Category: Technology

You are planning to build an app for your business. You zero in on the app features, then develop it and finally release your app to the respective stores. You actively monitor your app’s usage and analytics. Your users are happy and they actively use your app. After a while, you notice a dip in the curve. 

Your app starts falling behind in-store listings, garners poor user reviews, becomes frequently prone to errors. Then one day you get an email from the respective app store that your app might be removed if it does not meet the latest requirements of the app store.

So, what went down?

Well, did you ever take the effort to continue maintaining the app? Delivering updates on a timely basis so that your app stays compatible with current requirements and is well ahead in terms of features, functionality, security, and performance is a vital part of app development. 

You might have built the perfect app, but how long can it remain active and not pass out as obsolete without proper maintenance?

As an enterprise app development company, our experience has shown us that creating and deploying an app is sensible and profitable in the end only if you tie it up with a regular update cycle. We put equal stress on pairing a solid maintenance contract having timely release cycles into every mobile app that we built and always insist on our clients on the importance of having one.

Ongoing maintenance remains indispensable in app development 

In 2016, Apple made a relentless push clear its App Store for all outdated apps, delisting 50,000 apps according to a report by Sensor Tower. It further revealed that 51 percent of apps haven’t received any updates in a year. 

Source: Sensor Tower

Earlier, you build an app and launch it. You only need to focus on developing the app in the best possible way. However, things have changed. Ongoing maintenance is so important now that almost 80 percent of developers release some form of updates to their apps every month. 

Performing maintenance constantly, reinvent your app’s functionality and fix any bugs or other latent issues. It’s unlike the build and deploy once approach that was employed traditionally. 

To keep up with the ever-changing user and app store requirements, you should not only build, deploy and optimize your app for the store listing but also improvise it by regularly issuing new updates. Like a newly constructed house that requires additional investment and efforts at maintenance like cleaning, painting and other upgrades, your apps should similarly go through a regular upkeep cycle to keep it active.

Besides, setting up a maintenance plan is so important for your business as several external variables are prone to changes that can affect your app’s functioning. These include: 

• Mobile hardware: As handset manufacturers keep releasing newer models each year, mobile apps can become incompatible with the latest mobile hardware, causing them to fail. With a maintenance plan, you can continuously deliver new updates that extend support to all the latest mobile hardware specifications. 
• OS versions: Android and iOS are evolving with new version upgrades every year and your mobile app should take into account these changes to extend support and compatibility with upgraded OS versions. 
• User Interface: Material Design, Google’s visual design language, marks the change from Skeuomorphic principles towards a fresh approach to user interface design. Keeping up with the current user’s preferences meant adopting newer UI/UX design standards for the app interfaces offering immersive digital experiences. 

• Security: With data breaches now commonplace, you should integrate all recent security protocols into your apps to uphold privacy and security. 
• Programming languages: Programming languages use to build apps also undergo changes and your apps should stay updated considering these variations. 
• Software libraries: Third-party software libraries and dependencies used in apps go through frequent changes, which affects your app’s functioning and cause it to malfunction if they are not upgraded.
• Licensing: Apps that you build has to be tied down to licensing agreements and certifications. These agreements are a limited time offer and therefore need to be renewed periodically to keep the app intact and functional throughout its lifetime. 
• Hosting infrastructure: The platform where your app’s database and backend are hosted such as AWS can also be subject to changes throughout the year. 

Signing maintenance contracts save your app from getting outdated

Post completion, your vendor may ask about maintenance contracts for your app. Most businesses decline to sign up for a maintenance contract, because to them, the idea of paying extra amount for an already launched app sounds absurd and unconvincing. However, considering the necessity of app maintenance, it is mandatory that a maintenance contract is bought in and formulated alongside the actual development. 

Once you sign up for a maintenance contract, you get the dual advantage of both support and regular updates. Every issue that crops up will be dealt with immediately by the development team once you enroll for a maintenance contract, thereby significantly reducing your app’s downtime. Besides, when platform changes occur, such as new OS versions or hardware, your app development partner can review them in advance and plan all future updates accordingly. 

Understanding maintenance costs

Maintaining your app continually comes with its costs, which can vary depending on the scope and functionality of the app.  Clutch, in one of their surveys, outlines that post-launch maintenance can cost anywhere between $5000 – $10,000, a year after launching the app, which varies based on the vendor opted. 

However, maintenance costs follow the standard industry norm, which is 20 percent to that of the initial development cost. For instance, if your app costs about $10,000 to develop then maintenance would run around to $2000 a year. This is subject to vary as it takes into account several other factors like the type of app – native or hybrid, additional features – (push notifications, payment gateway), backend hosting platform, use of third-party analytics tools, etc. 

So, what real benefits does your business gain by signing up for a maintenance contract with an app developer? 

• Lower Uninstall Rates – AppsFlyer in their newest report shows that the global uninstall rates for apps in a month account for about 28 percent. Timely maintenance can come to the rescue by promptly applying all the first time fixes to the app, which retain your active users lowering the chances of uninstalling. 
• Sustained User Loyalty – Constant maintenance does finally pay off via sustained user loyalty across the app’s entire lifetime, which is one crucial ingredient that defines your app’s success in the long run.
• High Rankings in Store Listings – Keeping up with ongoing maintenance ensures that your app adheres to quality guidelines and is constantly optimized based on updates and user feedback for properly ranking in the store listings.
• Adaptation Centered on User Feedback – Recurrent maintenance helps monitor usage patterns to retrieve business insights and create individual user funnels. Using in-app analytics tools, developers can analyze the app’s functioning and resolve any crashes or bugs for flawless user experience and improved retention. 
• Increased ROI – With a solid maintenance plan in place, your app can achieve long term benefits for your business by reaping higher financial gains, leading to increased ROI. Besides, you can decrease the overall costs by monitoring the app post-launch and removing redundant features that most users ignore or skip by. 

Conceive a maintenance plan by collaborating with your app development partner

Finding a reliable app developer with expertise in building enterprise applications helps kick start your app’s development and maintenance cycle. Once signed up, the development team assesses your app’s performance after launch. Taking incoming feedback from users and identifying the potential issues mentioned, the team adopts an end-user development approach to detect bugs and other performance issues beforehand and proactively fix them. 

You can get the best in class maintenance support for your mobile app with our application development services. Talk to our consultants today and see how Fingent helps you conceive the right maintenance strategy for your app that yields new revenue streams and enhance your business outcomes. 

4 Top Reasons for Organizations to Move to Python 3

Python is one of the most widely used programming languages on the planet. Over the years programmers have fallen in love with Python for its increased productivity and capabilities. However, a constant argument persists on whether Python 3 is better than Python 2 and would it be a wiser decision to completely shift to Python 3.

Although Python 3 has been in existence for over 7 years, programmers are quite skeptical about using it. Most programmers tend to cling to Python 2, completely ignoring the capabilities of the new version.

This blog will walk you through some major drawbacks of Python 2 and will pinpoint 4 top reasons on why to choose Python 3 over Python 2.

Why Is Python 3 On The Rise?

Before we dive deeper into the major advantages of Python 3, let’s take a look at its origin. 

Released in 2008, Python 3 was introduced to overcome the flaws of Python 2. The major reason behind developing Python 3 was to clean up the codebase and remove redundancy. Although Python 3 is the newer version of Python, it is not necessarily backward compatible with code written in the 2.x version. 

Significant features of Python 3 makes it simpler, easier and incredibly efficient to use. Here’s listing the major differences between Python version 3.0 and 2.0, and the reasons why Python 3 can make a better programming partner.

1. Unicode Character Encoding

As mentioned earlier, Python 3 was introduced to address the vulnerabilities and drawbacks of Python 2. Hence, it reduces the complexities of coding and improves speed and performance. 

Where in Python 2 the character encoding is done in ASCII format, Python 3 is Unicode based. In Python 2 the strings are by default stored in the form of ASCII values. Programmers are required to add ‘u’, to store strings specifically in Unicode format. In Python 3 strings are stored in UTF-8 format, enabling a large number of storage features such as character value storage, different language characters, and emojis storage as well. 

Dropped deprecated features which were frequent sources of bugs in Python 2 have also been replaced by superior alternatives and retained solely for backward compatibility. 

For instance: If a file is created by олга with non-ASCII characters in the name, for the below-mentioned code, if you are using Python 2 your code is sure to throw an error 500. 

However, in Python 3  your error will be detected right away saving you the time on long code creation. Moreover, the error message is much easier to understand. Knowing that str object is owner and node is a bytes object, it is easy to recognize that the error is due to listdir returning a list of bytes objects. 

Adding listdir(‘.’) would make the bug disappear as this would appear as a Unicode string in Python 3.

The difference in the behavior is due to the difference in how each version handles the string type. Whatever is lumped together in Python 2 is split in Python 3.

2. Improved Library Standards

When it comes to libraries, there is a huge difference between the two versions. Many libraries developed in Python 2 are not forward compatible. Hence, the new 3.x version is developed focusing on providing good compatibility. Moreover, most of the actively maintained libraries are strictly created for use with Python 3. Hence, it’s suggested to keep codes compatible with Python 3 to help keep your test running on both versions. 

3. Improved Integer Division

Reducing a programmer’s confusion and frustration, Python 3 is created with a syntax that’s more intuitive. Python 3.x version has an elegantly designed structure that allows performing an action with fewer lines of code. Python 2, on the other hand, requires the exact input to perform a particular result or generate the expected results.

For instance: If you try a simple calculation like 5/2 (5 divided by 2), Python 2, after rounding up would give you the result as 2. To derive the exact result, that is 2.5, the input should be 5.0/2.0.

Whereas, Python 3 would right away, give you the answer 2.5 for the input 5/2, without converting the numbers to float data type.

4. End of Python 2 Support

Yes! Python 2 is expected to stop all support and maintenance by January 2020. Now, this is another major reason why you need to shift to Python 3 at the earliest. By end of support, we mean that all-new packages will be built on Python 3 and hence, it will be difficult to add any new features to the existing Python 2 projects. Major plugins are also being ported to Python 3 and thus, the upcoming updates of these plugins will be available only for the 3.x version. 

Moving forward, it will be difficult to find any Python 2 support services or developers. Also, the Python 2 hosting options will grow more scarce and costly. The Python 3.x version and the releases ahead are believed to have different syntax from that of the current version and thus, getting upgrades for existing features would be difficult to find.

Why Should You Stop Using Python 2?

Although programmers have widely accepted and loved working with the 2.x version of Python, there is quite a huge list of flaws and drawbacks experienced with it. Here’s listing a few of them. 

1. Firstly, as mentioned in this post, the Python 2 text model is not Unicode capable. It doesn’t handle non-ASCII files correctly. This is one of the major drawbacks of the version. Python 2 handles Unicode module names quite inconsistently, which is a source cause of multiple programming errors. That is why 3.x version of Python is designed to have a Unicode based string type by default.

2. In addition to not being Unicode capable, there is a large number of Unicode handling bugs in Python 2 standard library that might never be fixed. Fixing these bugs within the constraints of Python 2 is too difficult, and not worth the effort.

3. Python 2 iterator was designed long before the introduction of the iterator protocol. Thus, it has a lot of unnecessary and lengthy listings, which can now be made more memory efficient.

4. Programmers who have been involved with Python 2 for a long time might have noticed that the version interprets numbers in a strange way if they have leading zeros. Also, the version has two different kinds of integers. Python 2 beginners are often surprised to find that the version can’t do basic arithmetic correctly.

5. The print and exec statement is also weirdly different from the normal function calls like eval and execfile. Moreover, you need parentheses to catch multiple exceptions.

6. Although list comprehensions are one of Python’s most popular features, surprising errors arise on the local namespace. Also, if you tend to make a mistake in handling the errors, there might be chances where you’ll lose the original error.

Eliminating all these persisting errors and flaws of Python 2, the new version 3.x is specifically designed to enhance the quality and efficiency of the programmers. Thus, it is highly recommended to start preparing for a complete shift to Python 3. For developers who would like to check on to the Python 3 upgrade packages, here’s the command you can use:

[Don’t forget to create a test-requirement.txt file when using the command.]

With increased competition and high consumer expectations, programmers are under constant pressure to improve software performance. With the efficiency and ease of use offered by Python 3, programming is sure to achieve greater success than before. Although Python 2.7 will be supported until 2020, the sooner the switch, the better.

If you are looking for a technology partner to help your business transform with the latest digital trends, then get in touch with our experts today!

Data Mining Vs Predictive Analytics: Learn The Difference & Benefits

With big data becoming the lifeblood of organizations and businesses, data mining and predictive analytics have gained wider recognition. Both are different ways of extracting useful information from the massive stores of data collected every day. Often thought to be synonyms, data mining and predictive analytics are two distinct analytics methodologies with their own unique benefits.

This blog examines the differences between data mining and predictive analytics. 

Difference Between Data Mining and Predictive Analytics

Data mining and predictive analytics differ from each other in several aspects, as mentioned below:

Definition

Data mining is a technical process by which consistent patterns are identified, explored, sorted, and organized. It can be compared to organizing or arranging a large store in such a way that a sales executive can easily find a product in no time. Various reports state that by 2020 the world is poised to witness a data explosion. Therefore, data mining is a strategic practice that is necessary for successful businesses. It helps marketers create new opportunities with the potential for rich dividends for their businesses. 

Predictive analytics is the process by which information is extracted from existing data sets for determining patterns and predicting the forthcoming trends or outcomes. It uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In other words, the aim of predictive analytics is to forecast what will happen based on what has happened. 

Techniques and Tools

Although there are many techniques in vogue, data mining uses four major techniques to mine data. They are regression, association rule discovery, classification, and clustering. These techniques require the use of appropriate tools that have features like data cleansing, clustering, and filtering. Python and R are the two commonly used programming languages in data mining.

Unlike data analytics, which uses statistics, predictive analytics uses business knowledge to predict future business outcomes or market trends. Predictive analytics uses various software technologies such as Artificial Intelligence and Machine Learning to analyze the available data and forecast the outcomes.

Purpose

Data mining is used to provide two primary advantages: to give businesses the predictive power to estimate the unknown or future values and to provide businesses the descriptive power by finding interesting patterns in the data.

Predictive analytics are used to collect and predict future results and trends. Although it will not tell businesses what will happen in the future, it helps them get to know their individual consumers and understand the trends they follow. This, in turn, helps marketers take necessary, action at the right time, which in turn has a bearing on the future.

Related Reading: Predictive Analytics: The Key to Effective Marketing and Personalization 

Functionality

Data mining can be broken down into three steps. Exploration, wherein the data is prepared by collecting and cleaning the data. Model Building or Pattern Identification by which the same dataset is applied to different models, thus enabling the businesses to make the best choice. Finally, Deployment is a step where the selected data model is applied to predict results. 

Predictive analytics focuses on the online behavior of a customer. It uses various models for training. With the use of sample data, the model could be trained to analyze the latest dataset and gauge its behavior. That knowledge could be further used to predict the behavior of the customer. 

Talent

Data mining is generally executed by engineers with a strong mathematical background, statisticians, and machine learning experts. 

Predictive analytics is largely used by business analysts and other domain experts who are capable of analyzing and interpreting patterns that are discovered by the machines. 

Outcome  

Data mining enables marketers to understand the data. As a result, they are able to understand customer segments, purchase patterns, behavior analytics and so on. 

Predictive analytics helps a business to determine and predict their customers’ next move. It also helps in predicting customer churn rate and the stock required of a certain product. Additionally, predictive analytics enable marketers to offer hyper-personalized deals by estimating how many new subscriptions they would gain as a result of a certain discount, or what kind of products do their customers seek as a complement to the main product they bought from the seller. 

Related Reading: Using Predictive Analytics For Individualization in Retail

Effect of Data Mining and Predictive Analytics on the Future 

The global predictive analytics market is estimated to reach 10.95 billion by 2022. We are now in a period of constant growth, where businesses have already started using data mining and predictive analytics sift through the available data for searching patterns, making predictions and implementing decisions that will impact their business.

Both approaches enable marketers to make informed decisions by increasing productivity, reducing costs, saving resources, detecting frauds, and yielding faster results. To make the best use of data mining and predictive analytics, you need the right guidance and the best expertise. Talk to our experts and find out how Fingent can help your business scale up with the power of data. Get on your way to a digital-first future with Fingent.  

Can Data Warehousing Enhance the Value of Data Visualization & Reporting?

Organizations rely heavily on data to make crucial business decisions. Hence, it is important for your business to have access to relevant data. That is where a well-designed data warehousing comes to your rescue!

Besides gaining actionable insights, corporate executives, business managers, and other end-users make more informed business decisions based on historical data. 

Today’s Analytics and Business Intelligence solutions provide the ability to:

• Optimize business processes within your organization
• Increase your operational efficiency
• Identify market trends
• Drive new revenues
• Forecast future probabilities and trends

Before understanding how data warehousing can add more value to data visualization and reporting, let’s take a look at what these terms mean.

Analytics and Business Intelligence

Business Intelligence is a process that includes the tools and technologies to convert data from operational systems into a meaningful and useful format. This helps organizations analyze and develop meaningful insights to take timely business decisions. The information derived from these tools demonstrate the root cause of your business problems and allow decision-makers to strategize their plans based on the analysis.

Business Intelligence is information not just derived from a single place, but multiple locations and sources. It can be a combination of the external data derived from the market and the financial and operational data of an organization that is meaningfully applied to create the “intelligence”. 

Data Warehouse

Data warehouse is a repository that collects data from various data sources of an organization and arranges it into a structured format. An ideal data warehouse set up will extract, organize, and aggregate data for efficient comparison and analysis. Data warehouse supports organizations in reporting and data analysis by analyzing their current and historical data. This makes it a core component of Business Intelligence.   

Unlike a database, that stores data within, at a fully normalized or third normal form (3NF), a data warehouse keeps the data in a denormalized form. It means that data is converted to 2NF from 3NF and hence, is called Big Data. 

Key benefits of a Data Warehouse

• Combine data from heterogeneous systems
• Optimized for decision support applications
• Storage of historical and current data 

Why We Need Data Warehouse for Business Intelligence?

Before the business intelligence approach came into use, companies used to analyze their business operations using decision support applications connected to their Online Transaction Systems (OLTP). Queries or reports were retrieved directly from these systems. 

However, this approach was not ideal due to: 

• Quality issues 
• Reports and queries were affecting business transaction performance 
• Data resides in heterogeneous sources 
• Non-availability of historical data
• Non-availability of data in the exact form required for reporting

Connecting your organization’s business intelligence tools to a data warehouse can provide you benefits in terms of production, transportation, and sale of products.

Data Visualization vs. Data Analytics – What’s the Difference?

Data Warehousing and Business Intelligence Using AWS 

Today, traditional BI has given way to agile BI where agile software development accelerates business intelligence for faster results and more adaptability. Big Data is growing fast to provide useful insights for making improved business decisions.

There has been a paradigm shift in data storage with warehousing solutions moving increasingly to the cloud. Amazon Redshift, for instance, is one of the most popular cloud services from Amazon Web Services (AWS). Redshift is a fully-managed analytical data warehouse on cloud, that can handle petabyte-scale data, which enables analysts to process queries in seconds. 

Redshift offers several advantages over traditional data warehouses. It provides high scalability using Amazon’s cloud infrastructure to set-up and for maintenance, without the need for upfront payments. You can either add nodes to a Redshift cluster or create additional Redshift clusters to support your scalability needs.

You can use AWS Marketplace ISV Solutions for Data Visualization, Reporting, and Analysis.

Data visualization helps you identify areas that need attention or improvement, clarify factors that influence business such as customer behavior, and making decisions such as finding out a suitable market for your product or predicting your sales volumes, and much more.

TIBCO Jaspersoft, for example, is a solution that delivers embedded BI, production reporting, and self-service reporting for your Amazon data at affordable rates. It features the ability to auto-detect and quickly connect to Amazon RDS and Amazon Redshift. Jaspersoft is available in the AWS Marketplace in both single-tenant and multi-tenant versions. TIBCO Jaspersoft for AWS includes the ability to launch in a high availability cluster (HA) as well as with Amazon RDS as a fault-tolerant repository. Pricing is based on the Amazon EC2 instance, type as well as the chosen single or multi-tenant mode.

Image source: http://bit.ly/2IWWCDn 

Summary

By moving your analytics and business intelligence to a hybrid cloud architecture you will be able to handle huge amounts of data and scale at the rate of expansion required by your business. You will also be able to deliver information and solutions at the speed that your employees and customers demand, and gain insights that will enable your organization to innovate faster than ever.

Business Intelligence and Data Warehousing are two important aspects of the survival of any business. These technologies give accurate, comprehensive, integrated, and up-to-date information on the current enterprise scenario which allows you to take the required steps and make crucial decisions for your company’s growth. To know how your business can benefit from the latest technologies, get in touch with our experts today

How AI and Machine Learning are Driving Cyber Security in FinTech?

Being a subset of the financial services domain, FinTech is targeted by hostile cyber villains. Industries thus require secure mechanisms to keep their data safe and secure. Preventing data losses are critical for Fintechs. 

The World Economic Forum states that cyber-security is the Number One risk associated with the financial services industry.   

Cyber Security Risks Associated With FinTech

Cybersecurity has remained a pressing concern for FinTech. Ever since the global financial crisis of 2008 that challenged the traditional financial institutions significantly, technology-driven start-ups have started evolving increasingly to cater to finance, risk management, digital investments, data security, and so on. Presently, we are in the FinTech 4.0 era. 

The major cybersecurity risk that enterprises implementing FinTech face are from integration issues such as data privacy, legacy, compatibility, etc. Hackers target FinTech as they handle large volumes of customer data that include personal, financial, and other critical information.

FinTech offers a multitude of easily accessible services via its APIs. For instance, API banking. Here, the APIs are developed for the banks to access the FinTech platforms. It becomes open, API banking when open APIs enable third-party developers to build banking applications and services. 

Let us walk through the major cybersecurity challenges triggered by FinTech.

• Data Integrity Challenge

Mobile applications deployed for FinTech services play a predominant role in cybersecurity assurance. FinTech services require strong encryption algorithms to avoid integrity issues that can arise while transferring financial data. 

• Cloud Environment Security Challenge

Cloud computing services such as Payment Gateways, Digital Wallets including other secure online payment solutions are key enablers of the FinTech ecosystem. Though it is simple to make payments via cloud computing, it is equally crucial to maintain the security of data as far as banks are concerned. Appropriate cloud security measures are thus critical while dealing with sensitive information.

• Third-Party Security Challenge

Third-party security challenges include data leakage, service challenges, litigation damages, and so on. Banks should be aware of FinTech service relationships while associating with third-parties. 

• Digital Identity Challenges

Major FinTech applications are web apps that have mobile devices working at the front-end. Banks and other financial institutions need to learn about the security architecture of the electronic banking services offered by these applications before implementing the FinTech application.

• Money Laundering Challenges

The use of cryptocurrency for financial transactions makes FinTech-drive banks prone to money laundering activities. Thus, the FinTech ecosystem needs to be formally regulated based on global standards.

• Blockchain Challenges

Private keys can be stolen in case of weak security in blockchain architecture. Cryptographic algorithms need to be strong and transactions need to be confidential.  

The increase in the number of FinTech implementation of interfaces will cause a rise in the number of cybersecurity challenges as well. 

Can Machine Learning Predict And Prevent Fraudsters?

How Artificial Intelligence And Machine Learning Enables Cyber Security For FinTech?

Artificial Intelligence is both reactive as well as proactive or preventative. AI reinvents FinTechs by bringing in behavioral biometrics solutions. These solutions are used to monitor customer and device interactions that take place during transactions that enhance security and authentication. BB or behavioral Biometrics with AI provides problem-solving capabilities for FinTechs. FinTechs utilize Artificial Intelligence is an expert system that enhances decision-making abilities using deductive reasoning. Big Data analytics is used here to focus on quality data. 

The underlying technology in using Artificial Intelligence involves reasoning, learning, perception, problem solving, and linguistic intelligence to provide critical insights. It helps in understanding business in real-time operations. 

In this digital era of increasing cybersecurity attacks and malpractices, AI can be used effectively to prevent risks and attacks. The following are major ways of how AI and ML protect FinTechs:

1. Fraud Detection

AI and machine learning algorithms are used to detect frauds in FinTechs by being able to identify transactions in real-time accurately. The traditional strategy of fraud detection involved analyzing large volumes of data against sets of defined rules using computers. This process was time-consuming and complex. Unlike this traditional method, more intelligent data analytics tools for fraud detection such as KDD (Knowledge Discovery In Databases), Pattern Recognition, Neural Networks, Machine Learning, Statistics, and Data Mining have evolved. 

2. Controlling Access

Access control to critical data is crucial when it comes to security. Machine learning is used to derive critical insights from previous behavioral patterns such as geolocation, log-in time, etc to control access to endpoints. The risk scores are fine-tuned by combining supervised and unsupervised machine learning methods to reduce fraud and thwart breach attempts as well. 

3. Smart Contracts

Smart contracts are coded in a programming language and stored on the blockchain. With blockchain, new contracts can be added to existing ones without having to change the individual contracts, in case of agreement expansion. Artificial Intelligence has become an integral part of FinTech as more traditional banks are teaming up with FinTechs to leverage the benefits of both worlds. For instance, when customers face issues with a poor credit history while applying for loans. 

Artificial Intelligence is yet to be transforming the face of FinTechs in a multitude of ways. Drop-in a call right away and our strategists will guide you on how to leverage the benefits of AI and ML to secure operations and prevent breach attacks.

How Chat Bots Can Enhance Student Onboarding

Conversational interfaces have gone mainstream. The technology behind keeps crossing new milestones, the result of which chatbots have transformed from simple Q&A systems to intelligent personal assistants. As a result, bots found widespread application in diverse areas, most recently in education. 

Although education stayed backward in terms of technology adoption, lately it took on a renewed quest to incorporate it. Educators are on the lookout for innovative ed-tech systems for efficient tutoring and students increasingly prefer personalized learning environments. 

Deploying chatbots at numerous front-ends like college/university websites, internal student communication portals or even popular instant messaging platforms can help with that. Here’s how? 

Chatbots bring in a personalized and engaging learning experience optimized to the learning pace of each learner, which actively drives student-centered learning at the forefront. Configuring a bot to answer student inquiries related to curriculum, courses, admissions, etc. as well as deliver learning resources on request makes way for a personal always available assistant that every student can engage with. 

That’s exactly what we did, though in a different way. 

Recently Fingent was approached by a client, a leading public research university based in Australia to develop an intelligent chatbot for assisting prospective and freshly enrolled candidates with onboarding and orientation, course-related information, credit scores, etc. The client wanted to streamline its entire student orientation process using a chatbot and make all related information better accessible and context-based as well as systematically tackle the ‘summer melt’ rates. 

Here, we lay down a high-level abstract of this chatbot development experience powered by IBM Watson Assistant and backed by .NET Core. It briefs various facets and challenges faced during the design and development of the system.

The Plot (Objective)

“Build a chatbot to assist candidates during the orientation process of Monash University. The chatbot should be capable of handling different context-based scenarios such as listing available courses, providing credit score information, course structure, projects associated with each course and many more.“

Since it is a Proof of Concept (POC) project, and Monash University offers a wide range of courses based on various areas of education, the team decided to choose one particular area and focus on only two of the selected courses (Bachelor of Accounting & Bachelor of Actuarial Science). This is to repress the scope in control, considering the timespan and resource availability.

Foundation

Keeping in mind the idea of building a highly sophisticated chatbot, an ideal and matured chatbot assistant technology had to be finalized, which provides both comprehensive user intent identification and processing as well as a satisfactory response according to the user query. The system should also provide an extensive and less technicality included training interface. The hunt for such a tech ended up in IBM Watson Assistant.

Terminologies

The world of chatbots has some common terms which are essential key knowledge required while developing a chatbot. We can call them as the pillars of a chatbot.

• #Intent – Intent is nothing but the user’s intention in a query – basically covers all types of questions and their varieties, the user probably may ask. This can be queries within the scope or related to the scope.

Examples:

“What are all the courses available?”
– Intent associated: #KnowCourseInfo

“How much credit I require in the first semester?”
– Intent associated: #KnowCreditInfo

Remarks – There will be some stock #intent collection depending upon the chatbot engine, which is designed to handle the general greetings and conversation-oriented chunks. We can import or enable the intents as we want to make our chatbot more conversational and human-friendly.

• @Entity – An entity is a subject addressed in the user query. There are mainly two categories of entities. They are Scope-based entities and System entities. Scope-based entities are entities that belong to the scope we address whereas System entities are “primitive system-aware” entities.

Examples:

“What are all the courses available?”
– Entities associated: @Course

“How much credit I require in the 1st semester?”
– Entities associated: @Credit, @Semester, @system_number:1st

Remarks – On diving deeper, we may need the support of multiple types of scope-based entities and a system-aware way of specifying the relationship between the entities (which lacks in IBM Watson Assistant). This is to specify the entity characteristics as more descriptive as well as with the notion of “the system knows” the given attributes and relationships of an entity.

• Dialog – A dialog is a declarative way of specifying the possible questions the user may ask, and how should the bot respond to the corresponding questions. Generally, this will be a tree-based structure, rooted in the key user intentions and scope covered features. We will be handling the different scenarios of a single #intent as well as the edge cases.
• $Context Variable – A context variable is to store information, collecting from a dialog context or it can be any information related to the dialog context. It helps us to keep the dialogue context and facilitates conversational flow.
• Skill/Workspace (IBM Watson based) – A skill is a package that consists of the above-mentioned factors, in which all are aligned into a single chatbot capability, in our case, it was Onboarding skill.

Implementation

The entire development process streamlined into two major sections. The first one is aligned to the chatbot engine intelligence building and improvement activities while the other one is for the middleware and UI development.

1. Intelligence Build-up on top of IBM Watson Assistant

• Analyzed the requirements and fixed the boundaries of the scope. It includes what all are the functional areas to be covered by the proposed chatbots.
• Prepared the possible user queries and categorized them as #intents.
• Identified the underlying @entities in each question and classified them to form the actual set of primitive entities.
• Designed the dialog structure based on the prepared user query sets. See the resources: Intent structure and Dialogue flow. 

Fig 1. Intent Structure

• Continuously refined the dialogue structure based on detecting each edge cases and to incorporate new scenarios.
• Used some conventions on responses to extend the chatbot response capabilities, according to the requirements. This is to handle specific use cases such as clickable action list image response, map response, and show a list of items.
• Implemented WebHooks (IBM Watson based) to talk to external APIs to fetch the values for a dialogue node as well as validating user input (Not a comprehensive solution).

2. Middleware and UI Development

• Built a middleware backed by .NET Core with an intention to plug any chatbot service to the UI module. In fact, it is designed as a standard-framework to separate the chatbot logic from the application logic. This enables hassle-free maintenance of the app logic, code reusability, and extensibility.
• Built the UI using Angular to provide a sophisticated face for our chatbots.

Fig 2. Dayton Interface

Also, we built a diagnostics module, as part of the UI, which provides the service configuration information and session-based transcripts of conversations held with the chatbot.

Fig 3. The architecture of the Chatbot Middleware Application, Source Code

Challenges

During the development, we came across some development challenges with IBM Watson, which are listed below.

1. Unable to map relationships between entities. Due to this limitation, we were unable to link and pull the related values of the entities.
2. Conflicts between various entity values (Solved partially via entity split-up method)
3. API Limitations to manage chatbots dialog schema
4. IBM Watson doesn’t provide active learning, the self-learning capability to learn from user conversation sessions.
5. It also doesn’t provide an efficient way to talk to external APIs. Only one external API can be called, which leads to a bottle-neck on executing the webhook actions.
6. No built-in user input validation. This has to be done via WebHooks.

Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

Final Words

The application is now in a showcase/UAT (User Acceptance Testing) mode, also the refinement process being in progress. It has miles to go to reach the capability to converse with the user as a comprehensive onboarding assistant.

To know how chatbots can enhance your business growth, get in touch with our experts today!

Mixed Reality Promising a New Improved Healthcare Industry

Mixed Reality makes it possible for surgeons to perform an operation on a patient thousands of miles away. It makes it possible for nurses to hone their skills and perform hundreds of operations virtually before even touching a patient. Mixed Reality is making the impossible possible. 

Mixed Reality combines the real world with the virtual to create solutions that we never thought possible. It enables effective collaboration between physical and digital objects and has found an integral place and application in healthcare. This blog explores some radical ways in which healthcare is using Mixed Reality for better quality treatment and customer experience. 

Mixed Reality Working Wonders in Healthcare 

By 2026, AV/VR in the healthcare market is expected to reach 7.05 billion USD showing that Mixed Reality is finding increased application in the healthcare industry.  From enabling pre-procedural planning and visualization before surgery to training nurses as they virtually workout challenges they could encounter during real-life procedures, and improving the collaboration and communication between doctors and patients – Mixed Reality is making great things happen.  Here are some ways in which this is becoming possible. 

1. Immersive Learning for Nurses and Medical Students

Skilled nurses are critical to the healthcare industry. Simulations are the most effective method of educating and preparing nurses to respond appropriately to a variety of situations they might encounter. Mixed Reality can place a nursing student in those specific or rare situations, which may be difficult to arrange for in real clinical settings. Such immersive simulations are much more cost-effective than traditional nursing simulation devices. The education company Pearson has collaborated with Microsoft to launch apps called HoloPatient and HoloHuman. These tools use holograms of patients and help in training healthcare professionals as they diagnose and treat medical problems. 

As another achievement in Mixed Reality, St. George’s University in Grenada worked with SphereGen Technologies to develop what is called the ‘Learning Heart.’ The Learning Heart is a study aid that enables users to view the hologram of the heart from all directions and examine its functions. It responds to touch and voice commands and allows users to separate all the parts of the human heart, thus making learning immersive for medical students.

2. Reduced Time and Reduced Human Error in Reconstructive Surgery

Reconstructive surgeries enabled by Mixed Reality with the use of HoloLens have proven to be very successful at the Imperial College at St. Mary’s Hospital, London. According to the team guided by Dr. Philip Pratt, Mixed Reality helps surgeons locate and reconnect major blood vessels. With HoloLens, surgeons are able to use holographic overlays to see the bones and identify the course of blood vessels which aids them in their surgery, improving the outcome for the patient. An article in The Times entitled Holograms to get surgeons under the skin of patients showed examples of a 41-year-old man and an 85-year-old woman on whom such reconstructive surgeries were performed successfully. 

3. Revolutionizing Surgery

In December 2017, Dr. Thomas Gregory undertook a live transplant surgery with the help of HoloLens. It helped him access the patient’s medical information and anatomical pictures in 3D during the surgery. Since HoloLens is a standalone computer worn like a helmet by the surgeon, his hands are free for surgery. Additionally, the use of microphones and sensors allows the surgeon to communicate with other surgeons in different parts of the world making collaboration easier. All these features, along with the simulations and information it can pull up, make Mixed Reality a valuable asset in improving surgical performance.

4. Improving the Patient Experience

Building trust through efficient communication is an important aspect of a doctor-patient relationship. Mixed Reality makes this possible in a more immersive way.  For example, in a recent interview with Sirko Pelzl, CEO and CTO of apoQlar, he spoke about Virtual Surgery Intelligence (VSI) and said: “physicians can use VSI to show patients their own MRI scans and explain the surgical procedure in visual detail. We were able to illustrate in a recent study how greatly patients appreciated this education and communication.”

Mixed Reality also helps in reducing response time and improving surgical accuracy, which contributes to the smooth and successful patient experience. For example, when the surgery is complicated or the patient is critical, diagnostic images with Mixed Reality can serve as a twin of the patient. This helps doctors discuss, plan, and walk through their treatment protocol, thus reducing response time in patient care.

Mixed Reality also gives doctors all the information they need about a patient and enables real-life simulations to help him in his decisions and actions. Commenting on a prototype application called “Cinematic Rendering for Surgery” Christian Zapf, head of the Syngo Business Line at Siemens Healthineers said, “The error rate dropped from 14.1 to 0.8 percent for surgeons in training and from 11.1 percent to 0.8 percent for qualified surgeons.”

https://www.fingent.com/insights/portfolio/future-communication-security-using-augmented-reality/

The Future of Mixed Reality

Mixed Reality has already made deep inroads, especially in medicine. It will continue to improve the quality of healthcare and medical training as it positively impacts the medical industry. Fingent has helped many clients build custom healthcare software solutions, which integrate applications with the latest technologies like Mixed Reality. Get in touch with us and let’s discuss how we can make Mixed Reality possible for you.

Can Augmented Reality Improve Conversion Rates For Businesses?

Augmented Reality (AR) is a powerful tool that brands are using to bridge the gap between their products and their digitally empowered customers. With the power of AR, customers can see and experience their products without actually trying them on, helping them make purchase decisions instantly. AR is being widely used by many brands as the most cost-effective way to convert strangers into customers and promoters.

Let’s consider why and how top brands are embracing Augmented Reality for immersive customer experience. 

Top Brands Embracing AR 

When AR technology was pioneered by Ivan Sutherland in 1968, he described his concept of the Ultimate Display as: “The ultimate display would, of course, be a room within which the computer can control the existence of matter. A chair displayed in such a room would be good enough to sit in. Handcuffs displayed in such a room would be confining, and a bullet displayed in such a room would be fatal. With appropriate programming, such a display could literally be the Wonderland into which Alice walked.”

Today we are witnessing the massive affect AR is having on immersive customer experience. Wonderland looks close!

Here are a few examples of top brands that are using AR effectively to achieve this. 

LEGO Wear’s AR Shop

LEGO Wear’s first limited-edition clothing line for adults was featured on Snapchat with a uniquely designed Snapcode. This virtual cloth store had no clothes on display but allowed customers to shop virtually on a Snap-triggered portal. Studies reveal that Snachatters engage with Augmented Reality naturally on a large basis.

Around 70% of them play with an AR lens every day. So with the combination of AR and e-commerce leading to exciting customer experience, the company was able to drive sales rapidly.

Watch how AR simplify equipment maintainance

This video is made using InVideo.io

Patron Tequila’ s Immersive Distillery Experience 

Patron’s AR app is designed for its tech-savvy customer persona. Understanding the mindset of today’s socially connected generation to know more about the products they consume, the Patron AR app allows its customers to take a virtual tour of their distillery in Mexico giving them a glimpse of the history and origins of the distillery.

Speaking about the success story, the app-enabled more than a million consumers to have a memory of visiting Hacienda without even physically going to the place. Also, thousands of people have interacted with a virtual Patrón bartender, who is actually not a real person at all, but a robust data processing tool to simulate a real-world interaction.

Nike Snaps Up Customers

The Air Jordan Brand in collaboration with Snapchat and Darkstore, out together a virtual experience that brought in record sales last year. Using a special 3-D AR world lens of Michael Jordan circa 1988, taking off from the free-throw line in the slam dunk contest, Nike was able to target Snapchat users around the Staples Center where an NBA All-Star Game was taking place.

Users could walk around the lens and see Jordan changing into the All-Star uniform and the new AJ III Tinkers. In the following event, Snapchat brought out a QR code that users could use to buy the shoes from the Snap Store. The shoes would be delivered within 2 hours.  The shoes sold out in 23 minutes! 

MAC Cosmetic’s AR-enabled Video Tutorial

In June of this year, MAC cosmetics launched AR-enabled shoppable video tutorials on YouTube in partnership with the beauty influencer, Roxette Arisa. As the tutorial is playing, a ‘try on’ button appears below the video, allowing viewers to try on different shades of lipstick as they continue to watch the tutorial.

Once they have made a choice, they can order the lipstick without leaving the app. MAC sees a huge potential in this as beauty-related content generated more than 169 billion views on YouTube last year.

Speedo’s Customers Try It On Virtually

Early this September, leading swim gear company Speedo launched a mobile app that lets their customers try out their goggles before they buy it. This app is compatible with both Android and iOS phones. Commenting on this feature, Pentland Brand’s head of innovation Ben Hardman said, “This technology will undoubtedly enhance our customers’ shopping experiences by allowing them to interact with the product before they make a purchase. In this instance, it helps them address a well-documented human pain point: leaky goggles.” 

How Augmented Reality Can Simplify Equipment Maintenance

AR Is the Future – Are You Ready?

AR technology is working wonders for the sales and productivity of many brands. It is improving its business operations while giving their customers a more immersive and fun experience. Brands are able to market their products in fresh and interesting ways and are seeing great returns.

Fingent works with brands to achieve this for their business. By using AR technology like Microsoft HoloLens and more, we are making AR possible for our clients.

Reach out to us and know how Augmented Reality technology can be used to improve your customers’ experience and scale your business. 

Can Empowering AI and IoT Bring In Competitive Advantage To Industries?

It takes more than forward-thinking employees to gather customer purchasing trends and improve the customer experience. International companies depend on Artificial Intelligence (AI) and the Internet of Things (IoT) to drive data and forecast the next big wave of trends.

Studies predict Asia and North America to lead in the innovation of AI and IoT. Also, embedded AI in support of IoT smart objects will reach $4.6B globally by 2024.

Major vendors of IoT platforms such as IBM, Amazon, and Microsoft have started offering integrated AI capabilities like ML-based analytics. Scalable digital platforms are designed daily to understand the way customers think while using predictive maintenance in real estate, eCommerce, healthcare, and other industries.

It’s time for us to share the leading examples of how businesses use AI and IoT, and how these technologies benefit them.

AI and IoT: Leading Use Cases

Smart Cities: Making Life Easier

What happens when AI and IoT run a city? It turns into a smart city. Smart city technology can solve an energy crisis, help manage traffic, or improve the healthcare experience. 

One example of a smart city is the use of Advanced Transportation Controller technology linked to a 5G network in Los Angeles. There are road-surface sensors throughout the city, and cameras that monitor traffic, sending information to traffic management systems. Municipal employees can now analyze the data of traffic congestion and issues with traffic lights in high traffic areas. Overall, this improves the quality of living in Los Angeles and helps a business run smoothly without delays.

Convenience in Property Management

One of Fingent’s clients WRI Property Management, a US-based single-family rental provider with 10,000+ leased properties and 20,000+ managed houses experienced many challenges. Here are a few of the issues WRI Property Management faced:

• Tenant eviction
• Rent collection/accounting
• Scheduling property inspections
• Leasing properties
• Screening tenants

What happened next? Fingent introduced an advanced software platform, Honey Badger. The AI and IoT technology-supported WRI managers to conveniently communicate with multiple parties, renovating properties, view lives auction feed, track the construction of new properties, etc.

5G Network Vehicle Safety and Security

Machine Learning technology is improving the autonomous vehicle experience. How does it work? An automobile can stop when a driver is in dangerous tragic weather or unexpected situation. 

The 5G network can cause the brakes of a car to operate by tracking vehicle sensors of other drivers near prevent or relieve car crashes. 

The network can also send drivers a traffic update to use detours and avoid certain roads that are under construction or is unsafe.

AI and IoT Business Benefits 

1. Guaranteed Security and Safety

A company’s highest priority is protecting data in the workplace. As Artificial Intelligence scans security footage, IoT can close gates or doors if an intruder attempts to enter the premises of a head office. 

Organizations are now using machine-to-machine communication to determine potential security threats with an automated response to hackers or intruders. 

An example of AI and IoT in banking security is the detection of fraudulent activity in ATMs to communicate updates to law enforcement to protect customers.

The unexpected workplace accidents can be prevented by using sensors that monitor safety hazards as employees work. Employees at some organizations now wear wearable devices that alert the management of undetected dangers such as carbon monoxide released into the air on a work site. 

2. Convenient Shopping Automated Experience

Online shopping is more convenient than ever as websites personalize real-time suggestions to consumers based on a customer’s shopping history. As a result of this investment, Kinsta predicts that by 2021, Artificial Intelligence in e-commerce will increase sales to $4.5 billion from $2.3 billion in 2017.

3. Enhanced Healthcare Experience

NovitaCare, a Netherlands based healthcare company that treats patients with chronic and multiple disorders, wanted to improve the caregiver experience using an effective online platform. 

With Fingent’s help, NovitaCare now can communicate with non-profit organizations, patients, providers and researchers with an online platform that is HIPPA compliant.  

4. Simplified Management Of Supply Chain

The supply chain industry has experienced challenges in managing unexpected events that happen due to inaccurate forecasting. A solution to the problem is implementing AI and IoT. 

Supply Chain Digital recently stated the following about these technologies:

“Intel highlights that the world of IoT is growing rapidly, from 2 billion objects in 2006 to a projected 200 billion by 2020.” 

“AI is on most companies’ radars, with 78% of organizations implementing it to enhance operational efficiency by at least 10%.”

The use of real-time devices will feed data to executives to help create contingency plans for preventing unexpected challenges in the industry. As a result, the supply chain and a company’s reputation can experience fewer impacts.

A Guide for AI-Enhancing Your Existing Business Application

How Fingent Helps Businesses Achieve Success With AI and IoT?

Fingent has mastered the art of technology infrastructure to help companies resolve AI and IoT processes. As a result, it creates efficiencies in managing smart devices.

Implementing these technologies are small changes that can have a huge impact on your business. The ability to use raw data to understand customer behavior and forecast trends in the market can improve customer loyalty. Also, companies can track employees working in multiple departments and locations across the globe by partnering with Fingent.

Fingent is confident that AI and IoT work in your business context by delivering technologies to enable solutions in the cloud, networks and gateways, heterogeneous device support, systems capabilities, and data analytics. 

To Conclude 

Business Insider predicts that there “will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018.” 

Gartner observes that in three years (by 2020), more than 80 percent of enterprise IoT projects will incorporate at least one AI component. Artificial Intelligence and the Internet of Things is used to improve the safety of drivers on the road, enhance healthcare experiences, automate and streamline enterprise processes, stop intruders from hacking into IT systems or large organizations, and in numerous other ways. 

The combination of these technologies not only delivers a superior customer experience, but also forecasts what customers want in real-time, improves their experience of living in smart cities, maintains a high safety rating in challenging workplaces, and reinforces physical and cybersecurity. AI-IoT duo also avoids any unplanned downtime, increases operating efficiency, helps develop new products and services, and improves your risk management. 

Are you looking for an AI and IoT partner? Get in touch with Fingent experts today for a streamlined and error-free IoT implementation for your business.

Top 6 Reasons Why Implementing LMS Is Crucial for Your Business

With the rise of technology implementation in various business processes, the LMS market is forecasted to reach USD 22.4 billion by the year 2023. This figure was estimated to be USD 9.2 billion in the year 2018. With the increasing rates as shown in these figures, it is evident that businesses leverage a multitude of benefits from implementing a Learning Management System. 

The major drivers towards the increased adoption of LMS in businesses include digital learning, enterprise mobility, BYOD (Bring Your Own Device) policy, Artificial Intelligence technology implementation, Machine Learning, and so on.

What Is An LMS?

A Learning Management System is a software application that offers online training, educational content, and several crucial strategies for implementing LMS into a system. A quick example of an LMS is the SaaS (Software as a Service), which is a kind of web-based (internet-based) LMS. 

SCORM (Sharable Content Object Reference Model) and LTI (Learning Tools Interoperability) are two strategies via which content is integrated into an LMS, which is included in the LMS application. Let us walk through the key benefits that drive businesses in adopting a Learning Management System.

LMS of “Present” and Ways to Enhance It

Why Do Businesses Require An LMS Implementation

An LMS can help a business, streamline its procedures and improve the overall efficiency of the workforce. To sustain growth in businesses, it is crucial that industries employ an LMS into their processes. Let us walk through the major compelling reasons why it is necessary to implement an LMS for a positive business outcome:

1. Flexibility In Accessing Information

With LMS, employees can access information anytime, anywhere via their desktop, laptop or smartphone. Critical decisions can be made through the instant availability of data. With the advent of modern LMS platforms, centralized information can be easily accessed. The data accessed will be stored digitally, such as user profiles, training progress, and so on. This not only makes the data searchable but also reduces the time spent to retrieve the required information. 

2. Cost-effectiveness

An LMS platform can cut down costs associated with training expenses. Training and onboarding generally involve hiring multiple resources. For instance, a hiring manager has to train every newly joined employee in the company software and other implications. But with an efficient LMS implemented, the training can be customized. 

Additionally, providing online training can significantly reduce time and costs. Implementing a centralized location for training can avoid the need for sending employees to get trained in far off places. Since the data can be reused and accessed whenever required, it eliminates the need for excessive paper documentation.

3. Improved Productivity And Profitability

According to a recent study by ASTD (American Society For Training And Development) that was conducted on 2500 firms, the firms that invested in training had achieved a 24% higher margin than the rest. The study also found out that these firms had a 218% increased income per employee. 

LMS ensures that the employees get a thorough training that can help them perform productively and efficiently. An LMS also ensures that multiple users are trained simultaneously at the same pace. When employees are productive, they become profitable as well.  

An LMS can ensure the quality of the training provided to the employees via its data and analytics tracking. This includes factors such as the time duration of the training provided, how well performed is the training, and so on. The centralized training database of an LMS enhances the quality of training provided as well as significantly improves the productivity and profitability of the business. 

4. Effective Employee Onboarding

Employee orientation or employee onboarding is a tedious process in many companies. When the employee onboarding process has a modern LMS platform implemented, it significantly reduces the employee churn rate. This, in turn, increases the productivity of the employees. 

Third-party content can be easily deployed via an LMS platform. LMS works by enabling businesses to deliver, manage as well as track the hiring and training process of new employees. Being able to create courses, setting tests and assignments, and automating the process of onboarding are some of the major functionalities. 

With an LMS onboarding ecosystem, an effective and efficient training and onboarding process are ensured.

5. Measuring ROI

According to industry analysts, the LMS market is expected to grow from today’s figure of $2.06 billion to over $7 billion by the year 2023. A Learning Management System is designed and deployed to deliver increased ROI to businesses in a multitude of ways. 

The major return is in being able to replace traditional one-to-one training. This is a key cost saver. LMS is utilized as a centralized hub for housing large volumes of training as well as other content. LMS improves business outcomes by reducing travel expenses of employees sent for training externally and slashing down employee productivity losses. 

An LMS calculates resources allocated and identifies existing inefficiencies in training. It also lets employees focus on the core parts of their job. This significantly reduces employee turnover as well. 

6. Knowledge Retention With The LMS Centralized Hub

To drive innovation, it is crucial that the employees within an organization are intellectually capable. Technical know-how enhances the productivity of employees. Intellectual capital is thus one of the key benefits leveraged from an LMS. 

LMS ensures that the information does not remain consolidated in a single location and that it is shared with all the resources. With an LMS, employee performance can be tracked easily. The employees who underperform can be provided with additional personalized training and retained.

To become an LMS expert and to identify the current inefficiencies or areas of improvement in your business, talk to our LMS strategists and experts today!