Category: Chat

When I started writing about chatbots they were largely academic projects or intense in-house creations for firms with huge customer bases. In the fast-moving five-plus years since, they have become a staple part of business offerings, spawned a startup armada with fast-evolving services, and that’s just the beginning.

Call them chatbots, conversational AIs, virtual agents or avatars. Whatever the name, digital chatterboxes are fast becoming a familiar sight on the front of websites, within apps and business services. Soon we won’t be able to move for AI-based advice and information services.

But it was so different only a couple of years back. When Siri (10 years old in October) and Alexa arrived, they heralded the start of a personal assistant revolution. Yet, despite the Apple and Amazon hype, they were not all that useful and it has taken years of constant improvement for them to become the household buddies we use today.

Early chatbots were even starker. “I don’t understand that,” “can you rephrase that” or “please call our helpline” were constant blockages in our attempts to get things done. In the few short years of commercial bots, the situation has improved. But there’s a way to go yet.

How businesses adopted bots

The early bot-as-a-service firms were keen to take advantage of the hype around improved customer service. The proposition was a compelling one, chatbots handle the high-volume, simpler, customer queries; freeing up real agents to deal with more complex, high-value queries.

Many would offer to build the bot for the company, using scripts or simple natural language processing and machine learning concepts. Others would let firms build their bots and run them in the cloud, across apps and websites.

However they were built, bots started springing up everywhere — to the usual mix of responses. Many consumers took to Twitter or other platforms to complain about limited functionality and dead-ends. Quite a few early-generation chatbots were rapidly pulled from service, never to be seen again.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Chatbot evolution at speed

Lessons learned, bot developers and end-users have taken a more measured approach. Bots are being deployed where people want to use them, notably on Facebook Messenger, eBay and LINE through APIs and plug-ins. That enables many brands to continue a conversation across social media and bring people to their products.

Bots have also got smarter, showing off images instead of text, using emojis or multi-choice entry, anything to speed up the conversation, which is where they show the most value. Reducing the text content also limits the likelihood of an unsuccessful outcome.

New-generation bots can also use text-to-speech and speech-to-text analysis to deliver a more personal-sounding service, while with the rise of deeper machine learning and AI tools, bots can produce conversations that increase engagement and help deliver better quality results, while adding a wow-factor that can help create a great impression.

The new chatbot landscape

COVID saw a huge increase in bot adoption, driven by necessity. With stores and hotels closing, medical services overloaded and service provision across all industries changing at a frenetic pace, bots could deliver the latest news, advice and information, easily updated and save care lines for vital cases.

As well as for external customer service and sales, firms are using bots for internal use, like HSBC’s latest creation ORRA. Operational Resilience and Risk Application (ORRA), uses AI and ML to reduce the time employees are spending on manually intensive queries, improve the consistency of policy response, and understand what kinds of questions were being asked.

And soon bots will talk to each other on our behalf, driving machine-to-machine conversations that agree everything from delivery times to contracts, based on times or terms that the bot knows are acceptable to their user.

Redeveloping the bot market

Now the return to normal is on, as Gartner puts it, “When the world returns to a degree of normality, adoption of digital channels, including conversational AI technologies, is likely to continue at a much higher rate than before the pandemic. Companies already on the way to adopting these technologies will therefore have an advantage.”

Welcoming new adopters are the original bot companies like SnatchBot, which have learned a lot over the years and evolved their products to meet current and emerging market needs. There are also the likes of Microsoft, Salesforce and other big vendors who have acquired or bolted on bot technology into their services. SnatchBot provides a templated approach, allowing anyone to build a bot fast. Others will still deliver a bespoke design service if your company lacks the technical skills to develop your own.

Finally, there’s a new generation of providers, built around business automation services. Take BRYTER, they started off providing automation for lawyers, but are fast expanding into all areas of professional services. Their build-it-yourself automation approach enables any professional, knowledge worker, team or department to rapidly build, test and deploy a chatbot, modify it live and monitor outcomes.

The ability to do all that without extensive IT resources and the inevitable delay will be digital gold dust to many firms. Whatever type of bot or service your company uses, the need for smart interactive services is growing fast. A market prediction is that by 2025 almost 95% of interactions will be handled by an AI, with many customers unable to tell if they are “talking” to a person or bot.

Whatever the line of business, there are bots that can solve immediate and strategic needs. And any business not using them will be hugely inefficient compared to those that do.

Don’t forget to give us your 👏 !

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The changing face of chatbots beyond 2021 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Enterprises are grabbing every single opportunity in exploring proficient methods to deliver a promising customer service experience.

Because enterprises recognized customer experience/customer engagement as a significant competitive differentiator, and customer service plays a vital role in defining that experience.

According to the research, 61% of the customers stopped doing business with a brand because of poor customer service experience.

Contact Centers have long been the most important means of customer service across industries, and customer expectations are changing with the advancements in technology.

39% of people feel that reaching out to a contact center via phone call is the best option for the customers to make initial contact with a brand to resolve issues and receive customer support.

I think most of us reached to contact center at least once in their lifetime to resolve some queries and at that time you may come across some automated instructions such as:

• Press 1 for English
• Press 2 for German
• Press 3 for French

The automatic voice commands are promoted from IVR (Interactive Voice Response).

The traditional IVR can be defined as automated telephony systems that intermingle with customers via voice & DTMF (Dual-Tone Multi-Frequency) keypad pat tones.

According to marketsandmarkets research, the IVR market is anticipated to reach $5.54 billion by 2023.

Most of us (99%) got frustrated with the traditional IVRS telephonic menu options, long process to solve the issue, and long waiting hours to connect to live human agents.

Once a customer gets frustrated with improper customer service experience or lousy customer service, there is a higher chance of the customer shifting to your competitor brand.

“Enterprises across the globe spend $1.3 trillion on 265 billion customer service calls every year.” [Source: IBM]

33% of customers feel resolving issues in a single instance as a good service customer experience.

High call volumes and less number of human agents have necessitated enterprises to look for the perfect solution to resolve these issues.

It is where the need for Conversational IVR comes into the picture.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

With the latest developments in Conversational AI, now Conversational IVR is becoming a competent way to direct the users through a series of menus with zero confusion.

Before we move onto how Conversational IVR is helping to automate the contact center, let’s take a glance at some of the necessary information.

Continue reading!

Conversational IVR?

In simple terms, Conversational IVR is the most recent innovation in IVR.

The Conversational IVR is a self-service system that makes use of voice commands from users to enable them to intermingle with the self-service IVR systems when they reach out to the contact center of a particular enterprise.

The Conversational IVR reinstates the tedious, long, and intricate menu-driven structures with Natural language processing, Natural language understanding, Machine Learning, speech recognition, and quick response capabilities.

It can understand the user’s context, intent, and content spoken in the natural language and offers a much free form and go-ahead customer service experience.

The Conversational IVR applications can understand your customers better than ever by which customer satisfaction increases significantly.

Automate contact center with Conversational IVR

Now, let’s look at the diverse ways of automating the contact center with the help of Conversational IVR.

Handling a wider range of tasks

The traditional IVR can carry out monotonous and essential tasks, and it takes minutes to hours to solve a single concern.

But using Conversational IVR in your contact center can do tasks much more efficiently in less time by understanding the customer intent while maintaining the context.

As it is a Conversational AI-powered contact center, it provides advice on the brand’s services and products.

It can resolve thousands of issues with ease with minimal human intervention.

With the use of Conversational IVR in the contact center, there is a 45% reduction in agent calls.

If the Conversational IVR is not able to understand and respond to the customer query, it will automatically transfer the conversation call to the human agent.

Turn out to be smarter with time

We are aware that the AI-based contact center will handle thousands of queries, and with the help of that, Conversational IVR solutions learn over time.

As discussed above, if the Conversational IVR is not able to deal with a particular query, it will transfer that conversation to the live human agent, and he will guide the customer manually.

If some other customer enforces the same query, the automated IVR can resolve the issue with zero waiting time.

It’s possible because Conversational IVR is learning over time from the data gathered from the previous queries/instances.

According to a research report, 66% of users try to use self-service when engaging with customer service.

Most of the enterprises are using Conversational AI in the contact center to enhance customer self-service.

At present, most of enterprises started to use powerful automation capabilities to provide simple & easy to steer Conversational interfaces.

With the highly developed capabilities, the platform opens up this span to be more customer-oriented personalized self-service.

With the Conversational IVR, the customer can interact as they interact with regular call center agents, or else they can use their town of voice.

In this case, the Conversational IVR will automate the contact center to provide personalized self-service to the customers based on their requests.

Cost-effective

Handling a traditional IVR is so expensive for enterprises. Enterprises are showing interest in Conversational IVR to automate their contact centers, which can reduce costs per call to 1/8th of traditional IVR by minimizing dependency on human agents.

The Conversational IVR also decreases AHT (Average Handle Time) & costs and maximizes agent productivity.

Smart routing

With the traditional IVR systems, the user needs to select options, and routing to a particular department/section is very time-consuming & irritating.

It became a headache for customers and enterprises.

The automation of the contact center can resolve these problems. The platform can involuntarily examine and comprehend the flow of statements and determine the user intent to allocate the customer to the right agent.

It helps to remove call center navigation issues and provides high-quality automated service to contact center customers.

Call routing accuracy increased by 50% with the help of Conversational IVR.

Enterprises need to remember a few things while shifting to Conversational IVR

The sudden shift in technology always comes up with its challenges. To handle those challenges, you need to consider below key facets while stirring from traditional to Conversational IVR experience:

• Understand your customers
• Capture & study use cases
• Tracking performance

Final words

There is no doubt that the coming up future is all about voice interactions, and contact centers are no exception.

Shifting from traditional IVR to Conversational IVR makes a tremendous positive impact on the customer service experience.

It is a perfect opportunity for contact centers to improve their productivity, efficiency, customer satisfaction, and call routing.

Is your contact center still using the traditional IVR system, which affects your business & customer satisfaction?

Switch to Conversational IVR systems and automate your contact center.

If you have any queries about Conversational IVR, reach out to us.

Stay in touch with us!

To get more updates on Conversational AI, Artificial intelligence, and automation.

Don’t forget to give us your 👏 !

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How is Conversational IVR Helping to Automate the Contact Center? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Banks, which have typically had a heavy offline interaction with customers, today, face challenges to business continuity as employees are stranded in various locations with uncertain return dates. Apart from this, dramatic changes in customer demand are putting banks under huge stress: sharp declines in demand present serious financial challenges to many businesses, while those facing demand surges and resource shortage risk disappointing and disengaging customers. COVID-19 has disrupted operations and will have prolonged impacts on continuity of operations, modes of working, and growth patterns. CIOs need to respond to the crisis with both short- and long-term actions to increase resilience against future disruptions and prepare for rebound and growth.

Banks have long had on their agenda to streamline and automate processes. The Banks who were leaning more towards cost-saving were exploring RPA for automation of administrative processes and those banks which were cash-rich and were looking to better their customer experience were looking towards Conversational AI to make workflows more efficient. And in times like these, the best of business continuity plans and sustainability forecasts have fallen short of making predictions for a global lockdown and disruption.
Let’s walk through the challenges faced by the banking industry and a few solutions to undertake for the short-term and long-term.

Digital transformation challenges faced by banks:

One of the biggest challenges top executives at banks face is the gamut of transformation required. From internal processes to customer-facing processes, all areas require digitization and automation.

As per the research done by our Analyst partner Gartner, data-driven technologies are still seen as a game-changer, but many banks are perplexed on how to move forward?

The 2020 Gartner CIO Survey asked banking IT leadership to identify which technology capabilities they see as crucial to their organization’s evolution. For the 2020 survey, data analytics, and artificial intelligence (AI) were at the top of the list and roughly equal for financial services CIOs (see Figure 1).

Figure 1: Game-Changing Technologies

During the last few years, there has been much discussion about how customer engagement in banking can better serve and provide new product opportunities to customers, considering all the points of engagement offered by banking institutions. Often-used buzzwords like “omnichannel” or “multichannel” have punctuated this discussion. Often, however, banks find that they are unable to digest and analyze customer data and transactions well enough to support the user interface or customer journeys.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Challenges faced by bank’s agents and employees:

With business continuity completely disrupted, employees are finding it hard to run the business as usual, for processes that were offline and even for the processes that were online.

Various quarantine measures and travel limitations undertaken by different Countries and cities have created big uncertainty around employees’ return to work dates. Even returning employees are often asked to self-quarantine for seven to 14 days. Internationally, indefinite travel restrictions by many countries are causing similar uncertainties to business operations. Operations have either been suspended or run on a limited capacity. Since the outbreak, demand for digital collaboration tools has skyrocketed as organizations are deploying these tools so that employees can work remotely.

Challenges faced by the bank’s customers:

Customers today are more available on conversational channels such as messaging apps and IoT devices. Whereas banks have successfully transitioned to mobile banking and app-based banking.

Where banks are focussing on icons, menus, and clicks; users have moved to expect a conversational user experience on voice and chat. Where banks are focusing on transactional coverage, customers are expecting transactional, service-related, and even advisory services to be made available, on the channels of their choice.

In Figure 2, Gap between Mobile Banking and Conversational Banking.

How can Conversational AI help banks drive business continuity and growth?

Conversational channels have the potential to help banks solving this customer interaction conundrum, capitalizing on three major consumer and technological trends:

Trend #1 Messaging is now the preferred customer touchpoint

Messaging apps are now the dominant form of mobile interaction, enabling easy, fun interactions on the move. Their simple, intuitive text or voice-based interfaces are loved by Millennials, as well as by consumers typically more reluctant to embrace digital channels too. They’re also AI-ready, offering easy integration with chatbots and cognitive agents.

Trend #2 AI is becoming ready for B2C

As AI continues to develop, bots are becoming more human-like in their interactions, and can now be built with self-learning capabilities. That enables not only the automation of repetitive customer care tasks but also low-value advisory services.

Trend #3 Mass personalization and liquid expectations

By leveraging new data-driven insights, companies are able to offer unmatchable customer experience and personalized digital services at a mass level. This creates competition across, as well as within industries, as customers’ “liquid expectations” means each digital interaction is expected to be as good as the best last experience, regardless of brand or industry.

#1 Solution: Conversational AI for Agents in Banking:

Using Yellow Messenger’s service desk automation, you can leverage the true power of AI by closing the automated-learning loop between humans, platforms, and bot. Empower your agents to do more with less at hand, by:

a. Providing agent assistance:

Using conversational AI, you can enable your agents to be highly efficient by enabling access to customer sentiment and the past context of the conversation. Integration with CRM and service desk backend tools enables the bot to show relevant data like past case references, customer history, and journey map. Agent assist feature searches for past resolutions by agents for similar queries to provide recommended responses which can be audited by the agents and sent across without typing a single letter. Entire agent workflows can also be automated and triggered by the bot on behalf of the agent at the click of a button.

b. Live Agent Transfer: Deploy Virtual Assistants to be the first line of respondents and take the load off ~65% queries which are standard, and only handoff to a live agent the queries that really need their input. Cognitive agent routing capabilities ensure that each ticket gets assigned to the most relevant agent-based on concurrency, ticket details, agent availability, past conversations, and several other factors. Agents can either chat with the customers or use voice and video calling capabilities to resolve issues on call. Our intelligent queue management allows you to manage and allocate ticket flow even when agents are offline. The service desk can be customized to show relevant data using integrations with your CRM, IT requests and support systems and is available as a mobile app for agents who do not have access to laptops at home.

#2 Solution: Conversational AI for Customers in Banking:

The implications of conversational AI in banking are far-reaching, especially when it comes to Customer Experience. Here’s how Virtual Assistants can help Banks enrich their customer experience:

a. Banking Virtual Assistant: Virtual assistants can provide a personalized banking experience, on-demand, across channels like Web, App, Messaging, Voice, and more. These virtual assistants can help answer FAQs and even take the role of advisory and upsell products that are more likely to be appreciated by the customer.

b.Contact Centre Automation: No more long wait times on calls. No more repeating your query at multiple handoff points. With IVR automation, customers can reap the benefit of being served in less than 1 minute and banks can enjoy cost and experience efficiencies.

Summary:

Conversational AI in banking is a necessity to drive business continuity at customer and employee end. For more information, visit: https://yellowmessenger.com/

Don’t forget to give us your 👏 !

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The need for Chatbots in Banking in a post-COVID-19 world was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Nowadays there are more than 120 000 Alexa Skills available for everyone. Considering this, it’s a bit difficult not to find one that can help you learn new things. Even if your interest to learn goes for less popular topics. Probably there are Alexa learning Skills that can help you gain new information.

Amazon Alexa is attracting more and more attention lately. This has made it a launching point for many industries. Be it for pure entertainment or for more technical ones, everyone is rushing to have their Skill published.

All of this with the intention of making their presence in the voice world, a new market opens for everyone.

How to learn new things with Voice Applications

Using a smart speaker, like Amazon Echo, to learn new things might seem kind of strange. However, every Skill has been built with the intention of offering a plausible experience for every user.

First, let’s clarify what Voice Applications are

• What are Voice Applications?
  Voice Applications are similar to applications you use on your smartphone. They can offer an experience similar to visiting a website online to receive a certain information, or to interact with others.

These Voice applications, called so for the simple fact that you control them using only your voice, when referring to Amazon Alexa are called Skills.

If you want to find a new Skill, all you need to do is go to the Alexa Skill Store. There you can do a simple search with certain keywords or search by category.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Amazon Alexa Learning Skills

There are already plenty of different Skills to learn from. You can find Skills to help you Meditate, teach you historical facts and events, learn soft skills, and how to make something with step-by-step instructions.

• For example, a great way to learn how to do something is the wikiHow Skill. It has over 180 000 articles accessible from everyone. From soft skills to practical and technical ones, it can be really helpful.
• If you need a guide on how to meditate you can try Real Simple Relax or a Guided Meditation from Headspace and take you first steps towards meditation.
• In case you want to learn new facts by testing yourself, you can try Question of the Day and start everyday with a challenge.

What about the Kids?

When it comes to kids and teens, there are thousands of Skills available. They help kids to be entertained or help them with their homework. With it, they can learn new facts, play games, storytelling, learn new languages.

They are a great way to have your kids learn new skills, listen to different stories and audiobooks for their age. Also, they can learn new words, and develop their practical skills (math, physics, etc.)

Don’t forget to give us your 👏 !

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How Alexa Education Skills can Help You Learn New Things was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Review Analysing Application using NLP — Part 1

In my previous article, I talked about how machines communicate with humans. In there, I have mentioned the NLP — Natural Language Processing. NLP did a significant role in communication with computers and humans. Today I am going to implement a Simple Review Annalysing Application using NLP and python. I used PyCharm IDE for coding.

Review Analysing Application is a Text Classification application. This article has four sections. they are

1. Import dataset
   2. Cleaning dataset
   3. Create bag_of_words Model
   4. Create a Classification Model
   5. Test Accuracy

1. Import the dataset

#CSV dataset vs TSV dataset

CSV means Comma-Separated Values, and TSV means Tab-Separated Values. Simply if the dataset is in .csv format, the values in the dataset are separated by comma(“,”). If the dataset is in .tsv format, the values in the dataset are separated by a tab. Usually, for NLP, we are getting .tsv form. Because in NLP, we are dealing with sentences. In sentences, there have most commas. See the example text,

Loved it...friendly servers, great food, wonderful and imaginative menu.

In this example, we have more commas. So if we used .csv, the separated values might be incorrect. But in here no tabs. Therefore we usually get the dataset format .tsv for the NLP.

#Importing the dataset

import pandas as pd

# Importing the dataset
dataset = pd.read_csv('DataSet/review_dataset.tsv', delimiter ='t', quoting = 3)

To import the dataset, I used pandas library’s read_csv function. I set the delimiter as tab because the dataset is in .tsv format. And also, I set quoting to 3 because I want to ignore double quotes also.

Here I have 1000 reviews. That dataset has two columns. They are Review and Liked or Not. The review section has the String text given by someone. And Liked or Not section has a Boolean value about his satisfaction.

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1. How Chatbots and Email Marketing Integration Can Help Your Business

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3. My Journey into Conversation Design

4. Practical NLP for language learning

2. Clean the dataset

Firstly I clean one review. And then I apply that method to all reviews.

# get one review
review = dataset['Review'][0]

For clean the reviews, I use a library called re.

# Remove all things except letters

For the review analysing I want only words. I don’t want any punctuation marks or any numbers. Therefore I remove all unwanted stuff. And I store that sentence again in the review variable. For that, I used sub function in re library.

# remove all things except letters
import re
review = re.sub('[^a-zA-Z]', ' ', review)

Now you can see there haven’t any punctuations or numbers.

# Set all letters to lowercase

and then I change all letters to lowercase. because I want to number of words in the bag_of_words

# set all letters to lowercase
review = review.lower()

# Convert the string to a list

I convert the string value to a list because another step I’m going to remove non-significant words. For that, I use a for a loop. therefore I convert string to a list

# convert the string to a list
review_words = review.split()

# Remove non-significant words

In the review, there have words to unwanted for the review analysing purpose. Such as I, am, are, this, that… So I also remove those words. For that, I used the nltk — Natural Language Toolkit package. In nltk have a list named ‘stopwords’. Using that word list, I can remove non-significant words.

# remove non significant words
import nltk

nltk.download('stopwords')
from nltk.corpus import stopwords

review = [word for word in review_words if not word in set(stopwords.words('english'))]

# Stemming => taking the root of the words

we are ding stemming because we want to remove unwanted words from the bag_of_words. Directly stemming means taking the origin of the word. As an example, I get a sentence,

Loved it...friendly servers, great food, wonderful and imaginative menu.

In this sentence have a word named ‘loved’. But this is not the root of that word. The origin of that word is ‘love’. ‘love/loved/loving’ these all words are given the same sense about the review positive or negative. Therefore we don’t want those all words. We can use only the root word for that. If we use the origin word only, our bag_of_words also will be small. We use stemming to reduce the number of words in the bag_of_words.

Simply we,

loved/loves/loving/love   =>   love

for that, I used PorterStemmer in nltk.stem.porter library.

# stemming => taking the root of the words
from nltk.stem.porter import PorterStemmer

stemmer = PorterStemmer()

def stemming(word):
    return stemmer.stem(word)

review_after_stemming = [stemming(word) for word in review]

# Convert list into a string

Now the cleaning process is over. Therefore I again convert that list to a string.

# convert list into string
review = ' '.join(review_after_stemming)

# Doing this method to all reviews

Now I’m doing that cleaning process to all reviews.

# doing this method to all reviews
corpus = []
for i in range(0,1000):
    review = dataset['Review'][i]
    review = re.sub('[^a-zA-Z]', ' ', review)
    review = review.lower()
    review_words = review.split()
    review = [word for word in review_words if not word in set(stopwords.words('english'))]
    stemmer = PorterStemmer()
    review_after_stemming = [stemming(word) for word in review]
    review = ' '.join(review_after_stemming)
    corpus.append(review)

Now 1 – Import dataset and 2 – Cleaning dataset is done. We will see other sections in Review Analysing Application using NLP — Part 2.

Don’t forget to give us your 👏 !

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Review Analysing Application using NLP — Part 1 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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