5 Tips To Select The Ideal Chatbot For Your Business
Chatbots have opened up a whole new realm of communication between humans and machines. They enhance a company’s customer service and improve operational efficiency, driving better engagement, reduced churn rates, and overall sales growth. They have become immensely popular and their popularity only continues to increase. It is clear that the use of chatbots is imperative for your business success.
In this blog, we will take a look at the types of chatbots available and how to wisely select the chatbot that suits your business.
The Wide Array of Bots
Studies predict that by 2021, more than half of the enterprises will increase their investments in chatbots, creation than traditional mobile app development. Customers would prefer to get real-time answers from bots on a company website.
Chatbots can do just about anything. They can help you deliver a surprise gift to someone you love. They can also help you break up with your lover and much more!
Broadly, chatbots can be classified as follows:
- Action Chatbot: In order to follow through with a specific action, this type of chatbot requests relevant data from the customer.
- Social Messaging Chatbot: It utilizes social messaging platforms and allows customers to interact with the chatbot directly on social media.
- Scripted Chatbots: It uses a predefined questionnaire to interact with the customer.
- Natural Language Processing (NLP) Chatbots: Being an application of AI, NLP enables chatbots to understand the written or spoken language and come up with the best response.
- Contextual Chatbots: It is the most brilliant of all chatbot types. Since it is based on artificial intelligence and machine learning, it can self-improve over time.
Tips To Choose A Perfect Chatbot For Your Business
As a communication agent, chatbots play a vital role in automating mundane tasks in an “always-on” work environment. Chatbots can handle day to day queries until an emotive or complex issue arises, which might require the intervention of a trained human agent to address it.
Related Reading: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How
Here are a few pointers to select the perfect chatbot for your business needs:
1. Think about your target audience
Like every business that has a target customer, the chatbot too must have a target audience. It is important to remember that the chatbot should serve as the bridge between you and your customers. The bot should be able to understand the preferences of your customers and cater to their convenience.
2. Define objectives
Identifying and narrowing the specific tasks or areas you want to automate would yield maximum benefits. There are a few points that could help your business define those objectives. Carefully consider factors such as the platform where the chatbot would be used, the queries it would answer, the queries it would direct to a human customer care executive, and how it would manage the hand-over process smoothly.
3. Define your value proposition
The value proposition involves ensuring that the most vital factor of your business, is given prime consideration. It determines whether your customers will come to you or go to your competitors. A higher value proposition might require AI or ML capabilities; so gauge and determine your value proposition to select the right chatbot that fits your budget and your business needs.
4. What is your response speed?
According to the 2018 State of Chatbots Report, customers want quick and easy answers. Customers might get frustrated if the answers are delayed. The appropriate selection of chatbots can help you avoid such kind of delays effectively. When dealing with a complex issue, ensure that your chatbot is capable of collating information quickly without delay. If there is a need to hand over the query to a human customer care agent, it should be done seamlessly and fast.
5. Evaluate features and functionalities
Evaluation aids your business in identifying the essential features and functionalities required from a chatbot to run your business successfully. To begin with, you could create a set of standards that would analyze all solutions. Decide on which features are required, such as NLP, integrations, contextual awareness, analytics, and so on. Proper documentation is required while evaluating the features. Such a candid evaluation helps a business choose the right chatbot that could be fine-tuned later or could self-learn.
Download our case study: Using chatbots to create an enhanced and engaging learning experience
Make Your Business Chatbot Ready
In the 24/7 era where customers want instant services, chatbots help businesses to keep pace with such expectations. By evaluating your own objectives and keeping in mind your customers’ expectations, your business can maximize the benefits of chatbot technology. However, choosing the right chatbot that fits your organizational needs and implementing it without any flaws require a good deal of expertise.
Our team at Fingent has been doing amazing things with Chatbots for our clients. Recently, we provided a matured chatbot assistant technology to a client, which provides comprehensive user intent identification and processing as well as satisfactory response according to the user query. Chat with us to identify the best chatbot solution for your business, and learn how we can implement it for you quickly.
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
A Chatbot Story – How We Built a Comprehensive Onboarding Assistant for a Leading Research University
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.
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.
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.
“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.
“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.
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
During the development, we came across some development challenges with IBM Watson, which are listed below.
- Unable to map relationships between entities. Due to this limitation, we were unable to link and pull the related values of the entities.
- Conflicts between various entity values (Solved partially via entity split-up method)
- API Limitations to manage chatbots dialog schema
- IBM Watson doesn’t provide active learning, the self-learning capability to learn from user conversation sessions.
- 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.
- No built-in user input validation. This has to be done via WebHooks.
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!
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
Chatbot or Chatbaby: Why Chatbot Technology Needs Time To Mature?
Automation is at its height in this digital era and artificial intelligence is the core technology behind successful industries today. A chatbot is a computer program that interacts with human users via the Internet. These auto-operating conversational bots, artificially replicate the patterns of human interaction. This is made possible via Machine Learning algorithms.
Amazon’s Alexa and Apple’s Siri are well-known examples of chatbots. They respond to queries based on the data they are provided with. Since a chatbot is programmed to perform tasks independently, it can respond to queries based on predefined scripts and machine learning applications as well.
Enterprises that have enhanced IT infrastructure, implement chatbots in varying departments. For instance, ERP, on-premises to cloud, etc., improve customer experiences and increase business value.
Chatbots As Conversational Tools In The Workplace
The potential benefits that chatbots provide to companies are immense. On one hand, when chatbots provide personalized and efficient search results, on the other hand, companies find cost savings a prominent feature on implementing chatbots.
Enterprises enhance their workflows and operations increasingly with the help of chatbots as co-workers. This helps companies in serving a larger market segment. Chatbots automate redundant tasks that sap the productivity and efficiency of the workforce and enable them to focus on their core functions. Chatbot technology enhances customer experience by providing them with personalized content.
Chatbots: Influence In Industries Today
Enhances Customer Service
According to a recent survey, 83% of customers need online assistance to complete their shopping. Customers will need online real-time assistance from a real store. The assistance required by customers include areas like during registration, logging in, adding products into the cart, payment, checkout, etc. This is where chatbots serve as a salesperson by interactively communicating with customers via text, voice and so on and provide them with rich content.
Additionally, chatbots provide automated answers to redundant queries of customers. It also forwards the queries to real sales personnel when the need arises. This saves the time of human customer service resources, avoiding the need for customers to wait for responses. This scales up operations of enterprises to new markets globally as well.
Customer service that chatbots offer is proactive. This means that chatbots facilitate a 24/7 service for interacting with its customers real-time. Initiating communication with the customer periodically, is another benefit of the prevailing chatbot technology, thus enhancing your brand perception considerably.
Monitors Data To Provide Critical Insights
An enterprise benefits from gaining traction of customers on its website’s landing page. But it is equally important to ensure that the landing page also generates enough organic traffic. Chatbots reaches out to the customers who visit the landing page and gathers critical data as to why the customer left the page without a purchase and so on.
Online customer behavior and buying patterns are tracked effectively via monitoring the data thus derived.
Generate Leads Effectively
Chatbots ensure a better lead generation by helping in determining leads via KPIs such as timeline, budget, etc. Chatbots thus ensure higher conversion rates.
Implementation of a fully functioning chatbot is much faster than hiring individual employees for each task. With great speed, chatbots also ensure error-free operations. It is also easy to implement and maintain. This reduces costs and other overheads significantly.
Chatbots In Its Chatbaby Phase: Conversational Limitations In Current Chatbots
Chatbots have evolved into a phase where it can integrate Natural Language Processing or NLP technology to support the workforce in performing extensive searches. That being said, chatbots currently face a limitation. AI-based chatbots, for instance, are restricted to duplicate results. For example, a flaw in a chatbot can result in the chatbot responding to a high engagement level content such as an office party album instead of retrieving business-related documents.
Why Do Chatbots Need To Mature?
Though chatbots support customer services, they lack a human element in them. A full-fledged implementation of a matured chatbot requires addressing numerous technological gaps. Only then the services of these chatbots can be extended from a chatbaby level, offering just employee assistance to a serious enterprise level.
Even when chatbots interact in an automated manner, they sometimes cannot answer even simple queries. For instance, chatbots lack becoming conversational in a detail-oriented manner. Thus, it is necessary to learn the complex machine learning technology and its bottlenecks initially before you proceed to implement it in your business.
Around-the-clock support is what customers look forward to while they search via queries online. This 24/7 ability to converse with the customers real-time is what is yet to be achieved.
In 2019, chatbots are still in their chatbaby phase and need to mature. 75% of global consumers prefer human interaction rather than a chatbot or any other automated service at the time being.
The data required to establish a fully conversational chatbot is immensely huge. This explains the need for a much higher understanding of machine learning and challenges around natural language processing technique implementation.
Can There Be An Alternative To Chatbots?
Chatbots can handle simple tasks to help the human workforce. But when it comes to complicated tasks, assistants that can proactively work to streamline interactions with customers, are required. For this, employees must be able to access from a centralized location.
Employee Intranets, for instance, are hubs that allow organizations to access data in real-time and whenever required. This not only allows them to store data in an easily accessible manner, but also help in connecting with resources and tools at a fast pace. This now becomes a perfect collaboration system.
As more and more employees become tech-savvy and with the growing requirement of companies to expand their mobile technologies and strategies, connecting remotely is a necessity. With the growing remote working, it is important to implement social media features into various communication channels as well. This enables a human element to be present and can act as an alternative to chatbots.
2019 can be seen as a year to understand machine learning technology and to develop the digital workplace. With evolving technology, it is also important that an organization ensures a perfect environment where resources and tools support these digital assistants and help in making critical data simple and accessible. Call our strategists right away to learn more about how chatbots can improve your business effectively and productively.
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
Top Five Barriers To Growth and Adoption Of Virtual Customers
“There is only one boss. The customer. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else.”
Wise words from Sam Walton, the founder of Walmart. Yes, customers can either make or break an organization. The key then is to know how to treat them the way they want to be treated. In this blog, we will discuss a particular segment of these customers – virtual customers. We will also consider some of the challenges for growth and adoption of these customers.
The Customer Is The Only Boss
Every day, we see significant developments in technology. The rate at which consumers and companies are adopting such technology is astonishing. For example, the adoption rate for computer technology grew from 0% to 50% in just a five year period, and the use of tablets grew from 3% in 2010 to 64% in 2017.
Customers create customers, and so will virtual customers. Virtual customers will be able to communicate with each other and encourage knowledge creation and knowledge sharing, thus aiding the growth of the business. In this regard, innovation and new ideas are rising at an exponential rate. Here is where dealing with virtual customers become extremely important. Many large firms have integrated virtual customer assistants (VCA) or chatbots as part of their strategy.
Emphasizing the support operations, which integrate VCA, the managing vice-president at Gartner, Gene Alvarez says: “As more customers engage with digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks.” He adds that “this is underpinned by improvements in natural-language processing, machine learning, and intent-matching capabilities.”
According to Gartner research, companies report a reduction of up to 70% in calls, emails and/or chat inquiries after implementing a VCA. They also reported greater customer satisfaction and a 33% savings per voice engagement. Yet, we may have to wait for several years before we can create autonomous virtual customers that can function without human intervention. Meanwhile, certain hurdles need to be acknowledged and overcome by live customer support and services. Consider a few of them.
Related Reading: Check out how AI is redefining the future of customer experience.
Barrier 1: Brand Strategy
Without the benefit of face-to-face interaction, businesses will need to work out how to maintain their relationship with the customer. A business might design a VCA and forget it. Instead, they should continue to check if their design is functioning as they would like it to function.
Brands should continue to collect data from their customers and ensure that they treat them not just as a number. In this regard, algorithms play a major role. Engaging aspects of humanism provide the best virtual customer support.
Barrier 2: Capability and Capacity
One of the advantages of having a virtual customer is the ability to record and process data quicker and more efficiently than humans. For it to be fully automated there are two things to consider. The first is the capacity to understand a customer’s preference and the second is the capability to influence the actions of the customer. Both of these are equally important to balance business transactions.
In machine-to-machine communication, compatibility issues may run the risk of slowing down the pace of acceptance and deployment with virtual customers. Employing VCAs that can learn from each interaction, detecting preferences, and making recommendations based on past requests is the solution to this challenge.
Related Reading: Read more on how Machine Learning is boosting customer experiences.
Barrier 3: Legal Impact
Legal liability can become a major issue if the virtual customer assistant goes ‘rogue,’ and offers wrong information or misguides a customer. Internal policies will have to be put in place and disclaimers should be considered for failed transactions and adjudications which could follow.
Barrier 4: Data Privacy
According to Gartner, 80% of all internally developed software are now cloud-native or cloud-enabled. In the course of interaction with the virtual customer, VCA may be exposed to and may collect a vast amount of personal data and other commercial information. Therefore, it is very important to be careful about security and privacy. Companies should ensure that their data controller registrations and privacy policies are up to date. It must be clear where the data is collected and if it is protected under constitutional law. Data protection measures must be put in place to safeguard data.
Appropriate measures must be taken to safeguard copyright-protected data. Companies should ensure that the virtual customer is real and that they can understand and use the equipment. Also, they should decide if they want to provide virtual customer support to devices which may affect their sales and service channels.
Barrier 5: Human Acceptance
No technology can ever replace or replicate human empathy. Though VCA can eliminate the awkwardness of technology, it cannot really emulate the human element. Ultimately, humans will make the decisions. When customer service is on the spectrum of high-emotion and high-urgency, getting people to trust technology will undoubtedly be a challenge.
Virtual customer communities affect the relationship between absorptive capacity and organizational innovation. Many firms are investing in virtual customer communities as they are capable of reducing communication barriers between firms and costumers.
Get On the Virtual Path Now
Companies would do well to consider these five challenges and use them to advance their virtual customer services. While this is an aspect of technology that is sure to grow by leaps and bounds, organizations would need to act wisely to stay on the cutting edge, or they risk being thrown off the bus, or losing out on the possible benefits, at great loss to themselves. Give us a call if you need help with getting this started for your business.
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
Understanding The Types Of AI Systems To Better Transform Your Business
In this digital era, industries are witnessing the ability of multifaceted artificially intelligent systems performing tasks that mimic intelligent human behavior or even beyond. Artificial Intelligence today, manage large chunks of data and perform redundant tasks, allowing the human workforce to focus on core tasks. This saves cost and time and improves productivity significantly.
According to Gartner, the number of industries adopting AI has grown over 270% in the last 4 years. Technology giant, Google pledges $25 million USD in a new AI challenge named ‘AI For Social Good’. Understanding Artificial Intelligence Types, is important to get a clear picture of its potential.
Types Of Artificial Intelligence Calculation: Two Main Kinds Of AI Categorization
AI makes systems imitate human capabilities. Though AI can be classified into different types, the 2 main categories are defined as Type-1 and Type-2 and are based on AI capabilities and functionalities. Let us walk through the major classifications of AI types.
Type 1: AI-Based On Capabilities
1. Weak or Artificial Narrow Intelligence (ANI)
Weak or Narrow AI is a type of AI which performs assigned tasks using intelligence. This is the most common form of AI available in today’s industries. The Narrow AI cannot function beyond what is assigned to the system. This is because it is trained to perform only a single specific task.
ANI represents all AI machines, created and deployed till date. All artificially intelligent systems that can perform a dedicated task autonomously by making use of human-like abilities, fall under this category. As the name suggests, these machines have a narrow range of responsibilities.
Apple’s Siri, for instance, is an example for Narrow AI. Siri is trained to perform a limited pre-defined set of functions. Some other examples include self-driving cars, image and speech recognition systems.
The category of complex artificially intelligent systems that make use of deep learning and machine learning, fall under the category of Artificial Narrow Intelligence systems. These machines are categorized under the ‘Reactive’ and ‘Limited Memory’ machines, which is discussed in detail going forward in this article.
Know more about the key difference between deep learning and machine learning.
This video is made using InVideo.io
2. Artificial General Intelligence (AGI)
General Artificial Intelligence is a type of AI which can perform any intellectual tasks as humans. AGI machines are intended to perceive, learn and function entirely like humans. Additionally, the objective of devising AGI systems is to build multiple competencies which can significantly bring down the time needed to train these machines.
In a nutshell, AGI systems are machines that can replicate human multi-function capabilities. Currently, researchers around the globe are trying to design and develop such AI. Since there is no example as of now, it is termed, General AI.
3. Artificial Super Intelligence (ASI)
Artificial Super Intelligent systems can be best described as the zenith of AI research. ASI is intended not only to replicate multi-faceted human intelligence, but also possess faster memory, data processing, and analytical abilities.
This is a hypothetical concept of AI where researchers are trying to develop machines that can surpass humans. This is an outcome of General AI.
Type 2: AI-Based On Functionalities
1. Reactive Machines
The reactive machines perceive the real world directly and react according to the environment. The intelligence of Reactive Machines focuses on perceiving the real-world directly and reacting to it. An example of reactive machines is Google’s AlphaGo. AlphaGo is also a computer program that plays the board game. It involves a more sophisticated analysis method than that of DeepBlue. AlphaGo uses neural networks for evaluating game strategies.
2. Limited Memory
Limited memory machines are those that can retain memory for a short span of time. These machines have the capabilities as that of purely reactive machines. Additionally, limited memory machines can learn from previous experiences to make decisions. For instance, self-driving cars are limited memory machines that can store data such as the distance of the car with nearby cars, their recent speed, speed limit, lane markings, traffic signals, etc.
The observations from previous experiences are preprogrammed to the self-driving car’s system. This data, but is transient. That is, it is stored only for a limited period of time. This is because it is not programmed to be a part of the self-driving car’s library of experience, compared to the experience of human drivers.
Nearly every artificially intelligent system today uses limited memory technology. For instance, machines that make use of deep learning is a prime application of limited memory. These machines are trained with huge volumes of data sets which are stored in their memory as a reference model. An example of this is the AI that recognizes images. Image recognition AI is trained using a multitude of pictures along with their labels, as data sets.
Artificial intelligent systems such as chatbots and virtual assistants are also examples of limited memory machines.
3. Theory Of Mind Machines
Theory of Mind can be defined as a simulation. To be crisp, when a person considers himself in another person’s shoes, his brain tends to run simulations of the other person’s mind. Theory of mind is critical for human cognition. Additionally, it is crucial for social interaction as well. A breakdown of the theory of mind concept, for instance, can be illustrated as a case of autism.
Instead of a pre-programmed engine, AI scientists are looking forward to developing a series of neural networks. This series will be used to develop the ‘Theory Of Mind’.
‘Theory Of Mind’ machines are aimed at figuring out someone else’s intentions or goals.
4. Self-Awareness Machines
Self-Awareness machines exist hypothetically today. As the name suggests, these machines are supposed to be self-aware, like of the human brain. The machines can be described as the ultimate objective of AI scientists.
The goal of developing self-awareness machines is to make these capable of having emotions and needs as of humans.
To learn more about AI capabilities and how it can benefit your organization, drop a call to us right away and gather strategies to implement AI for positive business outcomes!
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
Patient-Generated Health Data – A Powerful Tool To Enhance Healthcare
Remember a time when patients would queue up in line with a big dossier in hand, waiting for their time to be examined by the doctor? Lengthy queues, paper-heavy records to be maintained, time wasted as doctors reacquaint themselves with the patient’s entire history – no more of all of that. Today, a patient wants to be involved in his care and he doesn’t want to be waiting around wasting precious time. With innovative health technologies, patient-generated health data is making that possible by enabling patient-centered, citizen-engaging care.
Let us consider how improving patient care through technology is possible. We will also see how the use of information technology in healthcare is allowing the healthcare industry to deal with patients as valued customers, improving their experience. But, first of all, what is patient-generated health data?
Patient-Generated Health Data
Any valuable information a patient can garner about their health status is referred to as Patient-Generated Health Data [PGHD]. PGHD is much more than just a person’s health history with information on his symptoms, medication and treatment information. It also includes his biometric data, as well as information about his lifestyle choices. These records will provide a clearer picture of a patient’s actual health status, better than the patient can describe in his own words.
The use of information technology in healthcare is making it possible for healthcare providers to understand their patients better and in making better medical decisions. It also aids patients in receiving an increased understanding of their health condition. The importance of health information technology in healthcare today cannot be over-emphasized.
How Does The Use Of Information Technology In Healthcare Help Healthcare Organizations?
Patient-generated health data can improve the quality of patient care. When integrated into clinical care, patient-generated health data enhance shared decision making and patient engagement. The use of information technology in healthcare can provide an assessment of the patient’s symptoms and well-being, preferences and values, and goals for healthcare. Here are a few specific benefits of the use of IT in healthcare:
1. Improves nurse-patient relationship
Low health literacy can strain the nurse-patient relationship. When a patient comes to appointments armed with their health research, the nurse-patient relationship improves.
2. Clinical benefits
PGHD plays an important role in assessing a person’s health and monitoring treatment effects. It connects patients and healthcare providers during a care encounter. PGHD can be used to monitor a person’s overall health and allows healthcare professionals to detect potential health dangers.
3. High-risk patients
Keeping a track of PGHD helps providers identify and treat high-risk patients at the earliest. Leveraging PGHD proves extremely valuable in chronic and acute care.
4. Patient engagement
Patients have ownership over their data and control on how and when that data is shared with their care team. They are empowered to contribute to their care and can share insights. It improves communication and contributes to shared decision-making.
5. PGHD provides independence to seniors
PGHD can be used in assisted living to mitigate the risk of falls. It gives confidence to the elderly for independent living and it improves adherence to complex medication plans. PGHD also alerts healthcare providers when required, avoiding the need for expensive assisted living facilities and nursing homes.
Fingent Technologies worked with NovitaCare, an early-stage healthcare startup, in setting up an online platform to enable easy administration, streamlined workflow for many healthcare services and HIPPA compliance. Improving patient and caregiver interactions, eliminating waste, enhancing accountability, and raising the quality of care were the main accomplishments of this tool.
Related Reading: Read more about the case study here.
Innovative Health Technologies To Help The Patient
Innovations in health technologies go beyond just PGHD. It includes various devices and systems designed to streamline healthcare operations, lower the costs and enhance the quality of care. Among the most promising healthcare technologies are Artificial intelligence (AI), virtual reality, chat-bots and so on. Let us consider a few.
1. Artificial Intelligence
Artificial intelligence with machine learning is proving to be essential for healthcare organizations. It is capable of reducing the risk of preventable medical situations in three ways.
- Reminders: It can provide automated reminders to help a patient, reminding him to take his medication within a certain time-frame.
- Identify and alert: It helps to identify potentially dangerous situations and triggers alerts for the medical staff making timely medical intervention possible.
- Personalized dosage: Dosage recommendations are made depending on each patient’s body chemistry while considering environmental factors.
2. Blockchain in Healthcare
Blockchain can create and maintain a transparent and tamper-proof transaction ledger. This technology could change the way huge healthcare sectors operate. Personal data collected in blockchain remains with the patient instead of being stored on servers, thus enhancing confidentiality.
3. Voice Search
According to research, 46% of Americans use voice-assistance on their mobile devices. The figures are not much different for the rest of the world. We can expect that soon many more would be inclined to search for health-related information using voice search. Since it is increasingly used, the healthcare industry is also making good use of voice search. This technology would be helpful to both patients and caregivers who try to locate hospitals or clinics either near their homes or offices.
Chatbots in healthcare provide various benefits. They assist with medication management, first-aid or in emergencies. Chatbots offer a personalized service to patients and caregivers.
5. Virtual Reality in Healthcare
Patients are often anxious about hospital stays and procedures. Virtual reality helps calm their nerves and improve the patient experience.
6. mHealth Apps
Mobile applications provide patients with control over the options available to him. These apps can assist in making an appointment, uploading medical history or even checking into a hospital for medical care. Mobile apps ease the load of the non-medical staff at hospitals and reduce the patient’s waiting period thus reducing operational costs considerably.
An online platform Encourage was developed to empower patients with information related to their healthcare and educational material about procedures and illnesses. The tool further helps doctors keep track of a patient’s progress, assign tasks, reminders and care plans for the patient. It also gives patients the ability to include people as their caretaker and manage their engagement with them and the doctors through a rewards system and more.
Related Reading: Read more about the case of simplifies patient care here.
What is the Future of PGHD?
With no signs of slowing down, it is estimated that in a couple of years AI is expected to grow annually at the rate of 48%. Other technologies along with mobile apps help in communicating with patients even after they leave the healthcare facility. With such promising technologies, patient-generated health data will surely benefit patients and caregivers and aid healthcare organizations to provide better care.
Yes, the partnership of healthcare organizations with technology is a match that brings out the best in each other. We can confidently look forward to this relationship growing and maturing in the days to come. To empower your healthcare organization with the budding technologies, get in touch with our experts now!
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
How to improve real estate sales with chatbots?
One of the biggest technology disruptions that have occurred in the world of marketing over the past couple of years has been Artificial Intelligence powered chatbots. The growth of machine learning and natural language processing capabilities have made chatbots more human than ever in terms of engaging with prospects. Studies have shown that by 2020 it is expected that 85% of all engagements that a customer has with a business will be through self-service options and chatbots and not a human point of contact. Just like with other business sectors, the real estate industry also needs to make note of this rising trend of AI conversational tools that are gaining affection from consumers worldwide. If you are part of the real estate sector and wondering how to drive more results with chatbots, then here are 5 ways to try:
Automate your first point of contact
If you deal with rentals or sale of property or homes as a business, then a conversational chatbot can be deployed on your website to serve as the first point of contact for a prospective buyer who visits your site. No longer do you have to hire dedicated customer service representatives exclusively in shifts if you want to have a 24X7 live chat support on your website to help buyers make choices faster.
A conversational chatbot can easily handle multiple visitors simultaneously without forcing them to wait for a live agent to connect and handle their queries. This automation combined with instantaneous response will definitely reflect as lowered operational costs for your business as well as increased customer satisfaction via quicker responses.
Effective lead qualifying
Reports show that 63% of customers are likely to return to a website if it offers live chat support. So you definitely need to have an extensive chat support system for your internet presence. But then comes the downside. Not all who chat with your agents will be a prospective lead. Some people may just chat for information that necessarily doesn’t transform into a business deal. So is it worth investing a human agent to qualify all incoming chat queries?
It would be a big no! Your human agents need to be assigned to handle only important leads that have the potential to transform into a direct deal. The lead qualifying part can now be handled by conversational bots that can use a series of questions autonomously to gauge the interest level of a visitor. It can then qualify the visitor as a valuable lead or not by comparing their behavior to previous cases of lead interactions and thereby assign scores of lead maturity.
Leads with a higher score will have more chances of being converted into a deal and can be passed on to human agents for further communication. Those with lower scores can be continuously nurtured by the chatbot themselves by supplying them with more information about the property they required assistance. Thus, your human agents can be utilized for better high-level sales while an AI-powered chatbot can handle repetitive questionnaire and lead qualifying rounds efficiently.
AI-powered Chatbots can be trained on a number of behavioral patterns of typical real estate customers to facilitate a more personalized and insightful conversation. By carefully asking a sequence of questions, the chatbot can drive potential buyers into booking a deal for a property without even the need for a human agent to take over.
The bot can study individual preferences of all visitors and offer personalized recommendations that are bound to strike a chord with their interests. For example, if a prospective buyer with an interest in shopping wants to rent or buy a house through a real estate portal, then a conversational bot can seek their preferences through smart questions and recommend them properties near to shopping destinations. Same goes for recommendations based on interests such as budget, number of rooms or bathrooms required, car parking facilities and much more.
It may take more than a couple of engaging conversations to lure a prospective buyer into making a decision to invest in a property. Conversational chatbots can interview visitors, collect their contact information and offer them time to think about the prospects that were showcased. They could then automatically follow up with them by triggering emails requesting an appointment to view the property directly or take it to the next step which may involve a direct discussion with a human agent.
Either way, the chatbot can facilitate follow-ups much more efficiently and in a timely manner based on visitor preferences. It can also send email alerts to agents regarding upcoming visits by a prospective customer with their detailed history of conversation to help them convert the lead into a deal faster and with more profit. If the entire process of chat support and subsequent email follow-up was to be handled by human agents, then their chances of missing out on follow-up schedules and also higher chances of errors in data collection and information exchange.
Faster information discovery
If a human agent is deployed for chat support on your website, then he or she may need time to scour through the vast scathe of data available in your property database to recommend suggestions to a prospective customer. Add to this, the complexity of applying filters manually to find properties that suit the buyer’s interest will only make information discovery a lagging and slow experience for a visitor.
This time loss is a blow to engagement levels for a potential buyer and they could leave your website if there is too much delay in getting the information, they sought from a human chat support agent. A chatbot, on the other hand, is connected to your property database and can instantly extract all valid property features based on customer interests. They can provide information on the chat window or upon instructions from visitors, can email it to them and can even book an appointment with a local agent to arrange a visit to selected properties physically for the buyer.
Digital innovations such as an AI-powered chatbot has the potential to transform engagements between buyers and real estate businesses considerably. As you have witnessed the top 5 scenarios where chatbots can turn out to be more efficient representatives of your property business, the remaining aspect is selecting the most efficient technology to build your own custom real estate chatbot.
Machine learning and artificial intelligence are highly complex in terms of technology development and would require advanced levels of technical advisory and development support to enable the best for your business. This is where a technology partner like Fingent can be your saving grace.
Our consultants can help you build the most intelligent AI-powered chatbot to help improve your online property sales efficiencies. Get in touch with us to explore world-class digital solutions that the best players in the real estate sector are continuously investing to grow their business.
Stay up to date
on whats new
Get a free
Talk to our experts today
about your business
6 Chatbot Security Practices You Need To Implement
According to a survey by Oracle, regarding the benefits of using chatbots for their consumer-facing products, which included responses from 800 decision-makers, including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK, it was found out that “80 percent of companies wanted to have some type of chatbots implemented by 2020!
It is also forecasted that 90% of bank-related interactions will be automated by 2022. Moreover, 80% of businesses will have chatbot automation implemented by 2020. Also, 47% of consumers would buy items from a chatbot when 28% of top-performing companies are already using AI for marketing! With chatbots turning into the trend, it is vital to implement chatbot security measures.
A Back Door Open To Hackers
Chatbots are nowadays mostly used in industries such as retail, banking, financial services, and travel that handles very crucial data such as credit/debit cards, SSN, bank accounts, and other Sensitive PII (Personally identifiable information).
The aggregation of such data is crucial for the chatbot to perform. Thus, it is required that chatbots are not vulnerable to be exploited by any hackers.
A recently released report from MIT Technology Review and Genesys showed that 90% of companies are already using AI strategies to increase revenue. The research also found that on average, between 25% and 50% of all customer queries can be solved through automated techniques. This has made it easier than before to handle complex tasks.
Related Reading: Read on to know more about the top AI trends of 2019.
The HTTPS Protocol For Security Of Chatbots
HTTPS protocol is the basic and default setting required for a good security system. The data that is being transferred over the HTTP via encrypted connections are secured by Transport Layer Security (TLS) or Secure Sockets Layer (SSL).
Related Reading: Check out how Fingent helped create an enhanced and engaging learning experience through chatbots.
Types of Security Issues
Security Issues fall into two main categories:
Threats are usually defined as different methods by which a system can be negotiated or compromised. Threats can include incidents such as Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privileges, and many other threats.
Vulnerabilities are defined as methods that a system is compromised and cannot be identified and solved correctly and on time. A system becomes open to attack when it has poor coding, lax security, or because of human errors. The most effective way to solve the issues of a possible vulnerability is to implement SDL (Security Development Lifecycle) activities into the development and deployment methods.
As per the study by the Ponemon Institute, In 2017, the average total cost of a successful cyber-attack was over $5 million, or $301 per employee!
Here are 6 chatbot security issues that you need to consider right away:
Data while transit can also be misused. There exist different protocols that provide encryption, while addressing these problems of misuse and tampering.
According to article 32 (a) of the General Data Protection Regulation (GDPR), “it is specifically required that companies take measures to de-identify and encrypt personal data. So, chatbots have access only to encrypted channels and communicate through those”.
For instance, Facebook Messenger introduced the new feature called “Secret Conversations” that enabled end-to-end encryption based on Signal Protocol.
2. Authentication and Authorization
Authentication is performed when the user needs to verify their identity. This is often used for bank chatbots.
Generated authentication tokens verify data that are requested through a chatbot. On completing the verification of the user’s identity, the Application produces a secure authentication token, along with the request.
Another step of security measures is an authentication timeout. The token generated is used for only a certain amount of time, after which the application has to process a new one.
Two-way verification is another process where the user is asked to authorize their email address or to receive a code via SMS. This is a crucial process which is necessary to verify that the user of that account is the real user that is using the chatbot.
3. Self-destructing Messages
When Sensitive PII (Personally identifiable information) is being transferred, the message with this data is deleted after a definite period of time.
Personally identifiable information (PII) is any data which can be used to identify a particular person. It includes records such as a person’s medical, educational, financial and employment information. Examples of data elements that can identify and locate an individual include their name, fingerprints or other biometric (including genetic) data, email address, telephone number or even their social security number.
This kind of security measure is crucial when working with banking and other financial chatbots.
4. Personal Scan
When working with personal data, it is necessary to take security precautions and measures.
Apple was the first company that added finger authentication to their iPhones. This technology is now being used widely to verify an individual’s identity. This is performed when initiating a transaction or when you want to access your bank account using a chatbot that a personal scan is required.
5. Data Storage
Chatbots are effective because they retrieve and store information from users.
For instance, if you have a chatbot that performs online payments, this can mean that your clients are providing their financial information to a chatbot.
The best solution in this situation is to store such information in a secure state for a required amount of time and to discard these data later on.
Some other concerns are the following:
- Biometric authentication: Iris scans and fingerprint scans are popular and robust.
- User ID: User IDs involve processing secure login credentials.
- Authentication Timeouts: A ‘ticking clock’ for correct authentication input. This prevents giving hackers an opportunity to guess more passwords.
- Other strategies could include 2FA, behavior analytics, and kudos to the ever-evolving AI trends.
6. Tackling Human Causes
The one and only other factor or cause that cannot be altered is the human factor. With commercial applications in specific, that chatbot security and end-user technique have to be resolved. This will ensure the chatbots from being vulnerable to threats.
Related Reading: Find how artificial intelligence can drive business value.
To know more about secure bot building, get in touch with our IT consultants today!