Chatbot Archives - BotCore

Creating Intelligent Chatbots Using Power Virtual Agents & Dataverse

Satheesh Kothakapu — Thu, 18 Aug 2022 08:54:52 +0000

Creating Intelligent Chatbots Using Power Virtual Agents & Dataverse

Over the past two years, the shift in how businesses operate worldwide has been palpable. Dynamics have transformed, ushering in an era of rapidly changing customer and employee expectations.

One of the most significant trends witnessed over the past two years has been the unprecedented and unparalleled rise in the use of conversational AI — chatbots and virtual agents — toagents—to provide quick, meaningful, and more personalized service to customers while enhancing employee efficiency levels by minimizing repetitive and redundant tasks.

A well-developed chatbot has the potential to shape a company’s brand image, playing an integral role in attracting and retaining the right market for your product and keeping employees active, engaged, and motivated throughout their journey with your organization.

Agile, resilient, and future-proof — these are three words that best describe today’s business environment. It is not enough to implement chatbots; deploying them at the right time to hasten business operations and build a competitive edge in the market is paramount.

That’s where Microsoft’s Power Virtual Agents (PVA), a low-code SaaS platform that helps build and deploy bots quickly with minimum coding experience required, comes into the scene.

This blog will explore creating intelligent chatbots using Power Virtual Agents and the Dataverse.

An integral part of Microsoft’s suite of services, Power Virtual Agents helps organizations develop and implement virtual agents and chatbots leveraging the low-code approach. This means both pro and citizen developers alike can build chatbots in a short period with little or no coding knowledge needed.

Moreover, being hosted on the Microsoft Azure Cloud environment offers the added advantage of pre-existing infrastructure. Hence, organizations needn’t invest more resources in maintaining the chatbots and can enjoy seamless integration with other services.

In short, Power Virtual Agents capitalizes on its graphical interface and minimalistic coding requirements to make bot building a breeze for all.

PVA takes entire responsibility for the functional aspect of the chatbot, so all the user has to do is provide the logic and set the virtual agent up.

Below, we describe in brief the process of creating your first chatbot using Power Virtual Agents and the Dataverse environment —

Install Power Virtual Agents in Teams by selecting and adding it from the left navigation bar.
After installing, launch the app and choose the “Start Now” button.
Choose which team would own and manage your chatbot and then “Continue.”
Choose a name and language for your virtual assistant.
Select “Create” to complete the process of creation.
Define the topic nodes. Topics define chatbot conversations and how they take place. Types of topics include “trigger” phrases (phrases and keywords that indicate the user has asked a question) and “conversation nodes” (define how the bot responds to the user’s request).

How to create a topic?

Select + New topic. Name and Save the Topic.
Enter trigger phrases for the topic, “for example, “Who can I contact for a loan query?”
In the Message node, enter information for the trigger nodes. For example, “The contact details for a loan query are Steve Robbins, sr@xyz.com.” You can add multiple conversation nodes.
Select Redirect to another topic > End of conversation to define the end of the conversation.

Click here to know more.

7. Add inputs, variables, and conditions.

Instead of defining all trigger phrase information in the message node, you can add inputs, variables, and conditions to vary the chatbot’s response based on the user’s input.

Inputs define the user’s response when the chatbot asks a question. While variables store the inputs to be used later, conditions set forth the branching logic.

To add a node between the trigger phrases and end conversation node, select the Add node plus sign and then select the “ask a question” option.
Select “Multiple choice options” and define the options for the user to select from.
Select the variable name that will be triggered when the user configures the branching logic.
To define the next appropriate response for each of the multiple choice options, create a new condition node and add a new message node for each probable response that the user may give.

8. Pull in data from Dataverse for Teams

Dataverse for Microsoft Teams is a low-code service that offers relational data storage, editable data tables, rich data types, robust governance, and one-click deployment to Teams.

You can create a Dataverse for teams table in the same team as the chatbot using Microsoft PowerApps.
In PowerApps for Teams, select Build -> See all -> New -> Table.
Create a new table, add new rows, and columns.
Add a “call an action.”
Use Power Automate flows to pull in data from the Dataverse for Teams table, Lists, or any other data source.

To know more, click here

9. Publish the Chatbot

In the Power Virtual Agents app, open the chatbot for editing.
Choose the Publish bot menu item or button.
Choose the Publish option. Select Publish to confirm in the Publish latest content confirmation window.

10. Publish the Chatbot

Once the chatbot is complete, it must be published so that users can interact with it. You can send a link to team members or add the chatbot to the Built by your colleagues section in Teams or if the chatbot is for the organization, you’ll send it to your Teams admin for approval.

If the chatbot is only for your team members, you must instruct them on how to use and share the bot by choosing one of the following:

Copy link – Provide users with a link to the chatbot.
Add to the team – Add the bot to a specific team. With this option enabled, anyone in your team will be able to find the bot in the Teams app store in the Built by your colleagues section.
Show in Teams app store – Make your bot visible in the Teams app store, with the option of showing only to teammates and shared users or to everyone in your organization.

Click here to learn about how to publish the chatbot for the entire organization.

Subscription And Licensing:

Microsoft 365 offers two editions of PVA:

PVA for Teams: A free app that only permits the creation of internal bots.
Power Virtual Agents: Requires additional subscription & supports the development of both web-based external client bots and internal Microsoft Teams bots.

How can Acuvate help?

As a Microsoft Gold Partner and provider of next-generation AI and consulting services, Acuvate leverages Microsoft’s robust suite of services, including PowerApps, Power Virtual Agents, and Power Automate, to create custom apps, AI bots, and workflows that enhance employee efficiency and support the delivery of exceptional customer experiences.

We have assisted clients from different industries and geographies in creating intelligent chatbots using PVA and Microsoft Dataverse.

To learn more about our chatbots, please schedule a personalized consultation with one of our experts.

The post Creating Intelligent Chatbots Using Power Virtual Agents & Dataverse appeared first on BotCore.

Understanding The Role Of Sentiment Analysis In Chatbots

Abhishek Shanbhag — Fri, 01 Mar 2019 10:00:00 +0000

Understanding The Role Of Sentiment Analysis In Chatbots

Chatbot technology has had an undeniable impact on digital transformation of organizations; customer experience management, in particular. When we talk about conversational AI solutions and other AI-based applications for augmenting customer and employee experience, chatbots emerge as a front-runner.

According to Gartner, 70% of white-collar workers will engage with conversational platforms on a regular basis in the next three years. The research firm’s 2019 CIO Survey revealed that chatbots are the main AI-based application used by the participating companies. Therefore, we can see increased investment in chatbot development and deployment.

“There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years. This increase has been driven by customer service, knowledge management and user support,” shared Van Baker, VP Analyst at Gartner.

However, in the earlier stages, chatbots used to have limited capabilities and deliver standard responses. With the advancements in AI and machine learning, chatbots have become more powerful and incorporated new features that helped improve user experience. And one of these latest features that is taking user experience to the next level is sentiment analysis.

Sentiment analysis helps a chatbot to understand the emotions and state of mind of the users by analyzing their input text or voice. This analysis enables chatbots to better steer conversations and deliver the right responses. Sentiment analysis is also playing a key role in driving user adoption for enterprise chatbots.

Let’s deep dive!

understanding sentiment analysis

Sentiment analysis is a sub field of machine learning and natural language processing that deals with extracting thoughts, opinions, or sentiments from voice or textual data. It is currently widely used in marketing and customer service functions to analyse customer data from surveys, social media and reviews. This not only enables businesses to understand the impact of their products/services but also to tweak their strategies as per the end consumers’ opinions.

In the context of chatbots, sentiment analysis helps in developing the bot’s emotional intelligence.

While machine learning helps to personalize the chatbot’s performance by harnessing historical customer data, NLP helps to evaluate and interpret the information sent by the customer in real-time.

These two features collectively help chatbots to deliver relevant responses and conduct meaningful conversations. Sentiment analysis takes this a step further by enabling bots to understand human moods and emotions.

Let’s break down how sentiment analysis in chatbots works:

It first identifies sentiment types and gauges if the emotions displayed in the conversation are positive, negative, neutral or objective. The technology detects emotions like anger, happiness, disgust, fear, sadness, curiosity, positivity and other range of emotions.
NLP and AI work in tandem to measures the intensity of the emotions and assign a numerical score to each of the core emotions.
After detection and classification, sentiment analysis presents the final output that enables chatbot to steer the conversation in the right direction. For example, for a text with a high positive score (joy + happiness), the digital assistant can use that as an opportunity for product recommendation or sales conversion. And in the case of a high negative score (sad + anger), the chatbot can escalate the complaint and transfer the call to a live support agent.

how can it be beneficial for your business?

Be it banking, insurance, hospitality, healthcare, travel or eCommerce, all customer-facing industries can benefit from sophisticated new-age chatbots that are integrated with sentiment analysis.

Take, American cosmetics brand CoverGirl, for instance. The company developed an influencer chatbot enabled by sentiment analysis, which helped them to improve mobile commerce performance. 91% of the conversations via the chatbot earned positive sentiment, and on an average 17 messages were exchanged per conversation that reflects high engagement rate. In addition, 48% of those conversations led to coupon delivery and the coupons’ click through-rate was an impressive 51%.

The above example illustrates the effectiveness of sentiment analysis-powered chatbots in stimulating conversations, identifying customers’ intentions, providing relevant answers and delivering a meaningful customer experience.

Listed below are a few of the benefits of using a sentiment analysis enabled chatbot to augment customer experience.

Learn how customers feel about your brand

Emotions heavily influence a person’s decision making process. How a customer is feeling determines the length and the nature of the relationship with the brand. Make use of this technology to understand how customers are feeling about your brand and communicate effectively at any stage of the customer lifecycle.

Seamless agent handover

It is important to understand the impact of timely escalation of issues to human agents when it comes to customer service chatbots. In the absence of sentiment analysis, chatbots would not be able to sense the tone of the aggrieved customer. But a digital assistant with emotional intelligence will help businesses to deal with displeased customers in an efficient manner. If the customer sounds frustrated or angry, the bot can easily hand off the conversation to a human agent.

Learn more: Human Hand-off in Service Desk Bots

Memorable customer experiences

The basic intent of sentiment analysis is to personalize and modify a chatbot’s responses to match the customer’s mood. This will enable businesses to build engaging conversations with customers at a very early stage and create a delightful customer experience.

Keep track of performance

The biggest benefit of using sentiment analysis is that it provides unique and powerful consumer insights. The conversations of an emotionally intelligent chatbot can act as a treasure trove of accurate data, which can be used to measure effectiveness of products/service, design future strategies, segment the customer base and devise strong brand positioning.

Upselling and new user on-boarding

As exemplified by CoverGirl’s influencer bot, chatbots can assist companies in product discovery, recommendation and upselling their products & services to existing customers. It can also improve new customer acquisition metrics by retaining the interest of a new visitor by analyzing his/her sentiments.

As stated above, emotions influence decision-making. Sentiment analysis help chatbots to adapt to the users’ mood and respond accurately, effectively and in the right way. By deploying and investing in this technology, companies can not only improve customer experience but also allow human agents to focus on productive issues.

If you’d like to learn more about the role of sentiment analysis in chatbots, please feel free to get in touch with one of our AI chatbot consultants for a personalized consultation.

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The post Understanding The Role Of Sentiment Analysis In Chatbots appeared first on BotCore.

Chatbots: The Past, Present, And Future

Rakesh Reddy — Wed, 19 Dec 2018 10:42:00 +0000

Chatbots: The Past, Present, And Future

Chatbots currently are one of the most popular AI technologies in the enterprise world.

Bots are being deployed for different functions of an organization – be it engaging customers, training employees, driving sales, providing IT Helpdesk or HR support, generating leads etc. These intelligent machines provide instant service, round the clock – you don’t need to keep your customers/employees waiting 24 hours for the next support agent to come online. 80% of businesses want chatbots by 2020.

However, bots weren’t equipped for intelligent and smart conversations when they were first invented. Chatbots have undergone several advancements in the past few years. The history of chatbots is intriguing, and so is the future. Let’s walk through all that chatbots were back in time, are today, and can be in the future.

the history of bots: where and how it all began

The word chatbot comes simply translates to conversation enabled by bots. As such, there are two components to this word – chat, which means conversation, and bot, which refers to the ‘computational’ element.

ELIZA, a 1964 computer program was one of the earliest examples of chatbots taking shape. Utilizing Natural Language Processing – something that allows computers to understand human language, ELIZA was able to recognize keywords and key phrases (inputs) and respond using pre-written scripts.

Soon after, an ‘intelligent’ version of ELIZA followed. This one was smarter in the sense that if ELIZA could understand inputs from a real person – PARRY could impersonate a real person! Invented by Psychiatrist Kenneth Colby, PARRY was indeed able to impersonate a patient with schizophrenia.

More inventions kept taking place to one-up older, outdated bots. And, the world soon transitioned into times when chatter bots could be openly used by masses. The first and most popular of these humanoids is A.L.I.C.E (Artificial Linguistic Internet Computer Entity) or Alice. It was the most powerful NLP chatbot of its time and was awarded the Loebner Prize three times!

Interesting fact: If you’ve watched the academy-award winning movie ‘Her’, it would be interesting to know that A.L.I.C.E was the inspiration for the movie.

the present state of chatbots: where we are today

Bots were now being deployed by businesses across various industries, all over the world. With the advancements in AI and machine learning, bots have become more intelligent are able to conduct meaningful and personalized conversations. Now, bots could adapt and learn based on the interactions they had with people. They could now process tons of data, rapidly retrieve information, process information, and give the right output/answer in no time.

In a consumer world, chatbot adoption increased because of an increase in the usage of messaging platforms. Consumers now rely on a chatbot for connecting and interacting with their favorite brand, troubleshooting basic issues, purchasing products that have been highly personalized based on their interests and likes, and keeping up to date with the latest deals and discounts on their favorite products!
According to Business Insider, by 2017, messaging apps have ousted social media platforms. You can check the following chart to get the stats and figures on global monthly active users.

chatbots in today’s enterprise

Organizations today are using chatbots for a variety of use cases and the usage varies from industry to industry and function to function. And the benefits chatbots offer are plenty including enhancing customer experience, improving employee productivity, automating mundane tasks, reducing costs and simplifying business workflows.
Enterprises are now leveraging chatbot builder platforms to effectively build, deploy, manage and train AI chatbots.
Let’s dig into some detailed chatbot use cases in the present day scenario.

chatbots for customer service

Customer service is the most popular use case of enterprise chatbots today. Businesses across industries are using customer service chatbots as the first line of support to reduce costs, improve customer experience, and increase agent productivity. Capabilities like 24/7 availability, multilingual support, instant responses, agent handover have made chatbots the most desired AI technologies in customer service.

chatbots for IT helpdesk

A number of enterprises are implementing IT helpdesk chatbots to accelerate response time, improve support staff productivity, automating IT workflows, and to deliver service related information on-demand.

Deploying chatbots can help alleviate your IT help desk challenges by making the whole process less labor-intensive, less complicated, highly interactive, and less costly.

With a chatbot at your disposal to answer basic queries raised by your employees in real-time, your IT help desk staff can focus on more complex and key tasks, thereby increasing your support efficiency!

chatbots for business intelligence

Data is the oil of the 21st century! Many companies today are integrating AI chatbots into their existing BI systems like PowerBI, Oracle, SAP BI etc. to help decision-makers get super quick access all the information and reports they need, in no time.

Simply ask your bot “What is my predicted sales for December 2019” and the bot will generate an accurate response after analyzing tons of data instantly. That too in a format that you prefer (image, graphs, pie charts, etc.)!

Learn More: Business Intelligence Bots, Power BI Bots

chatbots for HR

HR is one of the most popular functions for which chatbots are deployed. HR chatbots simplify and automate workflows throughout the employee life-cycle. Employees can use bots to ask simple or complex HR queries, perform tasks like applying for leaves or giving an appraisal etc. Whether it’s talent management or onboarding or employee engagement or off boarding, chatbots have successfully reduced HR costs in all stages of the employee life-cycle and streamlined the entire process.

Learn more: How Chatbots are Revolutionizing The HR Department

statistics on adoption of chatbots

Chatbots are expected to cut business costs by $8 billion by 2022 – Juniper Research
Furthermore, by 2021, conversational AI-first will be adopted by the majority of enterprise IT – Gartner.
By 2021, 50% of enterprises will be spending higher on chatbot creation than on mobile app development. – Gartner

For more such insights and statistics on the state of chatbot adoption, read through this blog post.

the future of chatbots: where we are headed

As the chatbot technology continues to mature, the future of bots is becoming interesting. Here are a few important trends to watch for:

Chatbot-RPA integration: Enterprises are taking customer and employee experiences to the next level by combining the power of automation from RPA and cognitive intelligence from chatbots. RPA helps chatbots access legacy enterprise systems which lack modern APIs. Bots can trigger RPA robots to perform complex actions without routing to a human agent – increasing productivity.

Voice Bots: Gartner predicts that, by 2023, 25 percent of employee interactions with applications will be via voice, up from under 3 percent in 2019. In the future, chatbots will no longer be just text-based interfaces. As AI-powered speech-to-text and text-to-speech hosted services improve, voice bots will be used for a variety of enterprise applications including conversational BI, IT helpdesk, scheduling meetings etc.

Chatbots will be more human: As key chatbot components like NLP, Machine Learning, sentiment analysis, contextual and language understanding etc. become more advanced, chatbots will be able to conduct much more complex conversations – just like a human does.

There might be soon a time when your bot will be reminding you of your sales meeting scheduled for the day. There’ll soon be times when a smart speaker will take your order at a restaurant. Bots speaking to bots can become the reality of the future. What next? We can only wonder.

If you’d like to learn more about enterprise chatbots please feel free to get in touch with one of our chatbot consultants for a quick consultation!

FREE EBOOK

exploring the use cases of an enterprise chatbot

The post Chatbots: The Past, Present, And Future appeared first on BotCore.

Chatbot Archives - BotCore

Creating Intelligent Chatbots Using Power Virtual Agents & Dataverse

Creating Intelligent Chatbots Using Power Virtual Agents & Dataverse

Subscription And Licensing:

How can Acuvate help?

Understanding The Role Of Sentiment Analysis In Chatbots

Understanding The Role Of Sentiment Analysis In Chatbots

understanding sentiment analysis

how can it be beneficial for your business?

Learn how customers feel about your brand

Seamless agent handover

Memorable customer experiences

Keep track of performance

Upselling and new user on-boarding

FREE EBOOK

a guide to choosing an enterprise bot builder platform

Chatbots: The Past, Present, And Future

Chatbots: The Past, Present, And Future

the history of bots: where and how it all began

the present state of chatbots: where we are today

chatbots in today’s enterprise

chatbots for customer service

chatbots for IT helpdesk

chatbots for business intelligence

chatbots for HR

statistics on adoption of chatbots

the future of chatbots: where we are headed

FREE EBOOK

exploring the use cases of an enterprise chatbot