Conversational Chatbots Archives - BotCore

Improve Employee training and Engagement with Knowledge Bots

Abhishek Shanbhag — Fri, 16 Sep 2022 07:23:00 +0000

Improve Employee Training and Engagement With Knowledge Bots

Rapidly increasing digitization and the emergence of hybrid working practices have resulted in a permanent shift in the kind of experiences employees expect at the workplace. Just as modern-day customers want faster, more meaningful and personalized brand engagement, employees have come to value and like rich, contextual interactions based on their behavior and changing expectations at work.

The new-age employee experience (EX) warrants technology as a vital enabler to augment and enhance critical touch points right from “hire to retire.” In short, designing modern EX entails mixing digital components with the human touch.

That’s where conversational AI has come to play a significant role. AI-based conversational applications, such as chatbots and virtual assistants, also known as knowledge bots in this context, have rapidly gained traction as a key facilitator of the “employee-first” approach that most organizations have adopted to create a more committed, engaged, and motivated workforce.

Employee knowledge bots offer a guided chat interface and the ability to ask ad-hoc questions to automate simple and complex employee tasks, answer FAQs, and retrieve relevant information for employees.

Powered by AI, natural language processing (NLP), and natural language understanding (NLU) technologies, such employee engagement chatbots enable employees to increase efficiency, save time, and focus on more productive work that demands higher attention. It is anticipated that by the end of this year, 70% of white-collar workers will have daily interactions with conversational AI platforms.

Easy-to-navigate and well-designed chatbot services have the ability to deliver high-quality human-like interactions, improve employee training and engagement at all levels, and help design effective employee experience strategies.

Let’s explore more.

Knowledge bots in employee Training and engagement

Streamlined and more personalized employee engagement enriches EX, makes jobs easier, and allows organizations to better address employee needs.

Whether it’s recruitment, engagement, training, collaboration, or support, conversational chatbots play a critical role in re-thinking and re-designing employee experience to focus on improving employee engagement and increasing self-service at a broad level through four critical functionalities:

Pushing personalized alerts and notifications to employees based on role, location, interests, etc.
Answering queries within a matter of minutes/seconds, anytime and anywhere.
Automating day-to-day tasks that add little value to the organization, yet take up a hefty chunk of the employees’ valuable time.
Retrieving the right information for employees from the digital workplace.

Looking at the role of employee knowledge bots in personalizing engagement and enriching employee experience through a functional/departmental lens:

A) HR Knowledge Bot

Organizations with a very large workforce struggle with managing the thousands of HR-related queries and tickets generated everyday, along with maintaining human-level interactions with each member of the workforce. Moreover, they must try to reduce cost per contact while ensuring employee engagement and HR efficiency is at its peak. With a majority of the workforce operating from remote locations, it is time to bring all HR-related functions, including onboarding processes, learning and development, etc., at employees’ fingertips.

That’s where we can use Microsoft’s Power Virtual Agents (PVA) to quickly develop an HR bot for you.

As a Microsoft Gold Partner, Acuvate uses Power Virtual Agents to accelerate the delivery of HR bot solutions that help customers solve HR-related business challenges faster. For example, with more than 1500 employees worldwide, Neptune Energy, an independent oil and gas exploration and production company, wanted a one-stop-solution for all HR-related queries such as terms and conditions, rewards and benefits, HR policies, and offshore employee guidance. Within two weeks, built with PVA and Power Automate, Acuvate launched the HR Handbook Bot for Neptune’s Employee Experience Digital Platform.

Delving deeper into the use cases, we see that an HR bot serves the following purposes:

HR Assistant

Answer employee queries related to insurance policies, leaves, HR rules, benefits, etc.
Schedule meetings, apply for leaves, input daily hours, etc.

Onboarding Assistant

Schedule new hire interviews, send paperwork, and capture new hire details.
Complete onboarding forms via chat and create standard employment letters.
Resolve new hire queries.

Training and career development

Send personalized AI recommendations on suitable internal open positions based on current profiles.
Recommend training programs based on individual interests, needs, and passions.

Acuvate helped a Dutch British Consumer Goods company build an all-encompassing digital assistant to address routine queries for various departments, including IT, HR, logistics, and finance, for 160k+ workforce. The bot resulted in 47% time savings in getting answers to regular HR/IT queries, 30% increase in CSAT, and 40% reduction in cases.

Collaboration and engagement

Fetch relevant information and allow employees to access the right tools quickly for greater efficiency and productivity.
AI-based feedback collection, surveys, polls, and analysis to gauge and understand employees’ behavior, stress levels, emotion, and overall engagement with the organization.

Acuvate helped a major oil and gas conglomerate spin up a knowledge capture assistant quickly, to maintain the tacit knowledge base of the organization. Geoscientists possess a wealth of knowledge that they gather, interpret, and convert to meaningful insights. To preserve this knowledge even if one employee needs, geoscientists must manually capture and store the data, which can be highly tedious, error-prone, and may make discoverability of knowledge difficult. This process was made quicker and accurate with a knowledge capture assistant that could capture text, notes, and links with real-time voice input, convert geological terms into text, tag transcripts with relevant keywords, and make knowledge search and management a breeze.

B) IT Knowledge Bot

Growing digitalization has significantly increased the dependence on digital solutions and apps for everyday work. Consequently, the staff is constantly looking for help on various IT-related queries. While this has put tremendous pressure on IT helpdesks, failure to receive timely assistance hampers employee productivity and efficiency.

IT bots developed and deployed quickly using Power Virtual Agents can address routine queries and IT issues, and help with the following:

Get common IT queries resolved (for example, “How do I reset my password?”)
Support IT onboarding by allocating new laptops
Report a potential security breach, obtain outage reports, get stolen devices wiped or disabled, etc.
Send asset request and asset change request notifications.

Acuvate helped a client build an intelligent IT helpdesk chatbot to enable self-service support, answer IT-related FAQs, and help employees authenticate and resolve their queries using a guided conversation experience. The bot also integrated with the company’s ITSM tool to auto-create incidents upon the user’s request.

C) BI Knowledge Bot

To maintain competitiveness, make critical business decision, and stay up to speed with how the business is faring, business leaders need instant access to critical KPIs, metrics, and business insights. BI bots help executives access data-driven insights and make business-decisions in a quick, cost-effective manner.

Get business metrics and KPIs at the fingertips.
Receive quick answers to fairly specific queries, for example, “What were Product Y sales in Q1-2021?
Receive alerts on movements (rise/dip) in critical KPIs.
Get quick access to visual reports and dashboards.

Acuvate built a Data chatbot for a government agency responsible for supplying water to one of the largest municipalities in the USA. The chatbot was able to provide insights related to contracts, payments, vendors based on the natural language questions. Not just that, the unique feature of this data bot is that it can surface relevant visuals from Power BI on the chatbot itself so that users need not login to Power BI separately to view the visuals and drill down charts to find more info. All this can be done on the chatbot itself.

Due to the 24/7 access to real-time data with zero dependency on devices and portals, the client was able to achieve 90% reduction in invoice processing times and reduce cycle times for orders drastically.

How can Acuvate help?

At Acuvate, we help clients strengthen their employee experience strategy by building and deploying employee knowledge bots with our enterprise bot-building platform called BotCore.

As a Microsoft Gold Partner, our chatbot services leverage the best of Microsoft’s AI, machine learning, and NLP frameworks, including Azure Cognitive Services, the Microsoft Bot Framework, and LUIS.

Furthermore, keeping in mind our customers’ strong interest in building & deploying chatbots quickly, we leverage Power Virtual Agents (PVA) and Microsoft’s Azure services extensively to roll-out chatbot solutions with lesser time-to-market and help customers solve business challenges faster.

Along with their multilingual functionality, our employee engagement chatbots support popular enterprise messaging channels (Teams, Slack, ProofHub, etc.), thus helping clients to engage a globally dispersed workforce.

To explore more about knowledge bots, schedule a personalized consultation with our BotCore experts.

The post Improve Employee training and Engagement with Knowledge Bots appeared first on BotCore.

How Conversational AI is transforming Digital Marketing

Abhishek Shanbhag — Wed, 14 Apr 2021 06:58:00 +0000

How Conversational AI is transforming Digital Marketing

With customer experience (CX) becoming a critical deciding factor in the choice of brand, organizations are looking at ways to engage with customers across all digital marketing platforms.

As customers increasingly demand round-the-clock access to information, social media and instant messaging apps replace phones and email. Companies, particularly FMCG brands, have started responding by implementing conversational AI experiences.

Mark Zuckerberg once said, We think that you should just be able to message a business in the same way that you message a friend. You should get a quick response. And it shouldn’t take your full attention like a phone call would. And you shouldn’t have to install a new app.

Conversational AI chatbots leverage machine learning (ML) and natural language processing (NLP) to mimic real people and hold human-like conversations with customers. Chatbots embed easily in day-to-day messaging apps. They are reliable and accurate and enable companies to interact with their customers all-day. Moreover, they are intelligent. Capabilities like dialog management and sentiment analysis help chatbots gauge context and customer emotions and respond to their needs better.

Conversational AI bots help customers seek information, purchase products and services, and enable brands to push the latest offers and marketing campaigns.

Let’s understand further how conversational AI is transforming digital marketing.

Revolutionizing digital marketing with Conversational AI

1. Access information and perform simple tasks

As seen above, conversational AI chatbots are available 24X7, provide instant answers, and can be deployed on multiple channels.

They can answer simple customer queries, such as, “What is the status of my flight” or “When will my order reach me” or “Is this car available in red.” Moreover, they can handle multiple requests simultaneously, easing the burden off support staff and helping organizations provide quick, interactive support at scale. Additionally, chatbots can be used to execute customer requests, like booking a hotel room, transferring money from one account to the other, etc.

2. Educate customers on how to use the product

Recently, organizations have started using chatbots to recommend how customers can use the product or service.

Take this example – Quaker Oats, a famous food brand, uses its Facebook Messenger brand called Otis to recommend oats recipes, set reminders for overnight oats, and assist customers with online shopping. Users may use a combination of text and emojis to discover new ingredients and recipes.

3. Update customers about new products and offers

Brands can leverage AI-enabled chatbots to study customer profiles and proactively inform them about the new products, latest offers on products they may be interested in, upcoming sales, tailored product recommendations, etc.

Such services add a new dimension to their marketing endeavors and go a long way in building brand loyalty.

4. Capture customer feedback

To improve product design and quality, make meaningful adjustments in the product development process, and adapt to evolving customer needs, organizations can use conversational AI chatbots to capture customer suggestions and feedback.

Feedback may be in the form of a 1-10 rating scale, or the chatbot may request the customer to provide a written review, or else the customer may upload product images and videos.

The current scenario

Organizations deploy chatbots on various messaging apps and platforms, the most popular being Facebook Messenger, WhatsApp, Telegram, and official company websites.

With over 300,000 active bots on Messenger and approximately 1 million new WhatsApp users every day, Deloitte predicts that by 2022, “we’ll be talking to bots more often than we talk to our own spouses.”

5 Examples of companies using conversational AI in their marketing mix

1. POND’s

We, at Acuvate, built POND’s Facebook Messenger bot called SAL that was launched across nine countries. SAL leverages augmented reality to provide skincare recommendations across four areas – pimples, wrinkles, spots, and uneven skin tone. Customers need to upload a selfie, post which SAL gives a personalized skin diagnosis and recommends products accordingly. SAL managed a 98% positive customer review, with the brand generating 15 times higher purchase intent.

2. Adidas

Adidas added an exciting element to its marketing mix with its “Rent-a-Pred” chatbot on WhatsApp to assist recreational football teams hire a professional athlete if a player backs out at the nth hour.

The campaign, which included the likes of football star Kaka, generated significant buzz around the brand and helped Adidas market its Predator20 Mutator footwear.

3. Whole Foods

Whole Foods’ chatbot for Facebook Messenger lets customers find recipes based on their favorite ingredients, dish type, dietary preferences, etc. Again, just like Quaker Oats, customers may use both text and emojis to find the recipe of their choice.

Also, the chatbot helps customers check if their nearest Whole Foods store has the necessary ingredients for a particular recipe.

4. Absolut Vodka

When Absolut Vodka launched its limited-edition collection in Argentina called Absolut Unique, it organized an exclusive party to generate excitement and positive word-of-mouth.

Since only two members from the general public could attend the party, people had to convince Absolut’s virtual bouncer, Sven, to give them the tickets. The campaign was a resounding success, with the company receiving more than 1000 hilarious videos, images, and voice notes from users on why they should be invited to the launch.

5. Dominos

Dominos Pizza launched its Facebook Messenger chatbot named Dom to help customers view the menu, place an order, track existing orders, and chat with customer support.

How can Acuvate help?

At Acuvate, we help clients build AI-enabled chatbots with our enterprise bot-building platform called BotCore.

Our chatbots can be deployed with minimalistic coding requirements within a few weeks and across different channels, with channel-specific API. BotCore’s bots support multiple languages to help you reach out to a global consumer base.

To know more, please feel free to schedule a personalized consultation with our BotCore experts.

The post How Conversational AI is transforming Digital Marketing appeared first on BotCore.

Using AR and chatbots to increase customer engagement

Abhishek Shanbhag — Fri, 05 Feb 2021 07:15:00 +0000

Using AR and chatbots to increase customer engagement

Customers seek personalization while shopping for products. With most of the purchasing happening online, businesses are turning to advanced technologies to facilitate more interactive and engaging buying experiences for their customers.

An upcoming trend is the use of augmented reality (AR) in customer-facing chatbots. Since customers today have grown habitual to communicating with chatbots for queries and product purchases, adding AR to the spectrum opens a whole new world of immersive shopping experiences.

Estée Lauder’s Lip Artist chatbot allows you to upload a photo or click a selfie to try on lipstick shades and immediately see the results. Moreover, the bot acts as a personalized beauty consultant – suggests shades based on your skin type, asks questions about your color preferences and the occasions you are planning to wear the lipstick, and makes recommendations or gives surprise suggestions.
Based on what you like, you can click on “Shop Now” to purchase the lipstick from Estée’s website.

The Lip Artist is one among many examples of how AR is increasing customer engagement in customer-facing bots. So, let’s understand what AR is.

What is Augmented Reality (AR)?

As defined by Gartner, Augmented reality is the real-time use of information in the form of text, graphics, audio, and other enhancements integrated with real-world objects.

In other words, augmented reality is a live view of the physical, real-world environment.

Research shows, 10X marketers that employ AR see a 10x increase in engagement time, increasing retention and effectiveness. As such, 7 out of 10 media planners want to use AR to boost advertising and customer engagement.

When combined with chatbots, augmented reality allows brands the unlimited opportunity to interact three-dimensionally with customers and transform the entire customer journey into a personalized, interactive, and immersive experience.

Let’s look at some real-life examples and success stories of companies, which used AR to boost customer engagement in chatbots.

#1 POND’S

Skincare brand POND’S launched an AI-enabled skin-diagnostic chatbot called SAL, available in Indonesia via messaging app, LINE and Argentina, Columbia, UAE, and South Africa via Facebook Messenger.

SAL leverages a combination of advanced AI, AR, and skin diagnostic technologies to help consumers solve skincare problems across four significant areas – uneven skin tone, spots, pimples, and wrinkles.

Recognizing how confusing it can be for customers to pick the right skincare products, SAL provides them a more in-depth insight into their skin type and recommends the most effective products. Customers need to upload a selfie and complete a short quiz on SAL to receive a personalized skin diagnosis and product recommendations within a minute.

#2 Sephora

Beauty brand Sephora’s chatbot for Messenger helps customers make purchasing decisions on their own with its new offering, “Sephora Color Match.”

In partnership with a company that allows consumers to pick out and match colors using augmented reality, Sephora Color Match gives users the facility to hold up their camera to any face or image. Subsequently, the algorithm automatically detects and presents the identified lip color and other matching products from Sephora’s makeup line.

Additionally, customers can hold up their phones to ads and Instagram photos of personalities they admire, and the chatbot will assist them in finding suitable products to recreate the look. However, the feature is not just limited to faces. If users are looking

for matching palettes and products to complement a dress, they may hold up their camera to the outfit to locate the right products.

#3 Madison Reed

Madison Reed, a company that produces hair colors for an at-home hair treatment, talks to its customers via its Facebook Messenger chatbot, Madi. For customers, it’s like having a professional personal hair colorist right at their fingertips.

Customers need to upload a selfie, and using image recognition and hair localization, Madi identifies the primary hair color and secondary hair tones. She then asks questions about the desired outcome and suggests the perfect hair color, which can be purchased at Madison-Reed.com.

#4 IKEA

With IKEA’s Place app, customers can get a dynamic and immersive experience while shopping for furniture, keeping them engaged and convincing them about the utility of the purchase.

A chatbot built into the app assists customers in navigating the technology. The bot helps them point the camera at their surroundings, wherever they want to place the item in their living space. Customers can adjust the piece of furniture according to where they want to put it and test the shape, size, and functionality before purchasing it. In the AR environment created, they can walk around to look at the item from various angles.

Moreover, the bot comes up with suggestions and asks questions to guide the customer during the process.

The benefits of integrating augmented reality and chatbots

1. Unique selling point

A brand leveraging augmented reality in its chatbot stands out from the competition because it offers something no one else does. In such a case, a mere word of mouth is enough to make the brand “the talk of the town” and attract customers.

2. Increase engagement with personalized experiences

By showing customers how a particular product looks on them, augmented reality adds a touch of personalization to the shopping experience. Retailers can showcase their items more effectively, and the client can see the products more clearly and realistically. This leads to customized and immersive shopping experiences for customers and higher engagement rates for companies.

3. Higher revenue

Customers want to make informed purchase decisions. Chatbots that run on artificial intelligence understand customer needs better. Combine it with AR, and the customer has a memorable experience in shuffling through the various suggestions, trying out the different items, and selecting the right products. Since the customer’s decision is informative and smooth, the possibility of return orders declines, leading to increased revenues.

Final Thoughts

As seen above, using augmented reality in chatbots is the way to go for any industry where buyers require visual inspection and a look-test of the product.

The AR-chatbot customer experience model allows users to take a closer look at a new range of shoes, try out a lipstick or eyeshadow, check how a dress looks on them, and get tailored product recommendations before the actual purchase, all of this while sitting in the comfort of their homes.

Such unique and immersive experiences go a long way in generating buzz around the brand and increasing customer engagement.

To know more about AR in Chatbots, please feel free to schedule a personalized consultation with our experts.

The Evolution of Chatbots to Conversational AI

Abhishek Shanbhag — Mon, 01 Feb 2021 06:47:00 +0000

The Evolution of Chatbots to Conversational AI

Since their evolution, chatbots have grown from delivering linear, scripted user experiences to providing unsupervised and contextually-aware engagement.

One of the earliest chatbots created at the MIT Artificial Intelligence Laboratory, Eliza interacted using scripts and leveraged pattern matching and substitution technology. It had no built-in provision for contextualizing events. Like Eliza, many first-generation, rule-based chatbots were used for answering simple FAQs. Such chatbots did not leverage automated, machine-learning technology and required 6-9 months to train manually. Moreover, training them was an ongoing process, and the entire investment did not deliver the requisite ROI.

Over time, as customers and employees started demanding interactive, real-time, and personalized omnichannel engagement, organizations needed sophisticated AI-enabled chatbots to meet their expectations. Consequently, chatbots evolved to conversational AI with powerful capabilities, including machine learning, natural language processing (NLP), intent extraction, and sentiment analysis.

Let’s understand further.

What is Conversational AI?

Markets and Markets predicts, The global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.

So, it brings us to the real question, “What is Conversational AI?”

Conversational AI is a set of advanced technologies, including natural language processing (NLP), natural language understanding (NLU), machine learning, and speech recognition, to process written and verbal inputs and respond accordingly in a natural, human-like manner.

Conversational AI bots pull out entities and intents and can comprehend the nuances of the language, including grammar, slang, and canonical word forms. Moreover, they are trained to understand the type and intensity of the user’s emotion and respond accordingly.

For example, I am trying to find a new red dress. Here, “find” specifies the intent, while “red” and “dress” are the fundamental entities of the user’s request.

Or, take this example. The customer types, “I am pissed off with your delivery agent.” Here, the chatbot will identify the emotion (which is anger in this case) and rank the sentiment based on its intensity.

However, it is essential to note that there are fundamental differences between a chatbot and a truly conversational AI engagement.

Rule based Chatbot vs. Conversational AI

Chatbots can be of two types –

(i) rules-based and

(ii) AI-driven. As seen above, a rules-driven chatbot follows a pre-defined workflow or script. In contrast, AI-driven chatbots understand the conversation’s context and the user’s intent and engage in a meaningful, dynamic dialog. As a result, an AI-enabled bot makes you feel that you are interacting with a human and not a computer.

Rule based Chatbot	Conversational AI
Hi, how may I assist you? Type “Place Order” or “Check Menu.”	Hi, how may I assist you?
Where is my order?	Where is my order?
I’m sorry, I don’t understand. Type “Place Order” or “Check Menu.”	Your order is dispatched and will reach you by 8:27 pm.
I don’t need this. I need to know when my order will reach me.	Thank you.

It’s clear from the example above that while a rules-driven chatbot carries out a keyword-based chat, a conversational AI chatbot uses NLU to gauge what the user is looking for at the moment and how specific topics relate to each other. Additionally, simple chatbots are trained on 100-200 customer intents; an AI-chatbot, on the other hand, is pre-trained on thousands of industry-specific customer intents and use cases.

The evolution of chatbots: Where do we stand today?

In the journey of chatbots, conversational AI is where we stand today. Human language is complex, and conversational AI provides many advanced capabilities that let organizations go above and beyond scripted resolution paths. Some of these include –

Context management – With conversational AI, bots will always learn from past user interactions and remember important details, including client information, customer preferences, employee profile, etc., making it easier to hold personalized, context-rich conversations.
Sentiment analysis – As seen above, conversational AI bots comprehend the tone and emotion of a user’s utterance and respond accordingly; for example, they may steer the conversation in a different direction, alter the style, or bring in a human agent to take over the conversation.
Dialog management – Human conversations are strewn with twists and turns. Conversational AI empowers bots to handle such complex dialog changes, including entity change, processing multiple entities within a single utterance, etc.
Omnichannel and multilingual support – Conversational AI allows users to start a conversation in one channel (e.g., WhatsApp) and end it in another (e.g., Facebook) without losing context or continuity. Moreover, organizations can reach out to a global audience with chatbots that support different languages, like French, German, Italian, etc.

Take the example of BlenderBot, the largest-ever open-sourced chatbot by Facebook.

The bot blends a host of conversational skills – empathy, persona, and knowledge – together with improved decoding techniques and a large-scale neural model with 9.4 billion parameters and a 14-turn conversation flow – making it one of the most engaging and human conversational-AI chatbots. Compared with Google’s Meena chatbot, 67% of the respondents claimed BlenderBot sounds more human, while 75% said they would prefer having a more extended conversation with BlenderBot than with Meena.

In fact, Facebook has done in-depth research on how often human evaluators preferred their chatbots over human-to-human chats over time, and the results are depicted below.

With the plethora of benefits they provide, it is clear that organizations must adopt chatbots with conversational AI capabilities.

They deliver interactive, tailor-made, and value-adding engagements to build better customer and employee relationships.
Personalized and immersive customer and employee experiences boost customer loyalty, build brand image, and increase employee productivity.
Since conversational AI chatbots learn from past conversations and any new data that enters the system, they can accurately predict what users want to develop specific responses and upsell by offering personalized product recommendations.
Moreover, since such bots rely on their taxonomy and cognitive capabilities to deliver self-service resolutions at scale, the return on investment is also high.

The Path Forward - How to get started with Conversational AI

Here’s our 101 to help you get started with conversational AI.

Plan and Strategize on how conversational AI can be integrated with different business units.
Build a powerful case for conversational AI by spreading the word amongst the various stakeholders.
Choose the right conversational AI platform that helps you build, deploy, and train your chatbots.
Determine the gap between your existing human and technical resources and those required for smooth implementation.
Quantify the business value of conversational AI deployment, including improved CSAT, reduction in support costs, and other metrics.
Launch a pilot project.
Scale and optimize conversational AI for the entire organization.

Final Thoughts

Today, with its innumerable advantages to business, conversational AI is being deployed in various consumer and employee use cases and processes, such as IT and security management, marketing, human resource, insurance, retail, banking and financial services, and healthcare.

At Acuvate, we help clients build conversational AI chatbots with our low-code enterprise bot-building platform called BotCore. With minimalistic coding requirements and a visual interface, our bots can be built and deployed within a few weeks, support multiple languages like French, German, Italian, etc., and can handle simple and complex conversations alike.

To know more about BotCore, please feel free to schedule a personalized consultation with our experts.

The post The Evolution of Chatbots to Conversational AI appeared first on BotCore.

A Comprehensive Guide For Conversational AI

Abhishek Shanbhag — Thu, 08 Oct 2020 07:28:00 +0000

A Comprehensive Guide For Conversational AI

Planning to implement conversational AI in your organization? Read this comprehensive guide to get a full understanding of conversational AI, how it works, and its capabilities and use cases.

What is Conversational AI?

Conversational AI is a set of powerful technologies that empower computers to comprehend, process, and respond to human utterances and text/voice inputs naturally. Used in conjunction with chatbots or voice assistants, it helps organizations deliver meaningful and personalized customer and employee engagement economically on a large scale.

Why Conversational AI?

The conversational AI market size is expected to grow from AUD 6 billion in 2019 to AUD 22.6 billion by 2024, at a CAGR of 30.2%, during 2019-2024.
As per Gartner,

With Conversational AI, an organization can benefit from personalized, context-aware, and differentiated customer and employee experiences. Global organizations leveraging conversational AI technologies like chatbots, virtual assistants and voice bots are significantly reducing support costs, streamlining internal operations, improving agent productivity, and delivering powerful customer service. The scope of conversational AI is vast; the channels are rapidly expanding. You may experience conversational AI through the following means-

Social media platforms – Facebook Messenger,, WhatsApp, Twitter
Enterprise channels – Microsoft Teams, Zoom, Slack, Web
Web and mobile messaging apps
Voice devices – Amazon Alexa, Google Home
Contact Center – Interactive Voice Response (IVR) system

Additionally, advances in NLP and machine learning, the availability of vast amounts of data, flexible app integrations, and low-code bot-building platforms have made conversational AI a force to reckon with.

How Conversational AI works?

Conversational AI uses a plethora of advanced technology components, including natural language processing (NLP), intent and entity recognition, machine learning, natural language generation, dynamic text to speech capabilities. Below we present a walkthrough of how these technologies make conversational AI a reality –

1. Natural Language Processing (NLP)

Natural Language Processing (NLP) is a component of AI and is the ability of a computer program to understand human or natural language as it is spoken/written. NLP helps a bot/virtual assistant understand the semantics of the language being used, including synonyms, canonical word forms, grammar, slang, and logically respond using natural language, consistent with the user’s query.

Learn More: Understanding NLP and Its Need in Enterprise Chatbots

2. Natural Language Understanding (NLU)

Natural Language Understanding (NLU) is a technology that deciphers the context and meaning behind the user’s words. With NLU, the AI assistant can easily understand the user’s query, even overlooking grammatical errors, shortcuts, etc., and remember the context throughout the conversation.

Contextual awareness is necessary to recall information over interactions and hold natural, human-like back and forth conversations.

NLU goes above and beyond scripted conversational technology that involves giving a pre-programmed response to a particular phrase or keyword.

Natural Language Understanding extracts intent and entities – precisely what the user is trying to achieve (intent) and elements that define what is required to accomplish the task, such as dates, time, numbers, and objects, also known as entities.

Example – I am trying to find a restaurant that sells blueberry cheesecake.

In the above query, the intent is to “find.” The relevant information (entities) required to fulfill the user’s request are “restaurant” and “blueberry cheesecake.”

3. Training Models

- Machine Learning

Machine Learning is a subset of AI that studies algorithms and statistical models, giving computers the ability to perform a specific task without being explicitly trained to do so. With machine learning, bots rely on patterns, inferences, human-agent conversations, and historical interactions to learn and improve their performance.

- Fundamental Learning

Fundamental Learning ensures input information always produces the same output. It determines intent from user utterance using semantic rules, such as grammar, sentence structure, word match, language context, etc.

- Knowledge Graph

Knowledge Graph groups key domain terms according to similarities and differences. The model then associates them with context-specific questions, synonyms, and ML classes.

- Natural Language Generation

After understanding the user’s intent, the conversational AI assistant uses natural language generation to respond in a textual or voice output that is easily understood by the user.

Capabilities of Conversational AI

Through conversational AI, conversations truly feel human-like. Just as humans remember context throughout the conversation, a conversational AI chatbot retains context from one response to the next. Because of its conversational ability, interactions don’t feel scripted. You can hold conversations about anything – as long as the bot has the data to build the conversation.

Below we take you through the various capabilities of conversational AI –

Context Management – The most significant capability of conversational AI lies in the fact that interactions will always have the human touch. Conversational AI allows bots to remember critical details from past dialogue, user preferences, and client information, making it easy to deliver personalized user interactions.

Dialogue Management – Human conversations are seldom straightforward and are layered with twists, turns, and context-switching. Examples include but are not limited to pausing tasks, changing entities at any point in time, and processing multiple entities in a single message. Conversational AI allows bots to hold both simple and complex conversations by handling dialogue turns effectively.

Omni-channel experiences – Conversational AI’s distinguishing feature lies in its ability to allow users to start a conversation on one channel (for example, website) and complete it in another (maybe, Facebook Messenger) – without losing context or continuity, so customers receive consistent support.

Real-time personalization – Depending on the user context, needs, and profile, conversational AI enables AI assistants to provide personalized information, products, offers, and other services.

Multilingual support – Conversational AI supports multiple languages, including Italian, Dutch French, German, Spanish, etc. – allowing you to expand to global markets and serve more customers and employees from different geographical locations.

Learn More: Chatbot In Different Languages: A Guide To Multilingual Chatbots

Sentiment Analysis – Tone and emotion can significantly alter what you want to convey. Conversational AI identifies, extracts, and signals the type and intensity of a user’s sentiment, allowing bots to steer conversations, change the tone, or bring in human agents for support.

Learn More: Understanding The Role Of Sentiment Analysis In Chatbots

Approaches to Conversational AI

Conversational AI can take one or both of the following approaches –

Reactive Engagement – It can respond to a customer’s query by providing an easy path to find information and answers without reaching out to a human agent.
Proactive Engagement – It may anticipate the user’s demand in advance and push personalized and contextual information, thus creating opportunities to establish new relationships, intervene at critical moments (for example, when a customer toggles between two product options), and support users 24×7.

Advantages of Conversational AI

By incorporating conversational AI into everyday organizational functioning, you can reap the following benefits –

Reduce cost per contact – You can reduce the cost to contact depending on the human-agent calls deflected to conversational AI powered channels, thus improving service resolution and human agent utilization and productivity.

Increase revenue via cross-sell and up-sell – Through proactively reaching out and engaging with customers, conversational AI enables you to create personalized customer experiences, recommend the right products, notify about promotions, up sell, cross sell and build a loyal client base.

Uncover data-driven insights – Conversational AI gives your business a competitive edge by providing valuable customer data that can be used for product innovation, improving customer service quality, and personalizing marketing campaigns.

Reduce churn – By providing instant, accurate and 24/7 support for resolving customer issues, conversational AI helps reduce customer churn.
Improve employee productivity – Conversational AI improves employee satisfaction and motivation in more ways than one, some of which include –

1. The virtual agents can complete mundane, routine tasks, allowing agents to focus on critical and high-value work
2. The AI bot can draw-up information from the enterprise systems like CRM that helps human agents serve customers quickly
3. Employees can easily access necessary data insights from analytics systems, thus empowering them to take more data-driven decisions.

Scale efficiently – With conversational AI operating 24×7, you can serve more customers and employees across business units and geographies, even outside your organization’s working hours.

Learn More: 10 Powerful Benefits Of Chatbots In Customer Service

Conversational AI: Customer and Employee Use Cases

1. Employee Processes

- IT and Security Management

The IT helpdesk is often inundated with routine questions. As soon as a request is raised, IT chatbots help the user do basic troubleshooting and in most cases fix the issue and thereby reduce the employee downtime.

If the issue isn’t resolved or the user isn’t satisfied with the outcome, bots provide the option to connect with a support agent – thereby leaving the more complex queries to human agents. This leads to faster resolution times, improved incident management, improved security, better handling of outages and ensuring that employees are kept informed with steady and timely alerts.

Some of the use cases include:

Check the status of tickets
Answer common troubleshooting questions like VPN or password not working
Ask instructions for common IT issues
Reset passwords for devices and network
Talk to a live agent (human-handoff)
Raise tickets
Fill form fields via conversation
Access the knowledge base
Check on the pending case reports
Look-up case-related information
Receive information on – Incident notifications, New change request notifications, Task notifications, Access request notifications, Asset request notifications and Outage alerts

- Sales

AI-enabled sales virtual assistants can integrate with data warehouses, CRM, BI and LOB Systems to perform tasks such as creating new leads, updating lead status, getting visual reports in multimedia formats, updating CRM records etc.

Use cases include:

Check the lead status
Ask pinpointed prospect-related queries
Check on the sales KPIs
Get pin-pointed answers of any information available in the CRM or BI systems.
Fill lead details
Send email of the desired dashboard
Set and get alerts about dip or rise in any sales KPI.
Receive notifications about change in lead’s status
Access reports available in the CRM, BI or LOB or DWH systems.
Get links to the desired dashboards

- Marketing

Conversational AI marketing assistants can gather data about potential customers that equips marketers with essential information to design their products and advertising strategies. They can be integrated with various social media channels and used to reach out to customers of various demographics.

Following are the use cases:

Lead generation
Lead qualification
Book sales meetings
Schedule demos/consultations
Suggest relevant content based on user’s website activity
Capture email addresses and other visitor details in a simplified manner

- Intranet Assistant

Employees can use the company’s intranet chatbot to perform simple actions such as checking on internal company updates, accessing documents, applying for leaves etc.

Use cases include:

Proactively take the announcements and news in the intranet to the employee
Get intranet information via natural language questions
Get links to desired intranet documents
Content authors can update content with a chat interface
Get personalized alerts and timely updates
Perform tasks like leave requests, travel settlement requests, IT requests etc.

- Human Resource

AI-enabled virtual assistants can be used at different stages of an employee’s life cycle – right from recruitment and onboarding to engaging the employee and fostering retention, in order to optimize the whole process.

Use cases include:

Delivering updates about the status of an application
Responding to FAQs, thereby saving the recruiter’s time and efforts for other tasks
Conduct and record feedback surveys from the candidate about their recruitment process and gain insights on any areas of improvement
Reduce recruitment time by qualifying and disqualifying candidates swiftly at scale
Automate the manual and administrative recruitment work
Streamline and speed up the process of collecting and recording KYC documents, tax forms, etc. by tracking the same for employees and reminding them to submit the required documents on time
Share standard operating procedures and company policies, and severely reduce the HR workload by handling the process and queries online

2. Consumer Processes

- Banking and Financial Services

Conversational AI in banking aims at delivering personalized customer services to improve customer satisfaction and engagement. Some of the use cases include:

Checking the account balance, transaction history, credit limit etc.
Help in upsell
Finding the nearest ATM or branch
Inquiring about different offerings and products
Generating a mini statement for the desired time period and the interest rate report
Updating contact information
Connecting to a live agent (human hand-off)
Transferring money from one account to another
Suggest money saving ideas
Generating bill payment alerts and personalized financial advice
Resetting the card PIN

- Retail

CPG and retail companies are increasingly using conversational AI to transform customer experience. Following are the use cases in the retail sector –

Product exploration and discovery
Product recommendations
Check the shipment status
Add items to cart
Place orders
Book appointments
Connect to customer support agents
Provide product related information, and alerts on a new product launch, and suggestions on discounts or coupons or any other sales offers

- Insurance

Insurance companies may use conversational AI to handle routine customer questions, address minor insurance related challenges, provide quotes, automate the claim process, and reduce call center costs.

Therefore, some of the use cases in insurance are –

Help in filing a claim
Answer scheme and plan related questions
Provide recommendations to prevent loss
Provide guidance for choosing the right plan
Send Insurance documents of the customer
Send personalized quotes to users

- Healthcare

AI-powered virtual health assistants and health bots offer users personalized access to health-related information and natural conversational experiences.

Providers, pharmaceutical companies, and insurance companies can use conversational AI for certain healthcare-specific use cases, including –

Inquire about the status of an insurance claim
Ask queries about health plan pricing, benefits, and services
Triage patient issues with a symptom checker
Find general information about conditions, symptoms, causes, etc.
Schedule doctor appointments
Patient registration, delivery of post-op instructions, etc.

- Education

Chatbots are changing the face of education right from personalizing education, helping people learn new languages, spaced interval learning, student feedback, professor assessment, essay scoring, acquaint students with school culture and for administrative formalities.

Uses cases include –

Handling queries related to the university and courses during registration, assessment related questions, tuition fees, time tables, scholarships, grades etc.
Get university policy documents, enrollment certificates, academic information, disability and other personal information
Provide course and administration related information
Access course documents
Handle university registration
Send feedback about professors, courses etc.
Update contact information
Register for courses
Fill applications
Apply for permissions

Getting Started with Conversational AI

The technologies that support conversational AI like NLP, machine learning, conversational interfaces and advancing at a rapid pace. As virtual assistants and chatbots become more popular across the globe, your customers expect powerful self-service conversational experiences from your brand as well.

In order to stay competitive, enterprise can no longer ignore the business value delivered by these technologies. Laggards in this space will fail to keep pace with the evolving customer and employee needs and get left behind.

Here are some tips for organizations getting started on their conversational AI journey:

Defining the strategy – Understand how conversational AI can be integrated into your organization. Capture the right use cases by collaborating with different business units.
Build a powerful business case – An enterprise-wide conversational AI journey involves cross-functional stakeholders. Spread awareness internally on the benefits of conversational AI and clearly demonstrate the ROI.
Evaluating solutions – Choose the right enterprise conversational AI platform that helps you build, deploy, manage and train virtual assistants. You can read market reports from industry analysts like Gartner to compare different vendors.
Assess your readiness – Determine your existing human and technical resources and identify the gaps to be filled.
Define Metrics: Clearly quantify the business value the project delivers. Establish realistic metrics like increased CSAT, reduction in cost per contact, reduction in support costs etc.
Launch pilot projects: Pick a high-value use case and launch a pilot project. Monitor the virtual assistant performance over time and document the lessons learned.
Scaling and optimizing – Continuously scale, improve, and optimize conversational AI technologies and look for opportunities to streamline workflows.

Free bot workshops like Acuvate’s Build-A-Bot program helps you get a subject matter expert opinion to plan your bot journey, identify specific use cases within your enterprise and evaluate different technologies. Acuvate provides a 1 day bot strategy workshop within your company premises for both business and IT leaders.

Introducing BotCore

Over the past several years, we have helped several enterprises build & launch chatbots to meet the needs of their employees, customers and vendors. We leverage our enterprise chatbot builder platform – BotCore to build, deploy and manage custom chatbots for companies across industries. BotCore is fully deployable on both on-premise and cloud environments.

If you’d like to learn more about this topic, please feel free to get in touch with one of our enterprise chatbot consultants for a personalized consultation.

The post A Comprehensive Guide For Conversational AI appeared first on BotCore.

9 Components Of A High-Performing Chatbot

Rakesh Reddy — Wed, 22 Apr 2020 06:31:00 +0000

9 Components Of A High-Performing Chatbot

Chatbot conversations will deliver $8 billion in cost savings by 2022
– Juniper Research

Chatbots are being widely used by businesses. But what does it take to build a truly advanced and enterprise-grade chatbot? Let’s explore!

Conversational UX

An excellent Conversational UX is key to drive adoption and helps users reach their goal in the shortest time with maximum end-user experience. It enables bots to conduct human-like conversations.

Machine Learning

Machine learning algorithms enable chatbots to learn from previous conversations, and deliver better responses in the future.

Natural Language Processing (NLP)

NLP is a technological process that allows chatbots to understand the meaning behind users’ natural language inputs and then deliver relevant responses.

Sentiment Analysis

The technology with which chatbots can analyze users’ input text or voice and understand their emotions and gauge their mood.

Multilingual

Multilingual chatbots are capable of understanding and conversing in different languages. This ability enables bots to cater to a wide range of audience across countries.

Analytics & Administration

Chatbot platforms need to have an administration module in which you can track the bot’s performance, do maintenance activities and train the bot.

By integrating with back-office RPA bots, chatbots can capture information from legacy systems that lack modern APIs and perform actions on behalf of users.

Voice Bots

The next-generation of enterprise chatbots are voice-based. Voice bots relieve users from having to use their keyboard or mouse to send messages.

Cognitive Abstraction

Using cognitive abstraction, chatbot platforms can leverage any AI service available today and will scale for future services.

Build A Modern Enterprise Chatbot!

Ready to get started with your conversational AI journey? Check out BotCore – an enterprise chatbot builder platform driven by AI! Learn how Fortune 500 companies are using chatbots to drive employee and customer experience. Visit www.botcore.ai

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The post 9 Components Of A High-Performing Chatbot appeared first on BotCore.

How Chatbots Are Helping The Fight Against The COVID-19 Crisis

Jagan Jami — Mon, 16 Mar 2020 10:28:00 +0000

How Chatbots Are Helping The Fight Against The COVID-19 Crisis

As the impacts of the pandemic deepen and governments continue to enforce social quarantine to “flatten the curve”, it is innovative technologies like chatbots that are coming to the rescue. Their diverse range of applications, cost-effectiveness, and ease of implementation, is resulting in more and more organizations readily adopting them. From customer support, crisis management, internal communications to remote work support, organizations are using these conversational interfaces in different ways to fight this pandemic.

Let’s look into this in detail.

Functions Where Chatbots Are Making A Difference

1. Crisis Management

Chatbots are essential not only because they answer FAQs of employees, but also for their ability to conduct human-like conversations – something very desirable when people are isolated and going through difficult times.

In regards to crisis management, chatbots can help in

Sending public health information to employees from sources like WHO and CDC
Performing employee health checks
Keeping employees informed about the company’s latest advisories, and news
Sending work from home tips for remote workers

In fact, Global Telecom, a Filipino telecommunications company, has implemented a chatbot named Digital Usher for Disasters and Emergencies (DUDE) to keep the management updated on its employee’s health and well-being. The chatbot does daily health status checks for its 8000+ employees.

2. Human Resources (HR)

The lockdown has put a huge burden on HR. The economic downturn has piled up their inboxes with emails from employees concerned about their work status, paycheck, and lack of procedural clarity. Moreover, managing the hiring and onboarding process has become incredibly difficult in the remote environment, resulting in offers being rescinded and talent choosing to join competitors. In such a scenario, chatbots can play a major role in streamlining the HR workflows.

Not only can chatbots answer FAQ, provide policy-related information to the workforce but also automate the hiring process and run virtual training programs and assessments. For instance, in industries which are aggressively hiring, chatbots can screen applications, conduct background checks on thousands of people, collect candidate data and help the HR team to focus more on productive tasks. Moreover, chatbots can make the training and onboarding processes interactive and interesting by ensuring active participation.

Learn More: HR Bots, Chatbots for Employees,

3. IT Services Management (ITSM)

In the past few weeks, we have seen IT help desks in several companies struggling to manage the sudden unprecedented surge in incidents, issues and requests. Needless to say this is largely due to the sudden shift to remote working.

A huge volume of these requests are usually ‘basic’ or ‘simple’ questions that take a lot of time to answer. A chatbot is a powerful solution to address repetitive and low-value requests. They’re available 24*7 and can handle multiple requests simultaneously. If the chatbot is unable to handle a particular query, it can always hand over the conversation to a human agent.

This will allow your IT staff to focus on productive and proactive tasks.

Learn More: IT Helpdesk Bot

How Different Industries Are Leveraging Chatbots Amid COVID-19

1. Government

Chatbots can enable governments to relay critical and accurate information to the public as and when they require it, providing much-needed clarity on some FAQs related to lockdowns, COVID-19 symptoms, testing locations, and the affected areas.

Interestingly, the British Government has launched a WhatsApp-powered chatbot, to prevent misinformation and spread reliable health information by sharing the latest news updates and health guidance, as per the chosen option, reducing the burden on the National Health Services (NHS). Additionally, WHO has also started to utilize chatbots in its app to prevent misinformation and spread reliable health information. The chatbot also conducts interactive quizzes in multiple languages.

2. Healthcare

In these testing times, with people facing a global recession, financial uncertainty, and increased health anxiety, their mental health is taking a beating. In such a scenario, AI-powered chatbots can not only help relay vital information but also help to cope with the increased stress 24×7. As the health industry continues to champion our fight against the pandemic, chatbots can also ease the burden on it by directly helping people through guided meditations, diet, and fitness programs.

Chatbots also help in communicating with digitally disconnected front line workers and sending the latest information right to their mobile messaging apps. Another popular use case amid this pandemic is remote patient screening.

Jefferson Health, a leading academic health system in the USA, has deployed a conversational chatbot to remotely screen patients and prioritize cases without the risk of exposure. The chatbot also digitized the patient intake process to streamline appointment scheduling for COVID-19 testing.

3. Consumer Packaged Goods (CPG)

With a drastic fall in physical store traffic, much of the demand for CPG has shifted online, with many executives publicly stating that they are zeroing in on digital commerce to target their customer base. With convenience and accessibility as the driving factors, they realize that implementing chatbots can be a vital step in this direction.

Not only can chatbots be used internally to manage restocks, track orders, and research promotions but also for online customer support to enhance the customer experience.

Britannia Industries, a leader in the CPG space, has launched a GPS-powered WhatsApp-based chatbot in light of the current social quarantine and rising demand. The chatbot is helping consumers locate grocery stores which have the organization’s products. In fact, the chatbot also sends notifications to the customers automatically whenever the store’s stock is refilled.

Learn More: CPG bots, Chatbots for CPG

4. Retail

With physical stores being open only for a few hours and supply chains hampered, retailers are changing their policies related to shippings, products among many others. A chatbot can be used to inform customers about these changing policies and keep them updated.

Learn More: Retail Bots, Chatbots for Retail industry,

5. Restaurants

With restaurants transitioning into takeouts only, their menus, service offerings, pricing, and timings have changed. Letting people know about these changes is important. Moreover, with them operating with limited staff, order taking and tracking can become a handful. Chatbots can be used to relay any changes and updates to customers, as well as collect orders from them.

Conclusion

Any doubts that businesses may have had about chatbots prior to the COVID-19 pandemic, have been put to rest. With the market dynamics changing in face of the social quarantine, chatbots are growing in popularity. Their use case is no longer limited to customer service, they are becoming an integral component of an organization’s operations, both internally and externally. A chatbot’s range of applications, scalability, and ease of implementation are some key drivers for its adoption amid this crisis.

If you’d like to learn more about this topic, please feel free to get in touch with one of our chatbot consultants for a personalized consultation.

The post How Chatbots Are Helping The Fight Against The COVID-19 Crisis appeared first on BotCore.

7 Ways To Improve FCR At Your Contact Center

Rakesh Reddy — Fri, 10 May 2019 12:28:00 +0000

7 Ways To Improve FCR At Your Contact Center

First Call Resolution (FCR) is an indispensable metric for contact centres to measure and improve.

Research conducted by Ascent Group suggests that 60% of companies measuring FCR for a year or longer reported improvements of up to 30% in their performance. Measuring FCR is beneficial as it evaluates the efficiency of the agents, reflects the quality of customer services and provides insights on the improvement areas.

FCR is a valuable business metric as it not only improves customer satisfaction but also helps in minimizing operating costs.

For instance, let’s say a contact centre receives 25,000 calls every month and the FCR rate is 60%. This shows that 40% of calls require follow-up support. If the FCR rate improves by just 10%, there will be 2500 fewer calls every month or 60,000 fewer calls every year. This reduction in calls enables a contact center to allocate resources better, and saves a lot of time and money!

Here are 7 actionable ways you can quickly improve your FCR rate and enhance customer experience.

7 Ways To Improve FCR

1. Deploy Chatbots and Conversational IVR

Chatbots have become an essential addition at contact centres to streamline several parts of customer service. “25% of customer service and support operations will integrate bot technology across their engagement channels by 2020, up from less than two percent in 2017”, report by Gartner suggests.

Enabled by conversation AI, NLP and ML, chatbots today are capable of understanding the ‘intent’ behind customer queries and conducting human-like conversations. This ability enables them to handle tasks such as providing information, answering FAQ, sending instant responses, collecting user information, among many more. Chatbots provide self-service options to customers and can be used 24/7 for customer care.

Chatbots also act as the first line of support and only route extremely complex conversations to agents. All these capabilities of chatbots help them to engage customers until the issue is resolved – thereby reducing the need for a follow-up conversation and improving the FCR.

Another emerging bot technology to consider is conversational IVR. Powered by AI and Natural Language Processing, conversational IVRs provide a unique voice-based and hands-free solution where customers can interact using natural language as opposed to choosing the options from a long static menu offered by traditional IVRs.

Reduced customer service costs, improved CSAT, increased agent productivity, streamlined workflows, a decrease in the number of customer emails and calls are some more key business benefits of deploying bots in contact centers.

Read More:

2. Incentivize Agents to Reduce Call-Backs

Rewarding agents on resolving issues on the first call is a great way to motivate agents to perform better and an investment that companies should make as the returns that they reap on reducing the number of call-backs is much bigger.

Evaluating agents based on the average handling time can prove to be an ineffective strategy as they can close calls without successfully resolving an issue to save time. Incentivising agents on resolving customer requests on the first call would be a good long term strategy as it relieves them from the pressure of saving time and shifts the focus to effectively resolve issues. This reduces call-back from customers and in turn saves time.

3. Employ Customer Journey Analytics

Customer journey analytics can be an eminent tool at call centres to have a well-rounded understanding of customer service journey. It is one of the primary steps to resolve FCRs and build an effective interaction with customers to avoid voice calls.

Traditionally, companies have relied on customer surveys to glean insights on escalations to agents, which proved to have serious limitations. Companies are deploying customer journey analytics to accurately understand the events throughout the customer service journeys that lead to failure escalations and failure in service.

Customer journey analytics provides insights to predict the likelihood of escalation and is a great tool to proactively design self-service to customers. With customer journey analytics, structured and unstructured data from various channels like website, mobile app, chatbot can be integrated. This is useful in eliminating the silos across the channels and have a comprehensive cross-channel understanding of customer behavior, issues and drop-offs, which offers a proactive preparedness for contact center agents for potential escalations from various channels.

4. Speech Analytics

In order to measure FCR, contact centres traditionally were dependent on reports by agents, QA team analysis and customer surveys. However, these practices are becoming increasingly ineffective. Agent reports can be inaccurate and biased, QA teams usually are not so confident in the data they’re provided and customers don’t actively participate in surveys.

Organizations are therefore implementing speech analytics – a great tool to understand customer pain-points by recognising patterns and keywords in conversations that indicate customer distress or dissatisfaction.

It can be used to measure the number of customers who call more than once to resolve their problem. It also helps in identifying trends, attrition hints, process issues and any inhibitors to FCR.

Speech analytics provides actionable information about customer calls and supports root cause discovery. This allows call centers to reduce repeat calls and the need for callbacks.

5. Provide the Right Training to Agents to Improve First Call Resolution

Equipping contact center agents with exhaustive knowledge and training meticulously on their function is fundamental to improve FCRs. Agent training hours is found to be one of the biggest drivers for first call resolution that yields improved rates of FCR and higher customer satisfaction.

6. Make Customer Data Available to Agents in Real Time

Following up on the previous point, while training agents it is also essential to equip their knowledge with all the essential information they may need to effectively help a customer.

Dashboards that provide a 360-degree view of customers can be a useful tool for agents to have a better understanding of the customers that approach them. These dashboards equip agents with all the necessary information about customers like purchase history, preferences, conversation history that are essential during a live conversation to provide a seamless interaction and resolve issues on the first contact.

This reduces the handling time as agents spend less time in gathering necessary information to understand the issue.

7. Focus on next issue avoidance

Next Issue Avoidance (NIA) is a useful metric to enable agents to predict prospective customer issues. NIA is derived by analyzing data from large sets of tickets raised by customers to anticipate the issue that customers can come up with, which can be leveraged to eliminate a large number of calls.

NIA is a critical strategy adopted by several contact centres to reduce customer effort, improve contact centre effectiveness and establish loyalty from customers. Research suggests that 46% of customer support cases are avoidable by predicting the next potential problem.

Wrapping Up

As customer service costs continue to increase, measuring and improving FCR is an imperative part of any effective Customer Experience (CX) strategy. By using the right processes, approaches, technologies and resources companies can now predict and resolve issues with the least Customer Effort Score (CES), reduce contact center volume and improve CSAT.

If you’d like to learn more about improving FCR at contact centers, please feel free to get in touch with one of our contact center and AI experts for a personalized consultation.

The post 7 Ways To Improve FCR At Your Contact Center appeared first on BotCore.

Conversational IVR: The what, why and how

Abhishek Shanbhag — Mon, 29 Apr 2019 15:40:00 +0000

Conversational IVR: The What, Why And How

Today’s modern customers want more – They expect faster resolution to issues, personalized experiences and effective self-service. Even though traditional IVR systems are deployed with an intention to provide self-service via phone, reduce call volume and increase agent productivity, these outcomes are rarely realized. Customers usually choose phone-based interactions for escalations, disputes and complex issues.

Traditional IVR systems with touch-tone and dialog-based commands, complex routing and convoluted navigation menus often fail to resolve the needs of customers and end up frustrating them more.

Consequently, the calls are routed to live agents, which increases the operational costs and decreases productivity.

According to an IBM report, companies across the world spend more than $1.3 trillion to serve 265 billion customer calls each year. One can only imagine the savings companies will make, if they can reduce the customer call time handled by human agents.

Companies, therefore, need efficient ways of resolving the problems of traditional IVRs and at the same time address customers’ issues effectively. And the solution? Conversational IVR.

Powered by AI, conversational IVRs provide a unique voice-based and hands-free solution where customers can interact using natural language as opposed to choosing the options from a long static menu offered by traditional IVRs. Conversational IVR systems can also anticipate the caller’s needs and can determine the context of a conversation with the help of Natural Language Understanding (NLU).

The What – Understanding Conversational IVR

Powered by automated speech recognition (ASR) and NLU engines, conversational IVR can comprehend both the meaning and the rationale of the question. Getting the intent of the customer right can in itself reduce handling times by eliminating detours and ineffective routes to resolution.

With conversational IVR users can now talk to the system to address their queries without having to press buttons and select options. This IVR system understands the semantics of the request, determines the user intent and guides the user through the correct sequence. This improves the quality of self-service and eliminates complex navigation through the menu.

In a traditional interaction, the agent authenticates the customer and evaluates the history to provide a tailor made solution. This entire process can be automated using an intelligent IVR system to provide a personalized experience to customers.

The Why – Benefits of Conversational IVR

Reduce operational costs

It’s imperative that enterprises optimise contact centre costs without compromising on customer experience. Conversational and intelligent IVR systems have been integral for achieving cost-efficiency in many ways.

One of them is by improving call resolution and deflection rates, thus lowering contact center costs. Conversational IVR also fosters better agent utilisation, thus reducing labour costs.

Reduce contact center call volume

Since traditional IVRs comprise of list-based menus, they are not very effective in addressing customers’ requirements. Often, after taking the caller through a long list of options, they may not address the issue faced by the caller at all. In such a scenario, calls are directed to live agents for resolution.

With conversational IVRs however, customers can directly speak about their issue in natural language and the system suggests the best possible solution for it. This shortens the entire process of customers having to make several attempts for resolution, thus reducing the contact center call volume.

Agent Productivity

With ineffective resolution of issues through traditional IVRs, customers are compelled to contact human agents. With more calls stacking up, agents will be incapacitated to handle the large volume of calls leading to call abandonment, long waiting and handling times, in turn affecting their productivity.

With conversational IVR, customers can be engaged effectively through the IVR system without having to connect to the agent, unless in rare scenarios. This leaves the agents with more time to attend to critical matters that need human intervention and resolve issues conclusively leading to improved agent productivity.

Learn More: Human Hand-off in Service Desk Bots

Improve Customer Experiences And Improve CSAT

Since conversational IVRs foster better call resolution and quick turnaround time, the customer experience naturally goes up. This leads to greater customer loyalty and improved brand perception, increasing the overall CSAT.

Customer-centric Self-Service

The motto of self-service IVR platforms is to resolve issues quickly and reduce IVR abandonment.

Conversational IVRs address this challenge effectively by seamlessly authenticating customers, quickly understanding complex requests, and predicting customer requirements to conduct business transactions and resolve customer issues efficiently.

Adapts To Customer Behavior And Needs With The Help Of AI

Backed by AI and machine learning, conversational IVR systems can build their own database and intelligence based on customer interactions. This helps them improve their overall capability in solving customers’ issues.

This data is then used in successive interactions to efficiently adapt to the customer’s behaviour and needs.

Higher Resolution Rates for Better CX

With the ability to discern complex requests, conversational IVRs can route calls to agents who are best suited to resolve specific issues than just routing calls to the first available agent. This leads to higher rates of resolving customer issues while improving customer experience.

The How – Key considerations When shifting to Conversational IVR

Clearly, there are several benefits to adopting conversational IVR systems. However, the shift in technology comes with its own challenges. Following are some of the key aspects that companies should consider when transitioning to conversational IVR:

Understand your customers: To provide the best experience, companies should have an understanding of their customers journey, different touchpoints, their goals and challenges. They must also acknowledge that different customers will respond differently to the new self-service option. Incorporating a feedback program is essential to understand customers’ experiences and expectations.
Capture use cases: Shortlist the use cases/customer journeys which would be the most beneficial for conversational IVR. Each use case needs a different conversation flow. So, you must also consider any new use cases that can come up due to new product launches or initiatives.
Tracking Performance: The shift to conversational AI platforms must be measured in order to evaluate its success and track progress. An important KPI to be considered is the Customer Effort Score (CES) – a metric that indicates the effort customers have to exert for their issue to be resolved. Effort is a key influencing factor of customer loyalty and organizations should aim to keep it as low as possible.

Conclusion

With customers demanding more agile, quicker and faster ways of getting their issues resolved, intelligent IVR systems are becoming key to enhance and modernize customer service. In the coming years, we can see many organizations deploy conversational IVR systems to cut costs, improve agent productivity and enhance customer experience.

If you’d like to learn more about this topic, please feel free to get in touch with one of our Contact Center AI experts for a personalized consultation.

The post Conversational IVR: The what, why and how appeared first on BotCore.

7 quick tips for designing a chatbot personality

Abhishek Shanbhag — Wed, 20 Feb 2019 12:52:00 +0000

7 Quick Tips For Designing A Chatbot Personality

One of the key factors for creating better conversational user experiences (CUX) and driving chatbot user adoption is the chatbot personality. Having the right personality enables the chatbot to conduct human-like, rich, personalized and relatable conversations with users and establishes an emotional connection with the user.

If the chatbot is built for a customer-facing function, its personality should ideally mirror that of your company’s and should be tailored keeping the end-user in mind. This is crucial as your bots are a representation of what your brand stands for and the experiences you want to deliver to your customers.

Humans are usually hard to please but can be frustrated easily. Hence, your chatbot’s personality should be consistent at every stage of the conversation – right from customer greeting, query handling, providing information to conversation sign-off.

7 quick tips for designing a chatbot personality

understand the persona of your target user

When designing your chatbot personality, keep in mind the demographic, age group and other key personality traits of the end-user the chatbot interacts with. For instance, if the majority of your end-users/customers are between 25 – 40 years, giving the chatbot an adolescent-like persona isn’t the best fit. Understanding the personality of the audience, their oft-used colloquial/slang language, verbatim, habits, mannerisms, interests, etc. can help in tailoring the personality of the chatbot to the customer base.

purpose of the chatbot

It is very important to design the personality of the chatbot according to its purpose. If the chatbot is built to conduct serious conversations like handling customer complaints or helping customers with time-sensitive actions, the chatbot should be efficient and straightforward with questions and responses. Trying to be clever with witty responses is the last thing the chatbot should be doing in such a situation.

brand tone of voice

Brands often use a specific Tone-of-Voice in order to successfully communicate their personality to the consumer. Maintaining a consistent tone of voice across all platforms of communication such as social media, marketing brochures, websites, etc., helps establish how the consumer perceives the brand.

Similarly, when developing a personality for the chatbot, it is important to factor in the tone-of-voice since users tend to perceive the brand through the chatbot. Maintaining consistency between the brand tone of voice and the chatbot’s use of it will inculcate user trust.

design chatbot personality at a country level

One of the typical strategies used when rolling out chatbots in multiple countries and languages is building the chatbot personality at a global level. This is not only incorrect but also risky for a chatbot roll out.

Cultures differ with regions. Some conversations that are polite in one country aren’t deemed the same way in another country. The word “crazy” might sound funny in the UK but it’s offensive in the US. So, it’s very important for Conversational Architects to build a chatbot personality at a country level than at a global level.

This means having a single Conversational Architect for a multilingual chatbot wouldn’t be enough – even if he/she has an exceptional cross-cultural understanding. Having a cross-cultural team of Conversational Designers is a better bet. This will help to bring in the flair of language in conversations which might be very locale-specific at times.

Learn More: Multilingual Chatbots: Benefits And Key Implementation Considerations

the greeting/opening a conversation

The greeting or the first message (first interaction) the bot sends to the customer is a crucial element of conveying the bot’s personality. Ideally, the bot should not only introduce itself but also convey the different services it offers. Instead of greeting with open-ended questions like “How can I help you”, the bot should send specific messages like “I can help you with raising an HR ticket, answering common HR questions, or connecting with an HR agent”

There are several ways of opening a conversation – “Hello”, “Hi”, “Yo”, “Greetings” etc. and all these reflect different personalities.

handling unexpected and unknown questions

Even when a human asks the bot a random question or something that has nothing to do with your offerings, the chatbot must still be able to offer a reply, no matter how rudimentary the question is. This characteristic, in turn, helps the human form an emotional connection with the chatbot. The chatbot should also have multiple none-intent responses. Sending a standard “Sorry, I didn’t get that” response every time a user asks an unknown question leads to bad CUX.

humour

Just as in everyday social interactions, humor tends to have a positive effect on how humans perceive conversations. It helps engage the user, especially in interactions or processes that may be long and arduous. A chatbot that is capable of humorous parlance helps the human user get more involved in the communication and perceive the chatbot as an emotionally intelligent entity. With the help of machine learning and NLP, enterprise chatbots can be trained to recognise humorous expressions, assess the user’s mood and respond appropriately.

conclusion

Designing a personality for your chatbot seems like a lot of work, but a chatbot with a great personality ultimately enhances the user experience for your customers who interacts with it. This, in turn, promotes greater user adoption, customer experience, and sales.

If you’re interested to learn more about this topic, please feel free to get in touch with one of our chatbot consultants.

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