the level of resources and depth in the market that these other company’s have.

It is important to know your competitors very well. By analyzing them, you will learn about their mistakes and how they overcame as well as the strategies they followed for success. You can apply it to your startup.

Evaluate Whether Your Idea Is Profitable:

You should research whether your product is profitable or not because you can’t invest your own money all the time your business should generate revenue for you to sustain your business.

It does not make sense if you are about to start a business, and you don’t know whether it has the potential for profitability or not.

If you are going to launch your startup, you need proper planning that will make your business profitable.

Prepare Your Product Concept:

The purpose of creating a product concept is to finding key questions for testing in the market. These questions could relatable to the problems that might occur to your target market.

• Who is your customer: There is a specific market for every business, so you have to make sure which one you are going to target and how big the market is. If your product is for a particular market, so what is your title for the buyer.
• Why are you in the market: You should be aware of your target customer’s problems. When you will get to know your customer’s problem you will be able to validate and solve them.
• How your product will help: You can elaborate to customers how it will help them to resolve their problems. You can represent a prototype to your customer, and by their feedback, you can improve your product further.
• Key features: Your product should provide plenty of benefits to your customers as it will make their life easy and better. Like the value of money and time saving etc.

Interview With People:

You can hardly get people for free surveys because people don’t want to waste there time on any kind of free surveys.

But this is a very crucial part to validate your startup idea it helps you understand customer’s needs and problems, and it will give you some valuable insight.

You can start with a list of questions to learn more information and make sure you can visit them or secure their precious 10 minutes over a video call.

This natural face to face interview is very essential to see their reaction. Some keys you should remember while interviewing any customer-

• Thank them for their valuable time and tell them how they will help you to make the best product for customers.
• Explain to them how your product is different from others that are already in the market and how you are going to tackle those issues.
• Explain to them about your product and the nature of your business.
• You should explain to them all versions of your product and ask them for their thoughts. Observe their body language and their reactions and their feelings and thinking about your startup Idea.
• There is a difference between liking and buying a product. People are only buying the product when that is needed by them. So if someone likes your product asks them if they want to buy that kind of product or not

Review And Decide:

Eventually, you have to review all feedbacks and decide what work would be best for your potential clients and how can you change or modify your product for your targeted market.
Lean market validation helps you to get enough information and data to make a decision. This concept helps a startup to validate and succeed.

Validate With MVP (Minimum Viable Product )

The MVP(Minimum Viable Product) is a technique to create or develop a product with sufficient features for early customers. The final product(having all features) is only designed by considering the feedback of early customers (users of the initial product). It is a prototype version of your product.

• It has enough value for people to buy and use it initially.
• Early customers can have enough future benefits.
• MVP provides a future guide to develop your product for future development.

Idea Validation Landing Page

It is an easy way to validate your startup idea. Because building a prototype takes less time and effort than creating a complete product. You can create multiple prototypes for better future development using customer reviews and basic market research.

Some important features you should keep in mind while creating a prototype –

Simple and Consistent with design:

The prototype should look professional. Means, it has to look like you already have established business. You have to make sure about design and image quality as you are going to sell your MVP. A prototype with user-friendly functionality and simple yet clean designed to attract your customer easily than a prototype with complex designing and functionality. The design and functionality of a product are important for brand building, improving conversions and adding legitimacy.

Product Description:

As you are writing a copy to describe the product, you need to address users’ problems and the solution you are providing as well as your product benefits or services.

A/B testing:

A/B testing is a very important feature during validation. Because it allows users to compare and vote between two or many products. This statistical analysis determines which one is best for a conversion goal. It will give a better result to

Image Credit: Adobe Stock

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