Category: Chat

WhatsApp has come far from its usual avatar of being an instant messenger. From mom-and-pop businesses to large-scale enterprises, almost everyone has started integrating WhatsApp chatbots into their customer service channels. However, WhatsApp does offer capabilities that can serve in lead generation as well.

Why use WhatsApp chatbots for lead-gen?

There are four traits that make WhatsApp ideal for lead generation:

1. Familiarity — The world knows WhatsApp is the primary tool for real-time messaging. With five billion+ downloads, it is one of the most downloaded and widely used apps in the world. WhatsApp needs no introduction and hence is an excellent platform for lead generation.
2. Friendliness — You don’t need special training or introduction to become familiar with the WhatsApp chat interface. The intent of the design is to make real-time instant messaging easier and simpler for all kinds of users. This also leads to higher adoption.
3. Mass accessibility — WhatsApp enjoys 2 Billion Monthly Active Users and is still growing at an active rate. Its mass accessibility ensures that businesses have enough ground to source leads from.
4. Built for business — WhatsApp for business makes it easy to attract, interact with, and qualify leads. It offers a built-in catalog to showcase products, canned responses to quickly respond to queries, and also sort chats using labels.

61% of marketers rank lead generation as their number one challenge (HubSpot). To add to their woes, only a fraction of leads generated convert into sales. As a result, there is always ongoing pressure to maximize lead generation.

Compared to traditional lead-generation channels and tactics, WhatsApp offers a novel way to reach, attract, and interact with leads. Here are some such features:

1. Real-time lead qualification

Almost every business faces the challenge of junk leads. Junk leads are those leads that do not fit into the Ideal Customer Profile (ICP) that the business has created for itself. Or, the leads carry incomplete information, which makes it difficult or even impossible for the business development executive to pursue. Also, junk leads come in large volumes, which further makes it difficult to find warm leads that have a high potential of becoming a customer.

With WhatsApp chatbots, you can mitigate that challenge to a large extent. It aids in real-time lead qualification by segmenting customers based on their queries.

For example, customers who ask for preliminary information about the business or its products are early-stage prospects. Customers requesting pricing information are warm leads with a high potential for conversion.

Further, WhatsApp for business allows labeling conversations based on labels. You can tag new customers/leads using a label to ensure that they are not lost amidst other conversations.

2. Marketing using status/stories updates

WhatsApp rolled out its ‘status’ feature in December 2020. It was an attempt to mimic the Snapchat stories that had by then become a social media rage. However, statuses in WhatsApp have a larger use case than social sharing.

In business, you can actively use it for marketing purposes. Imagine sharing your product images, explainer videos, or even customer testimonials as WhatsApp stories or status updates? They enjoy a high level of engagement and can also invoke immediate responses from customers. Based on their queries, it is easy to segment them as leads or prospects who have chances of conversion in the near future.

3. Multilingual customer service

Does the language you use for customer communication have a say in your lead gen or even sales? Turns out, yes. Stanford scholars found in their research that using specific words in product descriptions can predict sales. Also, if the product descriptions or business communication is in a language that is native to the prospect, it invokes a culture or authority that helps products sell.

In other words, multi-lingual selling is necessary for your business if it is operating at a global level. However, employing sales reps who are polyglots could be a strain on the budget. With WhatsApp chatbots, such a problem does not arise. You can easily integrate language apps into a WhatsApp chatbot and enable it to converse with the customer in the language of their choice.

4. Sets the stage for consultative selling

Under the hood, selling is a personal relationship-based transaction. If a sales development executive is able to nurture a positive relationship with the customer, the chances of seeing the transaction to completion are higher. Consultative selling is the term used to describe the sales approach where relationships and open dialogue with the customer to understand their problems and wants are prioritized.

Until recently, phone calls, emails, and surveys formed the foundation of consultative selling. The obvious downsides of these channels were that they were slow and did not always collect accurate information. WhatsApp chatbots ensure that there is real-time communication, a natural conversation where the sales rep can easily gather from the prospect what they are expecting from the business. Setting the right stage for consultative selling WhatsApp aids in lead-gen and ultimately in boosting sales.

5. Share engaging content

Millennial customers want to be engaged, and content provides the right context for that. Content that is engaging, educational, and informative can enable a customer to make an informed decision. As a result, they also feel positive about the business, thus leading to a long-term relationship.

How can WhatsApp chatbots aid in engagement? WhatsApp allows sharing of multimedia content instantly without any hassle. In fact, WhatsApp for business allows businesses to upload product catalogs, videos, and such collateral that can engage the customer and also enable them to make an informed decision. It also forms the basis for active engagement, which leads to more product awareness and brand recall.

How to use WhatsApp chatbots for lead generation

Although WhatsApp chatbots set the perfect stage for lead-gen, don’t expect leads to flow in without some effort from your side. Here are some tactics that will help you turn WhatsApp chatbot into a lead magnet.

1. Use WhatsApp chatbot links in email signatures and social media

If your sales team is sending cold emails to prospects, their email signatures offer splendid real estate to pace WhatsApp chatbots. Email signatures are not just the tail end of your emails. They are used as email footers that can provide more information about your business, its offerings, and even upcoming events or ongoing offers.

Email signatures are traditionally used to insert the contact details of the sales rep and the business at large. They are also the perfect location to place your WhatsApp chatbot link and label it as an all-in-one communication channel to find any information about the business. Customers will find the WhatsApp link easy to use hence leading to lead generation.

2. Use chatbot links in paid ads

Google text search ads and display ads. Facebook ads, Twitter ads, Instagram ads. There are countless paid channels through which you can run marketing campaigns. The common practice is to ask users to share their contact information through a pop-up form or lead sign-up page.

Naturally, this is a long process since you need to share the lead information with the sales rep. They have to qualify it and then initiate a conversation. However, if a WhatsApp chatbot link is provided instead of a form, you can eliminate unnecessary steps in the conversion journey. You can instantly initiate the conversation with the prospect and qualify them as a warm or cold lead.

3. Share WhatsApp QR codes

Dropshipping and doorstep delivery has taken away the personal interaction with customers. Or have they? It is still possible to create a WhatsApp channel through which customers can engage with your business. Your existing customers or new prospects can always reach out to find information about your other offerings or make repeat purchases.

Compared to other channels of customer communication like email, social media, telephone, etc., which take longer to complete, WhatsApp can provide instant communication, thereby helping with the lead-gen cause. WhatsApp links are easy to share through email or social media handles. However, when you want to make it easily accessible offline, too, it is wise to use QR codes. You can place WhatsApp QR codes on your store, product packaging, or even on product labels for lead-gen.

Getting leads is easier with WhatsApp chatbots

With 2 billion users, it is definitely a popular channel that prospects will not think twice about using WhatsApp for communicating with a business. If your business is struggling with lead generation or finding it difficult to segment leads based on conversations, WhatsApp chatbots offer a feasible solution. They are, perhaps, the most intuitive and user-friendly conversational interface you can find for lead generation.

The fact that WhatsApp is popular globally and does not have any friction in adoption makes it perfect for lead generation.

So, how is your business going to use WhatsApp for lead generation?

Originally Published at https://www.kommunicate.io/ on 14th July 2022

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WhatsApp Lead Generation: Use Chatbots to Generate Lead was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Virtual assistants or chatbots hosted on channels such as Apple Messages for Business (AMB), Google Business Messages (GBM) and WhatsApp for Business (WA) meet users in apps they have already adopted. Adding virtual assistants in these channels leverages interfaces that users are familiar with, but that can also lead to user assumptions on how chatbots can function in each of these channels.

For example, when using one of these channels for a human-to-human conversation, if a user sends an attachment such as a file, image, video or voice recording, their messaging partner will be able to view, play or otherwise open the attachment.

To further complicate user expectations, Apple Messages for Business, Google Business Messages and WhatsApp chatbots are all capable of sending images to a user as part of a bot flow.

From the user perspective, it’s easy to follow the thought process: if a virtual assistant can send an image, I should be able to send an image back.

However, depending on the conversational AI platform and its complexity, your virtual assistant may be unable to recognize attachments. This often happens when the bot is in its infancy and has limited initial use cases and feature sets. This can easily create user frustration, with a high frequency of utterances like:

The Challenge

Often, the user is reaching out to customer support due to a negative experience, and emotions like frustration or stress may influence user behavior. When a user has an issue, they may expect that providing an attachment using the chat is the fastest or most direct way to get connected to customer service. Users also often send attachments so they don’t have to type detailed messages or select multiple menu options. Sharing a photo/video is often faster than typing out a message. Additionally, users may not know the correct words when referring to specialized products or parts, and rely on the visual component. The user may also be anticipating the need to provide “proof”, which is common in replacement or refund customer support cases.

Common industries that experience this behavior include retail and food service. Other cross-industry use cases include sending attachments related to billing or product/technical support.

Chatbot files recognition option: Did you see what I sent?

As a Conversational AI company, Master of Code has conducted our own analysis. We are seeing a growing amount of users expecting the chatbot to be able to parse an intent from the image or attachment they have sent. These users expect the virtual assistant to be able to take action based on the attachment provided, such as detecting a damaged item and triggering a refund flow.

Additionally, even if your chatbot clearly introduces itself as a virtual assistant, sometimes the user gets confused and thinks they are already speaking to an agent: “Did you see what I sent?”

If a user sends an attachment and receives a normal fallback message, they often get frustrated and abandon the conversation. This results in negative containment and negative survey scores for CSAT and NPS.

Your general fallback is therefore not a ‘one prompt fits all use cases’ message. Instead, Master of Code recommends adding in specialized fallback handling.

Our Solution: virtual assistant parse attachments option or conversation flow improvements

As a first step, we recommend investigating if your bot platform has the ability to parse attachments and add to your roadmap.

How to Choose Conversational AI Platform. Get the checklist

However, even if your virtual assistant is currently unable to parse attachments, that doesn’t mean there’s nothing you can do to improve the user experience. The bot can still recognize a user is attempting to send attachments. By capturing when and where users are attempting to send images, you will also be able to determine opportunities for improvement, including creating or optimizing self-service flows.

To alleviate user frustration and improve user experience, your assistant should have a customized fallback response, so the user feels heard. Users are more forgiving if they learn the bot cannot process the attachment, and that’s why they’re hitting the fallback.

If your chatbot cannot parse any type of attachment, work with your technical team to update the bot to detect when users are sending attachments. The bot should provide a contextual response of what it can and can’t do:

This assures the user that once they are escalated, the agent will be able to view the attachment.

Further mitigate user friction by providing options. Allow the user to decide if they want to escalate immediately, or return to the flow and continue to follow the prompts: “Would you like to chat with an agent, or return to the previous step?”

An experienced conversation designer can help customize these fallback messages to best suit your persona and tone.

Additional conversation design considerations

Work with both your technical and operations teams to verify if agents can view attachments from their agent dashboards, and if the ability is the same across all channels the bot is available on.

If agents cannot view attachments, then we recommend modifying the message to: Thanks for sending an image or attachment, however we are unable to view it. In a few words, how can I assist you today? The bot can then compare the user’s input to your NLU model.

Also read: Three Secrets Behind Impactful Troubleshooting Chatbot Conversation Flows

It’s also important to review at what points within the conversation flow that users are sending attachments. Is it their first message? If so, we recommend presenting the customized fallback and then presenting the main menu. Once the user understands that they have to use menus, they are less likely to loop or abandon the conversation.

Download a Conversational Flow Chart Diagram with the scenario of building dialogues for your chatbot

If your bot can parse some types of attachments but not others, then you should make that explicit: Thanks for sending a video. I can’t view videos, but I can view images. If you’d like to resend as an image, we can try again. If not, we can continue and I’ll pass the video along to the agent.

One of the keys of the Conversation Design process is to be transparent about the virtual assistant’s limitations. Instead of apologizing with a generic “I’m sorry I don’t understand” style prompt, the bot continues to move the conversation forward.

Results

Since Master of Code has started applying this strategy to relevant projects, we’ve seen significant increases in CSAT and containment.

Recognizing that attachments are being sent and creating a transparent fallback about your virtual assistant’s capabilities is a quick win. It’s a small change but has a big impact on the user’s overall experience.

You will be able to measure the chatbot improvement via:

• Higher CSAT / NPS scores
• Increase in containment
• Decrease in users hitting the normal fallback
• Decrease in looping

Remember, it’s always more important to provide a prescription to the user, even if it means sharing technical limitations of a conversational solution.

Need assistance with designing Conversational AI strategy for your customers? We can help!

GET IN TOUCH WITH US

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Attachment Detection Strategies for Virtual Assistants was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How can OCR help with RPA and Document Processing? — Techashton

OCR or Optical Character Recognition is a fun way to read and understand documents. But why does it even make sense? Let’s find out. But before we proceed, we need to wrap our head around a less common machine learning term: RPA (Robotic Process Automation).

For the unversed, RPA is Robotic Process Automation, which is instrumental in helping businesses eliminate repetitive tasks. Document processing happens to be one such task- where the focus is often on invoices and handwritten tasks. Simply put, OCR can help with this aspect of document processing- the one involving tons of error-prone identification.

So let’s dive right in and understand the process better:

What is OCR in RPA?

Trust us; we wouldn’t overcomplicate things. OCR or Optical Character Recognition is a machine learning tool capable of capturing printed text and handwritten notes. Once captured, OCR is also responsive for converting these unstructured data chunks into decipherable data that machines can read.

How OCR achieves all of that is a different question altogether? Here is a bird’s eye view of the same:

• Working alongside different light patterns
• Pre-processing images, if any, by de-skewing letters and even smoothing.
• Detect discernable lines, which are characters and words
• Producing machine-readable elements

Every OCR trait can be put to use by RPA bots, depending on the short- and long-term requirements.

What are the use cases of OCR in RPA?

Now that we have touched upon a more oversimplified version of OCR, let us take a closer look at the RPA relevant use-cases:

Human Resources, across organizations, can make good use of Optical Character Recognition for resume screening (for specific keywords that match job description), document management, expense management and reimbursement, and more.

As per estimates, sifting through massive new data chunks (close to 2.3 exabytes each year) is one of the top physician burnout reasons. And that is where OCR can help by assisting specific RPA initiatives, including patient registration, trial matching, and even creating EHR snippets for identifying patient progress.

Did you know that nearly 42% of financial processes can be automated? Paired with RPA initiatives, OCR can help achieve the same, especially when it comes to tracking invoices and relevant data, receipts, insurance documents, and even credit scoring.

Benefits of Intelligent OCR and RPA Integration

In a nutshell, every bit of data captured by OCR-driven RPA can be used as standalone assets or as usable machine learning information in the form of an OCR dataset or categorized training data.

Didn’t we just talk about the use-cases? We did, but there is still some scope to discuss the benefits. In brief, OCR integrated with RPA lets you digitize and decrypt any document. Here are the more targeted benefits of the approach:

An OCR-backed RPA application can make extracting data more accurate than ever. Robots meant to scan through and scrap data work more accurately with OCR functionality embedded within.

Well, this benefit requires no validation. Anything automated is always faster.

OCR integration makes RPA resources read emails, images, and even PDFs better.

And most importantly, OCR implementation helps you save a lot of effort and also money in the process.

Wrap-Up

Author Bio

The world is changing. And so are the processes defining it. As we move further and further away from grunt processes, document processing often needs to be the first roadblock to cross- due to the sheer size and volume of organizational data. At this point, shifting focus to RPA applications and tools powered by nifty artificial intelligence technologies like OCR is advisable. From robots scanning text to setting up a visual automation process, optical character recognition can be the game changer for several global verticals, as mentioned descriptively in the sections mentioned above.

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip.com, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

Originally published at https://techashton.com on July 28, 2022.

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How can OCR help with RPA and Document Processing? — Techashton was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Macroeconomic issues such as supply chain disruption, input cost inflation, and employee shortages continue to dominate headlines. Amidst the uncertainty, organizations are turning to technology to solve their “heart of the business” challenges. Here at Master of Code, we continue to see a significant uptick in technology investment in automation and Conversational AI to streamline internal workflows and scale customer communications.

Conversational AI scope withing the company activities

A major theme we continue to see evolve is cross-team technology adoption. Conversational AI, with its battle-tested business case in a customer support setting, is spreading throughout the organization. For instance, organizations want an integrated approach to engaging with a customer across the customer journey through Marketing, Sales, Customer Success, and Customer Support teams. In the past, these teams typically made technology investments in isolation. As these silos break down, technology investments need to consider the unique needs of each team and how they are interrelated in an increasingly matrixed organization.

In uncertain times, planning often takes a backseat to urgency and action. Over the past year, we’ve helped our clients invest in Conversational AI innovation and increased 7.67x weekly bookings or conversion rate 3x higher since chatbot was launched, while avoiding several common mistakes during the planning and scoping phase.

Top mistakes during Conversational AI implementation planning

Validate Conversational AI Use Case First

In our experience, teams that experiment with low-risk projects focused on use case validation typically introduce more innovation over the long term. We also find that many organizations underutilize their existing technology investments. We encourage teams to use technology that already contains Conversational AI capabilities built into the platform. Typically, it’s much quicker to develop and validate a proof of concept than to evaluate and implement new technology. For instance, using common enterprise software, we can build a virtual agent in less than one sprint cycle using tools including but not limited to:

• Digital Transformation in the Cloud (Google Cloud, Microsoft Azure, AWS)
• Workflow Automation (ServiceNow, UiPath)
• Call Center Automation Software (Genesys)
• CRM (Salesforce Einstein)
• Customer & Agent Support (LivePerson, Zendesk, Hubspot)

Measure Twice Cut Once

Urgency and excitement can lead teams to make sub-optimal technology decisions. Within the Conversational AI ecosystem, there are a significant number of platforms, software, and other vendors offering cutting-edge AI solutions. Changing technology platforms is a costly endeavor including the porting of Natural Language Understanding (NLU) models, existing chatbots and virtual agents, and building new integrations. A transition can set back a team 6–12 months of development time and spend. We work with clients to implement a Technology Evaluation Framework to ensure the best technology decisions are made. Choose the platform that addresses your unique use case, industry, organizational size, architecture, and administrative needs.

In the first quarter of 2022, Master of Code has helped a record number of clients navigate Conversational AI and Conversation Design solutions. What is your next opportunity to innovate?

Exploring technology platforms? Check out the Checklist available below.

How to Choose Conversational AI Platform. Get the checklist

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Removing Obstacles for Companies to Innovate with Conversational AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the healthcare industry, every fraction of time weighs significance. People do not want to engage in waiting lines or sit by the phone looking out for a response from medical professionals. In this post-pandemic world, healthcare providers have to be more keen-eyed with their approach to customer service. Given the sense of fear and watchfulness the virus has evoked among people, it is vital for the healthcare industry to stay ahead of the game. This takes them to the implementation of healthcare chatbots.

The global chatbot market was valued at US $526 million in 2021. The research estimates that it will be US $3,619 million by 2030, at a CAGR of 23.9% during the forecast period. This technology trend has more rewards for healthcare service providers than you know. If you’re planning to implement a chatbot to boost your operations, there’s a lot you’d expect it to offer. Since that totally depends on how you design it, we’ve brought you the top 10 benefits of chatbots in the US healthcare industry that indicate how healthcare chatbots should work.

10 Benefits of Healthcare Chatbots You Cannot Miss

The medical industry is as vast as it gets. From guidance on prescriptions to health emergencies, people reach out to healthcare providers for several reasons. While a call or email may be a straightforward mode for interaction, it is not necessarily effective.

Chatbots in the medical sector are booming owing to many such reasons. The healthcare chatbots market stood at around US $184.60 Million in 2021 and is forecast to reach US $431.47 Million by 2028. Let’s take a look at the benefits of chatbots in the medical industry that are adding to their whopping success.

Enables Prompt Response

People expect medical professionals to provide a quick response to their queries. Delays in responsiveness can lead them to lose trust in the brand they’re seeking assistance from.

Chatbots give a hand in taking care of customers’ queries and issues anytime. They make instant responses and resolve the case on a chat. It eliminates the need for writing an email or making customers wait.

The medical concern is not something that can wait for the resolution to come their way after hours. It is rather a time-sensitive stance. Medical brands employ chatbots to reply to the standard queries, which wrap up various conversations without human intervention and save time.

Boosts Customer Service

One of the most effective ways to engage and retain customers is to be there for them. When the staff is communicating with customers via chat or a phone call, they can only go up to a certain number in a day.

In the case of chatbots, things are the opposite. Chatbots are not restricted to a specific number of customers to handle in a day or at the same time. They are devised to address unlimited queries simultaneously.

In manual customer service, the requirements for more live agents increase with the spike in the number of customers. The implementation of a chatbot enables US healthcare providers to maintain their customer service without losing consistency.

Medical Assistance on Time

Medical emergencies happen all the time. Whether someone wants to know how to deal with a situation or how to proceed with a prescription, people immediately call the healthcare providers for assistance.

However, a number of people seeking help can exhaust the calling service. So, people can now opt to chat with a healthcare chatbot and get medical advice on what and how to move ahead with a circumstance.

Options like a menu of general queries, links to relevant solutions, etc., make chatbots a primary way to address an inquiry. Healthcare chatbots, if devised well, can work wonders. The best part about them is that they are available 24/7 to assist people.

Assistance with Medications and Reminders

Doctors typically guide their patients about the medications they’ve been prescribed and how they must consume them. They may prescribe different medications to help patients treat various health conditions.

The challenge is making sure that patients are taking the prescription seriously and following the course as recommended. According to a study, about half of patients don’t follow their medication course routinely or simply forget to do that.

Healthcare chatbots act as an amazing resort to make sure this gap is bridged. First of all, they help patients with medical advice. They also send daily reminders to them. Many chatbots in the US healthcare industry work as personal health trackers and medication reminders for patients that use them.

Scheduling

Scheduling is one of the topmost benefits of healthcare chatbots. Making a phone call may be a common way to schedule an appointment but it can be time-consuming for both parties. In this process, a patient calls their local health care provider and waits while the agent checks what slots are available. It can result in a good waiting time and higher costs.

Today, many medical professionals in the US are using healthcare chatbots that provide patients with an option to book appointments with the right doctor. Patients can easily use the scheduling bot from a website. They can also use it on their mobile device no matter where they are.

Some healthcare chatbots are even designed to send reminders and let people know when they have an appointment coming up. Moreover, these reminders can also communicate the specific actions they must take.

Reduced Costs

Healthcare chatbots are capable of managing a myriad of healthcare inquiries, including medication assistance and appointments. So, healthcare providers can be assured of a timely resolution to their patients’ queries. Moreover, people can access options like reminders, scheduling, and informational content.

The extensive range of concerns these services cover boils down to reduced costs. Since healthcare chatbots eliminate a pretty good slice of manual effort, it boils down to reduced costs. It is one of the well-enjoyed advantages of chatbots in the US healthcare industry or any industry for that matter.

Chatbots can help healthcare businesses save a good deal of money and contribute to other crisis investments the entrepreneurs might want to make. By implementing a chatbot, a healthcare service provider can eradicate the costs spent on hiring additional customer support agents and providing training.

Easy Time Receiving Feedback

Every task a healthcare provider performs, and every goal they set is an effort to provide the best services to their patients. This is one of the reasons why medical assistants are not shying away from implementing a chatbot to ease their job. One of the greatest reasons they are using healthcare chatbots is to have an easy collection of feedback. Healthcare providers can leverage the feedback they receive to make smarter decisions and improve their practices.

Healthcare providers send out customer surveys to collect feedback. However, only a few of them return with a response. Customers do not want to invest time in filling out a feedback form, or they are simply not interested. Businesses have started resorting to chatbots to measure customer satisfaction. Patients can chat with the bot, reply to the instant questions that pop up and rate their overall experience.

This method of collecting feedback works more efficiently, given that chatbots make communication faster and quite straightforward. Collecting feedback is a great way to boost relationships with customers as it shows that you value your patients’ opinions. With an automated pinch and instant response, making it possible just becomes easier.

Seamless Invoicing

Invoicing and tracking every payment can be a costly deal. Not to mention, the manual process demands more time and effort. Healthcare chatbots are making the process of medical billing easier than ever.

The chatbot enables healthcare providers to receive the amount due for the treatment they offer to their patients. The automation capabilities of a chatbot help healthcare providers create invoices and receive compensation for the service. Ultimately, it minimizes the expenses incurred by administration practices.

Increased Employee Productivity

Yes, chatbots do act as a productivity booster in the organization. Generally, a bot is employed to host customer queries and resolve them effectively. However, healthcare companies can also leverage them to support collaboration among employees.

There are times when your employees want to confirm something or learn how a specific service works. When such cases occur, they can navigate to the website of the company and ask the chatbot for assistance. If you choose to build a custom healthcare chatbot for your company, you can devise it to link to various forms of content, including blogs and training videos.

More Value to Business Growth

Considering the top 9 benefits of healthcare chatbots we read, it is easy to surmise the role a chatbot plays in the growth of a healthcare company. Keeping in touch with patients 24/7 is beneficial in the long run. Moreover, a chatbot also improves brand visibility.

There are a multitude of factors that affect your website’s presence on online platforms. The time users spend on your website is one of the most powerful ones out there. The time your patients spend interacting with your chatbot adds value to your page. So, utilizing chatbots is an incredible way to boost customer engagement on the website.

Final Thoughts

Chatbots are growing exponentially in every industry. Healthcare companies can introduce them to their pages and make sure their customers are getting the best service. From on-time medical help to a quick reminder to take meds, a bot can be your patients’ support. It is imperative to do your research and define your goals before you build a healthcare chatbot. Being mindful with the planning and setting expectations will pose a beneficial factor for implementing this software.

Originally Published at https://www.kommunicate.io/on 29th July 2022

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10 Benefits that Chatbots Are Bringing To The US Healthcare Industry was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Reading, comprehending, and summarizing long documents won’t be that difficult from now because linguistic startup AI21 has unveiled a tool that will automatically summarize, comprehend and read any long document in just a few seconds. This new Wordtune Read will make managing important documents easier for any kind of commercial and enterprise use.

Word and Wordtune

The company is considering this Wordtune Read as an important tool to save a lot of time for enterprises as it automatically detects and analyzes texts from a link or a given pdf and then summarizes, highlights, and identifies important phrases. Also, there’s a new feature called Spotlight that will allow Wordtune Read to reexamine and summarize with a different emphasis.

This Wordtune read has been created by AI21 intending to make it a part of its larger developing language models.

“ Our mission at AI21 Labs is to fundamentally reimagine the way people write and read, so we thought it was high time to share our vision for the reading part of that equation. Underpinned by our sophisticated language models, Wordtune Read was specifically designed to help professionals across a range of industries and academia navigate the issue of information overload,” AI21 stated in a Blog Post.

Don’t forget to give us your 👏 !

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Startup company AI21 unveils Wordtune Read as a solution for “Too long, didn’t read” was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In every business, it is important to understand your customers but no more so than in the world of finance — where the stakes are comparatively much higher. For a bank, each customer presents a risk, especially when it comes to loaning money. Bad loans are full of risk, good loans are not. Ultimately, deciding whether that risk is worth taking is a decision of paramount importance and can affect the bank balances of people the world over.

Investment in risky loans is more likely to end up with customers defaulting on their payments — the consequences of this have been made painfully clear in recent history.

With this kind of responsibility at stake, banks tend to analyse their customer’s finances with the utmost scrutiny. Loans are granted — only — when an applicant meets certain criteria. But for people outside of the financial sector, these characteristics often seem mysterious, tangled and daunting.

We were interested in finding the characteristics of loans that were most important to banks and lenders.

Aside from a person’s credit history, there is a multitude of factors that help banks make decisions on the suitability of a loan. Factors including whether or not someone is a homeowner, a person’s age, their direct debit history, and how much they earn all contribute to a risk profile.

But disclosure of these characteristics, how applicants are scored and which features make it more or less likely for loan applications to be approved, are not as transparent as many of us would like them to be.

Due to the privacy with which datasets of loan applications are protected, the challenge of finding a dataset to explore proved to be more difficult than usual. We had to change our angle … and our time period. Putting on our beaded necklaces and frayed jeans, the team began to look into a dataset of loan applications … from 1970.

Putting on our beaded necklaces and frayed jeans, the team began to look into a dataset of loan applications … from 1970.

Our team decided to build a project using the features of an application to predict whether an applicant would have a good or bad risk rating. This meant reverse-engineering the features of a loan application in order to understand how they related to the loan’s risk rating. With Graphext, you can do this using the visual editor in under 10 minutes.

Our model used Risk as it’s target variable and every other characteristic of a loan application as factors, clustering loan applications based on the similarity of these factors. Our intention was to uncover the constitution of high-risk or low-risk applicants.

A Successful Model

A model’s error tells us whether the model was able to recognise a relationship between the factors and the target variables.

After executing the model, the team started to inspect the Graph, the network visualisation wherein loan applications were grouped together into clusters. A models accuracy — or error score — is important. This helps us recognise whether the model was able to recognise a relationship between the factors and the target variables.

The model we built had a low error score — 57 incorrect risk predictions out of a dataset of 1000 loan applications. It seemed to be clear that the model was able to understand which factors were most significant in leading to a risk rating. Next, using the Graph, we set about analysing the data to identify the factors most strongly contributing towards a bad risk rating.

Value Distribution in the Dataset

Cluster 9: A Bad Risk Cluster

With Cluster 9 making up just 5.6% of the dataset but having double the average score for bad risk, we started to inspect this important cluster in order to analyse the make up of a bad risk applicant.

After filtering our project to select data inside cluster 9, we could explore the features of this cluster dynamically. Cluster 9 loan applications are for higher sums of money and for long payback periods with smaller instalment rates. Moreover, applicants are generally younger people between the ages of 20 and 36 looking to start a business.

Cluster 1: A Good Risk Cluster

In order to evaluate the reverse of this, we could see that Cluster 1 had the highest number of good risk applications. It made up 10% of the dataset and in this, 79% of applications were classified as being good risk — 10% higher than everything in the dataset.

This cluster was predominantly made up of people in their thirties who were single. I found the fact that single was a positive factor for good risk very interesting, as I would consider that two incomes would be better than one, however this may be influenced by a lower likelihood of a married couple having two incomes in the 1970s.

The features of cluster 1 confirmed a few suspicions I was beginning to have about the dataset. Loan applications here were for short durations and low amounts. Additionally, the purpose of the loan was to buy either a TV or radio. Can you imagine taking a loan to buy a radio these days?

Social vs Financial Factors

The characteristics of a loan application can be seen as either financial or social factors. Age and status are variables relating to an applicant’s social status, whereas savings account and loan duration are variables relating to their financial status.

Social Factors

Selecting bad risk loan applications and inspecting their features suggested that women were more likely to receive bad risk ratings. The same is true for younger people, especially loan applicants under the age of 26.

It was also interesting to note that homeownership helped to increase the positivity of an applicant’s risk profile. Tenants or those in free housing were more likely to receive a bad risk rating.

With women and younger people coming out worse, I sincerely hope brokers and banks have changed their attitude changed since 1970. It would make for an interesting comparative study to find out just how much of this bias still applies.

Financial Factors

I am less persuaded that the influence of financial factors will have changed over the past half a century. Longer loans for higher sums of money are profiled as bad risk — and this makes sense! Not only this, but applicants with more savings and longer periods of current employment are more likely to be awarded good risk profiles.

So, with all this in mind, the ideal candidate to receive a bank loan in 1970s Germany would be a single man who already owns their own property and who is over 32 years old. He would have been in his current job for a sustained period and would have a reasonable amount of savings. Furthermore, he would be looking for a low sum of money over a short payback period.

If you want to explore this project yourself, you can find it at this link: https://public.graphext.com/2f34c397c7af9697/index.html

Or use your own dataset and create your own project: https://www.graphext.com/

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Good Risk vs Bad Risk: Deconstructing the Features of 1000 German Loans was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

At Master of Code, we talk a lot about Conversation Design, how to plan a conversation flow diagram and the importance of the flow and feel of a chatbot. Customer support, user or patient management, and answering customer questions are all great use cases that a conversational AI chatbot is perfect for. Another great chatbot application is one that can help frustrated customers solve issues and give back some time to agents: troubleshooting use cases.

In a troubleshooting flow of a conversational solution, a bot’s main goal is to automate simple fixes to free up agents’ time for more complicated issues. The customer’s goal is to get their issue fixed without waiting for help.

While it’s nice for a chatbot to be empathetic and conversational, it becomes more important to get each point or troubleshooting flow step across in the least amount of words (and the lightest cognitive load possible).

Here’s how you can make your troubleshooting chatbot conversation flows more impactful

Edit, edit, edit

For your chatbot, you can borrow from the world of UX, copy editing, and heuristics to drive troubleshooting conversation flow strategy and find clarity in your messaging. Taper messages down as much as possible by:

• Removing unnecessary words that clutter the chatbot messages.
• Removing redundant language that dilutes the meaning.
• Using imagery for guidance.

By simplifying messages, you’re already one step closer to helping customers achieve their goal by alleviating some of the cognitive load for them.

Download a Conversational Flow Chart Diagram with the scenario of building dialogues for your chatbot.

Let the customer set the pace

Another way to make chatbot troubleshooting content more digestible is to break longer messages into two messages. Keep in mind that it takes two to converse and a bot should never hog the conversation or overwhelm the customer with too much information at once.

Review your conversational flow diagram and be cognizant of how many messages your bot is consecutively sending. To ensure you’re not sending more than three messages in a row, invite the customer to let you know where they’re at in the process by interacting with you. You can use quick replies or give options that:

• lead to other relevant information in the conversation flow or in another flow.
• let customers connect to a live agent if they’ve run into an issue that the bot can’t help with.
• use Natural Language Processing (NLP) to let customers stop troubleshooting whenever they want to.
• give the customer the option to let the bot know when they’re done and ready to move on.

Troubleshooting Conversation Flow! Download an example that applies the writing, pace-setting and escalation principles.

Keep in mind that not every point in a troubleshooting chatbot conversation flows should be an escalation point. In other words, there’s no need to add the option to connect with a live agent after one or two standard troubleshooting steps to call center automation. Doing so could lower your containment rate and increase wait times. Offering escalation at a key step in the beginning and at the end of the conversation flow is plenty.

Consider other paths of conversational flow

Another important piece of troubleshooting with a chatbot is to remember that customers may finish the troubleshooting conversation flow and move on to another flow in the bot. For example, a customer may need to activate a product or service before they set it up. This means that they could start in an activation troubleshooting conversation flow and end up in a set up flow.

To understand your customer’s journey, review bot conversations and analytics to see where customers are going and identify patterns into subsequent flows. This will help you anticipate where your customer needs to go next and how to map customer journey. Keep flows smooth and consistent by paying attention to how much content the customer will see when they transition flows.

Also read: How to collect feedback about your AI chatbot or voice agent?

The takeaway of troubleshooting chatbot conversation flows

Technical jargon and language in a chatbot is inherently more difficult to understand than checking out or asking an FAQ. On top of that, customers are often frustrated when one of your products or services isn’t working for them. To make their lives easier, always keep your troubleshooting messaging short, to-the-point, and cooperative.

Well-designed troubleshooting conversation flows require a good amount of upfront work and continued data-based optimization by Conversation Design specialists. Following the tips above will help you build a useful, frictionless and efficient experience that’s a win-win for you and for your customers.

Ready to build efficient troubleshooting conversation flows as part of your impactful conversational AI solution? Let’s chat!
Get in touch with us!

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Three Secrets Behind Impactful Troubleshooting Chatbot Conversation Flows was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Can you answer one simple question? What does it take to stay alert on road? Coffee, tea, music, conversations, and window down for some people. But, is it the safest solution?

The answer is no.

As per National Highway Traffic Safety Administration (NHTSA) report, around 42915 people have died in motor vehicle traffic crashes in 2021 which is a 10.5% increase from the 2020 report.

Then what’s the solution? With the recent advancement in AI technology, there is one technology solution is at your rescue and that is the Driver Monitoring System.

Let’s go through this blog and learn about Driver Monitoring System from scratch and how it is a life savior jacket for drivers.

The driver monitoring system is an advanced safety feature that utilizes a camera mounting on the dashboard to monitor the driver’s alertness and drowsiness. In case the driver is getting sleepy and distracted driver monitoring system generates an alert and recommends taking a break.

This advanced safety feature is gaining huge attraction from car manufacturers, and as per a report the global driver assistance system growth is expected to reach $32 billion by 2025. And why not, who doesn’t want more safety while driving on highways and traffic.

Driver monitoring system typically uses a driver-facing camera equipped with infrared-light-emitting diodes or laser light that can see the driver’s face even in dark. Using these diodes, the driver monitoring system can also check whether the driver is wearing sunglasses or not.

Isn’t it amazing? This advanced driver monitoring uses training datasets points to check the driver’s action and generate points. Let’s understand how the driver monitoring system works.

• This safety system monitors the driver’s most recent behavior right from the head position, and eye, and further gives a signal from cars such as vehicle speed and traffic signs. According to the data accessed by the driver monitoring system, driver alertness is graded from 1 (attentive) to 4 (distracted). That’s how this information can be used to dramatically adapt driver assistance functions such as emergency car brake assist and blind-spot detection warnings.
• Also, the driver monitoring system diagnoses the driver’s drowsiness level based on blink duration and frequency as well as eye-opening and closing frequency. Based on these points, the driver monitoring system calculates the drowsiness scale from 1 (alert) to 4 (sleepy). If the driver is sleepy it generates an alert immediately.

The driver monitoring system is a boon in disguise to maintain driver safety and lower the count of accidents that occurs due to drowsiness and non-focus during driving. Some of the driver monitoring system benefits include:

• Increase driver safety by generating alerts for drowsiness and alertness detection
• Enhance comfort by offering automated parking and easy traffic sign recognition feature
• Give a boost to autonomous driving L2+ and scalable solution for small and niche application
• Help in avoiding collision and fatal accidents

The first and most important step in building a driver monitoring system is a massive amount of training data. This training data helps the deep neural algorithm to perform multiple automotive AI process such as face identification, voice and image recognition, and object identification, by training this interference dataset give outcomes.

To identify objects, the driver monitoring system has to be trained rigorously on thousands of images that made it easier for them to identify the objects and the content in the image. And to make an autonomous driving monitoring system requires a large amount of labeled and unlabeled trained datasets that can navigate through complex driving scenarios at a faster rate.

Adapting to the next frontier technology is a must-have in the digital era when safety is the biggest concern. From maintaining driver’s focus to generating alerts to drivers in the case of emergency, requires the safest solution that can be easily accessible. And drive monitoring system is a solution that allows drives to maintain their focus on road and the rest will be handled by the system itself.

Its high time to adopt the design and innovation in drive monitoring system technology and offer drivers a more safe, secure, and comfortable ride without any fear of accident and casualties.

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Originally published at https://www.techrika.com on June 15, 2022.

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What is Driver Monitoring System and why do you need it? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Have you ever wondered about the amount of data that an organization holds in the form of papers and multiple formats in its system all across the organization? The answer is innumerable. Data is the key to business growth and its keeps accelerating with time. For example, you have a pdf life and you can’t make any changes until it’s in editable format. To copy the text directly requires you to convert the file into text format and then process it. Considering the time and effort involved in the whole process makes the place for technology adoption to fast-track the data processing. And the answer lies in Optical Character Recognition (OCR). Do you think OCR can add value to your business and organization, let’s dive into the basics of Optical Character Recognition (OCR) and its most relevant use cases all across industries?

What is Optical Character Recognition (OCR)?

When we talk about Optical Character Recognition (OCR), it is a field of Artificial Intelligence (AI) that is specifically related to computer vision and pattern recognition. OCR refers to the process of extracting information from multiple data formats like images, pdf, handwritten notes, and scanned documents and converting them into digital format for further processing.

Using OCR, organizations can easily encode the text from an image and allow it to be electronically formatted, searched, and compacted on a digital system or web for use.

Optical Character Recognition (OCR) Use Cases across Industries

Banking and Insurance

Think about the data banking and financial organization carry from transactions, KYC applications, contract orders, reconciliation, and loan applications to customer services. Leveraging OCR technology offers the banking industry to streamline and digitize the whole process for end-to-end document processing and easy data access to employees. For example- with advanced machine learning algorithms OCR technology offer banks to recognize their bank card with accuracy even if the image data quality is low.

Traveling

OCR technologies also make traveling an easy path for you by fast-tracking the passport checking and travel application for security and data storage purposes. Also, OCR helps in reducing manual errors in data verification and processing the data faster than humans. From booking the checking process to travel expense management, OCR technology can be leveraged for reimagining the travel industry and customer experience as well.

Government

Many government and legal industries still rely on the paper trail method to process information. From license registration to voting ID cards, OCR technology can be used to streamline the workflow. Instead of manual verification, OCR technology can easily check the registered users’ information just by scanning the card and accessing information in a smooth process way.

Healthcare

The healthcare industry can get the most out of OCR technology. Using OCR features healthcare organizations can get a medical history, past illness records, and prescriptions just with one click. Also, medical providers can encode this information and access it in the future anytime using the OCR feature.

Food Industry

Using machine learning algorithms with OCR features can help the food industry in creating a digitized menu, and a database of multiple recipes with calorie information and their apt intake amount. That’s how OCR feature not only give enhance the customer experience but also give the food industry an idea for creating more innovative process.

Logistics

The logistics industry spends most of the time on routine input and manual data process of logistics documents, training modules for engineers, design, and development of new machines. Leveraging the OCR feature can streamline the process by checking the in [put data autonomously and removing the chance of error while loading.

Retail

OCR feature can up-level the retail operations by offering retail industries to scan and extract the relevant information from bills of payment, packing lists, invoices, purchase order’s, and many other processes. In addition, OCR can create structured format data for easy data access and offer end-to-end data processing.

Time to act now

In the digital era, the most critical factor for any business and organization is the customer satisfaction and data accessibility. And Optical Character Recognition feature has all on its plate to serve you. From extracting data faster to making the user’s lives easier, OCR has a lot in store for you. All it requires is adaptation. Build a streamlined business process with OCE before it’s too late.

Author Bio

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/ , which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

Originally published at https://datamagazine.co.uk on June 18, 2022.

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Top 7 Optical Character Recognition Use Cases in 2022 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.