Artificial Intelligence in Retail

Posted on by Keagan Diniz

Digitalization in the retail industry has been evolving every day for years, which resulted in increased speed, performance, and efficiency across all segments of the retail business. Predictive analytic systems and high-level data have led companies to make data-based marketing decisions. Almost all industries are interested in AI as the profit offered by this innovative AI technology is immense. Among others, there is an increasing demand for AI in the retail industry. The application of artificial intelligence in retail facilitates a better shopping experience for customers and the advantages are many which are going to be discussed in this article. 

Uses of AI in Retail 

Artificial Intelligence services have contributed a lot of profits in the retail industry; However, here are some basics that retail professionals can rely on: 

Visual and voice search  

Customers may find it difficult to visit a shop and make a purchase of the product they have in mind. They may face a lot of trouble while making the salesman or shopkeeper understand what they are actually looking for. Visual search features offered by AI make the process so easy and smooth. Customers simply have to upload a picture of a product they are looking for and you will get a list of similar products depending on the shape, color, size, and pattern. Likewise, there is a voice search option in which the customers have to give commands through their voice. The system will analyze the voice and give appropriate results based on the command. This works just like the way you explain things to a shopkeeper. Both these features save a lot of time during shopping and customers can easily pick the product of their choice without any tension. 

Virtual trial and decision-making  

When it comes to purchasing products, AI provides business insights for retailers in order to help them make quick decisions. For instance, you may feel tired and irritated while trying out different clothes in a physical retail store. AI-induced virtual trial rooms can save you from this frustration as they have digital mirrors that enhance the decision-making power of the customers. Customers can make use of a touch-based screen, choose any option like dresses, shoes, etc., and can pick the one that looks good on them. This same technique can be used even while selecting cosmetics too. These virtual fitting rooms not only save time for offline and online customers but also help them to pick the perfect dress that matches all the elements. 

Product categorization  

AI uses its machine learning capabilities to categorize more than one million products from different vendors. Machine learning-induced systems tag products and classify them into different categories for the convenience of customers looking for a specific product. The ML-driven software with computer vision easily identifies, classifies, and prices the product to be sold, based on the pictures uploaded by the vendor. One of the best examples is Lalafo, a platform that can process over 900 requests per second, enhancing sales with significant content using machine learning models. 

Price predictions and adjustments  

Price prediction refers to the process of forecasting the price of products on the basis of the item’s demand in the market, seasonal trends, features, the launch date of updated models of the same product, etc. Artificial intelligence gives retailers the opportunity to see the end result of different pricing strategies. This way they can introduce promotional offers, gain more customers, and boost sales. To implement this, systems gather information on promotional activities, selling prices, and relevant data about other products. AI thus helps businesses to predict prices to improve customer loyalty and optimize prices on the basis of the information obtained by the system. 

Consistent experience across channels  

If the information is not consistent across different channels, then it leads to less customer satisfaction and experience. For example, imagine the trouble a customer would have to face if the product bought online is shown out of stock at the collection point. This happens when the retailer tries to sell more than he has on hand. AI and ML help to optimize stock, automate inventory fill-ups, and optimize pricing to increase revenue. Artificial intelligence solutions not only minimize out-of-stock rates by up to 80% but also improve returns and profits by more than 5%. This will help customers to expand their omnichannel experience. 

Customer behavior prediction  

Artificial intelligence platforms allow entrepreneurs to utilize behavioral economics to develop a personalized approach to every customer. Such artificial intelligence platforms use an algorithm that processes the behavioral patterns and emotional reactions of the customer from past shopping experiences and brings the most favorable pricing offers to a specific visitor. This intelligent incentive platform maximizes purchases by evaluating the psychology and emotions of every customer. 

Common Benefits of AI in Retail 

Here are some of the common benefits of AI for retailers and customers: 

  • Customers don’t have to wait in a long queue for billing or purchase. 
  • It saves time and satisfies the customer’s needs in minutes. 
  • AI-driven virtual assistants to help during the purchase. 
  • It maximizes the revenue while holding the marginal profit tightly. 
  • High customer satisfaction and smooth functioning. 
  • Offer personalized experiences to the customers. 
  • Maintains and re-fills the inventory. 

Wrap Up 

The retail industry is trying to extract the power of artificial intelligence technology to achieve business benefits. The implementation of artificial intelligence provides an opportunity for retailers to become strong competitors in the industry. This article tries to explain the major benefits of using AI in the retail industry. Automated processes, improved statistics for your business, and better customer service in the retail industry – all are results of AI. 

How Chatbot Benefits E-Commerce

Posted on by Saish Kakodkar

Over the last two decades, the development of e-commerce has brought revolutionary changes in the way we make purchases in the retail sector. Even while offering the ease and speed of online shopping, it cannot offer one important factor – that is, the personalized service we are provided at a physical store by a customer care representative. Businesses have shifted to automation when they have found difficulty in meeting and adapting to the ever-changing requirements and demands of their customers. This resulted in using conversational AI chatbots that can improve customer-centric practices in e-commerce.   

7 Ways chatbots help e-commerce business  

The basic objective behind using or deploying a conversational AI chatbot in a business is to serve the customers better. Customer relationships and management are essential for the growth and success of any business. If a business fails to address the requirements of its customer, then there begins the failure of the business itself. There are many other uses for these automated chatbots. Let us talk about the different ways through which these chatbots help e-commerce businesses.   

Delivers personalized services  

AI chatbots are capable of recollecting previous conversations or any kind of interaction between a particular customer. The chatbot uses the data to initiate a customized conversation with the users the next time they interact with the chatbot. Moreover, these bots keep their complete attention on the customers while leading them throughout the whole journey of shopping by giving product recommendations and necessary assistance/support. Additionally, through these personalized services, you are increasing engagement rates and also save a lot of time for the customers by recommending only significant products.   

Offers quick, cost-effective, and reliable services  

Customers are happy when they get to shop 24*7 but that is not practical when it comes to physical outlets because they don’t function the whole day and night. But the e-commerce chatbots are live 24*7 to provide the best quality customer service to the customers. E-commerce chatbots are capable of building a live connection with users by enabling two-way communication similar to that of a real customer service agent. These conversational chatbots can answer up to 80% of repetitive questions by customers. Also, if the bot fails to resolve the problem, it will forward the issue to the live chat agent. Moreover, it makes the customer understand the advantages and offers of making payments online.  

Display catalogs across multiple channels  

Sometimes customers tend to purchase products they see or like while using a social media platform or any other app. In such situations, they wouldn’t want to go through a particular site or app to find out about the product they saw on another platform. Here, the most convenient way is to let them purchase directly over that app they are using. You can deploy your AI chatbot on multiple touchpoints like Instagram, Facebook, Snapchat, or any other platform. This way you can allow your customers to browse your catalogs directly from any application they use and make purchases from there, without switching to another app or website. This not only minimizes the customer’s effort and time but also provides them with an omnichannel experience.  

Records real-time interactions  

Enabling real-time customer interaction is a remarkable feature of AI chatbots in e-commerce businesses. The information collected during the interaction can be used to provide quality services based on user input. Quick access to crucial data helps in minimizing functional costs and increases the efficiency and effectiveness of a business.  

Act as the perfect knowledge base  

Providing the necessary information to the customers and creating awareness among them is a difficult process but quite easy for AI-driven chatbots. For instance, the bot will give single-touch access to the Frequently Asked Questions (FAQ) section on the screen whenever a customer chooses a particular product. Also, the chatbot can display any additional required information the customer asks for, within seconds. It helps in simplifying the purchasing process as there is effective communication between the chatbot and the customer, which in turn increases sales and customer engagement.  

Gather analytics and boost sales   

In e-commerce, no other factor is important as an interested customer who is ready to buy your product. These customers choose the products they need from the website and either add them to the cart or wish list, which will remain there for months without any action being taken. Because of this reason, many products remain untouched by customers and somehow, AI bots are changing this method altogether. Chatbots can learn and understand the preferences and requirements of customers by analyzing their previous activities on the website or application, their cart, and their product wish list. Based on this analysis they recommend products and services to the customers to develop the customer base.  

Gather customer feedback  

An e-commerce business chatbot mainly uses two ways to gather customer feedback – one is through reviews and the other is through forms. If a business pays close attention to gathering reviews manually, at some point they will realize that it is not cheap. It is indeed a time-consuming process. Therefore, the number of genuine reviews you get will be very less. The other thing is if your company is delivering a defective product to the customer, then definitely the customer will post a bad review. Negative reviews adversely affect the business, which is why the customer experience needs to be improved. Deploying AI chatbots in e-commerce businesses stimulates the feedback collection process within a limited period of time. Chatbots can interact and sympathize with customers, thus gathering feedback from them. In addition, they ensure that their problem is forwarded to the relevant team in real time. This minimizes the impact of poor marketing as well. Here, people are more likely to support your platform among their circle as one of the most trusted ones.  

Wrap Up  

The e-commerce chatbot always strives to satisfy its customers with an unprecedented experience. When your customer gets the best experience, it will automatically result in giving the best customer satisfaction rate. In addition to the business perspective, getting feedback from customers is very important. This is one of the best ways to identify weak points in the chatbot conversation stream, including wrong answers, poor speech design, continuous responses, and knowledge gaps.  

UX Design Process Explained  

Posted on by Vince Baby

User Experience (UX) is defined as the value your user gets when he/she uses your product. User experience design (UXD or UED) refers to the process of improving customer satisfaction with any product by offering an interaction that gives the user more accessibility, usability, and enjoyment. Building a user experience that gives high satisfaction to the customers is not just the responsibility of an employee or a team in an organization, but rather the main vision of a company. If your UX design process is not firm, it is unlikely to develop a product with a good UX. Contrary to this, a well-structured and well-defined UX process enables companies to deliver amazing experiences to users.   

UX Design process   

Each project needs a unique and different approach which means that the approach or design you use for a corporate website is not the same as the design you use for a food delivery app or a shopping site. The design process you choose will always be based on the kind of product you are designing. The UX Design process involves five important phases:  

  • Product definition  
  • Research  
  • Analysis  
  • Design  
  • Validation  

Let us go through each phase one by one.  

Product definition  

The most significant step in the UX design process is the one before the company develops any product. It is important to learn and analyze the context in which a product exists before you create it. This phase lays the basis or stepping stone toward the final product. This is also the phase in which the UX designers start thinking about the basic concept or idea of the products at the highest level along with the shareholders. The product definition phase usually involves:  

  • Shareholder interviews: interviewing main partners to collect insights into business objectives.  
  • Value proposition mapping: identifying the main features and value propositions of the product like what kind of product it should be. Who will be the target users? Why would they use the product? etc. Value proposition enables the product team and shareholders to build consensus on what the product will look like and how it will adapt to customer and business requirements.  
  • Concept sketching: generating a demo mockup of the upcoming product (a rough paper sketch of product architecture).  

This phase usually culminates in a project kick-off meeting, which brings together all the major players and fixes the appropriate expectations for the product team and shareholders. It includes a high level of product objective design, team structure (those who design and build the product), medium of communication (how they coordinate the work), and the expectations of the shareholders (like Key Performance Indicators and ways to evaluate the success of the product).   

Product research  

The second phase involves research. Once the product team has developed the concept for the product, they move on to product research. This phase usually involves consumer research as well as market research. Experienced product designers consider research as a sensible stage and a proper investment of time. Investing in healthy research at the early stage saves a lot of time and resources along the way by informing design decisions. The research phase varies according to the project as it depends on factors like time, resources, product complexity, and many more and the phase includes:  

  • Individual in-depth interviews (IDI) – without proper customer analysis and understanding, no product can deliver a good experience. In-depth interviews give insights into qualitative data like the needs, desires, expectations, fears, and behavior of the target customers.  
  • Competitive research – UX designers can easily learn and understand the industry standards and find out the available opportunities within a specific area of the product through thorough research.  

Analysis   

Analyzing information from the data gathered during the research phase is the goal of the analysis phase. The insights range from what customers need to why they would buy a product. At this point, the most relevant assumptions of the team are considered correct. The analysis phase mainly includes:  

  • Creating user personas – personas refer to imaginary characters who represent various types of users of your product. When designing your product, you can refer to these personas as an actual characterization of your intended audience.  
  • Creating user stories – explaining the perspective of a user about your product or service is called a user story and it is an excellent tool that gives a better understanding to the designers. It follows a particular structure that involves what the user has achieved from the product/service and the motivation behind using it.  
  • Storyboarding – this is a tool that enables the designers to connect with fictional characters and user stories. As the name implies, this is primarily a story of a customer interacting with your product.  

Design   

During this phase, the product teams work on a variety of functions ranging from Information Architecture (IA) development to the real and final UI design. The active collaboration of every team member involved in the product design process is important for a design phase to be effective and it should be repetitive (as it should go back to validate ideas). This phase includes:  

  • Sketching – is the best and easiest way to envision your ideas. You can either draw on paper or use any digital tool. It allows the team to envision a wide range of design solutions before picking one.  
  • Creating wireframes – this tool acts as the backbone of a product as it is used by designers to understand the basic structure of an upcoming page, with all the major components and how they fit together.   
  • Creating prototypes – refers to the real experience the product delivers during an interaction.  
  • Creating a design specification – This includes all the visual design resources that developers need to convert the prototype into a real final product.  

Testing or validating  

Testing or validation of the product is an important phase in the design process as it allows the team to get better insights into the working of the product when it reaches the customer. The team validates the product with all the shareholders and customers during different series of testing sessions. The phase includes:  

  • Testing sessions – the testing session includes conducting a wide variety of testing formats like focus groups, beta testing, and A/B testing, among the target audience.  
  • Surveys – UX designers can gather both quantitative and qualitative information from the users through surveys, where they can ask open-ended questions to end-users.  
  • Analytics – it helps to detect the user’s interactions with your product.  

Wrap Up  

In the User Experience Design process, you cannot find a solution that fixes all the problems at once. There process you follow may be different but the objective behind every process is the same – to give the product to your users. Use the best for your project, skip the rest, and expand your UX process with the product’s development.   

Chatbot UI Examples and Design Tips 

Posted on by Laxman Pednekar

Chatbot, nowadays, is one of the most commonly used terms in many businesses. Many of us turn a blind eye and think of it as another technology that has no real practical application. Well, if appropriately designed, they add real value to the business or area where it is implemented. Therefore, designing your chatbot UI is important to satisfy the users. Here, in this article, we are going to discuss some of the major tips for designing chatbot UI along with examples. The important thing is that you should know what your chatbot user interface means, its role, and its expectations.  

Chatbot UI: What does it mean? 

The Chatbot User Interface (UI) is a set of graphical and linguistic components that enables communication between humans and computer interaction. User interfaces are of many types and chatbots are language user interfaces that help users to communicate based on their own terms rather than the computers. Anyhow, the communication capabilities of the chatbot will be different based on the UI you create. A chatbot user interface that depends on predefined answers like button options has a limitation on the questions a user can ask and the language it can understand. On the other hand, there are contextual and rule-based chatbots that are specifically built to learn and respond to different textual and voice inputs.   

Chatbot UI design tips 

A chatbot UI design aims to create communication meaningful and smooth. Here are some of the design tips for crafting the best chatbot UI.  

Understand the platform   

Before embarking on the design process, designers should have a good understanding of the limitations, abilities, and opportunities of the platform on which they operate. It is significant to be realistic and aim for a balanced plan with design limitations. Better ideas may come from the product team for the chatbot design, but if the platform does not support the UI components, the conversation flow will fail.  

Identify the chatbot’s purpose  

It is important to define the purpose of your chatbot because it affects the design of the chatbot you create. For example, the chatbot you create for the human resources department is different from the chatbot you create for the Insurance sector. Therefore, Chatbot designers should start by recognizing the value a chatbot gives to the end user and referring to it throughout the design process. This is where UX designers add great value in shaping the purpose of the chatbot through user-centric design techniques. The purpose can also be repeatedly expanded based on user feedback.   

Set a voice tone  

Users generally know that chatbots have no emotions but they would still prefer the responses of a bot to be humane rather than robotic. For a chatbot to be accepted well, thorough research should have been conducted on the intended audience so that the designer can shape the bot with the appropriate personality. It is important to be aware of how tone can affect a user’s experience. By selecting a clearly defined sound tone, designers can view the data of all conversations that are initiated by the bot.  

Create user flow  

A chatbot flow decides the way a conversation should take place and it is important for designers to think about the possible questions the users may ask and the various answers the chatbot should give. Chatbot flow is a series of paths that can trigger a user’s responses. Building your whole chatbot flow in a single path is not a good idea but creating unique paths for various situations makes it easier not just to understand your flow but also to edit it in the future. When you have finished your flow, polish the messages on the nodes. Ensure that they match the personality of your bot.  

Guide the users  

One of the main principles of chatbot UI design is to give users the guidance they need to know where they are in the system and their expectations of them. It is important in a conversation for each question to be very clear so that the bots can understand what kind of information should be provided. Instead of open-ended questions, select the closed question to keep users in the flow. Moreover, to avoid dead-end conversations add buttons that provide specific answers to the user.  

Anticipate misunderstandings  

A creative solution is one of the best alternatives a designer can find to avoid misunderstandings. Designers should create fallback situations assuming that users cannot understand technical terminology, and hence error messages must be expressed in simple language to make sure that nothing is lost in translation. One way to avoid misunderstandings is to change the way the chatbot gives responses. Many failed responses can be created to give meaning to an actual conversation. Moreover, the response of a chatbot can direct the user to the current flow strategically. An alternate button can also be provided to bring the user to the conversation whenever a chatbot fails to understand.  

Track and analyze user behavior  

A chatbot needs to be tracked and analyzed to improve repeatedly. An analytic platform can be used to track data for chatbots as it give information on the way chatbot is used, where they failed, and how users interacted. They also give information related to the total number of users, most used flows, and words from users that the chatbot could not understand. Conducting surveys is one of the best methods to collect user data on satisfaction. This was very useful in trying to improve our chatbot automation and understand the user’s pain points. 

Chatbot UI examples  

Lark – a healthcare chatbot  

Lark is designed to assist patients and it is a contextual chatbot that initiates conversations with users similar to that of humans, through the mobile application. Lark is a friendly, kind, and humorous chatbot that attracts adults, its biggest customer. There are voice, chat, and button options through which users can interact with the chatbot. The chatbot user interface used in Lark gives adults control over their health and is easy to use without any assistance. The green color which is used as the primary color of the chatbot indicates rest, serenity, and good health. Lark’s messages go well with the calm color scheme as it is inspiring and mood-lifting.   

Replika- self-help chatbot   

There are many human-like chatbots in the market and Replika is one of the best among them. Replika is a contextual chatbot that can easily understand and analyze human conversations to the level where it starts to imitate the user’s way of speaking. This clever chatbot was built to serve the role of a companion to those in need. Moreover, the conversational tone and mood of the chatbot can be adjusted by the user according to the topic or mood the user chooses. That chatbot can also be given a name by the user to make the companionship more personal. To continue the conversation, users can choose from a wide range of topics or problems they want to discuss. It even helps you write a song and then gives personal badges as it understands more about the user. The chatbot can be accessed through the web or mobile and the user interface is customizable, which enables users to change to different modes and customize the background. The user interface of Chatfuel emphasizes building a personal and comfortable “environment” to have conversations.  

Erica – a chatbot for banking  

Erica is a chatbot designed for banking but it is similar to Siri. It is used in Bank of America to assist customers in making commands in the form of text or voice, thus making it easy for the customers to check anything related to banking activities. The chatbot uses text, images, and graphs to show the customer their account balance, spending habits, and recurring rates. Erica has a navy-blue color interface that symbolizes trust and authenticity, and it uses emojis and compliments to give a human touch to the conversation.  

Benefits of Custom Chatbot UI  

  • Offers you a customized and branded Customer experience.  
  • Interacts with consumers more like humans resulting in improved customer experience
  • Manages multiple tasks with multiple levels of difficulty.  
  • Helps to handle more than one customer at a time. 

Wrap Up  

Most businesses are not even in need of a chatbot, but their popularity and fun components make them very attractive. The point is, if you don’t have a proper plan to use the chatbot, then it is better not to spend too much time and resources on that. Instead, define the purpose of a chatbot so that it will not be limited to a talking website accessory. It can provide a voice to your brand to help consumers with simple tasks. All you need to do is make a list of everything you want your Artificial Intelligence chatbot to get and divide it into ones that fit your resources, time, and consumer needs. Decide where to use it, like whether it should be only on your website, or should be on different channels, and how to use your chatbot.   

How to Create the Perfect Digital Transformation Plan?

Posted on by Prem Kumar

Digital Transformation has now become the focus of many companies, and it is even visible in Google Trends search volumes. Executing digital transformation throughout your business is an exciting endeavor. You should be aware that it is not as easy as it seems and you may have to face many challenges or discomforts when stepping into the process for the first time. This uncertainty can lead to paralysis where organizations make no move as the process seems too complicated. In this article, we are going to discuss how a perfect digital transformation plan should be implemented.   

Steps to Create the Perfect Digital Transformation Plan  

Here are some steps that you should follow while implementing digital transformation so that your business won’t have to face any inactivity.   

Pinpoint your objectives  

Identifying your objectives is the first step in implementing a digital transformation strategy. Your digital transformation plan should be able to give you a clear idea about your goals and the steps that need to be followed to achieve your objectives. Pinpointing your objectives will help you identify the areas that need special attention in your plan or the elements of your strategy that need to be addressed first. 

 Moreover, it is important to consider that the goals of each organization will be different. Though digital transformation is seen differently by each organization, there are some common objectives that every business wants to achieve through digital transformation:  

  • Improve customer satisfaction and experience.  
  • Create an efficient and cost-effective function using the latest technology and processes.  
  • Be an organization that can accept and embrace change.  
  • To develop processes to collect data and information through analytics to support faster growth.  

Focus on customer needs  

Digital transformation aims to increase customer value by utilizing all the available digital tools. Therefore, taking the requirements of the customers into consideration plays a significant role in determining the success of the digital transformation. For instance, you can integrate user management and communication in one place through communication channels. Recording all emails and details of earlier conversations with customers will enable you to develop and handle customer relationships better. If digital interactions are what your customers prefer, then it is your responsibility to make the required internal transformations.  

Set up new processes  

When you decide to implement a digital transformation strategy into your business, you cannot let your business move as it was before. The previous approaches you have taken in your business may not go in coordination with the digital transformation project. Embracing new processes can help your business achieve success in the long run and sometimes businesses need to re-validate the previous processes to begin a digital transformation. To bring about such a change in the organization, it is crucial to mobilize employees together to make sure that updates are understood by all, like who is responsible for what, and what mechanisms are in place to assist the team should be decided.  

Choose the technology you need  

When we talk about digital transformation you may think that you need all the digital tools or the existing technologies to implement digital transformation. But that is not true, you just need to opt for a technology that is going to back the processes, you optimize. Choose what is most important to your organization and identify the technology that meets the requirements and standards of your organization, whether you want to:  

  • Systematize common processes and workflows,  
  • Coordinate with clients and bring them into your workflow,  
  • Gauge time and cost to assist in decision-making. make better-informed decisions, or stir up workflows by sharing client data.  

Apparently, the technology you choose should match your goals and enable your organization to achieve its goals.  

Restructure to adapt to changes  

Though the word ‘restructure’ may seem frightening, there is no other way to be a ‘digitally-fit’ entity. Organizations are beginning to leave the ‘hierarchy’ in the workplace to give employees equal opportunity to achieve goals without conventional top-down controls. These types of models are project-oriented and more dynamic, which enables employees to have functional autonomy in a lot of work they do. This leads to employees becoming more involved in their organization. Restructuring the hierarchical classification in an organization is a way of adapting to digital transformation.  

Execute your plan  

This is the stage where you are ready to put the plan into execution once all the preparatory work has been finished. But your plan is likely to come with several moving parts. Take a step back and work in a cycle instead of trying to integrate every change you made at once. A repetitive plan means a flexible plan, each step you take during the execution process may vary over time – from a week to a month.  

Bring your whole team together and divide your plan into obvious questions at the beginning of every week. Evaluate the progress of assigned tasks, make sure you are working within limits, and analyze problems that may need adjustments in your plan. Essentially, a repetitive approach will ensure that resources are focused on the most influential areas.  

Give space for agility  

According to the survey conducted by McKinsey & Co., it is found that the percentage of companies that succeeded in implementing digital transformation is less than 30%. In addition, not more than 16% of companies have received successful results in terms of performance, after implementing digital transformation. To sustain your digital transformation strategy for a long period, enable proper planning, monitoring, and adaptation. When your plan is complete, monitor and evaluate the performance of your team as well as if they are accepting changes in the process or meeting your objectives.  

When the process starts functioning, you may recognize problems or opportunities that may lead to further success. In each set of your plan, take the time to think about the lessons learned so that you can reach future settings with an in-depth knowledge of your new process.  

Wrap Up  

Digital transformation is a journey you have to go through step by step or stage by stage, it’s not a race to finish in a single stretch. Take your time, even if you are slow, it doesn’t matter when you have the right tools, the best team, and the perfect digital transformation plan. If you want to remain active in the digital world, your organization should be open to changes and progress.  

NLP Engines and Chatbots: Enhancing Conversational Experiences

Posted on by Mahes Kumar

Natural Learning Processing (NLP) is a critical aspect of chatbots and is the key element that comprehends the input given by the users at any point in time and translates it into a structured format that the system can process. Chatbot NLP engines include modern machine learning algorithms that identify the language of the users and match them with the list of functions the chatbot supports. NLP engines use either limited automation models or deep learning methods to read, analyze, and understand what users say. Finding the right NLP engine is very much integral to determining the success of the chatbot.   

Why NLP Chatbot? 

Chatbots are becoming more and more common and are a potent tool for keeping online visitors engaged by communicating with them through their natural language. Previously, there were live chats on websites where agents would chat with online visitors and respond to their queries. But when websites get high traffic, it is now an outdated and costly strategy to hire agents who interact with customers 24/7 live. It is a huge liability and takes a lot of time and resources for businesses to train and pay the agents. Chatbots can resolve the problem by being active all the time and interacting with website visitors without any human assistance or intervention. Natural Language Processing and other machine learning technologies increase the efficiency of chatbots by making most conversations easier without human intervention. 

NLP engine and chatbots  

When it comes to chatbot automation, what’s more crucial is deciding on the chatbot architecture. Similarly, opting for the right NLP engine is also important as it is mainly based on the preferences and objectives of the organization. Developers and businesses are often confused about choosing an NLP engine. The decision to choose between cloud and in-house is something that influences the characteristics your business requires. You can build an engine of your own if what your business needs is a chatbot that is a highly capable one with high security and custom dialog features. In certain situations, in-house NLP engines offer mature natural language comprehension components, while cloud providers are comparatively weak in dialog management. 

If you want to have contextual chat, in-house chatbots can be even more useful. For instance, if customers have to ask a certain question such as “give the details of the red product”. Chatbots will not be able to understand what the “red product” is from the database. Here the context is very important, and these situations are dynamic. In such situations, cloud-based providers may not be appropriate because they are developed for the target market use cases and this is where in-house NLP engines serve value.  

Here are some reasons why chatbots should have NLP:  

To overcome the challenges of language variations  

The main issue with the method of pre-fed static content is that languages show a boundless number of variations while expressing certain statements. There are uncountable ways through which a user can produce a statement to show emotion. A lot of efforts have been taken by researchers to make the systems comprehend the language used by human beings. Making a connection between an input message from a human being and a response generated by the system is easy and practical with the help of NLP. Responses may include an answer to a query or a customer request-based activity. NLP is capable of distinguishing various types of requests generated by a human being and thus improving customer experience.  

To change focus on more relevant tasks  

Usually, a lot of roles and resources are deployed to enable an organization to function, anyhow, it results in the repetition of manual tasks throughout multiple verticals such as customer service, invoice processing, human resources, Insurance, and catalog management. Natural Language Processing-induced chatbots significantly minimize human effort in many manual activities such as customer service or invoice processing, so these activities need lesser resources along with efficient employees.  

This helps employees to concentrate on crucial tasks that have a positive influence on business in a creative way, rather than wasting time on repetitive tasks that tire of each day. NLP-based chatbots can be used for internal purposes as well specifically for human resources and IT helpdesks.  

Increase profitability with reduced costs  

Cost is an integral aspect when it comes to the growth of a business and business profit maximization. As efficiency improves and workflows become more efficient, NLP-based chatbots can dramatically reduce the cost of human resources and other resources associated with monotonous tasks, along with customer retention costs.  

Increased customer satisfaction due to efficient systems   

The generations today need a sudden response and immediate solution to their questions. NLP allows chatbots to learn, analyze, and prioritize queries based on their complexity and allows bots to give more rapid solutions or responses to customer queries than human workers. Quicker responses help build the trust of the consumer and then more business. You may feel that the customer retention rate has increased once the chatbot has been implemented. This minimizes the effort and cost of gaining new customers by developing the loyalty of prevailing ones each time. Chatbots provide customers with sufficient time and attention they need, thus making them feel valued.  

To conduct Market Research and Analysis to make impactful business decisions  

It is possible for you to get or create a significant amount of flexible and unstructured content from social media. NLP allows you to create unstructured content and find meaning in it. NLP also makes it easy to understand the meaning or concept behind reviews, comments, or queries by consumers along with a glimpse of customers’ feelings towards your services or brand.  

Wrap Up  

Chatbots based on NLP will help you improve customer experience and take your business processes to a greater extent while contributing a lot to development and profitability. It offers technological innovations to keep the market competitive in terms of time, effort, and expense, which in turn enhances customer satisfaction and leads to greater engagement in your business.  

7 Essential Tips to Reduce Technical Debt

Posted on by Poulose Tyson

Technical Debt: An introduction  

Technical debt (TD) or code debt is a concept in software development that defines the cost of additional restructuring resulting from opting for an easier solution rather than going for a better approach that takes more time. If the technical debt is not fixed, it will become worse, making it difficult to implement the changes later. Unresolved technical debt may result in software entropy. Experts define technical debt as an outcome of incorrect or inappropriate technical decisions, which later lead to problems related to system maintenance and expansion.   

Technical Debt: Causes  

Here are some major reasons or causes that normally lead to technical debt:  

Knowledge gaps  

Sometimes businesses make decisions without proper knowledge or understanding and this can result in increased technical debt. Various resources may need necessary corrections later. In other cases, a developer writes code without enough knowledge, which then causes unexpected complications. Or even more, outsourced work appears to be substandard and needs to be rebuilt further down the line. All these are the nature of the technical debt. 

Change of context   

Sometimes certain requirements that were compatible with the initial system may no longer be appropriate or the technology you use may have gotten out of date. It may demand changing the technology stack or tools. 

Development process  

Technical debt happens when there is inappropriate documentation in the initial concept. People won’t be able to explain what needs to be done and why something needs to be done. If you cannot include sufficient resources and lack test automation in this equation, there emerges technical debt.  

Team/ people  

The most intricate cause of technical debt is the team/people. This is not to blame or point fingers at people, but to analyze the best thing human resources can do. If your team of developers is inexperienced, the communication between available teams is inefficient, or sources transfer from project to project – all these can get you stuck in technical debt.  

How to reduce technical debt?  

If an organization wants to reduce technical debt, it has to find out the source and the root cause of the debt. Some of the common causes of technical debt include:  

  • limited resources  
  • Time constraints  
  • An insufficient incentive to concentrate on long-term negative effects  

Once the source of the technical debt is identified, companies can rearrange their technological approaches and institutional mindset to reduce technical debts.   

Tips to reduce technical debt  

Now, let’s talk about some tips that may help your organization to reduce technical debt.   

Acknowledge the debt  

Organizations often take out technical debt without being aware of it. However, there comes a point where technical debt no longer remains an achievement but becomes a painful problem that creates many problems. The sooner you acknowledge it, the faster and cheaper it will be to reduce your technical debt. As mentioned before, fix the debt before it becomes too much to deal with.  

Identify the source of the debt  

Identifying the source is an important factor in reducing technical debts. Without finding the source, you won’t be able to find the reasons or causes behind the debt. Once the source of the technical debt is analyzed by the team via interviews and surveys, the team will be able to identify insufficient time as one of the main causes of technical debt. IT team members, who face strict time limits, adopt custom coding point-to-point integrations to get projects into the business faster. Here the team works under pressure and justifies the short-sighted approach in order to satisfy business needs and that results in generating more technical debt. Here, the IT team identifies the source, and lack of time, as the main cause of the technical debt, hence finding the first way to reduce technical debt.   

Adopt new technology  

Now the team is already aware that the custom point-to-point integration is the root of the problem. If they intend to reduce technical debt, they need to opt for a new integration method that works long-term. A new technology that motivates the team to deliver the projects within a short period, on or before time, while developing a long-term view for upcoming projects. 

Communicate and deliver cost-benefits 

Technical debt assessment is important for both tech executives and decision-makers in the organization. First of all, it gives proper visibility and enables you to communicate the problem. It puts a price on a problem to show how it may affect the project and explain why paying a debt in time is important. Secondly, analyzing cost-benefits helps to better prioritize and plan the payment strategy. 

Schedule regular and frequent pay-off 

One of the most effective strategies is to split up the additional work into smaller milestones and gradually integrate them into a regular workflow and pay them off. This approach allows the team to avoid any chance for technical debt without interrupting the ongoing process and ensures that it becomes less over time. 

Stick to the best practices 

Businesses should develop a standard coding document for developers that explains the best practices to follow. When proper coding standards and best practices are strictly followed by developers, it can significantly reduce technical debt.  

Keep a record of changes  

To resolve any problems easily and appropriately, it is best to keep a continuous record of changes in a collection shared by a team. That way, if there is a problem, it will be easy to find the source. This is a very important process when migrating software or upgrading legacy software to a new environment, where changes must be made very carefully so as not to affect other system parts.  

Wrap Up  

Even if you have a legacy system or are developing a new one, it is impossible to avoid technical debt. This is a complex issue that all organizations have to deal with. Irrespective of its character, it is important to notice its presence, document it, and spend money on it. You should be honest and eloquent with the development teams and stakeholders on this issue.  

Software Platform Vs. Application: Understanding the Differences

Posted on by Jithu Daniel

Over the past few years, we have seen a rapid leap in the use of the term software platform. All kinds of software applications came to be called platforms but the fact is that the term platform has an entirely different meaning that distinguishes it from software applications. A platform allows software applications to function on it so that the end-users can use them. It is very much necessary to distinguish both terms in order to make it easier to answer relevant questions like how to decide which platform should be used for software development services.

What is a Software Platform?

A platform refers to any hardware or software where an application or service is hosted. A software platform is an environment designed to write and run applications. It consists of many software tools like GUI builders, compilers, and utilities. Software platforms are designed to enable applications to function together without interruptions or integrations. It can also support different programming languages, engines, and web services.  

A software platform lies between the hardware of a system and every other application on the system. It consists of APIs and services that can be integrated in an unrestricted manner to build an unlimited number of applications. The OS (operating system) in a desktop or a mobile is an example of a software platform, which is not only responsible for the functioning of the in-built apps but also responsible for other third-party applications like Instagram, Google services, Spotify, etc. Software platforms are designed for scaling up and operating different programs based on business requirements. Businesses that purchase a software platform also have the option to customize the platform that needs certain development on the existing framework. In short, a platform is an infrastructure for applications to run.   

What is a Software Application?

A software application, also known as an application program, is designed to fulfill or run a specific task either for an end-user or for another application. A software application can be a single or a bunch of programs and it normally uses the operating system of a computer or the native programs to function. It is impossible for any user to perform any task without a software application. Moreover, a software application interacts directly with the user. 

Software Platform v/s Application  

  • A software application is a set of programs used to perform a specific task on a computer or mobile while a software platform is a kind of operating system that supports in-built as well as third-party apps.  
  • A software application converts data into information. On the other hand, a software platform serves as an environment for hosting applications.   
  • A software application is a combination of computer programs, related configuration files, documentation, etc. functioning together. On the other hand, a software platform includes a hardware device and operating system on which an application, program, or operates.   
  • Some examples of software applications include Google Chrome, skype, slack, WhatsApp, MS Word, etc. Some examples of software platforms include IMB I, Microsoft’s .NET framework, and SUN’s Java.   

Wrap Up  

You can see a clear difference between a software application and a platform from the basic characteristics, yet many still get confused between a software application and a platform, not in terms of their functioning but when it comes to addressing each term. This article explains the differences between a software application and a software platform in the simplest way.   

Applications of Blockchain Technology in the Healthcare Industry

Posted on by Naizam Hussain

Developed countries are spending a good portion of their gross domestic product (GDP) on healthcare. Anyhow, this doesn’t stop increasing hospital costs, inefficient practices, and breaches in the health data of patients. This is where blockchain technology can be used to bring changes. Blockchain technology helps in improving performance, security, and transparency while transferring medical data throughout the healthcare system. The application of blockchain also helps in identifying serious and dangerous errors in the medical field. The main reason for the adoption of blockchain technology is its whole digitalized facets and its use in healthcare applications.  

Applications of Blockchain in the healthcare industry

Blockchain helps healthcare institutions to gain insight into the functioning of the entire system and improve the analysis of medical records. Eliminating wastage of time and resources while enhancing productivity and increasing workflow is one of the most convenient applications of blockchain in healthcare.  

Here are some major use cases of blockchain in the healthcare industry:  

Secures patient data

Safety is always a major concern in the health sector. Currently, one of the most popular blockchain applications in healthcare is keeping vital medical data secure. As per reports, around 180 million patient records were revealed in data breaches between 2009 and 2022. Banking information, credit cards, and health and genetic test documents have been stolen by criminals. What makes blockchain a suitable technology for security applications is its capability to keep a record of all patients’ data intact, decentralized, transparent lucid. Although blockchain is transparent, it hides the identity and keeps the privacy of any individual with intricate and secret codes. This technology also enables patients and other healthcare providers to pass the same information securely and rapidly.  

Analyze the outcomes of specific procedures

Researchers have given verified access to patient data, which is used to evaluate every specific procedure on a proportionate number of patients. This results in bringing relevant improvements in managing these patient groups. It also gives updates to the clinicians on the patient’s current condition using data collected in real-time, notifies them of any emergencies, and provides exactly prescribed drugs or services for patients. They can also deliver effective advice to patients regarding the use of medication based on the outcomes.

Avoids dangerous and costly mistakes

Lack of proper communication between medical professionals may result in a loss of $ 11 billion a year in the healthcare sector. Most importantly, it is even dangerous to the health of patients. Error-prone medical records or delayed addition of updates to the record are the major reasons for improper communication. Also, gaining access to the medical records of a patient is a process that takes a lot of time, wears out staff resources, and slows down patient care.  

Blockchain-based medical records are the best alternatives for these ailments. Since blockchain solutions are decentralized, information is scattered across multiple network nodes, instead of being stored in a single database. Any change made to the information in a node will be updated automatically in all the network nodes. This results in the efficient exchange of up-to-date information with any doctor or health care provider. This type of technology creates a database of patients, that allows healthcare providers, hospitals, and pharmacists to refer to anyone else involved in treatment. This way, the blockchain can help in quick diagnoses and personal care plans.  

Supply chain management and drug traceability

There are many questions like how much we are aware of our medicine. Is it possible to confirm that there is no forgery in it? Does it come from a legitimate supplier? – all that becomes the primary concern of the relationship between the medical supply chain or the lab and the market. Blockchain brings serious inferences in the pharmaceutical supply chain management, and its decentralized nature guarantees full lucidity in the shipping process. When a ledger for a drug is generated, it notes the point of origin, which means the laboratory. Until it comes to the customer’s hands, the ledger will then keep recording step-by-step data, like who handled it and where it was. These records bring transparency to the supply chain and enhance the safety of drugs or medical equipment.  

Reduce data transformation time and cost

Blockchain networks reduce the time and cost during data conversion. Blockchain networks ensure to address the problem of quick and effective testing of medical credentials along with ensuring the anonymity and privacy of patients. It can also bring out essential new ideas and discoveries that could change the way healthcare functions in the world. The application of blockchain facilitates the transfer of monetary data while respecting the value and privacy of the data. Blockchain technology also enables you to save transaction history and documentation along with time stamping. Every node in this network is checked and recorded for each data input.  

Breakthroughs in genomics

The possibility of genetics making vast changes in the future of human health was a dream once and is now a scientific and economic reality. Back in 2001, $ 1 billion was the cost incurred to process a human genome but it takes $ 1,000 and some proficient companies to bring DNA tests that can open doors to our past and future health. Blockchain is perfectly suited for the growth of genomics as it can securely store a lot of genetic data points. It has even helped people to share encrypted genetic information to create a vast database. This, in fact, gives researchers access to relevant data more quickly than ever before.  

Wrap Up

When it comes to health care, the need for development is accelerating at an even more incredible pace. What is needed today are quality healthcare facilities supported by modern and innovative technologies. Here, we have discussed some of the best ways through which blockchain plays a crucial role in transforming the healthcare sector.