All posts by Mindfire Solutions

Thriving in an Age of Tech Disruption

Blockchain Technology, Custom Application Development, Enterprise Application Development, Mindfire Solutions, Web Application Development, , , , Mindfire Solutions

tech disruption by mindfire solution

Since the past decade, the pace of tech disruption has significantly grown with the increasing applications of technologies like AI, ML, and IoT. The global pandemic has only accelerated the wave of tech disruption by creating the demand for innovative and dynamic solutions.

Companies are constantly experiencing the need to innovate faster while keeping up with customer expectations so as to stay competitive. According to McKinsey, businesses adopted digital solutions 25 times faster than their own estimates during the pandemic.

In this blog, we will explore how IT firms are helping customers thrive during this period of rapid change and what they should do to prepare for further disruptions.

Leveraging Cutting-Edge Technologies

One of the key ways IT firms are allowing their clients to overcome tech disruption is by leveraging cutting-edge technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). These technologies enable IT organizations to create innovative solutions that can address problems quickly and efficiently.  

  • Artificial Intelligence & Machine Learning

AI technology is used to automate manual processes, while ML algorithms provide a way to analyze large volumes of data at high speeds. A survey has revealed that 72% of business leaders believe AI gives a focused edge on scaling enterprises. The AI market is expected to grow from $89 billion in 2022 to $407 billion by the end of 2027.

With the help of AI and ML, IT firms offer several solutions like customer segmentation and targeting, fraud detection, inventory management, behavior prediction, product recommendations, testing software, and many more. These solutions can enable their clients to maximize the value derived from their data.

  • Natural language Processing

Natural Language Processing, or NLP, is a subfield of AI that deals with analyzing and generating text and speech. IT companies leverage NLP to allow their clients to offer customized customer support services by using conversational bots. These bots can understand natural human language and respond accordingly.

In addition, tech companies are leveraging NLP to develop solutions like voice biometrics, automated content moderation, and sentiment analysis. With the rising application of NLP, its market capitalization is compounding at a CAGR of 18% and is projected to grow from $26 billion in 2022 to $161 billion by 2029.

  • Internet of Things

The Internet of Things (IoT) is another technology that IT companies are using to help their clients overcome tech disruption. Reports suggest that by the time we reach 2030, there will be about 30 billion IoT-connected devices.

IoT solutions enable IT firms to build connected systems which gather real-time data for their clients from any device in the network. Businesses can then use this data for predictive decision-making, such as identifying when a machine needs maintenance.

Additionally, these technologies are combined with existing business intelligence tools, such as analytics and reporting software, for deeper actionable insights into customer behavior.

IoT devices have applications across different industries. For example, IT firms are utilizing IoT with a combination of technologies like AI, ML, and cloud computing to offer improved healthcare services so that healthcare providers can monitor and treat patients remotely.

  • Cloud Computing

Cloud computing has been one of the most transformative technologies over the past decade. According to studies, the cloud computing market is currently worth $480.04 billion and is predicted to reach about $1.7 trillion by 2029.

Cloud solutions make it possible for IT companies to offer scalable and secure services that can be accessed from anywhere in the world. With cloud technology, tech companies can facilitate agile business operations by allowing their clients to scale their resources depending on their requirements.

Future-Proof  Your Business: Prepare for Tech Disruption

Along with leveraging cutting-edge technologies, IT firms also need to be proficient in the tech that is set to transform the market in the coming years. This will allow their clients to stay ahead of the curve. Some of these technologies include:

  • Blockchain Technology

Blockchain technology is a distributed ledger system that can be used to store data in an immutable way. It is a decentralized form of record-keeping that makes it difficult for cybercriminals to breach confidential data. IT companies can leverage this cutting-edge technology to help their clients protect sensitive data from unauthorized access.

It can allow IT companies to offer services like smart contract creation, asset tracking, and digital identity management.

  • Web 3.0

Web 3.0 is the upcoming generation of the internet that has been designed to provide users with complete control over their data. It utilizes technologies like blockchain, AI, and ML to provide a better and more dynamic web experience than the current version of the internet. This will enable IT firms to offer web-based services that can be used to create secure digital ecosystems for businesses.

As Web 3.0 becomes more prevalent, IT firms will have to focus on developing decentralized applications powered by blockchain technology to deliver integrated functionality.

The market cap for Web 3.0 was estimated to be $1.36 billion in 2021 and is projected to reach $64 billion by 2029.

  • IPA

Cloud computing has been one of the most transformative technologies over the past decade. According to studies, the cloud computing market is currently worth $480.04 billion and is predicted to reach about $1.7 trillion by 2029.

Cloud solutions make it possible for IT companies to offer scalable and secure services that can be accessed from anywhere in the world. With cloud technology, tech companies can facilitate agile business operations by allowing their clients to scale their resources depending on their requirements.

Conclusion

Technology is constantly evolving, and IT firms must stay ahead of the curve by implementing cutting-edge technologies in their services. Blockchain technology, Web 3.0, and IPA are some of the technologies that have the potential to transform the market in the coming years. IT firms should invest in these new technologies and focus on developing integrated solutions that utilize these tech disruptions for better results.

At Mindfire Solutions, we understand the importance of staying ahead of the curve and preparing for the future. That’s why we are always working on new and innovative technology solutions that can help our clients thrive in an age of tech disruption. Whether it’s using artificial intelligence and machine learning to make better business decisions or harnessing Blockchain technology for enhancing process flow, we have a team of experts who can help with your every business needs.

If you are looking for looking to leverage cutting-edge technology to fulfill your business goals, contact Mindfire Solutions today.

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Augmented Reality (AR) on E Learning by mindfire solutions

Transformational effect of Augmented Reality on E Learning

Elearning, IT Outsourcing, Mindfire Solutions, , , Mindfire Solutions

The ed-tech industry has witnessed tremendous growth in the past couple of years. With the advent of the pandemic, remote learning became a necessity, and the value of the ed-tech industry increased exponentially. According to an industry report, the ed-tech industry was valued at USD 106.46 billion in 2021 and is expected to compound at a rate of 16.5% (CAGR) from 2022 to 2030.

From these numbers, it is evident that the number of Ed-tech companies is only going to increase in the future. To keep up with the rising demand, Ed-tech enterprises are increasingly shifting towards adopting new technologies to gain and maintain a competitive edge.

One such technology that is transforming the e-learning experience is augmented reality, or AR. It is becoming more popular as a way to make learning more engaging and interactive. According to a report, the implementation of augmented reality in e-learning is expected to reach a value of US$5.3 billion by 2023.

In this blog post, we’ll look at what AR is and how it is changing the learning experience. We’ll also discuss some use cases of AR in e-learning.

What is Augmented Reality?

Augmented reality is a technology that superimposes digital elements or content in the real world. This means it allows you to view the world around you with computer-generated images overlaid on top. 

The technology enhances your existing surroundings with digital content without the need for any additional external device. This is different from virtual reality (VR), which completely immerses you in a digitally created environment with the help of a device called VR glasses.

The technology enhances your existing surroundings with digital content without the need for any additional external device. This is different from virtual reality (VR), which completely immerses you in a digitally created environment with the help of a device called VR glasses.

How is AR Revolutionizing the Learning Experience?

Here are some of the transformational benefits of AR in e-learning:

  • Offers a Better Understanding of the Concepts & Information Retention

Many studies have shown that teaching with AR technology is more effective than teaching from textbooks or videos. This is because AR adds information to the learners’ surroundings – making lessons more visual-driven, thereby enhancing the probability of retaining for a longer period.

  • Improves Engagement 

AR offers more engagement by making learning more interactive and immersive. This motivates the learners to repeat their AR learning experience. It is also utilized to gamify lessons, which can encourage reluctant learners to get excited about learning. 

  • Enhanced Online Training

The technology can be used to create AR-enhanced online training modules. Learners can get practical demonstrations as AR simulations can allow them to experiment with AR objects. This can help organizations or institutions provide remote training more effectively and efficiently. AR training simulators are also used for creating digital prototypes of products, which allows trainees to get used to the process and reduces errors when they are physically performing the task.

  • Reduced Cognitive Load

AR is used to reduce the cognitive load required to understand abstract concepts by providing learners with information in a more digestible format.

  • Save Resources

The technology can save resources by reducing the need for physical materials. AR simulations can also replace the need for expensive equipment or trips to real-world locations.

  • Assess Learners’ Progress

AR can assess learners’ progress and identify areas where they need improvement. This assessment can be in the form of AR tests, AR quizzes, or AR simulations.

Use Cases of AR in e-Learning

Here are real-life examples of how some organizations are leveraging AR to enhance their learning experience:

  • Augmented Reality for Medical Education

An Australian University uses an AR-based application to teach their medical students cardiology. The app is used to create a 3D model of the heart’s electrical activity and to remotely demonstrate the procedure of ECG.

  • Augmented Reality for Combining Coloring Activities with Learning 

An ed-tech company created applications for children where they can color any diagram and map. Once the children are done coloring, the AR app then brings their painting to life and also, at the same time, imparts knowledge about the topic. 

  • Learning Language with AR

A London-based ed-tech company uses AR for teaching foreign languages. The app places 3D images of objects in the real world. These objects are labeled with the word for that object in the target language. For example, if you’re trying to learn Spanish, you might see a picture of a chair labeled with the word “Silla.”

Conclusion

Augmented reality is making learning more interactive, engaging, effective, and accessible. It can be used in a number of different ways to improve the e-Learning experience. As the technology continues to evolve, we expect to see even more innovative and impactful uses for AR in e-Learning.

While AR technology does come with countless benefits, implementing it can be a complicated task.

You can collaborate with Mindfire Solutions to simplify the development process and reduce your workload. At Mindfire Solutions, we have over 2-decades of experience developing innovative e-learning solutions by leveraging cutting-edge technologies that can engage and educate learners.

Contact Mindfire Solutions to implement AR in your e-learning services.

 

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Adoption of Cloud Computing in Healthcare to Improves Patient Care Coordination

Cloud Native, Healthcare Application Development, Healthcare IT, Software OutsourcingMindfire Solutions

The cloud has revolutionized the way we live and work. It has brought about a new era of flexibility and convenience, allowing us to access information and collaborate with others from anywhere in the world.

According to a Gartner survey, global spending on cloud services is projected to reach over $482 billion this year (2022). The numbers are much higher than those recorded last year, i.e., $313 billion.

Now, healthcare providers are taking advantage of this technology to improve patient care coordination. Adoption of Cloud Computing in Healthcare with Cloud-based applications can help healthcare organizations manage patients more effectively, share important data in a secured manner, and reduce costs.

Let’s first get on with the basics of cloud computing and move on to the benefits of cloud computing in healthcare.

What is Cloud Computing?

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. 

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). This technology allows organizations to quickly scale up or down as needed and pay only for the resources they use.

It has many advantages over traditional on-premise IT infrastructure. With cloud computing, businesses can be more agile and responsive to change because they can provision new resources in minutes or hours instead of weeks or months.

Cloud computing offers greater scalability and enables businesses to scale up or down as they please, without having to make huge capital investments as they are required to pay only for the resources they use.

Cloud Computing in Healthcare

The healthcare industry is in a state of flux. With the ever-changing landscape of regulations, the increasing costs of care and the shift to value-based reimbursement models, healthcare organizations are looking for ways to cut costs and improve efficiency. One way they’re doing this is by adopting cloud-based solutions.

Cloud computing has already transformed many industries, and healthcare is next on the list. By moving to the cloud, healthcare organizations can reduce IT costs, improve patient care, and drive innovation.

Let’s look at how cloud computing is changing healthcare for the better.

  • Reducing IT Costs

One of the biggest benefits of adoption of cloud computing in healthcare is that it can help healthcare organizations reduce their IT costs. With on-premises solutions, organizations have to pay for hardware, software, maintenance, and support. But with cloud-based solutions, they only have to pay for what they use.

Additionally, cloud providers often offer discounts for long-term contracts or for paying upfront. As cloud providers handle maintenance and upgrades, healthcare organizations can further reduce costs.

  • Improving Patient Care 

Another benefit of cloud computing in healthcare is that it can help improve patient care. By moving to the cloud, healthcare organizations can improve collaboration between care team members, ultimately leading to better patient outcomes. 

In addition, patients are increasingly expecting more personalized and convenient care. And with the help of cloud-based solutions like telemedicine and patient portals, they’re getting it.

These solutions give patients 24/7 access to their health information and allow them to book appointments, refill prescriptions, and more from the comfort of their homes.

  • Improving Patient Experience 

Doctors and hospitals now have the ability to increase patient engagement and provide them with anywhere, anytime access to their medical data, test results, and even doctor’s notes thanks to adoption of cloud computing in healthcare. This gives patients more power and control, as well as increasing their knowledge of their medical conditions. Furthermore, because doctors can access the history of medical records, it provides a new level of safety for patients, preventing them from being overprescribed or avoiding unnecessary testing. 

  • Faster Deliver of Time- Critical Medical Services & Impact of Covid19 

Covid19 proved to be a powerful driver of rapid digital transformation across industries. Cloud computing in healthcare is cost-effective and quick to deploy, among other benefits that can be extremely useful, particularly during a pandemic. In many ways, the year 2020 has been unprecedented. Time was critical in combating the pandemic and constructing new hospitals, releasing the vaccine onto the market, and arranging a safe method of mass testing. Cloud computing should be used to help bring important technological solutions to market faster for Time – Critical Medical Services.

  • Data Security and Privacy 

Security and Privacy of patient data are the two most important factors that matter to healthcare providers and payers or for that matter any other healthcare stakeholder looking at leveraging software systems. All reputed cloud services providers undertake the required measures to ascertain that vulnerability of patient data to potential breach is either negative or as negligible as possible. It is, however, a moving target and it is advisable to hire the services of a reliable and experienced tech solution provider to address this concern while adopting cloud solutions.

  • Implementation of AI/ML & Access to Analytics for Data Driven Decisions 

Large datasets of patient information from ePHI, IoT devices, and consumer health applications are processed by cloud platforms. Technology can help to promote healthier patient behavior, improve disease detection rates, and aid in advanced diagnosis and decision making. Through data insights and analytics, AI/ML enables healthcare professionals to make data-driven decisions. It has the potential to personalize medicine, improve care, and deliver real-time information to patients and staff. Data from AI/ML is being used to drive innovation. Healthcare providers make better decisions with adopting cloud computing in healthcare, which improves service operations and increases hospital efficiency. Automated analytics provide significant benefits for patient scheduling, background checks, and managing all associated medical records. Data interoperability benefits research programmes by allowing researchers to quickly collect statistics from a diverse range of patients.

Cloud Computing Market & Opportunities in Health Care

Cloud Computing Makes Patient Care Coordination Easier

There is no denying that technology has revolutionized the healthcare industry. One of the most significant changes has been the move from paper-based systems to electronic health records (EHRs). This shift has resulted in more efficient and coordinated patient care.

However, as anyone who has ever dealt with a complex health issue knows, coordinating care can still be a major challenge. Many players are involved in the care of a single patient, including doctors, nurses, specialists, pharmacists, and others. It is difficult to track all the treatments and medications a patient takes. 

That’s where cloud computing comes in.

  • Cloud computing helps in storing and accessing data online. EHRs are often stored in the cloud, allowing different patient care team members to access them from anywhere at any time. This is a major advantage in coordinating care, as it allows everyone to see a patient’s complete medical history real time and in one place.
  • In addition to making information more readily available, cloud computing also enables sharing with ease large files such as x-rays or MRI scans. This is especially important when patients consult multiple specialists or receive care at different facilities.
  • In the past, patients would have to provide their records separately to each consulting physician, which was both time-consuming and inconvenient. With cloud computing, patients can give their doctors access to their records with just a few clicks.
  • Cloud computing in healthcare also makes it easier for doctors to communicate with each other. Earlier, if two doctors needed to discuss a patient’s diagnosis or treatment plan, they would have to do so by phone or fax (if they were lucky enough to have access to each other’s contact information). With secure cloud messaging platforms, doctors can easily send referrals, consult on cases, and request test results without ever having to pick up the phone.

Conclusion

Cloud computing in healthcare enables the doctors and other members of a patient’s care team to communicate and collaborate easily, resulting in more coordinated and efficient patient care. Adoption of cloud computing in healthcare also helps in implementing latest technologies like AI/ML.

If you are stuck with on premise healthcare software systems, now is the time to switch. Not only will you be able to improve patient care coordination, but you’ll also reap all the other benefits that a cloud infrastructure can provide you, leading to increased productivity and decreased costs.

If you are looking for a software technical partner who can assist you in developing custom solutions that are cloud-based, or in cloud migration, please reach out to us. With decades of experience, we have the necessary resources to help you achieve your goals. Connect with our experts today.

 

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Machine Learning In Banking

Utilizing Machine Learning In Banking To Prevent Fraud

Fintech, Machine LearningMindfire Solutions

Machine Learning (ML) is a vital tool for fraud detection in banks. It can spot potential fraud by examining patterns in transactions and comparing them with known fraudulent activity. It uses algorithms to identify these patterns, which are then used to predict whether or not a transaction is fraudulent. These algorithms are trained using historical data, so they can only identify patterns in existing data and cannot learn new ways as they occur. 

This means that companies must constantly update their machine learning models with further information for continuing to use machine learning in Banking to prevent fraud.

How Does Machine Learning Overcome The Traditional Security Techniques Used By Banks?

Machine learning pushes the boundaries of what can be done with security. A traditional security strategy is to make the system as difficult to access as possible, stopping the bad guys before they get in. Banks often use biometrics and key cards to access their accounts, which are more challenging to hack than a username/password combination. 

But machine learning in banking prevents fraud even when it’s not done by someone trying to access an account. It can also be used to flag suspicious behavior so that humans can investigate it and decide whether or not it’s worth taking action on.

Machine learning algorithms can analyze data from all sources—customer transactions, social media posts, etc.—and find patterns that indicate fraudulent activity or other risks. These algorithms are trained on examples of fraud so that they know what to look for when new transactions occur.

What Are The Benefits of Machine Learning In Fraud Detection?

Machine learning has been the buzzword in the tech industry for some time. From self-driving cars to automated customer engagement, machine learning is everywhere.

But what does it mean? Let’s look at some of the benefits of using machine learning in Banking to prevent fraud.

  • Speed

Machine learning can help improve the speed of fraud detection by reducing the time it takes to detect and flag suspicious activity. Machine learning algorithms can be trained to automatically flag transactions with a high risk of fraud. This can significantly improve your ability to identify fraudulent transactions quickly so you can act on them before they become too costly to remediate.

  • Efficiency 

Machine learning also improves efficiency by automating many manual tasks that waste time and effort. For example, machine learning in banking to prevent fraud can identify known bad actors who are likely to commit fraud in the future, so you can block their access to your business immediately without having to review every transaction they make manually. 

  • Scalability 

Machine learning allows you to scale up or down your fraud detection capabilities as needed. This is important because fraud patterns change over time as criminals adapt their approach or new types of fraud emerge. Machine learning algorithms are designed with built-in flexibility to adapt quickly when new threats emerge or old threats change tactics. 

  • Accuracy 

Finally, machine learning offers increased accuracy over traditional methods because it uses data from all available sources—including humans—to learn what normal behavior looks like and spot anomalies that indicate potential problems.

What Are Some Of The Ways Machine Learning Can Be Used To Detect And Block Fraud?

There are many different techniques to detect and block suspicious cases. Some of them include the following – 

  • Classification

Classification assigns a label to an observation based on a set of observed values used as predictors. The predictors are inserted into the algorithms, which use training data to learn what labels to give. These predictions can then be used for fraud detection. This is done by identifying fraudulent transactions or users by classifying them as fraudulent or not fraudulent.

  • Regression

Regression is a supervised learning method that predicts future outcomes based on historical data. The regression algorithms can be used in fraud detection to predict the likelihood that a transaction will be fraudulent based on historical data about previous transactions that were labeled as fraudulent or not fraudulent by humans.

  • Clustering And Anomaly Detection

Clustering and anomaly detection are unsupervised learning methods that can be used for fraud detection by identifying patterns within your data that suggest fraud may occur, such as many small withdrawals from an account or many large purchases made at one store over time.

  • Anomaly Detection

Machine learning algorithms search for patterns in existing data that are not typical of what you would expect. If a new transaction is entered into your system and doesn’t fit the pattern of existing transactions, it could be an anomaly.

  • Decision Trees

A decision tree is a tree-like diagram that shows all possible paths that can take place in a decision process. A decision tree algorithm takes in data and tests each piece of information against all possible outcomes to determine if they’re true or false. If any single piece of information leads to an inaccurate result, the entire transaction is flagged as fraudulent.

  • Neural Networks

Neural networks are used to detect fraud in several ways. They can be trained to recognize patterns that indicate fraudulent transactions, such as repeated requests for withdrawals from an ATM or many purchases at one store within a short period. 

Neural networks can also monitor customer behavior over time and flag suspicious activities like sudden changes in spending habits or changes in the type of purchases being made (from low-risk items like groceries to high-risk items like jewelry).

  • Natural Language Processing (NLP)

NLP refers to technologies that use machine learning algorithms to analyze text data and extract meaningful information. 

For example, NLP software might analyze customer statements and detect instances where someone has been using their bank account number on multiple credit card applications without having applied for those cards themselves. This could indicate that they have been victims of identity theft or another fraud scheme.

Summing It Up

If you’re looking to implement machine learning in banking to prevent fraud or other systems, Mindfire Solutions has got you covered. Our goal is to take the guesswork out of it and ensure you get the most out of your investment.

We have the experience and expertise to help you implement machine-learning algorithms for your security and other needs. Our team deeply understands this technology’s potential, and we can work with you to determine the best way to use it in your organization. Contact us today to see how we can help!

 

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UPI (Unified Payments Interface) – The Game Changer

Fintech, ThoughtsMindfire Solutions

Phenomenal growth may be the exact term to describe the increased volume of digital payments in India in 2022. If you inspect the March data of the current year, it shows that this payment ecosystem grew by 216% compared to the same period in 2019. The Unified Payments Interface (UPI), an initiative of the National Payments Corporation of India (NPCI) has been a critical driver of cashless transactions since its launch in 2016. The idea behind introducing UPI was to enable fast, secure, and seamless digital payments with the Immediate Payment Service (IMPS) infrastructure.

The immense popularity of this system is evident from the fact that UPI transactions exceeded USD 110 billion in January 2022.

NPCI has launched several innovative payment systems for developing the digital payment space and tied up with foreign companies like Japan Credit Bureau, China Union Pay, Discover Financial Services, etc. The international coordination would provide support by payment acknowledgment through RuPay cards.

Starting with IMPS and NFS in 2010, there have been RuPay cards, NACH, Aadhaar Payments Bridge System (APBS), Aadhaar-enabled Payments System (AePS), NETC, BBPS, UPI, and the USSD based *99#.

What makes UPI the game changer in digital payments?

Convenience

For using debit or credit cards online, you need to enter several details related to the card holder’s name, card number, CVV, and expiry dates. For UPI, you just key in the UPI ID, login, and complete the payment after putting in the PIN.

If you are using net banking, you must first add the beneficiary to your bank account. You require the beneficiary’s name, bank account number, branch address, and IFSC code. The process takes time to get activated, and then you can make the payment. To make a UPI payment, you can transfer funds to a peer account even if they do not have UPI access.

Besides, there are advantages over other existing payment methods. For instance, UPI allows you to make payments directly from your bank account without any third-party wallet or a card. Thus, making it much more convenient and secure than using a debit or credit card for online payments.

Moreover, UPI transactions happen in real-time. There is no waiting period for the funds to be credited or debited from the user’s account. This feature makes UPI an ideal choice for digital payments.

Simple

UPI is also very simple to use. You just need a smartphone with an active internet connection. You can then download a UPI-enabled app from your bank’s website or the Google Play Store. After installing the app, simply link your bank account and start making payments.

Versatile

You can also use UPI to request money from others or to send money to them. All in all, UPI is a very convenient and secure way to make digital payments. It is fast and easy to use.

UPI is particularly beneficial as a non-cash payment instrument when it involves person-to-person (P2P) fund transfers and transactions related to small-value person-to-merchant (P2M) payments.

Interoperable

With interoperability technology between different payment systems and UPI, and more people using digital payments, the costs of transactions are expected to decrease further.

India’s most prominent technology firms like TCS, Infosys, Wipro, etc., and fintech companies in various fields like insurance, payment, agriculture, microloans, crowdfunding, and wealth management are some of the major factors behind the growth of UPI. The role of an open Application Programming Interface (API) is also worth mentioning. 

How does UPI’s rise affect the Indian economy?

All the above factors have pegged the volume of UPI transactions at approximately 9 times that of debit and credit card transactions in FY22. But this is just the initial phase. UPI is forecasted to grow manifold and comprise around 73% of the total volume of digital transactions by the financial year 2026.

UPI transactions beyond borders

The tie-up between NIPL (NPCI International Payments Limited) and others like Liquid Group – Singapore, Mashreq Bank – UAE, Lyra Network – France, and PayXpert – UK, are significant steps toward enabling QR-based UPI payments in countries across the globe. Nepal and Bhutan became the first countries to adopt UPI.

RBI’s latest move entails linking RuPay credit cards with UPI. The decision to implement such a change has come at the perfect hour since credit card transactions are rising. A Goldman Sachs report states that the volume of transactions was around $130 Bn in FY22. The forecast is that it may reach $285 Bn by FY26 while the UPI volume growth in August 2022 was more than Rs. 6500 Mn with 346 banks going live on the platform.

An important point is the MDR (merchant discount rate) on transactions by RuPay credit cards. Like other credit cards, the rate will likely be around 2%, as specified by the National Payments Corporation of India (NPCI).

What can be the impact of this MDR on BFSI?

Payment companies like PayU, Razorpay, Billdesk, etc., would be able to acquire more transactions as credit cards would be an additional payment instrument on the UPI platform.

The proposed MDR of 2% (1.5% for the issuing bank and 0.5% for the payment company) on credit cards for payments through the Unified Payments Interface (UPI), would generate a revenue stream for the payment companies.

Currently, the MDR on UPI merchant transactions is zero, which is a deterrent for the payment companies as there is no direct income available for them on UPI transactions. This MDR would help the payment companies to manage and maintain the infrastructure for UPI volume growth.

For smaller merchants, providing a subsidy for MDR will mean the payment companies would lose their revenue percentage by an amount equivalent to the subsidy amount. In that scenario, the Government can incentivize the payment companies to maintain their payment infrastructure (technology, etc.) and operating costs.

Conclusion

With the RBI’s decision to allow credit cards for payments through UPI, the central bank is enabling access to digital payments for those who do not have a debit card or a bank account. It would help to expand the reach of digital payments to a wide demographic besides bringing in higher revenue for the payment companies. If you are looking for a game changer in the digital payments space, UPI it is.

If you want to develop a mobile payment solution application that offers a top-notch user experience and data protection, team up with Mindfire Solutions.

Mindfire Solutions is a trusted software service provider that can assist you in developing robust and secure fintech products. With our two decades of experience, we have learned to leverage modern technologies to offer customized solutions for your business needs. To know more about us, visit Mindfire Solutions.

 

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AI in critical care

Role of AI in Chronic Care Management

Healthcare Application Development, Healthcare IT, Wellness SolutionsMindfire Solutions

Effectively managing chronic diseases such as asthma, diabetes, cancer, and several others have been one of the biggest challenges for healthcare providers worldwide. According to a study, chronic diseases are responsible for 70% of deaths and about 50% of the disease burden globally. Owing to this, we will discuss how AI in chronic care management can change these numbers.

As the pandemic unleashed a global health crisis, many countries faced a shortage of healthcare professionals and medical resources. This caused several hurdles for patients with chronic conditions to get treatment at the right time. Such conditions forced chronic patients to adapt to remote and digital treatment, which is the new normal in the current world.

For medical professionals to deliver quality remote healthcare, it has become essential to leverage fast-emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML).

Both AI and ML have already shown how they can significantly improve the efficiency of operations in various other industries like e-commerce, manufacturing, automotive, etc. In this article, we will take a look at the role of AI in Chronic Care Management (CCM).

Key Benefits of Chronic Care Management

Before we understand the importance of AI in Chronic Care Management, let us look at some key benefits of Chronic Care Management: 

  • Chronic Care Management or CCM offers an organized approach to dealing with chronic conditions, making the process more coordinated for patients as well as the healthcare provider.
  • Healthcare providers can build long-term relationships with patients through their CCM services, which can result in increased revenue.
  • It can prevent unnecessary visits to partitioners.
  • Researchers have proved that CCM can offer quality healthcare to patients at a lower cost.

What is the Role of AI in Critical Care Management?

Here is how AI in chronic care management can turn around the situation:

●     Medical Data Analysis

An extensive medical data set will be required to utilize AI in chronic care management at its full potential. In today’s digital world, it can be easily gathered throughout the patient life cycle via mobile applications, IoT devices, and patient portal software. An Al-based algorithm can analyze this pool of data and generate new insights and opportunities for both patients and medical professionals in CCM. This can streamline the overall chronic care management processes.

●     Prognosis and Prevention of the Disease

One aspect of CCM is to prevent diseases from happening. With the help of AI, practitioners can identify the choric disease to which the patient is susceptible. This allows the doctors to take the correct preventive measures and circumvent the chronic condition.

In a study published by Yannis Paschalidis in Harvard Business Review, healthcare professionals were able to forecast hospitalizations due to diabetes and heart disease a year in advance using machine learning and Electronic Health Records (EHRs) with an accuracy of 82%. 

●     Diagnosis of Diseases

Many recent studies have proved that AI can also be indispensable when it comes to the diagnosis of diseases. Al-algorithms can easily detect diseases based on data points like medical imagery, ECG data, patients’ demography, and their medical history. Such developments have led to time and cost-effective CCM service. It also has significantly improved remote diagnosis.

●     Treatment

The healthcare data can be used to create AI/ML models that can assist physicians in medication modeling and treatment suggestions. These models can be further applied to suggest appropriate dosage and treatment plans for the patient.

A personalized plan and treatment for individual patients can also be created by AI. The personalized treatment can enable clinicians to intervene before a patient’s condition becomes critical, making CCM more effective.

Recently, a company named IBM Watson has achieved some remarkable results by utilizing AI in the field of oncology. The organization performed genetic data analysis and was successfully able to identify the rare secondary leukemia caused by myelodysplastic syndromes.

●     Remote Patient Monitoring (RPM)

With an AI-powered CCM, healthcare professionals can constantly monitor a patient’s vitals like blood pressure, pulse rate, temperatures, etc., from the comfort of their home. The AI can also send an alert to health professionals if there is any sudden or critical change in the vitals of a patient.

●     Virtual Assistant

Chronic diseases generally last for a lifetime. As the patient gets older, it gets difficult for them to keep track of medication and appointments, which causes disturbance in their chronic care management. In such cases, an AI-powered virtual assistant can come in handy for the patient. It can assist them by ensuring drug adherence and also monitor their vital data.

For example, a Meditech company created AI-embed nurse avatars that send notifications to the patient each morning for a check-in routine, record their vitals, and sends alerts for timely intake of medicine.

Another aspect of chronic care management is measuring and managing the patient’s chronic pain. Here again, AI can assist in detecting chronic pain by monitoring the facial muscle movements of patients who are unable to self-report the pain to their physicians.

Conclusion

Artificial Intelligence can offer actionable insights to guide clinical decisions and allow physicians to diagnose, treat and handle chronic conditions remotely, thus making chronic care management more efficient, accessible, and affordable.

That’s why many healthcare organizations have begun to utilize AI in their chronic care management services. If you are also looking to leverage AI to improve your chronic care management, you will need to hire or work with people who have relevant expertise and skill set.

Mindfire Solutions is an IT-service provider that has worked with several healthcare organizations over the years to provide customized healthcare tech solutions that are highly cost-effective, secure, and scalable. Visit Mindfire Solutions to learn more about us.

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Vulnerabilities of IoMT

Top 6 Vulnerabilities of IoMT devices to look out for

Healthcare Application Development, Wellness Solutions, , Mindfire Solutions

The internet of medical things (IoMT) is an innovative solution transforming the healthcare industry. With IoMT, healthcare providers can gather data from various medical devices and create a comprehensive view of a patient’s health. The solution enables healthcare professionals to offer more proactive patient care and better treatment outcomes while reducing expenses.

That’s why many healthcare providers are keen on leveraging the internet of medical things. In 2020, as the pandemic caused global turmoil and remote solutions became a necessity, the global market capitalization for the internet of medical things (IoMT) reached a value of USD 41.7 billion.

By the time 2028 arrives, the global internet of medical things (IoMT) market size is projected to be worth USD 187.6 billion, growing at a CAGR of 29.5%.

While there are several benefits of utilizing the IoMT, there are also certain risks associated with the technology. In this blog, we will bring forward some of the vulnerabilities of IoMT.   

Vulnerabilities of IoMT

Here are some of the major vulnerabilities of IoMT:

Multiple Entry Points

The interconnectedness of medical devices makes IoMT a groundbreaking solution for healthcare; however, it also makes systems more vulnerable to cyberattacks. Medical devices are difficult to patch. As the number of devices and sensors increases, the possible entry points for attacks also increase. Even a single point of a breach can be catastrophic for the entire system.

That’s why it becomes essential to deploy anti-malware mechanisms to ensure that devices are not susceptible to attacks.

Privacy and Security Issues

IoMT systems gather large amounts of data from patients. This data is often sensitive, and if it falls into the wrong hands, it could be used for malicious purposes. For example,  passive attacks such as traffic analysis can allow attackers to gather confidential information on patients like their medical conditions, medications, and treatments.

Active attacks are also possible. For example, an attacker could use a denial-of-service (DoS) attack to disable a device or system, preventing it from being used. Such incidents can cause serious consequences for patients who rely on the device or system for care.

To protect patient privacy and security, healthcare organizations must implement strong security measures like encryption of stored data and data-in-transit.  

Poor Authentication

Most healthcare devices are not equipped with proper authentication mechanisms. Research that aimed to understand the vulnerabilities of IoMT devices observed that many healthcare organizations adhered to default passwords and settings, which were available on the manual online. 

This makes it easy for unauthorized individuals to access the network and tamper with it. In such cases, cybercriminals can remotely take control of devices and use them to carry out hostile activities.

Therefore, your organization should have security protocols such as Multi-Factor Authentication in place to improve the safety of IoMT devices.

Unsecured Internet Connection 

Usually, healthcare enterprises run IoMT systems on the same network which is used for managing their infrastructure. This not only poses a threat to your IoMT system but also to your entire organization’s infrastructure.

Hence, it is advisable to segment the company’s network. Operate IoMT devices on a separate network. This way, even if there is any breach, the damage will be limited to a small portion of your entire network, which can be countered quickly.

Lack of Visibility 

Another major issue with IoMT is the lack of visibility into the network. With so many devices and sensors collecting data, it becomes difficult to track all the activity occurring within the system. According to a study, about 80% of IoMT devices are used frequently in a month. Therefore, it is difficult for the IT team to identify anomalies and potential threats.

Outdated Systems

A study has shown that many healthcare organizations leveraging IoMT are still using outdated versions of operating systems that are not designed to deal with modern cybersecurity threats. This leaves systems vulnerable to sophisticated attacks that can easily bypass traditional security measures.

Even if the organization is up to date with its operating systems, it is equally important to update the devices in the IoMT system regularly. 

Conclusion

IoMT systems offer several transformational benefits to the healthcare organization. However, healthcare providers also need to be aware of these vulnerabilities and take steps to mitigate them.

One of the ways to secure your IoMT system is to conduct risk assessments, implement security measures, and keep up with the latest trends in cybersecurity. By doing so, you can ensure that your IoMT system is protected and can be used to improve patient care.

As testing any system can be an overwhelming task, you can also collaborate with an experienced IT service provider to reduce the workload.

Mindfire Solutions is one of the leading agencies in the IT sector that has assisted several world-renowned organizations with IoT Testing. With our team of industry experts, we offer solutions that are tailored to your organization. If you are also looking to test your IoT or IoMT system, visit Mindfire Solutions to learn more about our services.

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Hybrid Cloud Strategy with Kubernetes

Develop a Hybrid Cloud Strategy With Kubernetes

Mindfire Solutions, , Mindfire Solutions

Initially developed by Google, Kubernetes or K8 is an open-source container orchestration platform that automates several application development processes, including day-to-day procedures like upgrading, monitoring, and logging.

It allows its users to utilize Google’s expertise in distributed systems consisting of highly complex clusters, making it one of the leading container orchestration platforms that support organizations in developing applications cost-effectively.

In 2021, 50% of the organizations globally adopted Kubernetes for application development.

Kubernetes is a fundamentally resilient platform that offers myriad benefits in terms of rapid scaling, deployment, and automated container rollouts. One of the critical benefits of K8 is hybrid deployments. In this blog, we will look into how to develop a hybrid cloud strategy with Kubernetes.

What is a Hybrid Cloud Strategy?

Many companies are migrating toward public cloud infrastructure. However, businesses do not want to abandon their on-premises infrastructure due to the critical data available on it. Hence, opting for a hybrid cloud strategy becomes an ideal option for businesses, where they can utilize on-premises resources with cloud resources.

A hybrid cloud strategy is a plan to deploy and manage applications and services on multiple clouds, i.e., public and private. Creating a hybrid cloud strategy is essential for enterprises undergoing digital transformations. With this strategy, organizations can easily bifurcate what data goes on on public cloud infrastructure and private cloud infrastructure.

Benefits of a Hybrid Cloud Strategy

Here is a list of the benefits that enterprises can get by developing a hybrid cloud strategy:

  • Scalability

With a hybrid cloud strategy, businesses can achieve true application portability. Meaning the application can be shifted instantaneously from one cloud infrastructure to another as per the requirement. It can distribute and manage the load without performing any complex configuration.

  • Data Safety

By replicating your on-premise data on cloud infrastructure, you can provide a backup for your database in case your on-premise infrastructure faces some technical disaster and you lose all your crucial information.

  • Innovation

A hybrid cloud strategy enables the IT team to develop and test new updates of an application on the private cloud infrastructure before making it available on the public cloud infrastructure. This allows the IT team to be more innovative and eliminates the need for any downtime required for developing new features.

  • Cost-effectiveness

Businesses using a hybrid cloud strategy require lesser time and resources in developing and deploying an application. Hence, making the overall process cost-effective. 

  • High Performance

By adapting to a hybrid cloud strategy, you can distribute the application globally, hosting critical services and data per the requirement, and provide high-performance to the users.

Benefits of Kubernetes

Kubernetes comes with several features that are very beneficial for organizations:

  • Auto Scaling is one of the most prominent benefits of Kubernetes. K8 has three different autoscaling capabilities- Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler. It allows the enterprises to run the workloads cost-effectively and efficiently.
  • Kubernetes acts as an orchestration system and enables the IT team to leverage a containerized environment.
  • Another impressive benefit of Kubernetes is its self-healing ability. If a container fails, K8 can replace it or restart it automatically. The platform also constantly monitors the health of nodes and containers.
  • With Kubernetes, IT teams can manage the network of multiple containers through a single console.
  • One can also build a micro-services-based application with Kubernetes. It allows the companies to scale the parts of the application that have received high traffic.

Challenges with Kubernetes

Even though there are numerous advantages of Kubernetes, there are also some drawbacks of the platform.

  • There are too many moving parts involved in Kubernetes; hence, new users would have to go through a steep learning curve while adapting to the platform.
  • The initial step-up and configuration of Kubernetes is complex and can be overwhelming.
  • The platform provides limited automation during the restoration of the containerized application environment.
  • The platform can be more expensive than its alternatives.

Kubernetes-Based Hybrid Cloud Strategy

Kubernetes offers an ideal foundation for hybrid cloud strategy because of its consistency. It does not matter if you are using Kubernetes as an on-premise infrastructure or a cloud infrastructure; they both work on the same commands and in similar ways. As Kubernetes is open-source, you can also deploy it in clusters of machines without licensing or contracts.

With Kubernetes, you can develop a hybrid cloud strategy using three different methods:

  • Clusters Without Direct Interaction: In this method, there are multiple clusters in a system, with each one having a different task to perform, and there isn’t any direct interaction among them. It is a beneficial strategy if you are looking for environment segregation to develop applications on one cloud platform and deploy them on another.
  • Cluster Federation: This is the opposite of clusters without a direct interaction method. Cluster federation provides a single point of view for all clusters. The method comes in handy when looking for a centralized configuration of data centers and a single API. It establishes a standardized Kubernetes deployment for all clusters.
  • Serverless Architectures: This method can be considered one of the popular trends for adopting a Kubernetes-based hybrid cloud strategy. The purpose of a serverless architect is to offload the cluster. It allows Kubernetes to be integrated as a virtual machine (VM) in case your physical machines are exhausted.  

Conclusion

In conclusion, a Kubernetes-based hybrid cloud strategy provides a holistic solution for deployment, management, and operational concerns. However, there are a lot of moving parts involved in the process. Coming up with a hybrid cloud strategy can be a challenging task. That’s why many companies prefer outsourcing this job to expert IT professionals.

To create a perfect Kubernetes-based hybrid cloud strategy for you, Mindfire Solutions has assembled a team of passionate individuals who are well-equipped with industry knowledge and have been working in this domain for a long time. Over the years, Mindfire Solutions has worked with several world-renowned companies like SAP, AsianPaints, and DHL. 

Visit Mindfire Solutions to learn more about our services.

 

 

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AI for healthcare

How Do AI Applications Improve Healthcare And Wellness?

Healthcare Application Development, Wellness Solutions, Mindfire Solutions

The COVID-19 pandemic overwhelmed the existing healthcare infrastructure. It has been a rude reality check for clinical administrators worldwide. Now, as the contagion subsumes, the persisting rise in the global burden for non-communicable ailments like lifestyle disorders is likely to keep medical practitioners on their toes in the days ahead. With this, the demand for preventive measures is increasing. By using AI for healthcare, we may step ahead in this crisis.

A Looming Crisis In Healthcare

Today, the demand for preventative intervention is skyrocketing. But limited growth in clinical ranks indicates an ever-widening talent gap with cascading implications. WHO assessed a projected shortfall of 18 million healthcare workers by 2030. This gap is likely to be manifested primarily in low and lower-middle-income geographies. Medvocation, in one of its recent studies, found that nearly 44% of doctors worldwide are already breaking under the immense workload and are unable to live happy and healthy lives.

The existing state of affairs can portend a crippling impediment as the global population ages. Reactive healthcare becoming more costly adds to the worries of the patients and clinical professionals.

AI For Healthcare: Reimagining Wellbeing

Fortunately, advancements in data-driven technologies like AI and machine learning have brought preventive medicine much closer to high-risk and healthy individuals. It has improved the possibilities of self-care and wellness like never before. For instance, today, AI applications can monitor every heartbeat and predict congestive heart failure (CHF) with remarkable accuracy. It permits the prospective patient a significant head start in seeking expert advice long before ending up on a gurney in an ICU.

Due to this the global market for intelligent self-care medical devices ($13 billion in 2020) is expected to reach a valuation of more than $30 billion by 2027, with a CAGR of over 8%.  

AI in Medicine: A Comprehensive Approach

For some years, AI-powered applications have made significant inroads across various clinical procedures and treatments, from automating medical front desk services, drug repurposing, vaccine development, building Smart EHR systems, and improving pathology to successfully predicting drug reactions. Today, at least 90% of healthcare establishments have an AI strategy. Indeed the benefits are substantial as cognitive computing allows practitioners to delegate repetitive tasks like clinical data extraction, assimilation, and report creation to the machines. Effectively AI for healthcare professionals can help with better decisions and focus on what they do best: care for their patients. 

However, the role of AI on the demand side of the story is equally spectacular! Proper self-care, yet elusive, is now feasible as wearable devices embedded with machine intelligence enable individuals to listen to their vital signs better. As algorithms operating on the edge get more intelligent and more emphatic, they will only expand the chances of proactively and precisely diagnosing physiological parameters and assessing the likelihood of acute events for individuals with chronic conditions. It, in turn, will preempt health risks, transform clinical deliveries and ease the pressure on the existing medical infrastructure worldwide.

Factors Fueling The Influx Of AI For Healthcare

The trend is in no way isolated and wholly synced with the cognitive technology maturity curve. Several factors advocate making personal devices like Blood Glucose monitors, Insulin Pumps, Sleep Apnea Devices, Blood Pressure Monitors, and Smart Watches intelligent enough to improve self-care and wellness for their owners. None is more telling than the dichotomy that although there has been an explosive growth of health data collected institutionally in recent years, it may still fall short in enabling patient outcomes.

For instance, an asthma patient typically visits the physician every three months and spends over 2,100 hours in between when the symptoms are not actively monitored, undermining realistic assessment. Now, data continuously ingested through smart devices can bridge this gap. Other factors include:

– Advancement in cloud computing: 

The computational power available for training AI models and algorithms has grown exponentially in recent years with the GPU revolution. Today, with easy access to bare metal servers from cloud infrastructure providers, it is easy to configure systems for running high-performance healthcare workloads.

– Development of Deep Neural Networks: 

The development of Artificial Neural Networks today is supplementing ML capabilities, providing for much better and more precise modeling. ML procedures like Capsule Neural Networks and Transfer Learning can transform how ML models are built and deployed, leading to far more accurate predictions even when trained with limited datasets. It will indeed make self-care medical devices smarter and more cost-effective.

– Shift in the healthcare delivery philosophy: 

As AI systems become more readily available, institutions worldwide are unmistakably considering how care is delivered and how precious healthcare resources and infrastructure are utilized. For instance, the National Institute of Health in the United Kingdom has launched an initiative to encourage the use of AI for healthcare of individuals to self-diagnose the onset of chronic conditions. The broader objective is to eliminate unnecessary outpatient visits and save operating costs, optimizing the resources available to the frontline care workers.

AI In Self-care: Use Cases

This research paper published by Nature.com manifests the overall apprehensions of patients around the role of AI in healthcare. However, advancements in cognitive technologies can bring in early and reliable insights and even formulate an effective response to some of the most prevalent chronic health conditions worldwide. These includes: 

– Diabetes: 

While the retroactive insights on blood sugar levels are currently derived from lab tests like A1C and self-service glucometer readings, AI-enabled devices can completely upend the diabetes treatment pathway. Intelligent insulin pumps can monitor blood glucose levels and other health metrics in real-time and auto-administer appropriate insulin doses based on the patient’s health condition and symptoms.

– Hypertension: 

Collecting blood pressure readings periodically through cuff-based devices is only half the game, pending further diagnosis. However, with Smart wearable devices connected to the cloud, blood pressure data can be assimilated with multimodal data sets like genomics and behavioral to pinpoint anomalies and preempt acute instances.

– Asthma:  

Asthma patients must regularly visit clinics for pulmonary function tests. They are monitored for environmental variables like air quality and moisture profile that can adversely impact their health. AI algorithms can extrapolate heart rate and blood oxygen level data from pulse oxymeters. Utilizing other pointers like pathophysiological analysis, natural history, seasonality, phenotypes, genetics, environmental monitoring, disease biomarkers, etc., AI predicts the possibilities of an asthma attack.

– Congestive Heart Failure (CHF): 

Conventional detection for CHF is done through a clinical diagnosis like ECG and studying factors like hereditary prevalence and lifestyle choices. Nevertheless, AI algorithms can accurately predict heart health through raw electrocardiograms to predict the possibilities of CHF, almost with 100% accuracy.

– Depression: 

Screening for mental health conditions depends on subjective evaluations to detect the root cause and respond to symptoms. However, AI algorithms can eliminate this subjectivity and clinical bias from the equation by evaluating symptoms through facial expressions, voice patterns, and online habits. Moreover, they can objectively assess treatment progress by interpreting brainwave profiles unique to patients with depression.

Final Thoughts

Bringing AI to improve the state of self-care and wellness is an idea whose time has come. Cognitive technologies can play a pivotal role in preventing catastrophic health events and saving lives. However, considering the wide range of variables and the risks involved, expert implementation becomes as crucial as the technology to ensure the first-time-right outcomes. Therefore alongside investment in technology, it becomes a strategic necessity to find an experienced technology partner who can aptly demonstrate the viability of self-care through AI.

Mindfire Solutions is one such leader in AI/ML, well-acclaimed in the global health tech market. Get in touch with one of their AI consultants to discover how the company simplifies self-care and wellness for millions worldwide.

 

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Brand Loyalty

Spoilt for choice – Is Brand loyalty of Buyers a thing of the past?

Digital Marketing StrategiesMindfire Solutions

The Covid 19 pandemic has seen variants across the entire spectrum of human behavior, including consumers’ brand loyalty. The latest McKinsey reports, “In the US, more than 60% of consumers who tried a new behavior plan to stick with it post-crisis.” A second study agreed, saying that 62% of people who changed their preferred brand pre-pandemic will most likely make it a permanent change. 

Research says during the pandemic, consumers have changed their shopping choices. When they didn’t find a particular product with a specific retailer, they moved on to a different brand. This causes supply chain disruptions globally. Availability, convenience, and comfort took precedence during times of scarcity. 

We cannot overlook Post pandemic changes in consumer behavior as temporary. It can shape the future trajectory of brands and customer relationships. 

Buying Behaviour Pre-Pandemic 

The change in buying approach is not entirely a post-pandemic trend. In 2019, a study of more than 34000 customers by Verint® Systems Inc. stated that brand loyalty has steadily declined for years. More than 60% of the people interviewed in the study said that it was very likely for them to switch to a competitor with better customer service. A report by Accenture Strategy found nearly 90% of buyers were quicker to shift to a new brand than they were three years back. 

Brand Loyalty Is A Thing Of The Past

Brand loyalty is not just declining. It is quickly diminishing. In the era of e-commerce and social media, buyers have plenty of choices. There has been a mass migration of buyers onto the internet. Billboards and television ads are almost dead.  Hence, consumers today tend to be more versatile and less loyal than ever. Research on consumer satisfaction indexes has long proven that despite having a positive experience with an existing brand, satisfied consumers are less likely to buy the same car, shampoo, or washing machine again. 

Previous research indicates that customers are invested in a brand for reasons like consistency, increase in value, quality, reward points, personalized gifts, etc. Today, brands are struggling to recall value. Between times of loyalty to no brand loyalty, the world around us has gradually but significantly shifted.

Reasons For Changing Consumer Behavior

Some of the reasons that can explain the changing consumer behavior are:

Product Availability  –  With shutdowns happening since 2019, many products went off the shelves. Despite preferences, consumers found more options – similar and sometimes better in quality. 

Exposure to online reviews – Customers used to listen to brands. Now, they have the option to read online reviews and assess the product’s quality. We see people reading an online review and saying, “I am going to try it,” now more than ever. 

Customer service – More than 85% of customers say that excellent customer service is a ‘make it or break it moment’ for them when selecting a service provider. 

Convenience – When a diverse range of products is available at a  screen swipe, an average buyer wants maximum comfort. 68% of consumers say they intend to engage with a brand that makes it easy for them. 

Technology – Close to 50% of customers say that they are more likely to stay loyal to brands with improved technology than their counterparts. We often associate good tech with better customer engagement. 

Human contact – In sharp contrast to the previous point, while many customers prefer self-service, they also find it comfortable to engage with a person across phone and digital platforms. Non-availability of people in customer service proved to be a firm no-no for buyers. 

While customer service has improved across services, some outperforming brands and retailers have set the bar high. Today’s world is fast evolving to provide the best shopping experiences online. Buyers don’t have to be on their feet all day hopping from one store to another. It is not enough to be good or better. Be the best to stand a chance at winning the buyer’s brand loyalty. 

Value for Money

“Value for money” is the primary reason to switch brands more often. Increasing global awareness of ethical, sustainable, and value-based brands (e.g., supporting local businesses) are some reasons why buyers choose to move away from certain brands or products.  

Another challenge for retailers when retaining loyal customers on the internet is competing for the buyer’s attention, engagement and consistency.  While it takes time to nurture such loyalty, in the face of the bombardment of information on the internet, people who want to buy do not have the attention span for one particular brand. Therefore retailers investing at least 50% of their budget in improving customer experience are set to gain in the long run because research says loyal customers are more likely to spend 67% more than new customers. It is always easier to sell to someone ‘again’ than for the first time. Technology can make personalized products and services for all buyers a reality. Brands can reap rich profits through early investments in robust IT. 

Conclusion

The soar in online shopping is unlikely to decline after the pandemic abates. For brands to maintain a solid relationship with their valuable customers, they must keep a firm grip on their brand loyalty programs. Instead of running isolated programs around marketing, companies should integrate brand loyalty goals into all departments such as operation, finance, and technology. It is crucial to ensure seamless coalescing of operation, marketing, and the tech footprint of the company to build brand loyalty. Swift and smooth returns, replacements, and refunds, with efficient and polite customer care, are essential success factors for a brand. Couple it with a user-friendly online interface. And you have the success mantra for customer retention in the long run.

Mindfire Solutions provides robust IT solutions and a user-friendly online interface that can help you bridge the gap with your customers. Connect with us to know more about solutions that can help grow your business. 

 

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