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Business Models

Netflix Business Model: Streaming is the Future

As absurd it may sound, but the cable TV is being slowly pushed towards obsolesce. Cable TV, which was considered a phenomenon, is being replaced by Netflix. It started out in 1997 when Reed Hastings and Marc Rudolph put it out as a rental for DVDs online. Over the course of years, the Netflix business model evolved, and now you can stream movies and TV shows on Netflix.

As of now, Netflix outranks cable TV in the United States of America. This particular statistic is evidence of the gradual demise of cable TV. You could say that Netflix was the last nail in the coffin of the beloved cable TV we once loved.

netflix business model

In the recent years, the subscribers of Netflix have grown exponentially. The subscriber base is so large that it accounts for more than a third of the entire traffic on the Internet during peak hours. The success of Netflix has everyone talking. Not only did they earn US$ 1.21 billion in 2018 fiscal year, but they also generated US$ 15.8 billion in the same year. Critics and users all want the answer to this golden question: how does Netflix make so much money?

Before we can answer that question, we need to give you important insight on how the Netflix business model operates. The success of Netflix is rooted in its business model.

How does the Netflix Business Model Operate?

Netflix is not only home to movies and TV shows, but it also has documentaries and other videos-on-demand. It uses a subscription model to earn money. The customers pay a monthly subscription to stream different kinds of visual content hosted by Netflix. Moreover, the US entertainment giant provides DVD rentals of movies and shows. They supply the DVD to their customers who need them.

The quality of the content depends on the type of subscription that a user pays for. The content is available in either SD, HD, or Ultra HD.

The Netflix Business Model

Netflix business model is an exciting prospect that has evolved over the years. Now that they have established their dominance over other streaming services, it is safe to say that their research and development has paid off.

The Target Audience of Netflix

Netflix currently leads as the largest internet television network with over 100million subscribers. They have subscribers in over 190 countries who stream 125 million hours of movies and TV shows in one day.

Netflix targets customers regardless of their gender. Moreover, their customers are between the ages of 17 to 60. Furthermore, most customers belong to households that have annual income of $30,000 or greater. If you study the statistics, you will realize that the subscribers belong to different lifestyles. This disparity shows that Netflix uses psychographics, not demographics, when targeting prospective customers.

There are three types of people that Netflix is targeting with their product. They are:

  1. Users that are too busy to go out and shop for movies and shows
  2. Consumers that love movies and who frequently rent movies
  3. Users that want the best bang for their buck

What Value Does Netflix provide to its Customers?

Netflix has been able to climb the ladders of success, all thanks to its huge database of movies, TV shows, documentaries etc. Moreover, to add more value to its service, Netflix has incorporated a smart suggestion algorithm to its streaming service that suggests new movies and shows according to the likes and watching habits of the user. It is one of the best suggestion algorithms that provides personalized suggestions.

Furthermore, Netflix is well-known for their no advertisement policy. No ads mean that the users can enjoy their favorite shows and movies without any disturbance. This makes Netflix a great option if you love binge watching.

netflix business model

Netflix has a wide array of supported devices. They currently have the best range of supported devices that includes PCs, TVs, mobile phones, and video gaming consoles. Last but not the least, Netflix promotes binge watching by releasing entire season of shows instead of weekly episodes. The viewers no longer have to wait days before they could see another episode of their favorite show. With Netflix, the users can view the entire season of shows in one sitting.

How Does Netflix Business Model Make Money?

Subscriptions fees is the main source of revenue for the US streaming service. Subscribers pay Netflix to get access to the huge database of movies and TV shows. The subscription fees pay for the expenses that are incurred by the company.

As Netflix does not host advertisements, subscription is the only significant source of income. The streaming service offers three different plans to it users. Each plan offers something different and has a different monthly price tag attached to it. The three plans are:

  • Basic – the content can only be streamed in SD
  • Standard – the content can be streamed in HD
  • Premium – the content can be streamed in Ultra HD
netflix business model

The subscription fees of each plan varies from country to country. The DVD rental service operates on a similar model. The monthly subscription in this case depends on the number of discs that are rented at a time and rented for the month. The online subscription and the DVD rental services are different and cannot be combined together for a better deal.

Expenses That Are Incurred By Netflix

While Netflix has been making loads of money in every fiscal year, most of the money that is made is reapplied in the business to pay for various expenditures incurred over the fiscal year. These costs include licensing cost of movies and TV shows, production cost of Netflix original titles, marketing, research and development, technology expense and other costs.

These expenses eat a huge chunk of the profits that are made by the streaming service. This is one of the reasons why the executives at Netflix are contemplating whether they should introduce advertisements. For example, they spent about US$ 8 billion in 2018 for content generation.

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Business Models

Craigslist Business Model: The Go to Website for Classifieds

When you mention classifieds to any American, the first thing that comes to their mind is Craigslist. Craigslist took off amidst the dot com bubble. What started in 1995 as an email distribution site, it has come a long way since then. Moreover, the Craigslist business model grew into a lucrative website that now handles around 50 billion views each day. So, you can literally say that most Americans resort to Craigslist when they need to buy or sell anything.

During the dot com bubble, Craigslist started off as a not-for-profit organization. However, when founders saw the scope of their business entity, they moved to a for profit model in 1999. In its initial days, Craigslist depended on word of mouth marketing. During the early years, digital marketing was not as strong as it is today.

Nowadays, Craigslist is synonymous to online classifieds. Furthermore, Craigslist paved way for different online classifieds around the world. Craigslist is very popular in the States and has no competition over there.

To learn how Craigslist makes money, we need to understand the entire business model of the classifieds company.

What is Craigslist in a nutshell?

Craigslist is an online classified website that is region-specific. Moreover, the website has separate web pages assigned for different categories of classifieds such as jobs, services, housing, gigs, buying and selling, forums and many more.

craigslist business model

Most of the advertisements that are put on Craigslist are free. Users can go on Craigslist and put up an ad in the respective category that adheres to the needs of their classified. However, depending on the region and the type of classified, you might need to pay to put an advertisement.

How Does the Craigslist Business Model Work?

Being the pioneers in the field of online classifieds, Craigslist works by listing different advertisements in their respective categories. Advertisements are categorized to avoid any confusion and to make sure the viewer can easily navigate all the relevant listings in one place. Also, the advertisements are listed according to their region.

Online classifieds have transformed over the ages. Classifieds can still be found in newspapers. However, there are more reasons now to switch to online classifieds instead of the ones included in newspapers. Advertisements that are uploaded on Craigslist have a longer life when compared to the classifieds in a newspaper. Most important of all, advertisements uploaded on Craigslist are usually free of cost. The little to no cost involved in uploading a classified is the biggest reason why Craigslist was so quickly adopted back in the days.

craigslist business model

The best part about Craigslist classifieds is that they are available online for a longer duration. Moreover, they can easily be searched through Google or their own built-in search engine. The region specific classifieds make it easier for the user. They no longer need to sort listings to find the ones that are in their region.

Lastly, the ads that you put on Craigslist can be easily modified. Unlike the ones that are put in newspapers, you can easily navigate to your listing and make the necessary changes.

How Does The Craigslist Business Model Make Money?

A question that bothers many users is that how does Craigslist make any money when they do not charge money for any classifieds? This question will be shortly answered as we probe deeper in their business model in the next few paragraphs.

The reason why Craigslist has been able to stay afloat over the years is because they have a solid revenue model. Their revenue model has helped them to grow as a unit and this is the reason why they have been able to sustain themselves over the years.

The idea to stop spam and the introduction of paid advertisements

A certain business practice was included by Craigslist in their business model to avoid spam listings. This inclusion was not part of the proposed revenue model in the initial days. However, the successful run of this strategy meant that it was quickly incorporated in the revenue model.

Craigslist started to charge fees for putting up ads in competitive categories. Categories such as jobs used to see a lot of spam listings added to them. To fight the spam, they started charging money to put listings in some categories. This fee varied from region to region. Moreover, this fee was only applicable to categories that saw thousands of listings added to them daily.

For example, customers were expected to pay $25 for putting job listings in San Francisco. Not only this helped combat spam, but it also helped to pay for the running cost of the website. This particular step proved to be lucrative for the founders and they introduced this scheme in other popular categories too.

Learn about Recommerce here.

You can refer to the image below to see the cost involved for putting up certain advertisements in different categories. Moreover, there is a repost cost involved in Craigslist as well. This repost cost is only included in the categories that require you to pay for a classified. Lastly, the reposting cost is lower than the posting cost.

craigslist business model

What is the net worth of Craigslist Business Model?

Craigslist is a private entity which has never relied on any investor to raise money for their corporation. Hence, we only have an estimated net worth of this online classifieds website. Craigslist had about $700 million worth of revenue with a profit margin of 80% in year 2016. Keeping the statistics of the year 2016 in mind, the estimate net worth of Craigslist is about $3 billion.

craigslist business model

It currently sits as the most successful and profitable online classifieds website. The reason for its success is the employment of the ‘network first, profit later’ strategy. The website was completely free for users in its early years. They only added a paid advertisement option when Craigslist got popular among the consumers.

Conclusion

Craigslist business model enjoys distinction as the pioneer online classifieds website. The region-specific listings with different categories for various classifieds helped to gain much needed approval from its consumers. After allowing the consumers to use their product for free, they introduced paid advertisements in popular listings. These paid advertisements are their biggest source of revenue.  

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Business Models

PayPal Business Model and the Crucial Service It Provides

In this digital age, online payments have been made easy. You can use various apps to complete different transactions. The path to online payments was paved by PayPal when it was founded in 1998. It started out as Confinity in 1998, but then has evolved since then. The PayPal business model has grown robust with passing time. PayPal allows online transfers which makes for easier and electronic transactions.

PayPal processes payments for various online vendors and users. The company charges a fee for all the transactions that are processed for respective users. It used to be extremely cumbersome to send money abroad in pre-PayPal days. Banks used to take at least a week to process money transfers. The inception of PayPal revolutionized the money transfer services. The American outfit coined the term which is now known as ‘online money transfers’.

paypal business model

PayPal and its business model has expanded to about 202 countries worldwide. Moreover, they are currently serving 197 million users from different parts of the world. The number of users are growing with every passing day as outsourcing and freelancing is on the rise.

The ever-increasing customer base has elevated PayPal to 222nd on the Fortune 500 list of 2018. The company is expected to see more growth in future years.

PayPal and Its Many Uses

PayPal started out as a simple online payment tool. However, it has evolved over the years. It is a robust financial tool that offers various monetary services to its users. Here are the services that you can use:

PayPal Business Model and Bank Cards

Save your Debit and Credit cards on PayPal so that you can go around without cards – PayPal allows the users to save the details of their cards on their PayPal account. The users can then process various payments directly through the PayPal app. You no longer need to worry about forgetting your cards at home. Your cards will be with you at all times as long as you have your smartphone with you. The app works as a digital wallet.

One Touch on PayPal

Complete transactions on website with one click – PayPal has baked a new feature in their app which is known as ‘One Touch’. The feature helps to save time for the users as they are no longer required to enter PayPal login details on every website. Your login details are saved on your account and you can check out at any website with one click.

Send and Receive Money through PayPal Business Model

Send and receive money from bank accounts and PayPal accounts – The financial service allows the users to send or receive money in over 202 countries. Furthermore, PayPal allows you to keep the money in your PayPal account in about 25 different currencies.

Payments made Easy

paypal business model

Receive payments – PayPal business model took off when they included it as a payment gateway on eBay. PayPal helped to grow ecommerce.

Introduction of Debit Card in PayPal Business Model

PayPal allows its users to apply for debit cards. These debit cards work the same way as the ones that are issued by different banks.

PayPal Credit (Bill Me Later)

You can take a credit of $99 from PayPal which is interest free if you pay it in full under six months.

PayPal Business Solutions

The app allows you to set up your business online with help of PayPal. Furthermore, you can acquire a working capital loan from them. The loan can be payable daily.

How does PayPal Business Model Make Money?

PayPal is responsible for starting a revolution in fin-tech sector. The financial company may not be a bank, but it offers about all services that you may expect from one.

paypal business model

eBay acquired PayPal in 2002 for a whopping $1.5 billion. The ecommerce giant made a crucial acquisition back in the early days of PayPal. While PayPal is no longer a subsidiary of the American ecommerce giant, but the inclusion of PayPal as a payment option helped in the promotion of PayPal.

PayPal paved the way for the likes of Paytm and other payment banks.

Sources of Revenue for PayPal Business Model

Transaction charges

PayPal has two type of user accounts – personal and business. Personal accounts are levied a transaction fee when they make a payment through PayPal.

Moreover, the business accounts are levied a fee of 2.9% plus $0.3 USD of the total amount of the transaction. This fee is reduced as the value of transaction increases. Users do not pay any withdrawal fees. However, in cases where users need to draw money through a check, they will be charged $1.5 USD.

Payments from Around the World

Users are levied charges when they receive international payments. Moreover, they need to pay conversion charges as well. These charges also increase international payment charge.

PayPal Business Model: Business Account Charges

You can register for a business account on PayPal for free. However, for extra features, you need to pay for a subscription. The Payments Pro business account costs $30 USD monthly and provides extra features such as customized check out pages.

P2P Payments

Users can establish custom links to receive payments. PayPal charges the business account rates in USA when users withdraw money from custom links.

PayPal Business Model: Interest

The users keep their money with PayPal. PayPal invests the balance in liquid investments. These investments incur interest. The company generates revenue from the money deposited by users.

Integration of Payflow in the PayPal Business Model

PayPal was initially used as a payment gateway by eBay. Times have changed and PayPal has developed their own. Users with a business account can integrate it on their websites.

paypal business model

There are two types of plans that users can subscribe to. The free plan has a page hosted by PayPal that allows the consumers to enter their payment details to complete transactions on respective websites. The premium version that comes at $25 monthly subscription allows the users to customize their check out page.

Users pay $0.1 USD to PayPal gateway charge to use this service, regardless of their plan.

Conclusion

PayPal started out as a fin-tech product in 1998 and has grown exponentially over the years. Now, it offers all the financial services that you expect from a bank. Moreover, they use various sources to generate revenue. The biggest sources of revenue are the fees that they charge and the interest they receive from deposited money.

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Data

Data Analytics in Healthcare Sector

Healthcare data, when used with data analytics, provides valuable insights for the hospitals to improve their quality and patient welfare. Hospitals have long been using descriptive analytics to diagnose the patient based on their medical record. Doctors also use descriptive analytics to find the current health state of the patients.

But descriptive is only the tip of the value that data analytics provides. The real use of analytics comes by using predictive and prescriptive analytics. Hospitals in developed and developing countries have started to use predictive analytics only recently aided by the vast collection of medical records and increasing computer resources.

The predictive analytics brings much value to the hospital than the descriptive. Instead of just analyzing the past data and presenting the information by the descriptive, prescriptive analytics predicts the future outcome by analyzing the past data. It gives the hospital a great advantage by staying one step ahead.

Here are some of the ways in which hospitals use predictive analytics.

Predicting the risk of illness

Healthcare organizations use predictive analytics to find the probability of a person getting a medical condition in the future. With the use of the patient’s medical data and the historical record of that particular medical condition, predictive analytics can find the probability for the person diagnosing with the medical condition.

The healthcare organization can also find the progressiveness of a medical condition in a person given the medical record. This process of predicting the risk of a medical condition for the patient helps the doctor get ahead of the condition and cure it.

Avoid patient readmission

In addition to finding the risk of a medical condition for a patient, predictive analytics can find whether a patient is about readmitted to the hospital. After being cured by a medical condition, there is always a chance for a patient to be admitted to the hospital again for the same condition or a related one. That is, the condition reappears for some patients. Data analytics can accurately predict whether the patient is about to readmit to the hospital. This helps the doctor to fully address the medical condition of the patient and avoid readmission.

Manage Supply Chain

Hospitals require a variety of medical and non-medical products each day for smoothly running the operation. The stock and the supply chain should be managed efficiently so that no product is limited or much.

This is where predictive analytics comes in. The demand for medical products can be found with a good probability by using predictive analytics. And using the result, the hospital can stock up their products. Also, the supply chain process can be optimized with analytics to save time and money. Optimizing the supply chain process has been used in many sectors successfully for many years.

Boosting patient and hospital satisfaction

Data analytics is also used to predict when the hospital might get busy or when a patient might skip an appointment. This can be found by analyzing the previous record of the hospital and patient to find a pattern.

This could help the patient to know the busy time at the hospital and avoid it. It also helps the hospital about the patient’s appointment and schedule based on it. On the whole, this process of analytics helps better satisfy hospital staff and patients.

Developing new medicines faster

Developing a new drug is a time-consuming process. There is a chance that the current drug variant might fail. If it succeeds, then animal trials must proceed with success. Then human trial and finally, the FDA approval.

With the introduction of the big data approach to the drug trial, the drug trial process can be accelerated. The predictive analytics helps in finding whether the current variant of the drug will succeed provided the relevant data and thereby saving valuable time.

Analytics also helps in finding new insights with medicine. For eg: aspirin, a pain reliever is found to have the property to treat colorectal cancer by using different analytic techniques. There is a huge potential for analytics to play in the field of medical science.

Conclusion

Analytics is transforming the Healthcare sector in a good way and it is a good example of true value in using data analytics. In this decade, we will see hospitals using predictive analytics often to improve patient care.

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Data

How Predictive Analytics is transforming the insurance sector?

Data analytics is not only used in the technology sector now. It has permeated through every industry. Each industry is implementing data analytics in its own way and finding value in it. Insurance Industry is one such were data analytics plays a big role.

To be fair, Insurance companies have long used analytics to find the loss in property or to analyze the damage. But, with enormous computing resources and data resources available right now, companies can do much more with data analytics than just analyzing risk and damage.

Predictive analytics is the process of analyzing the past data to find potential future outcomes in the scenario based on the data. In insurance companies, predictive analytics is the most used.

Predictive Analytics in the Insurance Industry.

The predictive analytics is extensively used in the Insurance Industry for various purposes. It is used to identify customers who are at the risk of canceling insurance. Using predictive analytics with the customer data, we can identify customers who are likely to lower the coverage or cancel the insurance. It gives insurers the knowledge to give special attention to retaining customers.

Fraud in insurance companies is always a thing. Though there are various measures taken by these companies to control it, it is not successful. Predictive analytics and Prescriptive analytics can help companies to find malicious customers who are likely to commit fraud. With the customer data from various sources like social media, internet, etc, the companies can find potential fraud with a high probability.

Insurance companies are using analytics to find potential targets to advertise. With data about the demographics and people, the companies can find the market to target their insurance for the people. Previously, the companies had to use manual advertisements without any real knowledge about the outcome. Now, they can target specific demographics of people with the help of analytics to get better results.

Not only identify potential customers, but the companies can also give a personalized experience to the customers using the analytics. With descriptive and predictive analytics, insurance companies can find the behavior of the customers and can anticipate their needs. With this result, they can provide more personalized service to them. This makes the customers be loyal to the company and in turn increase the profit of the insurance companies.

With the analytics, the insurance companies can get ahead of their competitors. By finding new trends using predictive analytics, insurance companies can create new insurance plans, services, or products. The analytics companies can also optimize their pricing for the insurance plans using analytics to give better plans to the customers and in turn, increase their profit. This gives a serious edge for the companies to grow and be top in their domain.

The analytic companies can also identify potential customers for risk before providing insurance services to them. With the data about the customer from the banking sector, and online, the insurance companies can find the customers who might be a risk to provide insurance with a certain probability. This helps the companies to make better decisions when it comes to providing insurance.

Conclusion

As you can see, the insurance companies are using predictive analytics in many ways to improve their business and provide better service to the customers. In this decade, as the data grow tremendously, we will see huge opportunities for not only the insurance companies but also all the organizations that use data analytics.

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Data

Different open-source data analytic tools

Data Analytics is a growing field that is becoming a significant part of any business field. Due to this popularity, there are lots of tools and software that are available for you to do analytics. Some of them are open-sourced and some of them are proprietary. But, both have their own advantages and disadvantages.

When it comes to analytic tools, open-source tools are more popular than the proprietary ones. The reasons are it doesn’t have any lock-in by the vendor, free to use, and a large community of fellow developers to help. The open-source tools also have good if not better documentation and update support than the proprietary ones.

Here are the major open-source analytic tools that you can invest to improve your career.

Apache Hadoop

It is the go-to tool for processing a large volume of data. Hadoop is a Big data framework developed by Apache Foundation. Hadoop can process data efficiently with low hardware requirements. It has its own file system called HDFS (Hadoop Distributed File System) that handles the data storage process.

The Hadoop leverages the parallel processing by using Map Reduce programming. Hadoop is usually used in conjunction with the Apache Spark. It is a high performing memory processing engine.

To write the processing program in Hadoop, you can use any languages like c++, python, java, etc. This property gives Hadoop a great flexibility for developers. Used in more than half of the fortune 500 companies, Apache Hadoop is a must-learn for aspiring data scientists.

Mongo DB

The main function of MongoDB is for data storage and not analytics. But it does have some features that make it a good choice for real-time analytics. MongoDB is a NoSQL based database storage system. It means that you store data that has no structure in it and manipulate them using queries. Most of the data that you’ll be analyzing in real-time will have no structure. So, it makes MongoDB a perfect fit.

MongoDB can also be deployed in the cloud. It offers great flexibility of configuration. The main reason why MongoDB is used real-time analytics is that it stores data in the form of JSON objects. Most of the real-time analytics are written in Java or similar language. These applications can easily convert the JSON object to Java objects. The unstructured data can be accessed quickly in MongoDB, and it makes it a good candidate for dealing with real-time analytics.

MongoDB is the most popular data storage tool used in companies like Google, Flipkart, Amazon, and Facebook where real-time analytics is a major work.

R Environment

The R environment is a suite of software and libraries that are specifically used for data analytics. You will be working with the R-studio tool that uses the R programming language for working with your data. It has support for data storage, graphical facilities for data visualization, a variety of data analytics packages, etc. Most of the popular machine learning algorithms that you wish to deploy are present as a package in the R. It makes the work of data analyst easy as you can just install and work with the algorithms.

Other advantages of using R framework are it can run in an SQL server, has support for Apache Hadoop, cross-platform, highly scalable, and easily portable. Companies like Microsoft, Google, Amazon, etc are using R for the analysis of data.

Python

Python is not a tool but a programming language. It is currently the most popular programming language for data analytics. The popular packages in python for data analytics are Pandas, Numpy, Scikit Learn, SciPy, and Matplotlib. These packages are used in implementing machine learning algorithms seamlessly and without much work. Python also offers data visualization capabilities in the form of the Matplotlib package.

Some other advantages of learning python are its simple syntax, large developer community, cross-platform functionality, etc. Almost all of the tech companies are using python for data analysis. So, it is a good place to start for aspiring data analyst.

Conclusion

With analytics gaining significance day by day, there are a variety of analytic tools available for developers and analysts. It is essential to select and learn about a good tool to advance your career in data analytics.

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Data

Learn analytics and become a data analyst.

Data Analyst is one of the high paying, in-demand jobs right now. Every company despite its field is becoming data-driven. They are trying to find value with their data. They can outsource the job to other companies or hire their own analyst team. Whatever the decision may be, a data analyst is the one who finds the value in data. Naturally, the demand for data analysts is booming. IBM predicts the demand for analysts will soar 28% by 2020.

If you want to know how to become a data analyst, then you have come to the right place. First, let me quickly give out the definition for the data analysis. As an analyst, you’ll be working with a huge volume of data to derive valuable insights for the organization using some analytic technique. This process is data analysis. The analyst role is not only exclusive to the data. There are a few types of analyst role present,

Data Analyst – As I said, the role of the data analyst is to retrieve valuable information from the data.

Business Analyst – It is closely related to the data analyst, but business analyst retrieves useful information that drives the performance of the business. They find insights on how to refine the day to day operations, mitigate risk, etc. They are more related to the business side than the analytics side.

Quantitative Analyst – They work in the financial industry to predict the stock or bond price, find any potential risk in investment, etc.

As you can see, there is a variety of data analyst role. Whatever you decide to become, you should have the following capabilities.

Have strong knowledge in Mathematics

Though there are tools and software that executes the mathematical algorithms, it is vital for a data analyst to have strong mathematical skills. Whatever analysis you may do with the data, it all has math at its core.

Data Cleaning uses basic statistics like mean, mode, median, etc. Linear algebra like matric manipulation is used in the development of neural networks. Gradient descent in the regression algorithm uses calculus techniques. Graphs and tree structures in discrete mathematics are used in the optimization algorithms.

The analysis process literally touches every area of mathematics. It is easy to get disappointed to see the hurdle in the form of maths. But as I said, there are many tools and software that mask the difficulties in implementing the algorithms. Scikit-learn, TensorFlow are free popular machine learning frameworks that don’t require any mathematical skills to work. But if you want to become a top person in the analytical field, you should take the difficult path and have the knowledge of mathematics.

Know your technologies

You should not only have mathematical skills but know how to deploy the maths using technology. You will work with any one of the popular technologies like excel, python, SQL, Tableau, Hadoop, etc in your company environment. These technologies help you in executing the analytical process like cleaning, visualizing, analyzing, etc seamlessly. So, have working knowledge in all the process of analytics from cleaning to visualization using a popular technology to land a role as a data analyst.

Data Analysts should also have knowledge of programming. All the popular tools like TensorFlow, Hadoop, spark are based on coding. So learning a programming language is a must. For analyzing and visualizing data, Python, and R is a popular programming language to learn.

Learning by doing

Learning by doing is the best way to learn anything. So after you master your skills in mathematics and analytics technology, practice your skills by solving real-world problems. You can check my previous post on a “data analytics project to advance your career” to have an idea about it, or there are a number of projects hosted on the Kaggle website that you can work on. Having a working knowledge will go a long way in grabbing the role of the data analyst.

Conclusion

Learning about analytics may seem difficult as it involves mathematics and programming. But once you start learning about it, you’ll be amazed by the potential of it. It is the most popular domain right now, and analysts are in high demand. There are numerous sources available on the internet to start your learning. It is never too late. Start now and become a part of this wonderful domain.

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Data

Trends in Data Analytics

Data Analytics has seen tremendous growth in the last 5 years. Data Analytics is now not just a luxury item that only big organizations can deploy. In the last decade, the availability of cloud, open-source frameworks, and tools made it possible for even small companies to deploy data analytics. With many small to medium businesses adopting it, the Data Analytics will continue to grow and become ubiquitous. In this post let’s see some of the trends in analytics that will shape up the future of modern business in this decade.

Augmented Analytics

A new step in improving data analytics, Augmented analytics is the process of using AI and ML to automate and enhance the data analytic process. In today’s analytics, data extraction, preparation, and analysis are done by data scientists. It is a tedious task and consumes lots of man-hours that could be spent elsewhere.

By integrating ML algorithms and NLP models, these tasks could be automated and data scientists could concentrate on more valuable tasks. The integration of ML in the data extraction and preparation also reduces the error when comparing it with manual data preparation.

The NLP models are used here to interact and interpret the data and result in the data analysis. When the data is generated on a large scale, it takes valuable human hours to analyze it. Whereas, a high functioning NLP can do it in less time with fewer errors. This is how Augmented Analytics enhances the data analytics process. It is predicted to grow significantly in this decade.

Embedded Analytics

The traditional analytics takes time in extracting data from other applications, analyzing it, and giving valuable insights. What if the analytic process is embedded into the user application so everything happens in real-time. It also gives insights in real-time, so decisions could be made faster.

For a long time spreadsheets have been hindering the process of visualizing the data. If the analytic tools can be integrated into the user application, it takes care of all the processing and visualizes the result that could be understood by humans rather than presenting as a number.

The embedded analytics technology has already been implemented in a lot of applications like google analytics, salesforce analytics platform, and amazon e-commerce site. These platforms are good examples of embedding analytics as they provide real-time information about the product to the customers. This trend will grow significantly and impact the process of decision making in the next 2 to 3 years.

Blockchain in Data Analytics

Whenever you are dealing with a large volume of data, you are likely to be faced with a major issue called data privacy. The privacy of user data is currently the nightmare for all tech companies right now. When you have complete control of data as an organization, people will get concerned and privacy issues will grow. This problem can be solved by using Blockchain.

A blockchain-based data storage system provides complete transparency and security for all the data stored in it. It is the reason why the cryptocurrency is becoming popular than the traditional centralized currency. But realistically, the blockchain-based data storage system hasn’t matured enough to be implemented with a data analytics system. But still, it has a very high chance to be successful in the near future.

The use of predictive and prescriptive analytics

In the current data analytics world, the use of predictive and prescriptive analytics is not popular. Most organizations are still using descriptive and diagnostic analytics to give a summary of their past performance and find any problems in it. They have not touched the full potential of the analytics which is predictive and prescriptive.

The predictive analytics is used to analyze the past to predict the future based on the scenario. The prescriptive does one more than that by presenting the organization with the best possible way to take to improve the business. This type of analytics will see a big growth in the next few years as companies start to realize the value in it.

Collaborative Business Intelligence

Today the business managers who take decisions and the analyst who create the result to take decisions are working in a different environment. There is a gap between. It is not essentially a big problem, but there is room to be improved here. Collaborative business intelligence is the integration of analytic tools and the collaborative tools to make sharing the results easier and faster.

The collaborative tools are nothing but social media tools to share the information but to the respective manager. It makes the decision making process faster and improves the performance of the business. This trend is sure to grow in this decade as companies start to see the value in collaborative business analytics.

Conclusion

I have listed only some of the trends in data analytics that will affect the organization in this decade. As data analytics is a vast domain, there are many trends and varieties of analytics that will become possible. Each organization is different and each organization use the data analytics differently according to their work. The analytics platform is evolving day by day. This is the reason why we will see many trends in the near future.

Categories
Business Models

Learn About The Franchise Business Model

Fast food chains grew all over the world, thanks to a phenomenon: the franchise business model. We all know and love McDonald’s. It is a common household name all round the world. McDonald’s is the largest food chain in the world. It has about 37,000 outlets in more than 115 countries. Their global presence and popularity is possible because of franchisees around the world.

Franchisees are companies that are in contract with a franchise to sell their goods while abiding to their established standards. The franchise business model increased the reach of McDonald’s and similar food chains. This newly established reach meant that their customer base increased and so did sales in the long run.

franchise business model

These franchisees use the name of McDonald’s to sell their products. However, they are not owned by McDonald’s. They can sell the products of McDonald’s as long as they maintain the standards set by the parent company.

This might sound astonishing, but around 80% of all restaurants and food chains are franchises of their respective parent companies. The success of McDonald’s and the wide adoption of this business model shows that the franchise business model is lucrative.

Let’s start with the basics and inform you about things such as franchise.

What is A Franchise in A Franchise Business Model?

To understand what a franchise is, it is necessary to understand franchisor and franchisee. We will use McDonald’s again as an example. McDonald’s is an American fast food company. However, you see their branches in different parts of the world. The owners of these branches are the franchisee, and McDonald’s is the franchisor.

Hence, a franchise is a contract agreed between a franchiser, who is the owner of the brand, and franchisee. A franchisee can be an individual or a corporate company that agrees to the set standards of the franchiser so that they can use the knowledge, products, trademarks and other proprietary things of the franchiser to sell their products in a certain region.

To make it sound simple, a franchise allows the franchisee to use the following things:

  • Use the franchisor’s brand name
  • Use the trademarks and patents of the brand
  • Operating knowledge
  • Marketing strategies of the franchisor
  • Software and other IT systems of the franchisor
  • Proprietary knowledge of the brand

Hence, this leaves us with an important question. How do they work?

How does the Franchise Business Model Work?

Like we mentioned before, franchise is an agreement between a franchisor and franchisee.

A franchisor is the brand that is looking to expand their business without making huge investments and with minimal involvement of their own.

Also, a franchisee is an individual or a corporate company that is willing to buy the rights to sell the products of a brand in a region. Moreover, the rights allow them to use proprietary material and marketing campaigns of the franchiser.

A franchisee uses the demand and goodwill of the brand to sell products of the franchiser to make profit. A franchisee is not an entrepreneur as they do not work on a new idea.

How do they operate then?

The operating model of a franchise is rather simple. The franchise agreement makes the franchisee eligible to use proprietary information and sell products under the name of the franchisor. The services are rendered under the name of the franchising brand.

There is a licensing cost that needs to be paid by the franchisee. A franchisee cannot operate without paying this fee. This fee depends on the demand and popularity of the franchiser. Furthermore, aspects such as the reach of the brand, their marketing strategies and scale of the brand are key factors that determine the licensing fee.

franchise business model

However, the licensing fee is not the only fee that needs to be paid. The franchisee is also required by the agreement to pay an ongoing royalty fee to the brand. This royalty fee is a percentage of the gross sales made by the franchisee. This percentage is decided when the franchise agreement is being penned down.

In addition, a franchisee is obligated to follow all the conditions mentioned in the franchise agreement. Moreover, they need to use the operation manuals that are provided by the brands. The franchisor provide special on-field training to make sure that the quality of service is up to the mark. This training may be provided through different means.

Lastly, the franchisee is expected to maintain the quality of service, price, product, discounts etc. that are introduced by the brand.

What Does the Brand Do in A Franchise Business Model?

The franchise business model sustains itself on the demand and popularity of the brand. A franchiser works hard enough to create demand and popularity of the brand so that it can be further franchised to others.

Franchisees help the brands to expand their business. Moreover, they also increase the revenue of the parent company. On the other hand, the franchisee uses the demand of the franchisor to make sales. It is easier to sustain a business that has a set demand and supply.

Lastly, the brands are responsible for marketing the products that are sold by franchisees.

What Makes Franchising Business Model Better than A New Business?

You may often hear that buying a franchise might be a safer investment than starting a business from scratch.

The market already exists

It is easy to sell things that have an established market and demand for them. The franchisee do not need to spend time and other resources in creating marketing strategies to sell their products. It is the brand’s job to take care of those things.

In addition to that, if there is definite demand of a service, it is easier to get sales.

Only Need to Follow an Operational Procedure

Franchisees are expected to follow standard protocols of operation that are regulated by the brand. No research and development is expected from the franchisee. They just need to follow the set rules to make profit.

There is less risk involved

Franchise of an Apple store

Franchising involves less risk because of an already established market for those products. You already have the list of things that need to be taken care of.

Conclusion

Franchising is the act of buying rights from a brand to sell their products in a certain region. It is a model that involves two parties: franchisors and franchisees.

McDonald’s is an example of the franchise business model.

Categories
Data

The strategy behind Data Analytics.

If you’ve been reading my post, then you’d know that data analytics is the most disruptive technology impacting all the organizations. Everywhere around the world, Businesses are trying to become data-driven. That is, they are using the data to improve their performance.

For a new business trying to adopt data analytics, a strategy must be created before delving into it. Without a strategy, a variety of things could go wrong implementing the data analytics. Companies could get stuck in one phase of the analytic process, or they could get drowned in the overwhelming amount of data, or they may choose a wrong algorithm for their use case. To avoid these scenarios, a business must have a data analytics strategy in place to govern the process.

Necessities required before developing an analytics strategy.

Before developing a strategy, an organization should assess if they have the following capabilities.

The business needs to make sure that they have the analytic capabilities to do the analytic process. Capabilities like tools, analyst team, data, etc should be present in the business. The infrastructure within the organization should support the analytic process so that every person in the organization gets access to the analytic tools. The culture within the organization also should enable the employees to know the value of analytics and to contribute to it.

Analytic Strategy

After a company verifies that it has the necessities required for developing a data analytics strategy, it can start forming the strategy. A good way to develop a strategy is to answer the following questions.

What problem are you trying to solve?

A company should clearly identify the problem before starting to collect the data. Everything in data analytics from data selection to algorithm selection depends upon it. Data analytics can improve a business in a number of ways like identifying potential customers, predicting performance, etc. It may look silly but a company should have a clear goal in their objectives and set their priorities right.

What data do you need?

After identifying the goal, determine the data that you need for achieving your goal. It is one of the daunting tasks in the analytics process. Identifying data and make it cleansed is not easy even for a big analytic team. This task should be done carefully as it affects the further process if not done right.

Wrong data or noisy data significantly affects the quality of the results you get. However good maybe your algorithm or analytic process, if the data is not good then your result will be not.

How will you analyze the data?

Once you identify your goal and the data to achieve the goal, you need to define how you are going to convert the data into valuable insights. You should define the tools, algorithms, and necessary process. This is the core part of the strategy build and an easy part too.

The analytic process may seem daunting but there are various tools and software that abstracts the complexities present in the analysis. The only difficulty here is to choose the right algorithm and tool for analyzing the data you have to meet your goal.

How will you present the result?

Getting the required result is only half the story. The rest is how you present them. Mostly the results from the analytic process will be in mathematical form. This cannot be easily understood by the management team. So, you need to pick a visualization technique to present your result.

There are lots of tools available for this step. These tools are mostly integrated with analytic tools itself. So, this process will be easy once you identify the way you should present your results.

Conclusion

The Strategy building task is the reverse of the analytics process. You identify the goal first and work your strategy upon that. After creating the strategy for your analytic process, you need to create an action plan and execute it. In addition to forming your strategy, keep key decision-makers of your organization involved in the critical stages of the analytic process. It helps them make a better decision.

Every organization implementing an analytic task should have a strategy. It lets them execute the analytic process seamlessly. Keep in mind that the action may not go as planned in the strategy. You may not meet your goal, or the result may lead you to a different path. If that happens, examine your strategy again to get the desired result.