Trending February 2024 # Top 10 Most Popular Chatbots In Different Sectors To Help Businesses # Suggested March 2024 # Top 7 Popular

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Today, 1.4 billion people use chatbots. Organisations deploy their top AI chatbots to have 1:1 conversations with customers and employees. Chatbots powered by artificial intelligence are also capable of automating various tasks, such as client assistance and sales.

We’ve collected the top 10 most popular chatbots in different sectors to help businesses of all sizes and industries find the best.

1. Netomi

Netomi’s AI platform helps companies to resolve client support tickets via email, voice, or talk. Because of its NLU motor, it has the highest level of natural language understanding (NLU). This chatbot can provide client support with unparalleled precision.

Netomi can therefore resolve more than 70% client queries without the need for human intervention and focuses extensively on AI client experience.

Also read: Best ecommerce platform in 2023

2. atSpoke

AtSpoke makes it easy for workers to access the knowledge they need. It is an interior tagging system with inherent Artificial Intelligence. It allows interior groups (IT help area, HR and other tasks groups) to quickly note 40% of all solicitations, which makes it 5x faster to reach their goals.

AI responds to worker queries by surface information base substances. Workers can easily get refreshed via the channels they use every day, such as Slack and Google Drive, Confluence and Microsoft Teams.

3. WP Chatbot

WP-Chatbot, the most popular chatbot within the WordPress environment is WP-Chatbot. It allows for live chat and site visits.

WP-Chatbot integrates a Facebook Business Page and powers live and automatic connections on a WordPress website through a Messenger talk gadget. It only takes one tick to set up the plugin.

Also read: How to choose The Perfect Domain Name

4.  Microsoft Bot Framework

The Microsoft Bot Framework provides a comprehensive structure to build conversational AI encounters.

The Bot Framework composer is an open-source visual authoring tool for engineers and multidisciplinary teams to design and construct conversational encounters using language understanding, QnAmaker, and bot responses.

Microsoft’s bot framework allows clients to use a wide-ranging open-source SDK, apparatuses, and tools to seamlessly interface with a bot to existing channels and gadgets.

5. Alexa for Business

Are you ready to connect with 83.1 million smart speakers owners? Amazon holds 70% of the market and has the best AI chatbot software to voice assistants.

With Alexa for Business IT teams can build custom skills to answer customer questions. In just three years, Amazon has grown from 130 skills to more than 100,000 skills by September 2023.

Also read: No Plan? Sitting Ideal…No Problem! 50+ Cool Websites To Visit

6. Zendesk Answer Bot

Zendesk is close to your Zendesk help group, so you can answer any client queries immediately. To provide clients with the information they require immediately, the Answer Bot pulls relevant articles from your Zendesk knowledge database.

You can add more innovation to your Zendesk chatbot, or you can let the Zendesk To Answer bot fly on its own on your site, in portable applications, or within inner groups on Slack.

7. CSML

CSML is an open-source language for programming and chatbot engines that aims to create interoperable chatbots. CSML is a chatbot engine that allows designers to communicate and build chatbots with their expressive punctuation.

Also read: 10 Best Android Development Tools that Every Developer should know

8. Dasha AI

Dasha is a platform for conversational AI. It provides developers with the tools to create conversational AI applications that are human-like and profoundly conversational.

These applications can be used to substitute call center specialists, to text talk or to add conversational voices interfaces to flexible applications or IoT gadgets. Dasha was named a Gartner Cool vendor in Conversational AI 2023.

Dasha does not require any knowledge of AI or ML to work with, and any engineer who has basic JavaScript knowledge will be completely at ease.

9. SurveySparrow

SurveySparrow allows you to conduct conversational studies and build structures. This stage includes consumer loyalty reviews, such as Net Promoter Score, Customer Satisfaction Score, or Customer Effort Score, and overviews of employee experience (i.e. Recruitment and Pre-enlistment, Employee 360 Assessments, Employee Check-in and Employee Exit Interviews).

Also read: Top 9 WordPress Lead Generation Plugins in 2023

10. ManyChat

In one year, Facebook Messenger will be used by 2.4 billion people. ManyChat is a great alternative if you are looking for an efficient way to send a simple chatbot to book items, sell products, request updates, share coupons, and make requests on Facebook Messenger.

You can choose from industry-specific formats or create your own interface. This allows you to dispatch a bot in minutes with no coding.

It is easy to interface with eCommerce tools such as Shopify, PayPal and Stripe, ActiveCampaign and Google Sheets. There are also 1,500+ additional applications available through Zapier or Integromat.

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10 Most Popular Guest Authors On Analytics Vidhya In 2023

Overview

Writing what you learn is at the core of Analytics Vidhya and we regularly engage with the community and encourage them to be guest authors on our blog

Here is a list of top 10 Guest Authors on Analytics Vidhya this year

Introduction

Analytics Vidhya has always prided itself on sharing high-quality comprehensive articles on a variety of topics related to ML, DS, and AI.

Be it articles dealing with the implementations of latest models, or dealing with covering the basic concepts, or the latest trends, our writers have always been at the forefront sharing our knowledge, and experience. The year 2023 was no different. We published over 500 articles and this number includes plentiful articles written by our Guest Authors.

In this article, I highlight the top 10 Guest Authors on our blog this year. These are in alphabetical order by their username.

We start off with an author who has written a couple of articles on different tools as well as how to start your DS career. All of Arka Ghosh articles combined have over 30K views!

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning? Which are the various roles in the DS industry? What are some tools and resources to start my DS career?

I am sure most of you who want to start their DS journey have these questions. This article by the author answers these questions and more in detail, using an example and easy-to-understand graphics. This article serves as a one-stop guide to starting your ML journey: How to Build a Career in Data Science and Machine Learning?

While we usually stick to either scikit-learn and pandas for our machine learning models. However, there are other libraries we can use to complete specific tasks more efficiently than the generic ones above.

His 2nd article deals with implementing Linear Regression. Too simple? Well, what makes it different is that the author implements linear regression using H2O’s AutoML tool and demonstrates the end-to-end process from loading the data to presenting the predictions. You can also learn about how to build AutoML models in this article: Exploring Linear Regression with H20 AutoML(Automated Machine Learning)

The final article by this author covers the Anomalize library in R for anomaly detection. The author has not only explained using this library but taken up an entire case-study to explain and implement anomaly detection in Time Series: A Case Study To Detect Anomalies In Time Series Using Anomalize Package In R

For instance, her article on Image Classification not only covers the basic code for using pre-trained models for the task but also to develop an Android app that uses such a model. This comprehensive article covers scraping images from the internet, preparing the image data, building a deep learning model based on VGG-16, and integrating a custom-built UI into an Android App: Build an End to End Image Classification/Recognition Application

The next article by this author deals with Stock price Prediction. While we come across various ways of dealing with Time Series tasks right from regression to ARIMA and to deep learning models like LSTMs, this article uses Reinforcement learning for the same. This is a totally new approach to solving a time series forecasting problem that is seldom used with comparable performance to the above methods. Check out this article here along with the Python code to implement it: Predicting Stock Prices using Reinforcement Learning (with Python Code!)

While the above articles were on Computer Vision and Reinforcement Learning, her latest article is from the NLP domain. Implementing the task of text summarization, the author now takes up scraping a Wikipedia Page(on reinforcement learning, no less) and using the nltk library to summarize this page. In the process, the article implements web scraping, text preprocessing, and summarisation: Tired of Reading Long Articles? Text Summarization will make your task easier!

The net author is one of our regular writers for our blogathons. His articles always cover interesting problems and provide easy and short to-do guides on how to get the problem solved.

His 1st article deals with one of the most time-consuming tasks in the DS process- data exploration. No matter how large your dataset is, this is one task one simply cannot ignore and have to perform to get an idea of the data provided. However, using just the basic tools of pandas, numpy and a couple of visualization libraries is too tedious and inefficient. What if there were a tool to make data exploration easier and one that we could integrate with Jupyter Notebooks as well? Well, this is what the ‘dtale’ library does. The article takes up a dataset and explains how to use the ‘dtale’ library in your Notebook for interactive data exploration: Data Exploration with the dtale Library in Python

While there are blogs and articles aplenty dealing with how to build machine learning and deep learning models but have you ever wondered what comes before and after model building? In the industry, datasets are not provided to us on a plate, ready to build our models on. Oftentimes, we have to collect the data ourselves. This is especially applicable when you want image data. There are not many image datasets available that you can use to practice your Computer Vision tasks. However, the internet is a treasure trove of images, and we can leverage this huge resource to get the images we want ourselves. Thus, this article by the author implements image scraping using the popular Selenium library in Python: Web Scraping Iron_Man Using Selenium in Python

Similarly, it is not merely enough to build a model and generate predictions on a test dataset. The important part is serving the prediction results in ways that can be further used to make decisions such as web apps. However, if you have a model ready, you need not consult a web developer to build a web application for it. The latest article by Kavish Jain demonstrates how we can use Streamlit, a popular Python framework to deploy our model: Streamlit Web API for NLP: Tweet Sentiment Analysis

The next Guest Author in this list also covers a wide range of topics in his articles catering to different audiences.

For beginners and professionals who want to become data scientists, it is often much easier to handle roles of data analyst or business analyst to get used to dealing with different types of data. However, there various opinions on how to become a data analyst. This comprehensive article covers all aspects of becoming a successful data analyst with useful resources to get you started: A Quick Guide to Become a Data Analyst.

What if you could create your own version of Alexa or your own personal digital assistant? Don’t worry, it is not as daunting as it sounds. This article provides the complete step-by-code to create your own desktop assistant powered by your voice: Build Your Own Desktop Voice Assistant in Python

So the next time you check the weather on your mobile, ask your own voice-based assistant first!

The importance of model deployment in the machine learning process can be gauged by the number of articles showing how to put your model into production using different tools and libraries is much needed in the domain of computer vision where you can show whether the image you uploaded is that of a dog or cat instantly. This article uses the fastai(v2) library and iPython widgets to classify x-ray images into Covid Positive or Covid-Negative: Develop and Deploy an Image Classifier App Using Fastai

One of our most prolific authors, Maanvi is fast becoming one of our regular contributors to the blogathons. For instance, Maanvi has submitted 5 well-researched and diverse articles over the last couple of months with each article exploring different concepts in R and Python.

Starting with the fundamentals of statistics, this article explains statistical modelling in great detail along with the definitions and key concepts of building statistical models: All about Statistical Modeling

It is a common misconception that machine learning involves collecting the data, cleaning it, feature engineering, model building, and prediction. However, that is not the case. There are quite a few statistical steps involved in between as well, such as Power Analysis. Power Analysis is a 4-step process that helps you perform a sanity-check on your data. This article illustrates this concept using Python: Statistics for Beginners: Power of “Power Analysis”

Are you more a proponent of Bayesian statistics over the frequentist approach? If so, conditional probabilities and Bayesian networks are your friends. More specifically, Probabilistic Graphical Models(PGM) form the base of making predictions using graphs and networks. This article covers PGM in great detail along with R code: Complete R Tutorial To Build Probabilistic Graphical Models!

Preparing your data before extracting and building features from it is one of the most vital tasks in the ML process. Occasionally, the data that we have collected is spread out across different files containing different features. It is not merely enough to just load these files and combine them into a single dataframe – we need to keep it consistent and remove the redundancy as well. This article demonstrates data preparation when it is split into different files: Tutorial to data preparation for training machine learning model

For beginners who have just started learning Python, data exploration can be a challenging task. It can be difficult to recollect and use a huge number of available functions to deal with different types of data. This article describes data exploration using Python on a dataset in easy-to-understand language and code: A Comprehensive Guide to Learn Data Exploration in Python!

Another of our highest contributors, he regularly churns out high-quality articles dealing with deep learning and statistics.

For instance check out his article on Data Exploration: Interpreting P-Value and R Squared Score on Real-Time Data – Statistical Data Exploration

While it may look like just another run-of-the-mill articles on data exploration, the author particularly highlights how to use statistical measures and techniques at this stage. In fact, we can derive the majority of insights at this step itself without even proceeding to build a predictive model on the data.

Have you thought of applying Python to improve call centers? It can be extremely helpful for customer service representatives who have to deal with thousands of calls every day, sometimes on the same issues but worded differently. This article uses plain Python code without any complicated model-building to create an extremely efficient log of issues and their corresponding resolutions by parsing large files for the critical baking sector: Modernize Support Logs Using Simple Python Commands

A continuation of the previous article, the author now uses NLP techniques to look up similar issues being raised and how to identify high-priority issues: NLP Applications in Support Call Centers

It is really fascinating to study how ML professionals use a combination of ML and DL models in their decision-making process.

If using NLP wasn’t impressive enough, the author now ventures into transfer learning and Generative Adversarial Networks(GANs). This article explains the fundamental concepts behind GANs, but also uses them to generate new images from a custom image dataset: Training StyleGAN using Transfer learning on a custom dataset in google colaboratory

Moving on to one of the most interesting articles in this list – dealing with Unsupervised Deep Learning. The concept of Autoencoders is a compelling one. They basically ‘learn’ to encode data in an unsupervised manner and thus are typically used for extracting and reducing features in a dataset. This article demonstrates using Autoencoders for the same purposes in a real-life energy sector problem: Deep Unsupervised Learning in Energy Sector – Autoencoders in Action

Like a few authors in this list, Sagnik Banerjee is also all about the tools. This author’s straightforward style of explaining practical concepts has been well-received by our community.

Time Series is one of the most common problems we come across in hackathons and in the industry. In fact, it is even a part of interview questions for ML roles across the board. There are various techniques to deal with time series forecasting like ARIMA, RNNs, etc. However, it is Facebook’s Prophet library that is fast becoming the preferred tool to forecast time series. This article provides a clear and concise implementation of the Prophet library using Python: Time Series Forecasting using Facebook Prophet library in Python

Similar to other articles that you will find in this list, Sagnik Banerjee’s next article also deals with Machine Learning model deployment. However, this time it is using the popular Microsoft Azure framework and Flask. The idea is to create a web application that can run 24 x 7 to generate predictions: How to Deploy Machine Learning models in Azure Cloud with the help of Python and Flask?

Feature engineering is one of the most essential steps in the ML process. You simply cannot skip this step and it is imperative to get the best possible features out of the data we have to get the best possible predictive model. Feature selection is one of the steps in feature engineering where we select the best possible combination of features. This excellent article describes the popular types of features selection techniques that filter out the redundant features: Most Common Feature Selection Filter Based Techniques used in Machine Learning in Python

Here we have an author who takes up the building blocks of machine learning models and explains them meticulously using examples and code each step of the way.

Generalized Linear Models(GLMs) are widely used in the industry despite the dee learning boom. Cheaper to build and easy to explain, they are the go-to models for building benchmarks in academia as well. In the case of classification, logistic regression, though popular can be quite intimidating for beginners. This article provides a complete guide to logistic regression along with the math behind it and also Python code: Demystification of Logistic Regression

Similarly, Entropy is one such term that we hear day-in and day-out in the case of decision trees and tree-based models. But what is Entropy and what is the intuition behind using it in tree models? This article provides a thorough, yet an easily understandable guide to Entropy and its usage: Entropy – A Key Concept for All Data Science Beginners

Dimensionality reduction is a much-needed step when we are dealing with large datasets. Working with thousands of features without reducing them can lead to poor-fitting models. Thus, using as few features as possible without losing too much information is essential for the ML process. Principal Component Analysis(PCA) is one of the most widely-used techniques for Dimensionality reduction. This comprehensive article explains PCA using interesting examples and clear explanations: An end-to-end comprehensive guide for PCA

I really like the topics Sreenath chooses to write his articles on. They are always engaging, dealing with new concepts and tools.

For instance, how many of you were acquainted with the idea of reservoir sampling? I am pretty sure that not many of us knew this simple technique of obtaining smaller chunks of datasets from a huge ‘reservoir’ of big data. It uses statistical techniques of accomplishing this. This article by Sreenath provides a comprehensive guide to Reservoir Sampling and provides the statistical intuition behind it: Big Data to Small Data – Welcome to the World of Reservoir Sampling

From statistics, let us move on to math. Most of the time when we are using linear regression, we hardly use the barebones model. More often than not, we combine regularisation with linear regression to get the best-fit equation for our data. Amongst regularisation, lasso and ridge regularisation are the most common types. However, what is the difference between the two? This article explains the math behind the two techniques and how they actually perform feature selection: Lasso Regression causes sparsity while Ridge Regression doesn’t! – Unfolding the math

Once we obtain the benchmark results for our model, the next step is Hyperparameter Tuning. It is Hyperparameter tuning that pushes our accuracy from 90% to 98% or our RMSE from 0.1 to 0.002. This finetuning of parameters can be a very tedious step when we are dealing with when we are using models with a large number of parameters like the Random Forest. It is simply impossible to try out various combinations of parameters by trial and error here. This article explores Optuna, a recent and lesser-known library that automatically generates the best possible combinations of parameters and also generates plots to visualize them: Hyperparameter Tuning using Optuna

Didn’t I mention earlier the diversity of topics that Sreenath chooses to write on? His most recent article tackles reinforcement learning. This concise article provides a really simple and intuitive explanation of reinforcement learning and how to use the Markov mathematical model to represent reinforcement learning: Getting to Grips with Reinforcement Learning via Markov Decision Process

Finally, I would like to include one of the most well-received authors among our readers. Vetrivel PS is one of the few authors who has won 2 awards in the same blogathon! An experience DS professional who is also a Kaggle Expert published 2 popular articles with us.

Participating in Hackathons is one of the most crucial prerequisites for building your DS profile. Achieving a good rank in hackathons could be the difference in your resume among hundreds of other resumes that could give you a boost.

However, it can be intimidating for beginners to start participating in hackathons and Vetrivel PS addresses this very issue in his awesome article on how achieved Rank 4 out of 3000 submissions in one of our hackathons: Ultimate Beginners Guide to Breaking into the Top 10% in Machine Learning Hackathons

Not only this, his other articles deals specifically with classification problems in hackathons and shares his approach of featured in the top 10% amongst 20,000 participants in Hackathon involving a classification problem: Ultimate Beginner’s Guide to Win Classification Hackathons with a Data Science Use Case

End Notes

Who was your favorite Guest Author of 2023? Share your feedback and answers below!

Related

The Impact Of Ml Dataops On Different Sectors

Know about how ML DataOps is creating an effect on multiple sectors worldwide

Commercial machine learning (ML) applications have progressed from conceptualization to testing to deployment over the past decade. The need for efficient and scalable operations has led to the establishment of MLOps as a vital function within firms developing artificial intelligence (AI) as the industry has progressed through this cycle. As a result, it is critical to understand what ML DataOps is and how it affects various sectors.  

What is ML DataOps?

ML relies heavily on the collection, analysis, and creation of data. Over the past year, the AI ecosystem has witnessed a push to move to a more data-centric approach from the current model-centric one. And data is the single biggest differentiation in ensuring the success of ML models in the real world. In the wake of this development, ML DataOps is in the spotlight as it allows us, if correctly structured, to handle data at scale as it flows through the cyclical journey of AI training and deployment. This becomes highly important to ensure the sustainability of the resulting AI solutions as there is a need for a transition from testing to production, which must be tackled through repeatable and scalable processes. Moreover, various insights can be derived from the data that can help customers accelerate the process of developing production-grade ML models. Companies focus on different aspects of the data pipeline within the ML DataOps ecosystem. However, the solutions provided broadly fall under the following categories: 3. End-to-end processes: Efficient, insight-driven processing can be a big saver of cost and time when dealing with enterprise-grade data pipelines. Thus, some companies focus heavily on end-to-end solutions for such streamlined processes.  

2024 is the year of ML DataOps

So far, 2023 has been a year of remarkable growth. Here’s why this year will see further investment and development.

Firstly, AI products are going into production and this is huge. Industries like finance and retail are taking cutting-edge models to production, which will provide feedback loops once released. A feedback loop of results will force enterprises to adapt their ML data operations to meet the evolving demands of their models. Algorithms in the field will come back with edge cases, which data operations will work to resolve before the algorithm is redeployed.

Second, data pipelines require scale and experts-in-loop. Scaling for efficiency, enterprises will need to ensure that annotators understand the domain and product requirements.. This will, in turn, result in faster market releases as they continue to improve the performance of their models.

Using the right processes

Technology is only as good as your ability to use it properly, which is why enterprises building AI applications must leverage the right processes across their ML DataOps. Leaning on AI data solutions providers like iMerit gives companies access to domain experts who can guide every phase of a company’s ML DataOps process, including requirements definition, workflow engineering, technology and tool selection, domain skill identification, execution, evaluation and refinement, and analytics.  

Impact across various sectors

Healthcare: Since the onset of the COVID-19 pandemic, healthcare has taken center stage across the globe. There are several challenges we need to tackle to make it accessible and impactful. Intelligent, data-driven insights enable organizations to predict the right clinician mix needed for a specific department. It can also aid in creating a value-based ecosystem by automating clinical operations such as investments in physician recruiting, clinical staff scheduling, and clinical systems. DataOps can assist in creating patient-centric systems to deliver enhanced operating processes and better customer engagement. Such DataOps-led architecture can help assess tools and capabilities to identify and recommend patient-centric approaches to improve connectivity, engagement, and collaboration with patients. Finance and Insurance: The sheer amount of data collected by financial services has prompted the industry to adopt technology-driven solutions to achieve a competitive edge. Employing innovative data and analytics capabilities can have a huge impact on the financial services sector, from decision-making to innovation. These smart tools enable financial service providers to optimize data analysis and enable companies to combine human expertise and machine intelligence to build a credible ecosystem. For example, data analytics can empower banks to gather customers’ insights and channel this into strategic decisions for introducing new products and improvising current business models. The use of AI and data-driven tools can also lower risk for banks with more effective evaluations and judgments based on risk profiles during credit applications, by considering more targeted details about an individual or business who is applying. Automobile: Countries are taking note of the rising need for and potential of autonomous vehicle (AV) technology and building initiatives to nurture its growth. For example, the US rolled out a $1 trillion infrastructure bill that makes numerous suggestions for modernizing infrastructure to facilitate the widespread adoption of AVs and mobility. However, manufacturers and innovators still need to master the art of creating AI models to perform on any road. With modern transportation at an all-time high, one of the biggest challenges we face in the 21st century is reducing the number of road accidents and safety breaches. AI-led solutions have the potential to significantly assist human drivers and enable driverless mobility. It’s not surprising that the sector has attracted many global leaders in AI, software development, and device engineering. Retail: The industry collects great volumes of data, from product catalogs and customer information to customer queries and complaints. This data could be overwhelming for decision-makers trying to solve a problem. Moreover, retail is one sector that appeals to all human senses, be it touch, smell, hearing, or sight. We need data operations to make sense of the information collected in any format – audio, video, or text. However, especially in retail, we also need human abilities to dive deep into the intricacies of consumer behavior and derive insights for effective decision-making. Data-driven solutions not only help retail businesses analyze the enormous volume of data but also accelerate decision-making for this dynamic industry. The eventual goal of industries adopting AI and data solutions is to build an ecosystem that can independently learn and develop to aid in decision making. This, along with human-in-the-loop processes, provides the right blend of technological innovation and human intelligence at work to drive business goals and problem-solving.  

Author:

Top 10 Most Innovative Companies In The World

What’s Business Innovation Important? As among the USA’s biggest production companies Apple Inc – 2023 revenue: $229.2 billion

Also read:

10 Best Saas Marketing Tools And Platforms For 2023

Netflix — 2023 earnings: $11.7 billion Square – 2023 revenue: $984 million

Besides its signature card reader, Square offers an iPad point-of-sale program. The business also functions Square Capital, which provides loans to its own retailers, along with a consumer-facing mobile wallet, Money App.

Tencent — 2023 earnings: $37.3 billion

Tencent Holdings Limited is a Chinese multinational investment carrying conglomerate whose subsidiaries specialize in different Internet-related providers and goods, amusement, artificial intelligence, and engineering both in China and internationally. Tencent’s WeChat messaging program is the planet’s most used messaging program with over 980 million active monthly customers as of January 2023. It is competitive R&D actions have made WeChat much more than only a social networking platform. Users may hail a cab, look up a restaurant inspection, make a booking, and pay for supper, without leaving WeChat.

Amazon – 2023 revenue: $177.9 billion

The business also generates consumer electronics–Kindle e-readers, Fire tablet computers, Fire TV, and Echo–and will be the world’s biggest supplier of cloud infrastructure providers. Over the recent decades, Amazon has made considerable investments in the physical world, starting its first bookstore in Seattle in late 2024. In 2023, the business completed its purchase of Whole Foods and at ancient 2023 opened an Amazon Go grocery store to the general public.

Patagonia – 2023 revenue: $209 million

Over time, Patagonia has grown to become one of the most admired and popular outdoor clothing brands in the world and continued to utilize its prevalence to boost awareness around environmental problems and climate change by investing in grassroots associations and in companies developing technology which will make its supply chain and products more sustainable.

CVS Health – 2023 revenue: 184.7 billion Washington Post — 1 million electronic subscribers Spotify — 2023 earnings: $4.7 billion

Spotify Technology SA is a Swedish amusement firm launched in 2008 that specializes in audio, podcast, and even video streaming support. It supplies DRM-protected articles from record labels and media firms. A lot of Spotify’s achievement is because of increasingly complex data collection, which lets it maintain releasing new products which captivate its customers around a specific mood or second.

NBA – 2023 revenue: $7.73 billion

It is now the planet’s most precious sports league, with over a billion viewers every year. In 2023, every NBA team. On average is well worth a record $1.65 billion, representing 22% growth from this past year. To push the boundaries of creation, NBA continues to be commissioning ground-breaking highlight-clip creation tools, pop-a-shot programs, and fantasy basketball matches and solidifying relationships with firms like Facebook and Silicon Valley startups, even investing in e-sports. Record-breaking TV deals and the international expansion of this game are the largest driving factors for NBA’s growth. Additionally, it helps the NBA’s socially-conscious practices revolve around the public; particularly one of the 4 big sports leagues in North America.

Prospective of the Top 10 Most Innovative Companies in the World

Top 10 Most Promising Cryptocurrencies In September 2023

There are more than a thousand cryptocurrencies in the market, at the moment, and the most popular one out of the lot is Bitcoin. With frequent market volatility, choosing the right cryptocurrency, apart from the ever-expensive Bitcoin, for investment becomes an overwhelming task. To help you make smart investments, here are the best cryptocurrencies with the most growth potential in September. 

1. Cardano 

Cardano is touted for its proof-of-stake validation, which reduces transaction time and uses less energy. Because environmentally friendly coins have become the latest hot topic, Cardano serves the purpose. It also has many use cases as it enables smart contracts and decentralized applications. Compared to other cryptos of its kind, Cardano sees less market volatility. 

2. XRP 

XRP is a token created by Ripple, a digital technology and payments processing company. To enable the exchange of other cryptocurrencies on the network, XRP can be traded for traditional currencies as well. XRP has seen massive growth over the years and now several banks are using this blockchain network for their modern banking functions. 

3. Binance Coin 

Binance Coin is used to trade other cryptocurrencies and pay fees on Binance, one of the biggest cryptocurrency exchanges in the world. It was launched in 2023 and can now be used for many functions like even booking travel arrangements. If you are going to invest in cryptocurrencies for the first time, it’s best to invest in Binance first and then trade it for other cryptocurrencies. 

4. Dogecoin 

While there is not much hype around Dogecoin at the moment, this digital coin still attracts many investors. Cryptocurrencies like Bitcoin come with a limited coin supply, but Dogecoin has no limit. What was started as a joke in 2013 is now seeing a myriad of supporters, from billionaires to celebrities? 

5. Tether 

Tether is a unique cryptocurrency as it is a stable coin. Stable coins are backed by fiat currencies like the US dollar or the Euro, which means anyone who buys 1 Tether coin will be guaranteed the value of one fiat currency. Theoretically, this means Tether’s value will be more stable than other cryptocurrencies amidst market volatility.

6. USD Coin 

USD Coin is also a stable coin with its value pegged to the US dollar. For every USD Coin bought, the investor will be assured the value of US$1. This coin is powered by Ethereum which means it can complete transactions on a global scale. 

7. Landshare

Landshare is created to leverage the real estate industry. Based on the Binance Smart Chain and DeFi principles, users can use Landshare for house flipping projects, and make passive income via rents. Launched in August this year, Landshare is gradually getting traction. Initially, it was priced at US$3.6, and at the time of writing, it is trading at US$2.67. 

8. Polkadot 

There are more than 7000 cryptocurrencies in the market that use various blockchain networks. Polkadot’s aim is to integrate them all by creating a cryptocurrency network that connects all the blockchains to work in sync. This ambitious mission has attracted many experienced investors, booming Polkadot’s growth. 

9. Ethereum 

Ethereum is the network that powers the token Ether. Ethereum is a developer’s favorite platform as it supports smart contracts that allow them to create apps based on the network. Ethereum has also seen massive growth over the years. Second to Bitcoin in market cap, it is now receiving more attention as the network announced its new upgrade Ethereum 2.0 that brings changes to this blockchain network and makes the token more environmentally friendly. 

10. Uniswap 

Top 10 Most Expensive Nfts Sold So Far In The World

With an increase in the popularity of NFTs, the investors are keen on delving deeper into the NFT marketplace. Talking about NFTs, what interests the investors is its pricing. There are a lot of elements that can impact how much an NFT is valued. For example, limited series NFTs with particular use cases typically have a higher value. The founding team, artists, and the local community also have a pivotal role to play. If you are curious to know which are the most expensive NFTs sold so far in the world, you have landed at the right place. In this article, we shall talk about top 10 most expensive NFTs sold so far in the world. Read on!

Merge

Merge was brought into existence on December 6, 2023 by an unknown artist. A point to note is that it was put up for auction on the Nifty Gateway marketplace and sold for a whopping $91.8 million. This Non Fungible token is made up of a collection of pieces that individuals could buy. The Merge is made up of three enormous white masses that have a dark background.

Everydays: The First 5000 Days

The reason why this NFT deal has become extensively popular is because it is most expensive NFT ever sold to one sole owner — and there’s a good reason for it. Needless to say, Beeple’s artwork is extremely high-tier and highly respected. This NFT piece is essentially a collage of 5000 pieces.

Beeple’s Crossroad

Who would have thought that just a short, 10-second movie of sorts, would bag a deal of $6.6 million. The movie depicts people walking past a large, fallen body with insults written all over it. Why this NFT had gained popularity is because of the sole reason that Beeple offers something slightly different from the regular NFTs you see — and the price speaks for itself.

Clock

This creation is amazing – it literally acts as a clock, counting the days that WikiLeaks founder Assange has been imprisoned. Created by Pak and Julian Assange, the goal of the NFT was to raise funds for Assange’s legal defense and was ultimately purchased by AssangeDAO — a collection of over 10,000 people pooling their money to purchase the NFT and support Assange.

Beeple’s HUMAN ONE

Yet another interesting and unique Beeple creation that has made it to the list of top 10 most expensive NFTs sold so far in the world is Human One. It was sold on November 9, 2023 for an amount close to $30m.

CryptoPunk #5822

CryptoPunks are bound to be expensive and following the same line, CryptoPunk 5822 was sold at a very high rate – as high as over $23m on February 12, 2023. Well, that’s not all. It broke all records by being the Non Fungible token that was sold at double the cost of the previous token.

CryptoPunk #7523

What makes this CryptoPunk stand apart from the rest is the fact that it is

one of the only 9 Alien punks minted by Larva Labs and comes with an exclusive series of accessories like earrings, knitted caps and medical mask. No wonder why CryptoPunk #7253 is the rarest in the collection.

TPunk

TPunk isn’t worth millions of dollars, but Justin Sun, the co-founder of Tron, purchased TPunk #3442 for $10.5 million in August 2023. Later, what followed is Justin giving the NFT to APENFT, a startup built on Tron that aims to tokenize art on the blockchain.

CryptoPunk #4156

CryptoPunk #4156 was sold for $7.7 million in February 2023. Since then, it showed no signs of seeing a downturn in enthusiasm for CryptoPunks, even after the 2023 NFT frenzy. Off late, Robert Leshner, the creator of the Compound DeFi protocol, bought this token.

CryptoPunk #3100

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