Trending March 2024 # Jetpack Ai Assistant: Pricing, Features And Use Cases # Suggested April 2024 # Top 6 Popular

You are reading the article Jetpack Ai Assistant: Pricing, Features And Use Cases updated in March 2024 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested April 2024 Jetpack Ai Assistant: Pricing, Features And Use Cases

Create compelling and professional content within WordPress with this powerful AI assistant.

About Jetpack AI Assistant

JetPack AI Assistant is an AI tool that creates engaging content within the WordPress Editor. It allows users to write blog posts, edit content, and adjust the tonality of the posts using AI. The tool can also suggest titles, generate summaries, and translate text into various languages.

JetPack AI Assistant has an intuitive interface with powerful AI capabilities to help users produce high-quality content faster. It can generate various types of content, including tables, blog posts, structured lists, or detailed pages. The tool is integrated into WordPress. So you can start using it immediately after creating your free account.

Jetpack AI Assistant Features

Jetpack AI Assistant offers several impressive features for WordPress users. Some of the best functionalities of this tool include the following:

It can easily be integrated into the WordPress editor.

It has an intuitive and beginner friendly interface.

It generates content on a diverse range of topics.

JetPack AI Assistant adjusts the tone to match the style and context of the blog post.

It detects and corrects spelling and grammatical errors.

Users can request the tool to generate a title or summary for a blog post.

It translates content into multiple languages.

It creates content faster, saving the time of writers and website owners.

Jetpack AI Assistant Use Case – Real-World Applications

JetPack AI Assistant can be used for various purposes. Some of its applications include the following:

Content creators can use it to write blog posts, articles, or website content.

Editors can use it to spot errors in the content and edit them.

Businesses can use it to ensure their content is of high-quality.

It can be used to produce content in various languages.

Jetpack AI Assistant Pricing

JetPack AI Assistant has a free and paid plan. The prices of its plans vary depending on the features and number of requests they can handle. Below is an overview of both JetPack AI Assistant plans:

Free – $0 per month – It can handle up to 20 requests, create tables, blog posts, lists, adjust tones, and solve grammatical issues.

Paid – $12.54 per month – It includes everything offered in the free plan, high-volume request access, and priority support.


Does the JetPack AI Assistant Premium Plan have a request limit?

No, the premium plan doesn’t impose any limit on the number of requests sent or processed by the platform. It supports an unlimited number of requests with priority access to the support team. However, the company says that it will impose an upper limit on the number of requests in the coming months. Keep checking their announcement page for the latest information.

Can the JetPack AI Assistant adjust the tone?

Yes, the JetPack AI Assistant allows users to modify the tone of their content. You can choose between a formal or conversational tone, and the tool will edit your content accordingly.

Is the JetPack AI Assistant available for free?

Yes, the JetPack AI Assistant is available for free. However, it only supports 20 requests and offers limited features. To enjoy all the premium features and get priority access to the support team, you need to switch to the premium plan.

Is the JetPack AI Assistant available within WordPress?

Yes, you can access the JetPack AI Assistant within your WordPress editor. It is integrated within WordPress and doesn’t require you to download any software or tool separately. You have to install the JetPack AI Assistant Plugin, and you will get all its features right within the WordPress editor.

Can I use JetPack AI Assistant to write blog posts for publishing online?

You can use the JetPack AI Assistant to write blog posts for your online blog. It can generate blogs on diverse topics and publish them online. It generates unique, plagiarism-free content that can be used for personal or commercial purposes.

JetPack AI Assistant is a powerful companion for writers and editors. It can rapidly write and edit various types of content within the WordPress editor. The tool is ideal for freelancers, editors, and businesses that want to save time while producing high-quality content.

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Copy Ai Review: Pricing, Features And Alternatives 2023

Copy ai is a copywriting tool and content writing assistant to create high-conversion copies.

About Copy ai

It can automatically produce highly targeted sales copies focusing on the needs & pain points of diverse customer segments. 

Launch Date – Oct 2023

Founder – Paul Yacoubian

Copy ai Features

Copy AI has over 90 templates & tools to level up your copywriting game 10X faster. Here are the Amazing features of Copy ai-

Copy ai writes SEO-optimized blog posts in a short period.

Copy ai can produce long-form sales copy to convert your potential audience into sales.

Copy ai can generate eye-catching digital ad copies for marketing campaigns.

Copy ai can produce engaging social media content.

Copy ai generates high-quality product descriptions and e-commerce copies for websites.

Copy ai lets you directly paste the final output to your publishing platform.

Copy ai offers over 30+ free AI-based writing generators to level up your marketing efforts.

Copy ai allows you to select pre-designed temples from the following categories – Business, HR, marketing, real estate, personal & sales.

Copy ai has its own AI chatbot known as “ chat by”  ( alternative to ChatGPT ) that delivers updated responses by extracting real-time data.

Copy ai chatbot lets you sum up Linkedin profiles into crisp bullet points.

Copy ai can create copies in over 29 languages making it an accessible tool worldwide.

Copy ai can generate SEO-friendly content to increase the ranks.

Copy ai Use Case – Real-World Applications.

Copy ai has emerged as a game changer for Small-to large businesses, email marketers, bloggers, social media creators, and teams. Here are some real-world applications of copy ai.

Businesses utilize copy ai to create personalized sales copy, long-form posts & product descriptions faster for their sales campaigns.

Copy ai assists in personalized cold outreach over emails and LinkedIn.

Copywriters can utilize copy ai to write compelling and high-converting emails for email marketing.

 Bloggers use copy ai for blogging to produce high-quality blogs & articles by simply entering titles & keywords.

Social media managers can write posts in bulk for over a month.

Copy ai chatbot assists market researchers by offering prebuilt prompts.

Youtubers can use copy ai’s chat feature to extract data for their Youtube videos.

 Copy ai chatbot assists in lead generation on LinkedIn.

Copy ai Pricing

Copy ai has a free plan that lets only one user write only 2000 words monthly. Here are two premium plans of Copy ai.

1. Pro Plan ( $36/month )

Five users can use it simultaneously.

Access to chat by

No word limit.

Unlimited projects.

29+ languages.

Access to new features.

Ideal for all copywriters.

2. Enterprise Plan

Customized plan 

Ideal for a team of over 5 users.

Chat interface.

Options to automate workflows.

SOC 2 security feature included.


Is copy AI free?

Copy ai lets a single user benefit from its feature through the free plan. To access the free plan, you can log in. The free plan allows you to write only 2000 words per month with limited access to new features.

Is copy ai better than ChatGPT?

Unlike Chat, Copy ai chat is trained with the latest real-time data. You can enhance your text quality by using copy ai prebuilt prompts, which are not there in ChatGPT. Copy ai can collect and summarize website information & data which is missing in ChatGPT. Further, Copy ai can also search the latest LinkedIn posts.

How to make money with Copy ai?

By learning copy ai, you can open the door to endless earning opportunities by becoming a blogger, freelance writer, or copywriter. You can write blogs, emails, social media copies, e-commerce product descriptions, etc., to earn 5 to 6 figures. You can use copy ai in affiliate marketing for writing product pages. 

Is copy AI better than Jasper?

Copy ai is better than Jasper ai primarily regarding less usage and pricing limits. You can cut down 83-92% of your monthly budget with Copy ai. Copy ai can generate unlimited words in its pro plan, while Jasper ai allows only 700,000. Copy ai offers 40+ more templates & tools than Jasper ai.

Will copy AI replace copywriters?

Copy ai has been designed to assist copywriters in fastening their work with 10X faster speed. Copywriters know how to add the element of empathy & emotions in their work which AI tools like copy ai cannot do as efficiently. Final editing work still requires human input. It’s most unlikely that Copy ai will replace copywriters.


Copy ai is the preferred AI writing tool for over 7000,000 teams & professionals worldwide. Copy ai constantly gets updated with new features, including more content types. Copy ai is a budget-friendly, must-to-have content generation tool for businesses. You can start with the free plan if you still need time to invest in Copy ai’s pro plan. 

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Top 6 Use Cases Of Generative Ai In Education

Due to the COVID-19 pandemic, the use of digital technologies to enhance education has significantly increased as many students around the world have had to shift to online learning. For example, investment in education for adopting innovative technologies increased from $7 billion to $20 billion during the pandemic as trends suggest. However, digital technologies also have the potential to transform the education experience in other ways beyond just online classes. The application of generative AI in education is an example to this.

Generative AI is a digital technology that can quickly create new and realistic visual, textual, and animated content. In other articles, we investigated its use cases in different  sectors, such as healthcare and banking. While other technologies like conversational AI and robotic process automation (RPA) are implemented in education, generative AI is not properly implemented in education. Despite this, it has potential use cases for improving it. This article explains the top 6 potential ways to use generative AI in education.

1. Personalized Lessons

Personalized lesson plans are a powerful way to ensure that students receive the most effective education tailored specifically to their needs and interests. These lesson plans can be generated by using AI-powered algorithms to analyze student data, such as:

Their past performance

Their skills 

And any feedback they might have given regarding content

AI-based systems can leverage such information to generate customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.

For example, Speechify is a generative AI-driven tool. It offers text-to-speech or speech-to-text generations on desktops or on online use.

2. Course Design

Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills and learning styles, such as practice problems or interactive exercises. 

Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.   

For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.

3. Content Creation for Courses 

Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.

Furthermore, generative AI can facilitate generating additional materials to supplement the main course materials, such as: 

Reading lists

Study guides 

Discussion questions 



Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses. Image generation is another important ability of generative AI for education. Teachers may want to generate images with specific modifications that respond to particular course needs.

For example, NOLEJ offers an e-learning capsule that is AI generated in only 3 minutes. This capsule provides an interactive video, glossary, practice, and summary for a target topic (see Figure 1 below).

More established companies are using AI to generate content that supports their main products. For instance, Duolingo, a language learning platform, uses GPT-3 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-3, second language writing skills of customers are increased.

4. Data Privacy Protection for Analytical Models

Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use.

For more on how generated synthetic data enables data privacy, you can check out these articles:

5. Restoring Old Learning Materials

Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media.

These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.

Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.

Figure 2. Image restoration with GANs. (Source: Towards Data Science)

6. Tutoring 

Another use case of generative AI is to provide tutoring. Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.

According to academic studies, private tutoring children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.

For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.

Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring. Chatbot Life’s 2023 chatbot report shows that education is the third biggest industry benefiting from chatbots.

Lately, Chat GPT from OpenAI stormed the internet with its ability to engage in highly personalized conversations and definitive answers. It can answer course-related questions from a variety of domains, and can even write essays on the target topic. 

On the other hand, implementing generative AI-based chatbots specified and regulated for educational purposes is a future plan. However, it offers potential uses and benefits:

One potential use would be to provide around-the-clock support to students and their parents, including help with homework.

Generative chatbots can also assist with administrative tasks, such as answering student or parent questions, freeing up time for educators to focus on other tasks, such as grading and lesson planning.

The flexibility and natural feeling of generative chatbots make them useful in educational settings, particularly with elementary and middle school children.

Challenges of generative AI in education

Although generative AI has a lot of potential to improve educational practices, it may also pose some potential challenges. These can be shortly listed as:

Biases in educational materials

False or inaccurate information

Abuse of it for self interest

Unemployment risks for some teachers or other education professionals

For a detailed discussion on the ethical challenges of generative AI, you can check our article.

For more on generative AI

To explore more about generative AI, you can check our other articles:

Discover the top generative AI tools from our detailed list sorted by category:

If you have questions regarding generative AI, feel free to reach out:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Top 13 Use Cases / Applications Of Ai In Manufacturing In 2023

The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.

Why is AI important in the manufacturing industry?

Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

The same study also reveals that the most popular AI use cases in manufacturing are improving:

maintenance (29% of manufacturing AI use cases)

quality (27%)

This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.

The COVID-19 pandemic also increased the interest of manufacturers in AI applications. As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.

What are the common AI use cases in manufacturing? 1. Predictive maintenance

Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.

2. Generative design

Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.

3. Price forecasting of raw material

The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes.

4. Robotics

Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.

Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.

Cobots are another robotics application that uses machine vision to work safely alongside human workers to complete a task that cannot be fully automated. Feel free to learn more about cobots with our comprehensive guide.

5. Edge analytics

Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:

Improving production quality and yield

Detecting early signs of deteriorating performance and risk of failure

Tracking worker health and safety by using wearables

To learn more about analytics in manufacturing, feel free to read our in-depth article about the top 10 manufacturing analytics use cases.

6. Quality assurance

Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected, and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is of lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.

You can also check the lists of data annotation and AI/ML tools and services to find the option that best suits your project needs:

7. Inventory management

Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning.  AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.

8. Process optimization

AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult.

By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action.

For example, a manufacturer that employed a process mining tool in their procure-to-pay processes decreased deviations and maverick buying worth to $60,000.

9. AI-Powered digital twin use cases

A digital twin is a virtual representation of a real-world product or asset. By combining AI techniques with digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing:

10. Product development

Manufacturers can use digital twins before a product’s physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.

11. Design customization

Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.

12. Shop floor performance improvement

A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.

13. Logistics optimization

Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.

What are the benefits of AI in manufacturing? Safety

Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year. The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents.

Cost Reduction

AI technologies can reduce the operation costs of manufacturers due to several applications:

Leveraging AI technologies can enhance organizations’ analytics capability so that they can use their resources more efficiently, make better forecasts, and reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.

This one is obvious but manufacturers don’t need to pay monthly salaries to robots. However, robots require CAPEX which needs to be weighed against the recurring cost of labor.

Faster decision making

Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions.

24/7 production in dark factories

Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity.

Read more on AI applications in different industries:

If you still have questions on how AI revolutionizing the manufacturing industry, don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Healthcare Hyperautomation: Use Cases & Best Practices

Hyperautomation was one of the top technology trends in 2023. According to IBM, healthcare in particular is a prime candidate to benefit from it, with so many repetitive processes and regulations to follow.

In this article, we’ll explore why hyperautomation is important for the healthcare industry, its use cases, its challenges and how to overcome them.

Why does the healthcare industry need hyperautomation?

Hyperautomation is an emerging approach to digital transformation that involves automating every business process possible while digitally augmenting those processes that can’t be automated fully. The need for hyperautomation is not different from the need for digital transformation: According to Gartner, hyperautomation is inevitable and is quickly becoming a condition of survival instead of an option for businesses.

The healthcare industry also has its own unique challenges that require hyperautomation to address:

Consumer preference is rapidly shifting to digital, and the COVID-19 pandemic has accelerated this trend. Patients demand more convenient, transparent, and personalized healthcare services. Healthcare providers are aware of this trend as more than 90% of healthcare technology executives say achieving a better patient experience is their top desired outcome when implementing digital technologies.

Legacy systems are still the norm in the healthcare industry. 80% of healthcare organizations use legacy systems that no longer receive support from their manufacturers. Replacing these systems is a challenge because it can disrupt operations and lead to integration issues. By leveraging hyperautomation tools with screen scraping and OCR capabilities, healthcare businesses can integrate these systems with modern technologies and automate the operations relying on them.

What are the use cases of hyperautomation in healthcare?

We have explored use cases of individual hyperautomation technologies in healthcare, such as:

Hyperautomation combines these technologies for end-to-end process automation. Use cases include:

1. Patient services

A combination of conversational AI and intelligent process automation bots can handle most patient service tasks, improving patient experience and employee productivity. Bots can:

Interact with patients about their health problems through different channels,

Enable self-service scheduling by providing patients with suitable physicians and time slots,

Send reminders and allow rescheduling or canceling appointments,

Collect data from patient interactions to be analyzed for customer service improvement,

Assist human customer service reps during their customer interactions.

2. Regulatory compliance

Healthcare providers, health insurance companies, pharmacies, and other healthcare entities must comply with regulations such as HIPAA in the US and GDPR in the EU. Failure to comply with such regulations can lead to fines ranging from $100 to $100,000 per violation. Since a fifth of healthcare employees would be willing to sell patient data to unauthorized parties for as little as $500, adopting digital technologies is imperative for compliance.

Hyperautomation can help with ensuring regulatory compliance for healthcare organizations:

Intelligent bots can log every action in healthcare systems and document the activity log when demanded,

AI/ML models can be used to predict potential healthcare fraud,

Automating internal audit processes can help evaluate risks and internal controls more efficiently and frequently.

3. Research & development

Hyperautomation technologies such as AI models and digital twins can accelerate pharmaceutical R&D:

Drug discovery: Deep learning algorithms can be used to discover drug candidates for specific diseases.

Testing new drugs: To test new drugs and treatments, companies can use digital twins to build digital representations of tools, drugs, human organs, genomes, or individual cells.

4. Health insurance processing

Processing claims efficiently is important for health insurance companies since:

Nearly 90% of customers say effective claims processing influences their decisions when choosing a vendor,

In the US, claim submissions account for $4.5 billion of medical industry spending, representing 13% of all administrative transactions.

Around $300 billion is lost each year due to health care fraud in the United States.

By leveraging NLP methods and AI/deep learning models, a hyperautomation approach can help health insurance businesses:

Minimize manual work during preauthorization and claims processing,

Reduce human errors,

Detect and prevent healthcare fraud more accurately,

Ensure customer satisfaction with shorter claims cycles.

What are the challenges and how to overcome them?

Data privacy: Medical data contains highly sensitive patient information protected by data privacy regulations. This can create a roadblock on the path to hyperautomation for healthcare organizations. Businesses must invest in privacy enhancing technologies (PETs) to develop innovative products without risking patient privacy.

Process understanding: Processes are often poorly documented, and businesses may lack a comprehensive understanding of them. Process mining tools and digital twins can help businesses understand how actual processes are carried out and how to improve them. In this way, healthcare organizations can prepare themselves for their journey toward hyperautomation.

Change management: Building a company culture around hyperautomation is just as important as selecting specific tools, since cultural deficit is one of the main reasons why digital transformation initiatives fail. Organizations should create opportunities for reskilling and upskilling and improve top-down communication about why these changes are needed. For more, check our article on the importance of organizational culture for digital transformation.

Check our article on intelligent automation strategy for more.

Further reading

If you have other questions about hyperautomation and its applications in the healthcare industry, feel free to reach us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Dynamic Bus Fare Pricing Comparison And Detection

This article was published as a part of the Data Science Blogathon.


Online bus ticketing ographies and proving value-added services such as insurance, various amenities, etc. Once a technologically backward and resource-intensive public transport industry is now transformed into a travel industry behemoth within a decade. The major players in the industry are Redbus, Makemytrip, Goibibo, EaseMyTrip all fighting to capture market share and assert dominance.

Though plenty of levers is available to capture market share, pricing still remains the most important in India. Pricing can make or break bus operators. In an industry that already has high operating costs and low margins, getting the price right is the most fundamental decision that every operator has to take, either a large or a smaller one. One Bain study found that 78% of B2C firms believe they can still improve their pricing strategies.

What pricing strategies can be used?

Zonal Pricing – Simple and direct pricing based on zones. Used by government public transport.

Distance Pricing – Pricing based on distance travelled, used majorly by buses on hire, tourist operators.

Origin destination Pricing – Based on the destination, if a major tourist destination, then higher prices.

Seasonal Pricing – Based on the season.

Last-minute Pricing – Some operators drastically reduce or increase prices to increase volumes.

Dynamic Pricing – Most common in eCommerce where marketplaces have higher price setting flexibility, sparse adoption in the bus service industry.

This article explores the world of online bus tickets pricing. We will cover the following:

Problem statement

Explore dataset

Data preprocessing

Exploratory data analysis

Exploring feasible solutions

Test for accuracy and evaluation

1. Problem Statement:

Large bus operators have higher pricing power as they are already well placed in the market. The problem statement is to identify operators who are pricing independently(ideally large operators) and operators who are followers. Identifying market leaders through a data-based approach helps businesses serve these players better. As resources are scarce, the question is on which operator should the company utilize its resources better? Other use cases of this analysis can be used for internal strategic decision making and can have a positive impact on the long term relationships between operators and online ticketing platforms.

Problem Statement – Identify price-setters and followers. 

2. Explore Dataset:

Data can be downloaded from here.

display(bus_fare_df.head()) display(bus_fare_df.shape) display(bus_fare_df.describe()) display(bus_fare_df.dtypes) Screenshot: AuthorThe dataset has  5 columns :

Seat Fare Type 1 – Type 1 seat fare.

Seat type 1 has a different set of prices.

Need to clean it and identify a single price, such that this analysis can be easy and less complicated.

Seat Fare Type 2 – Type 2 seat fare.

Similar to Seat Fare Type 1.

Bus – Bus unique identifier.

Service Date – Journey date.

Convert to pandas timestamp and get day of the week, month, year etc information.

RecordedAt – Pricing recorded date, price captures by the system.

Similar to service date.

Modern ticketing platforms enrich customer experience through robust UI. On the left-hand side, there are the filters such as bus types, amenities, the top has information about the bus timings, prices and the bottom space provides seat selected capability.

Operators and buses terminology is used interchangeably.

Platform refers to online ticketing platforms such as Redbus, Makemytrip.

3. Data Preprocessing: 

Functions to c

def clean_seat(x): ''' input is a string object and not a list ''' # a = [float(sing_price) for price in x for sing_price in price.split(",")] # a = [sing_price for price in x for sing_price in price.split(",")] # return sum(a)/len(a) a = [float(price) for price in x.split(",")] return sum(a)/len(a) def average_s1_s2_price(s1, s2): ''' pandas series as input for price 1 and price 2 all 4 combination covered. ''' price_output = [] # for i in range(len(s1)): if (s1 == 0) & (s2 == 0): return 0 elif (s1 == 0) & (s2 !=0 ): return s2 elif (s1 != 1) & (s2 ==0 ): return s1 else : return (s1+s2)/2 # return price_output

Calculate the average fare per bus, having one data point is easier than multiple.

Backfill prices so that missing values can be replaced.

Convert seat fare type 1 and seat fare type 2 to string and replace null

bus_fare_df = bus_fare_df.sort_values(by = ["Bus","Service_Date","RecordedAt" ]) # display(bus_fare_df.head()) test = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_1_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test = test[["Bus","Service_Date","Seat_Fare_Type_1_average" ]] test["Seat_Fare_Type_1_average_impute"] = test.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='ffill')) display(test.shape) display(bus_fare_df.shape) test2 = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_2_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test2 = test2[["Bus","Service_Date","Seat_Fare_Type_2_average" ]] test2["Seat_Fare_Type_2_average_impute"] = test2.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='ffill')) display(test2.shape) # display(bus_fare_df.shape) bus_fare_df["Seat_Fare_Type_1_average_impute_ffil"] = test["Seat_Fare_Type_1_average_impute"] bus_fare_df["Seat_Fare_Type_2_average_impute_ffil"] = test2["Seat_Fare_Type_2_average_impute"] # display(bus_fare_df.head()) ############################################################################################################################# test = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_1_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test = test[["Bus","Service_Date","Seat_Fare_Type_1_average" ]] test["Seat_Fare_Type_1_average_impute_bfil"] = test.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='bfill')) display(test.shape) test2 = bus_fare_df[["Bus","Service_Date","RecordedAt","Seat_Fare_Type_2_average" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test2 = test2[["Bus","Service_Date","Seat_Fare_Type_2_average" ]] test2["Seat_Fare_Type_2_average_impute_bfil"] = test2.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='bfill')) display(test2.shape) bus_fare_df["Seat_Fare_Type_1_average_impute_bfill"] = test["Seat_Fare_Type_1_average_impute_bfil"] bus_fare_df["Seat_Fare_Type_2_average_impute_bfill"] = test2["Seat_Fare_Type_2_average_impute_bfil"] # display(bus_fare_df.hea ############################################################################################################################# test_a = bus_fare_df[["Bus","Service_Date","RecordedAt","average_price_s1_s2" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test_a = test_a[["Bus","Service_Date","average_price_s1_s2" ]] test_a["average_price_s1_s2_bfil"] = test_a.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='bfill')) display(test_a.shape) test_f = bus_fare_df[["Bus","Service_Date","RecordedAt","average_price_s1_s2" ]].sort_values(by = ["Bus","Service_Date","RecordedAt" ]) test_f = test_f[["Bus","Service_Date","average_price_s1_s2" ]] test_f["average_price_s1_s2_ffil"] = test_f.groupby(["Bus","Service_Date" ]).transform(lambda x: x.replace(to_replace=0, method='ffill')) display(test_f.shape) bus_fare_df["average_price_s1_s2_bfill"] = test_a["average_price_s1_s2_bfil"] bus_fare_df["average_price_s1_s2_ffill"] = test_f["average_price_s1_s2_ffil"] # display(bus_fare_df.head()) ############################################################################################################################# bus_fare_df['average_price_s1_s2_filled'] = bus_fare_df.apply(lambda x: average_s1_s2_price(x.average_price_s1_s2_ffill, x.average_price_s1_s2_bfill), axis=1)

Create flags for buses with only Rs 0 as price point, these could be cancelled buses.

average_price_s1_s2_filled is the final average price point.

The level of data/detail(LOD) is Bus x Service_Date x RecordedAt

The preprocessing step creates all the cleaned columns and features.

The code can be downloaded from here as well.

Screenshot: Author

4. Exploratory Data Analysis

EDA is all about asking as many relevant questions as possible and getting answers through data. EDA on its own might not help solve the business problem but will provide valuable explanations as to why something things are the way they are. It also helps identify important features in a dataset and filter out discrepancies.

As only pricing data is available, certain assumptions help narrow down the analysis. :

All operators are charged a similar commission.

All operators have similar ratings/amenities.

All operators run on the same routes, covering similar distances.

All operators have similar boarding and destination points.

All operators have a similar couponing/discounting policy.

Null hypothesis – All operators price independently and there are no price-setters or followers.

Alternate hypothesis – Not all operators price independently and there are price setters and followers.

EDA problem statements and probable business intelligence gained by answering them :

Percentage change between the initial price and final price?

Do operators increase or decrease price over time – this information can be leveraged to help bus operators identify opportunities to keep prices constant instead of reducing.

It also provides initial impetus and validates our problem statement.

Days between price listing and journey date:

Identify the perfect window for listing. Is 10 days before the journey date too short or is it 30 days too long? Does the early bird always get the worm? The optimized window saves cost, If all seats are not filled within the time then it’s a loss, if all seats are filled before time, then an opportunity is lost, which turns out to be an opportunity cost.

Daily average prices across platform vs bus operators:

Identify market price expectations, and improve pricing decisions.

Price bucketing of buses:

Descriptive statistics.

Identify competition and deploy tactics to mitigate losses due to competitive pricing.

Day of the week analysis on price:

Weekend weekday analysis, to improve pricing decisions.

Price elasticity across the platform:

Based on prices, identify the different seats available:

Try to gauge price sensitivity on the type of seating.

Percentage change between the initial price and final price?

For 60% of Bus X Service_Date combinations change in the initial and final price is 0.

The remaining 40% buses majorly have seen a decrease in final prices.

The box and whisker plot shows on average 10% decrease in final price.

Operators list higher prices eventually to reduce them over time.

MOM(month on month) boxplot of the same would shed some light on how seasonality can affect price change.

Screenshot: Author


one_df_002 = pd.merge(one_df,final_cleaned_df[["Bus","Service_Date","RecordedAt", "average_price_s1_s2_filled"]], how = "left" , left_on = ["Bus","Service_Date","min"], right_on =["Bus","Service_Date","RecordedAt"], suffixes=('_left', '_right')) one_df_003 = pd.merge(one_df_002,final_cleaned_df[["Bus","Service_Date","RecordedAt", "average_price_s1_s2_filled"]], how = "left" , left_on = ["Bus","Service_Date","max"], right_on =["Bus","Service_Date","RecordedAt"], suffixes=('_left', '_right')) one_df_003["price_diff_i_f"] = one_df_003.average_price_s1_s2_filled_right - one_df_003.average_price_s1_s2_filled_left one_df_003["price_diff_i_f_perc"] = one_df_003.price_diff_i_f / one_df_003.average_price_s1_s2_filled_left one_df_004 = one_df_003[["Bus","Service_Date", "price_diff_i_f"]].drop_duplicates() one_df_005 = one_df_003[["Bus","Service_Date", "price_diff_i_f_perc"]].drop_duplicates() one_df_005.boxplot(column = ["price_diff_i_f_perc"]) one_df_004.price_diff_i_f.hist(bins = 50)

Days between price listing and journey date

75% list on or before 30 days.

Final 25% percentile list between 30 and 60 days.

It would be interesting to see if buses listed for longer periods operate at higher volumes as compared to average.

Screenshot: Author


groups = final_cleaned_df.groupby(["Bus","Service_Date_new"]).RecordedAt_new min_val = groups.transform(min) one_df = final_cleaned_df[(final_cleaned_df.RecordedAt_new==min_val) ] one_df["date_diff"] = one_df.Service_Date_new - one_df.RecordedAt_new figure(figsize=(10, 6), dpi=80) plt.subplot(1, 2, 1) plt.title("Date Difference Boxplot") plt.boxplot(one_df.date_diff.astype('timedelta64[D]')) plt.subplot(1, 2, 2) plt.title("Date Difference Histogram") plt.hist(one_df.date_diff.astype('timedelta64[D]'))

Daily average prices across platform vs bus operators

The flat orange price line till 20230715 is backfilled, hence appears to be constant.

This provides a view of the platform average prices vs a single bus operator.

Near the journey period, there is a shift of the peak to the right.

Screenshot: Author 


plot_1 = bus_fare_df[bus_fare_df["average_price_s1_s2_filled"] !=0].groupby(["RecordedAt_date_only"]).agg({"average_price_s1_s2_filled":np.mean}) figure(figsize=(10, 6), dpi=80) plt.plot(plot_1.index, plot_1.average_price_s1_s2_filled,label = "Platform") plot_2 = bus_fare_df[(bus_fare_df["average_price_s1_s2_filled"] !=0)&(bus_fare_df["Bus"] =="060c6d5595f3d7cf53838b0b8c66673d")].groupby(["RecordedAt_date_only"]).agg({"average_price_s1_s2_filled":np.mean}) plt.plot(plot_2.index, plot_2.average_price_s1_s2_filled, label = "060c6d5595f3d7cf53838b0b8c66673d")

Price bucketing of buses

Using price buckets, the different types of seats available can be identified.

For operators, helps understand competition, and provide more information on how to position themselves in the market.

Understanding the market helps set internal pricing business rules and guardrails.

The lowest price is 350, the highest is 1449.

The average is 711 and the 50 percentile is 710. Proof that the Central Limit Theorem holds good, the peak of the histogram is near about 700- 750 and that’s where the average is.

A lot of  0’s are present in the histogram, this is due to backfilling. In the boxplot, these 0’s are removed to provide a clear picture of prices.

Bucketing can be based on bins or percentile.

Preliminary bucketing can be done based on the boxplot:

Bucket 1 – 350-610  – Regular Seaters

Bucket 2 – 610-710  – Sleeper Upper Double / Seater

Bucket 3 – 710-800 –  Sleeper Upper Single / Sleeper lower Double

Bucket 4 – 710-1150 – Sleeper Lower / AC Volvo seater

Bucket 5 – 1150-1449 – AC Sleeper

Screenshot: Author


figure(figsize=(10, 6), dpi=80) price_f = final_cleaned_df[final_cleaned_df["average_price_s1_s2_filled"] != 0] plt.subplot(1, 2, 1) plt.title("Boxplot - Average Price") price_f = final_cleaned_df[final_cleaned_df["average_price_s1_s2_filled"] != 0] plt.boxplot(price_f.average_price_s1_s2_filled) plt.subplot(1, 2, 2) plt.title("Histogram - Average Price") plt.hist(final_cleaned_df.average_price_s1_s2_filled, bins = 100)

Price elasticity across the platform

The average price of the platform has been changing drastically from time to time and this can only mean that demand is varying as well. But due to the unavailability of demand information, the price elasticity of demand cannot be calculated.

This plot provides a proxy for the GMV change(gross merchandising value) of the platform.



Readers are encouraged to think about other hypotheses in support or against the problem statement which in turn will help in finding feasible solutions.  

5. Exploring Feasible Solutions

The problem statement states that identify independent price setters, and followers, the assumption being, the follower is following just one operator. We could be tempted to think, the follower could be checking multiple operators before setting the final price, just like users compare prices on Redbus, Makemystrip, Easemytrip before purchasing tickets.  This temptation is called conjunction fallacy! The probability of following one operator is higher than the probability of following two operators. Hence it’s safe to assume that comparing 1:1 operators pricing data and assigning setter and follower status is both heuristically as well as a statistically viable solution and the same has been followed in this solution.

How has the EDA helped with solutions?

On average 10% change in the initial and final price is strong evidence that operators are leveraging pricing to increase volumes.

The long-tailed wide bell curve for prices, hence the need to compare between similar buckets or similar price groups.

The majority of individual bus prices move along with the market prices. If this dint holds good, then it’s evidence of data anomaly or coding error.

1:1 operator comparison to identify follower and setter, rather than 1:2 or otherwise, to avoid conjunction fallacy.

Follower Detection Algorithm V001:

Randomly select 2 bus id, B1 and B2 with P1 and P2 having sufficient data points.

Join the two datasets and keep relevant columns only.

The basic hypothesis is, the price change ratio(p’) will converge at some timestamp, meaning the prices have intersected, and there is a setter-follower behaviour.

Calculate the difference in price (~P )of wrt to P1 as well as P2. (P2-P1)/P1 and (P2-P1)/P2, assuming  B2 is the follower.

Get the average price change by calculating the harmonic mean of the ~p. (Different denominators P1 and P2, hence harmonic mean )

HM_Score = (max(hm) – min(hm))/ min(hm). If score is low, then less confidence in setter-follower relationship.

 The second aspect of the solution is to identify the coefficient(beta) of the equation ~P2 = C + beta*~P1. Ideally, the scatter plot will be at a slope of 45 degrees or have a positive correlation. And p-value of 0.8 to 1 is considered(experimental) good confidence.  And the intercept needs to be close to 0, meaning little or no difference in price, which could be convergence.

A combined score/metrics of p-value and hm_score can help identify a follower and a setter.

This is an initial, crude solution and can be further refined to improve its accuracy.

f = final_cleaned_df.copy() b1 = f[(f["Bus"] == "a6951a59b64579edcf822ab9ea4c0c83") & (f["Service_Date"] == "15-07-2024 00:00")] b2 = f[(f["Bus"] == "ab479dab4a9e6bc3eaefe77a09f027ed") & (f["Service_Date"] == "15-07-2024 00:00")] recorded_dates_df = pd.concat([b1[["RecordedAt_new"]], b2[["RecordedAt_new"]]], axis = 0).drop_duplicates().sort_values(by = "RecordedAt_new").reset_index().drop(columns = "index") joined_1 = pd.merge(recorded_dates_df, b1, on=["RecordedAt_new"], how='left',suffixes=('_actuals', '_B1')) joined_df = pd.merge(joined_1, b2, on=["RecordedAt_new"], how='left',suffixes=('_B1', '_B2')) joined_df cols_to_keep = ["RecordedAt_new", "Service_Date_B1","Bus_B1","Bus_B2", "average_price_s1_s2_filled_B1", "average_price_s1_s2_filled_B2"] model_df = joined_df[cols_to_keep] model_df_2 = model_df.drop_duplicates() ## replace null of service date model_df_2['Service_Date_B1'] = model_df_2['Service_Date_B1'].fillna(model_df_2['Service_Date_B1'].value_counts().idxmax()) model_df_2['Bus_B1'] = model_df_2['Bus_B1'].fillna(model_df_2['Bus_B1'].value_counts().idxmax()) model_df_2['Bus_B1'] = model_df_2['Bus_B1'].fillna(model_df_2['Bus_B1'].value_counts().idxmax()) model_df_2.fillna(0, inplace = True) test_a = model_df_2.sort_values(by = ["RecordedAt_new" ]) test_a = test_a[["Service_Date_B1","average_price_s1_s2_filled_B1" ]] test_a["average_price_B1_new"] = test_a.groupby(["Service_Date_B1" ]).transform(lambda x: x.replace(to_replace=0, method='bfill')) test_f = model_df_2.sort_values(by = ["RecordedAt_new" ]) test_f = test_f[["Service_Date_B1","average_price_s1_s2_filled_B2" ]] test_f["average_price_B2_new"] = test_f.groupby(["Service_Date_B1" ]).transform(lambda x: x.replace(to_replace=0, method='bfill')) model_df_2["average_price_B1_new"] = test_a["average_price_B1_new"] model_df_2["average_price_B2_new"] = test_f["average_price_B2_new"] model_df_3 = model_df_2[model_df_2["average_price_B1_new"] != 0][["average_price_B1_new","average_price_B2_new"] ] from scipy.stats import hmean ## get the price change wrt to each bus price model_df_2["price_cng_b1"] = abs(model_df_2.average_price_B1_new - model_df_2.average_price_B2_new)/model_df_2.average_price_B1_new model_df_2["price_cng_b2"] = abs(model_df_2.average_price_B1_new - model_df_2.average_price_B2_new)/model_df_2.average_price_B2_new model_df_2["harm_mean_price_cng"] = scipy.stats.hmean(model_df_2.iloc[:,8:10],axis=1) model_df_2 = model_df_2[model_df_2["average_price_B1_new"] != 0] model_df_2 = model_df_2[model_df_2["average_price_B2_new"] != 0] model_df_2x = model_df_2.copy() hm = scipy.stats.hmean(model_df_2x.iloc[:,8:10],axis=1) display((max(hm) - min(hm))/ min(hm)) print("======================================================================================================") model_df_3 = model_df_2[model_df_2["average_price_B1_new"] != 0][["price_cng_b1","price_cng_b2"] ] model_df_3.plot(); # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets # (X,Y)["price_cng_b1"]).reshape(-1,1),np.array(model_df_2["price_cng_b2"]).reshape(-1,1)) # The coefficients print("Coefficients: n", regr.coef_)

6. Test For Accuracy and Evaluation

Manual evaluation B1 – 9656d6395d1f3a4497d0871a03366078 and B2 – 642c372f4c10a6c0039912b557aa8a22 and service date – 15-07-2024 00:00

Actual price data from 14 days time period. We see that B1 is a follower but with low confidence.


The harmonic mean score((max(hm)-min(hm) )/ min(hm)) is high showing that the difference on average is about 8, meaning there is a significant difference in prices at some point in time.


The co-efficient shows that (P2-P1)/P1 and (P2-P1)/P2 is linear, also a good R2 of above 75% would mean a healthy model as well. Intercept is close to 0 also supports our assumption.


Final confidence can be hm_score* coefficient = 8*0.8 = 6.4. This is absolute confidence. Relative confidence by normalizing across all combinations will provide a score between 0 and 1, which can be gauged more easily.

We can reject the null hypothesis and assume the alternate to be true.

While this isn’t an optimal solution, it’s a good reference point, to begin with. Smoothening the rough edges of the algorithm and refining it for accuracy will lead to better results.

Useful Resources and References

Data science can be broadly divided into business solution-centric and technology-centric divisions. The below resources will immensely help a business solution-centric data science enthusiast expand their knowledge.

Fixing cancellations in-cab market.

Don’t fall for the conjunction fallacy!

HBR – Managing Price, Gaining Profit

Pricing: Distributors’ most powerful value-creation lever

Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

Pricing Strategies for Public Transport

The Pricing Is Right: Lessons from Top-Performing Consumer Companies

The untapped potential of a pricing strategy and how to get started

Kaggle notebook with the solution can be found on my Kaggle account.

End Notes

This article presents a preliminary, unitary method, to figure out fare setters and followers. The accuracy of preliminary methods tends to be questionable but sets a precedent for upcoming more resilient and curated methods. This methodology can be improved as well with more data points and features such as destination, boarding location, YOY comparisons, fleet size, passenger information etc. Moreover, anomaly detection modelling might provide more satisfactory results as well.

The dataset can also be used for price forecasting using ARIMA or deep learning methods such as LSTM etc.

Good luck! Here’s my Linkedin profile in case you want to connect with me or want to help improve the article. Feel free to ping me on Topmate/Mentro as well, you can drop me a message with your query. I’ll be happy to be connected. Check out my other articles on data science and analytics here.

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