Category: Chat

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Chatbot space is diverse and there is a huge list of chatbots that are being used in various areas. Recently, Enterprise chatbots are picking up popularity and are the newest AI chatbot that is receiving wide attention.

Till now, you might have heard about AI chatbots for customer service that responds to users’ queries and routes requests to agents.

Think the same but replace the customers with employees of an organization and you basically would get an enterprise chatbot.

There are a lot of advantages for enterprise chatbots, but there are many challenges too. In this Guide, let us discuss all the important aspects of Conversational AI Chatbots for an enterprise.

We start with a brief discussion of what chatbots are and how they work, then understand clearly what enterprise chatbots are. We understand their use cases with an enterprise and see how advantageous it is to use enterprise chatbots.

Then, we will look into some challenges of using it, and finally understand how easily you can equip yourself with an enterprise chatbot. Also, we will see what you need to see on a chatbot platform.

What Chatbots are and how do Chatbots work?

When you see chatbot definition, it looks something like this: Chatbots are software that can understand human language. Lets people interact with businesses on their digital assets like websites, apps or social media channels in their natural language through various communication channels.

Chatbots use three natural language techniques: Natural Language Processing, Natural Language Understanding, and Natural Language Generation. The intents of these technologies are evident in their names.

An intelligent chatbot correctly identifies context and intent of an user and based on things it already learned from user’s big data, they fill in additional information and clues for solving particular problem.

Also, chatbots are now able to understand and interact in many languages and can be localized easily.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Why do Enterprises need Chatbots?

Enterprises need AI chatbot technology for many reasons. Just as a customer needs information quickly and easily, employees of an enterprise too would have certain needs for their queries or performing certain tasks.

An enterprise chatbot can take questions posed by employees and after recognition of the user’s (i.e, employees) intent, it can take appropriate action.

This shows that there is a significant degree of similarity between use cases of customers and those of employees for chatbots. Let us see some of use cases of Enterprise chatbot applications:

Chatbot Use Cases for Enterprise Chatbots

General Requests and inquiries

Employees would have a wide range of queries like they might want to check their allowances, reimbursements, and policies. Instead of referring through many documents themselves or asking others, employees could quickly reach out to chatbot.

Helpdesk

Conversational AI solutions can be extremely helpful in suggesting basic solutions for any problem and if not yet solved, raise a ticket request and send it automatically.

Intranet search Chatbots

There would be many files on the intranet and if a particular employee needs to find a specific file, instead of going through all the files and searching manually, they can just ask the chatbot.

Chatbot then uses intelligent natural language techniques to fetch data from the intranet and present them to employees.

Business Intelligence

Chatbots can have access to data reports and your employees could ask for any data regarding the organization and within their bounds, they could get all information in multiple formats.

These are only some general use cases. On the basis of your organization, there would be a lot more use cases for an enterprise chatbot.

Benefits of implementing Enterprise Chatbots

Just as use cases overlap, benefits also overlap between general customer chatbots and enterprise chatbots. Let us see some of important benefits of implementing enterprise chatbots:

Easily answer FAQs

Chatbots can understand questions easily and can give fast and more accurate answers to them than a search engine.

Decrease time taken for solving problems

Chatbots work pretty fast and they can finish requests of employees within seconds. If required, they can also rope in experts within an organization for solving problems.

Works all time

A conversational AI solution can work consistently throughout all times a year and as such your employees won’t experience any downtime and even a slight delay in getting their concerns addressed.

Increased Employee Productivity and Satisfaction

With all their Concern queries satisfied and work quickly done, employees would be more productive and ultimately this will improve their satisfaction.

Challenges with Chatbots

One of the main problems with implementing chatbot is to design it. What must it do? There must be a well-thought answer for these problems which probably involve multiple chatbots for multiple use cases.

As chatbots would have access to critical business information both in data and policies, its security is also a serious concern.

Another important challenge for having an NLP chatbot is its cost. Though many solutions are available relatively cheap, certain sophisticated solutions could take up a lot and be more costly than it actually saves.

How can you get a Chatbot?

It is certainly true that the underlying technology of chatbots is incredibly complex and hard to build from scratch. The challenges which we mentioned make it even harder.

There are a lot of open-source chatbot frameworks that make chatbot development easier. However, developing enterprise chatbots that address all use cases and concerns is still not an easy task.

Fortunately, there are a lot of organizations that provide customized chatbot solutions that are tailored to specific use cases and also can be built within minutes! If the kind of chatbots offered by the chatbot platform can align with your needs and budget, you can quickly go for them.

Final Words

Harnessing the power of natural language processing and natural language understanding technologies, plus armed with big data analytics, they can solve numerous problems and perform a lot of tasks.

Just as they have been helping end customers throughout the years, they can help employees in an enterprise if they are correctly designed and implemented. If you have not yet considered including chatbots, then this is high time to use them!

Don’t forget to give us your 👏 !

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Chatbots Powered by Conversational AI for Enterprises was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Businesses now realize the need for a customer-centric approach to transforming their customer experience (CX). According to the Zendesk Customer Experience Trends Report 2021, 75 percent of company leaders agreed that the global pandemic accelerated the acquisition of new technologies to get customer-centricity right.

But, there are challenges too.

• Some of the businesses don’t have the systems and technology to segment and profile customers.
• Some lack the processes and operational capabilities.
• Some of them don’t have all of the components in place to claim they are customer-centric.
• Few don’t know what their customers expect and how they want to interact with the business — not the products, features, or revenue model.

However, in the digital-first world, social messaging is the dominating communication channel consumers are using to interact with brands. Forward-looking businesses are tapping this trend to their advantage using industry-ready AI chatbots to manage customer-centric interactions and forge customer relationships online.

Superior Customer Experience is a Necessity

While adopting the latest AI technologies to improve customer relationships, it becomes imperative for industry leaders to keep an eye on the latest customer engagement trends. Here are a few reasons that explain why a top-notch customer experience is the need of the hour:

• To enable a Superior Omnichannel Experience

AI-powered chatbots are capable of preserving information across several digital touchpoints and even when it transfers the conversation to a live agent, customers don’t have to explain their issues repetitively. Such availability of information across the channels helps businesses provide a consistent omnichannel experience to their customers. This experience helps businesses save time for customers and amplifies the customer engagement graph.

• To improve Brand Loyalty & Differentiation

Another success metric for businesses is to consistently improve their brand value in this digital competitive arena. Brand loyalty involves an intrinsic commitment of a consumer to a brand based on the distinctive values it offers. Hence, it becomes an obvious reason for CXOs to leverage a Conversational AI technology that enables instant, relevant responses helping brands provide improved experiences and differentiation.

• To expand new Customer Base

The biggest success for brands is to acquire new customers and expand their customer base over time. Providing instant prompts with offers, product recommendations, and guiding customers through their conversational journeys enables businesses to broaden their reachability and increase conversions.

Intelligent AI chatbots are fast becoming key enablers to customer support and conversational commerce teams and are instrumental to improving the end-customer experience landscape.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Why are AI Chatbots not a “One-Size-Fit-All” Approach?

AI Chatbots are not a “one-size-fits-all” solution. No two brands have the same business needs, so no two chatbots can be the same. An all-in-one solution that goes right for all the business functions sounds like a myth. Hence, the approach has to be changed as per the business use cases while building and training an AI chatbot.

When catering to customer support and conversational commerce use-case, the “One-size Fit-all” approach is not able to solve all customer queries. The responses will sound generic to customers and increase dissatisfaction. Hence, the right approach is to replace this with the best and most common industry use-cases to improve efficiency and conversions.

Here are a few problems that remain unsolved with the one-size-fit-all approach:

• Every industry has its distinct use-cases. Today, every industry has its unique business use cases depending on the marketplace and audience they are targeting. Hence, a versatile approach that provides solutions to industry-specific use cases should be the topmost priority for businesses when adopting customer experience automation technology.
• Non-personalized responses don’t work anymore. A generic AI Chatbot will not be capable of providing contextual responses across omnichannel digital touchpoints. In the current landscape, this won’t work anymore. The need of the hour is a domain-intelligent AI Chatbot that can end-to-end resolve customer queries, providing a seamless experience to the customer.
• Customer satisfaction matters. Unhappy Customers are an outcome of poor customer service. A generic AI chatbot will not be able to deliver top-quality support and service as they are not supported or trained to handle domain-specific commonly recurring queries, resulting in increasing customer dissatisfaction.

NLP: The Technology Behind Intelligent Conversations

While it is established that a domain-specific, AI virtual assistant is core to enabling superior customer experience, it’s important to understand the technology behind it.

To understand the pain points, intent, and expectations of a customer in a conversation between a bot and a customer, NLP is the behind-the-scenes technology that makes the magic happen.

Natural Language Processing (NLP) is a subsection of Artificial Intelligence that enables chatbots to understand human languages. NLP analyzes the customer query, language, tone, intent, etc., and then uses algorithms to deliver the correct response. In other words, it interprets human language so efficiently that it can automatically perform end-to-end interaction with accuracy.

Key Capabilities that NLP provides:

• NLP allows chatbots to understand voice input as well as text.
• With NLP technology, the chatbot doesn’t need the exact correct syntax to understand customer’s expectations.
• Based on its programming mechanism, it can auto-detect languages, context, sentiment, and intent.
• Chatbots can either process their response through their NLP engine or by analyzing customer’s browser preferences.

Intelligent AI chatbots are now critical to strengthening a brand’s CX strategies. As cognitive AI-powered technologies continue to develop, business leaders must ensure they adopt chatbots technologies that are agile to meet the requirements of their businesses.

Key Capabilities a powerful AI Chabot Should Have

An AI-powered full-stack Conversational AI platform enables brands to comprehensively solve business problems end-to-end, and at scale. While looking to adopt a conversational AI solution, some of the key characteristics which CX leaders should look for are as follows:

• Powerful NLU & ML Intelligence: The turning point in the evolution of chatbots was the advent of two key AI technologies — Natural Language Understanding (NLU) and Machine Learning (ML). The architecture of Natural Language Understanding (NLU) is built on a combination of modules such as Language detection, ASR classification, Context Manager, that work in tandem with deep learning-based encoders to accurately understand natural language and handle user queries with higher precision. Businesses should go with a Conversational AI solution that has a high precision, powerful NLU capability.
• Ability to Create Domain Intelligent Conversations: Industry-specific AI chatbots embedded with domain-specific intelligence, data dictionaries & taxonomies are trained on thousands of user utterances to deliver human-like conversational experiences at scale. The in-built Named Entity Recognition (NER) engine helps chatbots to understand user intent and context better. As customer conversations are unique to a business, the Conversational AI solution must be agile and help create domain intelligent conversations.
• Quick to Launch: AI chatbots built using smart NLU and advanced domain intelligence capabilities Smart Skills deliver desired output with minimal effort and training. This platform consists of a comprehensive library of 100+ ready-to-use, domain-specific intelligent use cases for your business. Technology is getting easier to deploy and domain-intelligent chatbots can now be launched in a matter of minutes. Businesses should go for a Conversational AI solution that is faster to value and give quick ROI.
• Comprehensive integration to build a Full Stack solution: An AI solution that can be easily integrated into your existing CRMs, help desk software, etc helps create a full-stack solution with only one source of truth. The best scenario, in this case, is that the integration of these AI solutions should not require deep-coding dependencies or complex technical processes. Businesses should adopt an easy-to-integrate Conversational AI solution that has a comprehensive integration ecosystem.

CX Trends to Look out for in 2021

While the above-mentioned capabilities of Conversational AI sound interesting and intriguing, it is only the tip of the iceberg. Technology has just entered the digital space and is expected to evolve further with time. Talking about the same, here are the top four customer experience trends businesses might come across in 2021 and beyond.

• The build-to-buy switch: Considering the increasing popularity, organizations find it optimal in terms of cost to purchase already-built tools and then customize them, instead of building one from scratch.
• Emphasize on what and how of the customers: 2021 conversational AI tools are more efficient now. They are designed to understand human language quicker, faster and give human-like responses.
• Deploy models (process-oriented) that are more than a messaging bot: Since organizations are on the lookout for automating a large part of their customer interaction funnel, emphasis is laid on the creation of tools that are one step ahead of the basic designs and can automate end-to-end queries and processes which are repetitive.
• Consolidation of customer support, marketing, and sales departments: To offer an omnichannel experience, the next wave of conversational bots is bringing together the different departments in an organization to achieve a common goal of customer experience.

Final Words

CX transformation is a catch-all phrase that means something different for every business. There should be different strategic approaches when it comes to deploying AI-powered technologies. However, it is established that a simple AI chatbot will not deliver the kinds of experiences that a Conversational AI solution can enable.

In case you’re interested to explore more, here’s an eBook we’ve put together that shares the experiences of a diverse set of CxOs as a part of their journey to identify feasible, realistic solutions to solve the challenge of repairing a broken customer experience and scaling high-volume customer queries with AI Automation. Get your copy here.

Join us in our journey to transform Customer Experience with the power of Conversational AI.

Interested to explore more or want to try out a chatbot of your own?

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CX Transformation with AI Chatbots is not a “One-Size-Fit All” Approach was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to Create a Chatbot Using DialogFlow ?

What is DialogFlow ?

DialogFlow is a development platform created by Google that can help us to create Chatbots. It’s based on NLP (Natural Language Processing) which offers our chatbots the possibility to be very powerful.

What is the ChatBot ?

A chatBot is an intelligent program that can interact with people like a human and serves them in the specific domain where it has been created. The chatbot analyzes the intention of the client and researches the response that will be more adapted.

Now you know what DialogFlow and chatbot are, let’s see how we can create a chatbot using Dialogflow.

Note: You should have a google account and login in to the Dialogflow platform before following these steps.

In this article, we will create a chatbot that can serve clients who will want to do a reservation for a bedroom in a Hotel.

Step 1. Create an Agent

An Agent is an intelligent program inside the chatbot, it’s that program that interacts with the clients or users.

To create an Agent, go to the left section of your screen and click on the first button below the Dialogflow logo and go down to the create new agent button.

After that, the new screen will be loaded, and you will be ask to specify the name of the Agent, the language that it should be speak and the time zone. For me, I type reservation-bot for the name and the rest, i leave the default values. After that, you must click on the CREATE button and DialogFlow will create an agent for your chatbot.

Step 2. Create intents

Intents is use by the chatbot to understand what the clients or users want. It’s inside the intents that we should provide to the chatbot the examples of phrases that the clients may ask and some responses that the chatbot should use to answer to the clients. Let’s show how we can do it.

Note: When we create a new agent, it comes with two defaults intents named Default Fallback Intent and Default Welcome Intent

For create a new Intent, click on the Create Intent button

After that, you must give the name of your intent. Then go to the Training Phrases section and click on add training phrases. This section concerns the way where you should give the example of the phrase which represents the different questions that clients may ask to the chatbot. we recommend giving many examples to make your chatbot very powerful.

For this example, you could take the same phrases as me.

We have added some phrases that clients may ask to our chatbot, for your own chatbot, feel free to add another phrase to improve the power of your chatbot

In this image, we can see that two expressions are overlined. In fact, DialogFlow has identified these expressions as an entity. DialogFlow recognizes three types of entities such as systems entities, developer entities, and session entities. this night and today are recognized as systems entities, it refers to the date or period of time, this type of entity is already set in Dialogflow. Later we will create our own entities which will recognize by DialogFlow as Developer entities. For more information, check out this documentation

Now, let’s define some Responses that the agent may use to answer to clients. Go down to the Response section and click on the Add response button, and add some responses statements.

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1. How Conversational AI can Automate Customer Service

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3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

You can see that inside these responses examples there are some expressions that start with the $ symbol, these expressions are considered as variables that will contain the values that clients will mention in their questions, and that DialogFlow will have recognized as a certain entity. On the image above, we have three variables such as $time-period, $date-time, and $reservation-type. $time-period and $date-time are systems entities variables and $reservation-type is a Developer entity variable, which means $reservation-type should be created by the developer, before that DialogFLow may recognize it. After added some responses that the agent should use, click on the Save button, we will come back hereafter.

Step 3. Creation of entities

In reality, entities are keywords that help the Agent to recognize what the client wants. To create it, just follow me.

Click on the Entities button

After click on the Create Entity button

After, specify the name of the entity (you should give reservation-type as name of your entity, because you have use it as variable when you gave some responses to the agent). Then, add an entity bed-room and some synonyms like below.

make sure to check the case Define synonyms before, and then click on Save button.

The role of synonyms is that, when clients should talk about bed-room, bed or room, all of this should refer to the bed-room.

Do the same with the entity reservation-action and save it.

Now, we have two entities ready to be used.

Step 4. add our entities inside training phrases expressions

back to the reservation intent interface and go to the training phrases section.

When you are there, select an expression, and inside this expression select the word bed-room like this

Then, research for @reservation-type

And click on this, and the color of bed-room will change.

Do the same thing to all the bed-room inside all expressions.

For the words booking, reservation, and reserve, do the same things but instead of research @reservation-type you will research @reservation-action.

Step 5. Definition of parameters and actions

It’s not required, but in some cases, it will be very important to obligate the user to give to the chatbot, some information.

Go down to the Actions and parameters section, always inside the reservation intent interface. you should have this image below.

For our chatbot, we want that clients provide the reservation type and the date of the reservation. Make sure to check it.

After that, we should specify the prompt text that the Agent should display to the client when they haven’t specified the required parameters. You need to click on the Define prompts… space on the right place of this section, after defining prompt text, close the box dialog.

for the date-time parameter

for the reservation type parameter

After this, save the intent.

Now you can test your chatbot.

You can test your chatbot here.

Step 6. Integration on the web platform

Click on the integrations button

You can integrate your chatbot inside of many platforms, like Facebook messenger, WhatsApp, telegram, and so on.

For this article, we are going to choose the Web Demo

click on the link, and test it again.

My Demo on my phone.

reservation-bot-dialogflow.mp4

Thank you for reading…

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How to create ChatBot using DialogFlow ? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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So far we have discussed the idea of dialogue systems and the various types of architectures around which to model our chatbots which you can read here. In this post, I would be talking more about the techniques and algorithms that are widely being used to implement the architecture and touch upon the idea of social and emotional awareness in chatbots.

Techniques and Algorithm

To design a chatbot, there exist several techniques and algorithms that are useful to a programmer. Some of these are based on natural language processing techniques and were quite common in the earlier versions of the dialog systems, while others make use of deep neural network techniques. Bradesko and Mladenic [1] and Abdul-Kader and Woods [2] investigated some of the design techniques and algorithms used in Loebner Prize Competition conversational chatbots, some of which are mentioned below:

1.Pattern Matching: One of the most widely used techniques for designing chatbots that match the input message with the patterns present in the knowledge database using sentence measurement techniques like bigram methods [3].

2. Parsing: This technique takes the user’s message as input and looks for objects and subjects like verbs, nouns, and common phrases in the message to find dependent and related phrases and to determine the grammatical structure which can then be analyzed to check if it forms a valid expression. By this method, a chatbot can cover varied input sentences with similar keywords using a limited set of patterns.

3. AIML: A universal, easy-to-understand derivative of XML which consists of data objects called AIML objects which contain two units topics, and categories, consisting of either parsed or unparsed data. Topic includes a name attribute and set of categories related to the topic, wherein each category consists of patterns which represent user input and a template implies chatbot response. Categories can be atomic, default, or recursive [4].

4. Markov Chain Models: The idea behind Markov Chains is that there is a fixed probability with which each letter or a word appears in a dataset. This idea is used to generate responses that are probabilistically more applicable. As explained by Serban et al. a two-step procedure is followed. Firstly a seed reply is created using a sequence of keywords extracted from the message and secondly, two separate Markov chains generate the words following and succeeding the seed keywords starting from the seed reply. In this way, many candidate responses are produced and one with the highest entropy is returned as the response from the chatbot [5].

5. Ontologies: Modern task-based dialogue systems are based on domain ontologies which contain the knowledge as a set of concepts that are hierarchically and relationally interconnected. Ontologies contain concepts that are interconnected into graphs and this structure can be used to represent the intentions that the system can extract from messages. An ontology defines one or more frames with each frame having a group of slots and defines the values that each slot can take. Milward and Beveridge examined the use of ontological domain knowledge for dialogue based breast cancer referrals and control of networked home appliances [6].

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

6. Word Embeddings: Bengio et al. proposed a distributed vector representation of words called word embeddings [7]. Word embeddings are words converted into numbers that capture the meanings, semantic relationships, and contexts and may have different numerical representations for the same word. Different types of word embeddings include frequency-based embeddings and prediction based embeddings.

7. TF-IDF: It intends to capture the importance of a particular word to some documents present in the corpora [8]. The ‘term-frequency’ term simply denotes to the contribution of a word in a document or the count of the number of times a word appears in a corpus and the term ‘inverse document frequency’ is used to penalize the words that appear more often in the corpora. The final score is calculated as the product of these two terms.

8. Recurrent Neural Networks: A variant of neural networks that stores the state of previous input and combines with the current input which helps preserve some of the relationships between the current state and the final state. This leads to the formation of an internal state which models the time-dependent data and the internal state is updated at each time step [8].

9. LSTM: To model longer dependencies the hidden units in Recurrent Neural Networks are changed to Long-Short Term Memory units [9]. It maintains two different memories- a cell state which is the long-term memory and a hidden state which is the short-term memory. A series of gates is used to determine if a new input is to be remembered, forgotten, or used as an output.

Social Chatbots

One of the primary objectives of artificial intelligence is to make machines act like humans. As suggested by, H. N. lo et al. research in the field of chatbots and conversational agents has seen a sudden increase from 2015 but more attention should be given to the new technologies like deep learning and new trends like mobile chatting apps for further research in this topic [10]. Keynar et al. investigated the importance of Open Data as a new trend that enables government transparency and citizen participation. They have used machine learning models for entity recognition and intent classification and a neural network that selects the response from a predefined set of actions [11].

Young, Cambria et al. made the first attempt to augment a large common sense knowledge base into an end-to-end conversational model [12]. Common sense knowledge in artificial intelligence research refers to facts about the everyday world that humans are assumed to know. Commonsense knowledge has to be integrated into a conversational model in order to make human-computer interaction more interesting and engaging and since this kind of knowledge is very vast in itself the model in this paper uses an external memory module which is better than forcing the system to encode it in model parameters. This model takes into account both the message content and related commonsense for selecting appropriate response and employs retrieval-based methods.

One other important issue that needs to be taken care of is the emotional intelligence of these dialogue systems or chatbots. Cambria et al. study the use of common sense knowledge for developing emotionally sensitive systems [13]. Such systems or social chatbots must be designed in such a way so as to enhance user engagement while taking both intellectual quotient (IQ) and emotional quotient (EQ) into account [14]. Social chatbots are designed in such a way that their primary task is not solving all the problems but acting as a virtual companion to the user. They need to have an emotional connection with the user and can interact through a number of modalities including text, speech, and vision.

Conclusion

We studied and analyzed various types of dialogue systems that exist including rule-based and corpus-based systems. From using simple natural language processing techniques, including pattern matching, parsing, and AIML for designing chatbots, dialogue systems have come a long way and nowadays implements complex neural network architectures for response generation. From rule-based approaches such as ELIZA to data-driven approaches which can either be based on information retrieval methods such as DBpedia Chatbot or generation based methods implementing recurrent neural networks or hierarchical neural networks to model short term dependencies. The architecture was further improved with the introduction of long-short term memory units which made dialogue systems more engaging and helped produce more natural and humanly responses. Use of common sense knowledge and other techniques are being worked upon to develop intellectual and emotional chatbots like XiaoIce. We can certainly say that there has been a lot of research and improvement going on in the field of designing a dialogue system.

References

[1] Bradeško, L., & Mladenić, D. (2012, October). A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian Language Technologies Society Eighth Conference of Language Technologies (pp. 34–37).

[2] Abdul-Kader, S. A., & Woods, J. C. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7).

[3] Setiaji, B., & Wibowo, F. W. (2016, January). Chatbot using a knowledge in database: human-to-machine conversation modeling. In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS) (pp. 72–77). IEEE.

[4] Shawar, B. A., & Atwell, E. (2002). A comparison between Alice and Elizabeth chatbot systems. University of Leeds, School of Computing research report 2002.19.

[5] Serban, I. V., Lowe, R., Henderson, P., Charlin, L., & Pineau, J. (2015). A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742.

[6] Milward, D., & Beveridge, M. (2003, August). Ontology-based dialogue systems. In Proc. 3rd Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI03) (pp. 9–18).

[7] Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137–1155.

[8] Lowe, R., Pow, N., Serban, I., & Pineau, J. (2015). The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909.

[9] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.

[10] Io, H. N., & Lee, C. B. (2017, December). Chatbots and conversational agents: A bibliometric analysis. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 215–219). IEEE.

[11] Keyner, S., Savenkov, V., & Vakulenko, S. (2019). Open Data Chatbot. arXiv preprint arXiv:1909.03653.

[12] Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., & Huang, M. (2018, April). Augmenting end-to-end dialogue systems with commonsense knowledge. In Thirty-Second AAAI Conference on Artificial Intelligence.

[13] Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2010). Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. In Development of Multimodal Interfaces: Active Listening and Synchrony (pp. 148–156). Springer, Berlin, Heidelberg.

[14] Shum, H. Y., He, X. D., & Li, D. (2018). From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–26.

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A Comprehensive Survey of Existing Chatbot Architectures and Techniques(Part-2) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are you a small business owner? 24 hours just don’t seem enough. Getting tangled in between endless To-Do lists and wishing everything could be automated with a click of a button…all this while saving costs?

According to a recent Salesforce study, more than a third of small business owners worry about not having enough time in the day.

A major problem small business owners face is that most mundane daily tasks get divided attention; leading to quality being compromised or even tasks being abandoned before completion.

Enter Artificial Intelligence.

AI allows companies to automate numerous steps in both core and support functions thus increasing efficiency and reducing costs. With Artificial Intelligence one can assure consistency in business processes, especially for those mundane and repetitive tasks.

For example, you are a real estate agent, the job demands you to be in and out of the office throughout the day. As such, there are chances you could lose a potential customer while you are out of the office. But certain answers to clients’ questions can be delivered only by you. Therefore, cloning your expertise with an AI-enabled chatbot will ensure consistent dispensation of knowledge and commoditized expertise.

As a small business owner, there would be several doubts about applying “AI for my small business”. Will the investment give good returns? Do I need this cutting-edge technology? Botspice provides an easy customizable Workbot model that is easy to implement and grows with time.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Here’s are some benefits to cloning yourself using Artificial Intelligence,

· WORKBOTS NEVER TAKE A DAY OFF!

Workbots are available 24 hours a day 7 days a week and can often answer customers’ questions more quickly and efficiently than humans. Cloning your expertise with a Workbot will not only allow consistent answers to queries but also make your expertise available during non-business hours.

· NO ADVANCED I.T. SKILLS REQUIRED

83 percent of the small businesses surveyed don’t have IT staff to help them when technical difficulties arise.

With a Workbot business can grow without any complexities of adding technical knowledge. Workbots are future proof and it does not need much supervision once deployed. Botspice provides a variety of activity nodes to support a multi-lingual and multi-dimensional conversational flow. And it is easy to create and maintain a Workbot.

· VALUABLE CUSTOMER DATA

Gone are those days where one would fill a form and call it a lead. Replacing lead-generating forms with an intelligent conversationalist will enable a business to understand customer behavior. This valuable data can be used to identify the overall demographics and identify how they can improve the customer experience through these conversational funnels.

· GENERATE LEADS WITH AI-POWERED INSIGHTS

Businesses can analyze the statistical data and record market behavior over time to generate valuable insights for strategizing and forecasting future business plans. Through an analytics dashboard, one will be able to obtain a 360-degree view of customers. It also gives an idea of which lead is likely to convert into a sale.

It is benefits like these that make Artificial Intelligence the best fit for small businesses and business owners. Cloning expertise helps automate time-consuming and costly processes. While you may not be able to biologically clone yourself anytime soon, an AI-enabled ecosystem could be the answer to digitize and automate your business processes.

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Clone your Expertise with AI-Enabled Workbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

As a tool to build and deploy interactive chatbots, AI-based bot platforms are a rage today. Organizations utilize these tools to create intelligent and conversational bots to interact with their website visitors, offer them the required information, and improve customer satisfaction.

In this blog, we explore chatbots and their types along with the best chatbot builder in 2021 to help you build an interactive chatbot.

Building a Chatbot — Use Cases, Requirements, and Types of Chatbot

Building a chatbot is a complex process that requires everything from a detailed strategy, tools, conversational process flow, reporting, and much more. Among the key things that you need here include-

• Problems you are trying to solve with chatbot or use cases
• Time required to address the issue to figure out the ROI
• Functionality-Manual, automated or both
• Integrations

Types of Chatbots

There are mainly three different types of bots that you can build here-

• Rule-Based Chatbots

As the name suggests, rule-based bots work on predefined rules. These rules form the basis for the types of issues the chatbot is familiar with and can deliver solutions for.

• AI Chatbots

AI chatbots leverage NLP to understand the intent, followed by processing the information and offering relevant/contextual answers instead of simply relying on a predetermined output.

• Hybrid Chatbots

Hybrid chatbot combines the power of both rule-based and AI-powered bots to offer a superior customer experience.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

14 Best Chatbot Development Platforms

Some of the best AI-chatbot builder platforms include-

1. WotNot

One of the best ai based chatbot builder platforms available, WotNot helps you build intelligent chatbots and also offers a complete range of conversational marketing solutions for multiple industries.

2. Landbot.io

Landbot.io is an intuitive tool that allows you to build both rule-based chatbots and AI-powered bots. The platform enables you to interact with your prospective customers and generate high-quality dialogues.

3. Bold360

As one of the most popular chatbot development tools, Bot360 leverages NLP technology to help customer service executives be more efficient and effortlessly take over conversations.

4. Intercom

Intercom is a well-known name that offers custom chatbots for use cases around multiple fields, including marketing, sales, and support. This AI bot builder can also be integrated with social media platforms and e-commerce websites and have live chat options.

5. Drift chatbot

If you want to build a bot to book meetings for sales teams and seamlessly facilitate conversations with leads, Drift is a great choice. Besides identifying the right sales executive and scheduling an appointment, it also qualifies website visitors without using any cumbersome forms.

6. LivePerson

An excellent platform to help you comfortably build, deploy, and optimize AI-powered chatbots, LivePerson is an ai based chatbot builder that enables you to use advanced analytics for real-time intent detection.

7. Flow XO

Flow XO is a platform that lets you build bots without any coding. You can use the platform across multiple platforms while also integrating them with other 3rd party platforms.

8. Botsify

One of the best chatbot builders, Botsify, helps you create bots for websites, Messenger, and Slack with various ready-to-use templates. The platform also allows you to hand over the chat to a human agent seamlessly.

9. Aivo

Offering full customer service and the ability to respond to customers in real-time, Aivo bots can be easily programmed under different rules and conditions across channels to interact with customers.

10. Chatfuel

Chatfuel bot builder allows you to leverage NLP to identify intents and utterances, followed by sharing predefined answers.

11. BotsCrew

A popular choice for SMBs and SMEs, BotsCrew is among some excellent chatbot development tools that provide a managed service.

12. Pandorabots

Pandorabots is an excellent AI-based chatbot platform that offers comprehensive solutions for chatbot development. As one of the oldest and largest chatbot builders, it is also a multilingual chatbot.

13. Manychat

Manychat allows you to build and deploy bots on Messenger for use cases ranging from sales, marketing, and customer service.

14. Octane AI

Octane AI is an excellent choice if you wish to integrate a chatbot with a Shopify store via Facebook Messenger, answer customer questions automatically, and send shipping information.

In Conclusion

To pick the best chatbot builder, it is essential to develop a thorough understanding of your specific use case to help you determine what exactly you want out of your chatbot.

Once you do thorough research based on features, pricing, and integrations, it becomes easier to pick the platform that suits your individual needs. Want to know more about the topmost chatbot platforms? Read the detailed blog here.

Don’t forget to give us your 👏 !

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A Comprehensive List Of The Best Chatbot Platforms was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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