Month: April 2021

Rule-based chatbots are a good starting point to gain experience in designing chatbots. These decision-tree based bots are following a set of rules and use a planned, guided dialog.

Behind the apparent simplicity, there are plenty of operational-level difficulties for conversation designers in the maintenance. When a chatbot reaches a certain size, it becomes extremely difficult to handle and follow any changes in the conversation model. Logic jumps (to create different paths under conditions) and loops (to go back to a previous element of conversation) are giving you a wide range of flexibility in this closed environment, but they often make the conversation difficult to handle.

Once any change has been implemented, you have to make sure that it did not cause any confusion in other convos and that each conversation path stayed “healthy”.

Botium Crawler is here to help

Botium Crawler is the newest member of the Botium toolset. It was designed to imitate users going through all the possible combinations of the dialog structure at the same time. It automatically detects all conversation paths for navigating through the whole conversation model.

The following graphic shows the concept for an e-commerce chatbot and one of the possible paths a user might navigate (red arrows).

Botium Crawler simulates user clicks on all of the options in parallel, following all paths down until it reaches the end of the conversation or a certain criteria.

In the flow option you can see the visual representation of the conversation model of your chatbot.

This is something you can most probably see on your conversation design platform as well.

In the Crawler script view you can see each convo path separately.

As long as you have a green tick on the side, it means that the crawler successfully reached the end of the current path, without any failure. It suggests that the user will be also able to do the same in production.

Other cases with an exclamation mark are worth examining. In this case, the crawler could not reach the predefined depth in the conversation path. There could be more reasons behind this:

1. The conversation is stopped before reaching the maximum conversation steps

Before starting the crawler session there are several criteria you can add. One of them is the predefined number of conversation steps, that the crawler should reach. In case the specified criteria is not met, the crawler will not succeed.

2. Wait for prompt

You can define the maximum time a user should wait for a message from the chatbot. There are several conversation steps where the chatbot sends more than one request. In this case it is advisable to define a longer waiting time, but short enough to provide the feeling of a dynamic conversation. If the waiting time exceeds the predefined time limit, the conversation will be labelled with an exclamation mark

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Additional Crawler features

1. Exit criteria

You have the option to give a certain message as exit criteria. This means that when the crawler reaches this step, it will not go any further. This function is extremely useful, if your chatbot deals with a great amount of convos, but you’ve only done a tiny change in the conversation model and you don’t want to wait for the crawler to track through all conversation paths. If the crawler stops by meeting the exit criteria, it will be closed successfully.

2. Entry point

The conversation start message is a similar setting to the exit criteria, except that it does not specify when to stop the crawler session, but where to start. You may want to use this feature in situations where you have made changes at the end of a conversation model and it makes no sense to run the crawler from the beginning of the conversation. In case you establish an entry point, that the crawler can not find, the conversation will stop at the first step and it will fail.

Failing crawler session could also result in cases when the chatbot does not respond!

Summary

Botium Crawler will help you to identify missing paths and dead ends in the conversation flow and will help you to provide great user experience no matter what path the user takes.

The additional benefit of the crawler is that all detected conversation flows along all paths can be saved as Botium test cases and utterance lists and can be used as a base for a regression test set.

In the end Botium Crawler helps conversation designers to untie the inscrutable threads of human communications and to examine each conversation path as an individual part of the final user experience.

Don’t forget to give us your 👏 !

Detecting dead ends in the Chatbot Conversation Flow was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

function hook(bp: typeof sdk, event: sdk.IO.IncomingEvent) { const eventDestination = { channel: event.channel, target: event.target, botId: event.botId, threadId: event.threadId } // Don't process event and send content in 5 minutes if (event.type === "") { event.setFlag(bp.IO.WellKnownFlags.SKIP_QNA_PROCESSING, true) setTimeout(function() { bp.cms.getContentElement('supportbot', 'builtin_card-FvRfLb').then(cardPayload => { bp.events.replyToEvent(eventDestination, cardPayload, event) }) }, 5*60000) } } 

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With the rise of the digital era, chatbots have launched innovative ways for banking and financial services to interact with customers. Chatbots for banks can hold natural interaction with your customers and respond to convoluted queries related to the banking transaction with the help of conversational AI.

By using Nuacem’s branded bots, you can develop customized chatbots for your financial process to focus on any necessity of their clients, contact center representatives, or sales advisors.

Using Nuacem’s bot as a unique offering, the banking sectors can provide a facelift to their existing customer engagement platform or help build a new bot for better business. The most sought conversational AI will also help the financial sector to enhance their competencies by divesting significant tons of superfluous jobs engaged in claims, responding to inbound voice calls, and other transactional processes spread across the department.

Finance sector using AI

Chatbots are projected to disrupt all areas of finance — Banking, Insurance, and more much like several other businesses. Some of the major banking corporations that have already adopted chatbots include Bank of America (Erica), American Express (Amex), Eva of HDFC bank, and more. Similarly, Nuacem’s Botjet, Observejet, Engagement, and Convojet are the most powerful bots which can be plugged in with your APIs for customized output to serve your customers flawlessly.

Is this all about ROI?

Now is the judgment to realize Conversational AI is all about ROI? Yes, you got it right, and it should be. However, many corporates are taking advantage of such automation to not only cut down the cost burden but also to serve the customers on digital platforms. This would certainly help your customers to finalize their decision or to get a quick response to their queries with natural language.

By satisfactory response through your business can build a long-term relationship with your customer, thereby enhanced revenue. This would encourage a superior level of customer loyalty.

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Conversational AI platforms are so advanced that one of the major research firms — Juniper Research has predicted that conversational AI technology has the potential to cut down the OpEx — operational expenditure nearly $8 billion by 2022.

European banking and financial sectors are moving ahead even more swiftly than their US counterparts in implementing Nuacem’s chatbot — The conversational AI.

Let’s take an example to understand one of the reputed banks in the EMEA region. The top-performing European financial body — DNB, was also able to slash client chat assistance by a whopping 49 percent while supervising more than 10,000 automated conversations every day. Nuacem’s conversational AI provides unique features for all your banking needs proving to be a real game-changer.

Nuacem is an AI-powered Omnichannel Customer Engagement Platform that presents the full features and capabilities required to build sophisticated customer engagement, experience, and support solutions built for businesses. The Nuacem’s Conversational Platform — Botjet, offers comprehensive features and abilities needed to build advanced and intelligent enterprise chatbot solutions.

Nuacem’s AI platform powers natural language business products that are continually enhanced through AI-powered tools and platforms that empower human capital to evaluate performance manages the conversations and enhance end-user experience seamlessly.

Don’t forget to give us your 👏 !

Conversational AI: The Game Changers of Banking & Financial Services was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

You walk into a retail store. A salesperson approaches you, asks you questions, tries to understand your needs, and then recommends a perfect product based on your responses.

In another case, you go to a shop, and the sales rep directly slaps you with the plethora of products they have at the store.

What do you like more?

The first case, right?

And this is how every customer experience must be personalized, whether it is in-store or via chats!

In today’s world, where more and more customers are purchasing online, chats replace physical representatives. Even customers love it! Statistics suggest that nearly 63% of the customers would return to a website that offers chat support.

Despite the chat’s popularity, surprisingly, most of the chat conversations are impersonal. However, the brands that tailor messages to the user’s real-time behavior, location, interests, etc., outperform others. Thanks to companies like Amazon, buyers are expecting personalized messages at all touchpoints.

But there’s nothing to worry about! You, too, can personalize chat and carve a niche for yourself amidst the cutthroat market competition. Check out the proven tips-

Establish Stronger Connections

Who doesn’t love to be called by their names? Even as you offer support through chat, it is no excuse for you to skip your customer’s username while addressing them. Welcome customers like they are your old friends, even if it means going ahead and tapping into their location.

When you see where they are located, you can speak their language, greet them in a familiar way, and provide them with more reasons to stick around.

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Prepare yourself to travel the extra mile by remembering your customer’s purchase history, previous chats, and more. This practice will only help you establish a better relationship with them and show them that they’re more than just a number.

Take a look at Amazon’s chat options, for an example!

Isn’t it amazing how well this chat understands the customer without wasting a lot of time!

Make Way for Clearer Conversations

One thing that customers are head over heels in love with is a faster resolution. You can take chat personalization to the next level with features like audio and video chat, remote access to your buyer’s account, etc.

Not only can it help you get close with them but also reach the heart of the matter, more quickly and in a friendly manner.

Prepare, Personalize and Chat!

Chats for businesses are the 21st century’s gateway to enhanced sales, unparalleled satisfaction, healthy relationships, and many more. The only question is, are you willing to tap into its potential with personalization? The sooner you start, the better you will reap its benefits!

Don’t forget to give us your 👏 !

How to Win Customers With Personalized Chats? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Advancements in technology have brought changes to various industries in many ways. One of the most effective technologies available is Artificial Intelligence which is extensively used these days. Among the available AI technologies, multiple businesses are opting for chatbots to enhance customer service and user engagement.

Statistically, 50% of the brands are currently investing in chatbots compared to mobile apps. It is estimated that by 2021, 85% of the customer engagement activities would occur artificially. For your brand, you can use the integrated tool of Drupal 8 and Dialogflow to create custom chatbots. Brands of different sizes and industries can use the content management system of Drupal 8 to create dynamic chatbots with the help of Google’s Dialogflow SaaS tool.

In this article, you would learn about what each of them does and how to use them to create a successful marketing chatbot.

Define Drupal 8

Drupal is a type of open-source content management platform that developers use to customize and optimize web-based services. It contains a range of robust tools that brands can employ to edit web content and components like admin tools, views, and lists.

Drupal 9 is the latest version available and includes extra features like WAI-ARIA integration, Schema.org native markup, and flexible object-oriented coding.

Importance of using Drupal 8

Brands that use drupal 8’s Chatbot API integration can utilize its content on different platforms. In web development, developers have to code separately for each personal assistance/chatbot platform protocol. The coding steps were complex, increasing the chances of errors that can push back the development time.

In contrast, with the Chatbot API, developers can complete the coding in one sequence. The tool handles continuous responses or requests automatically. Here, it is important to mention that the Drupal 8 chatbot API requires another module, like Dialogflow. The accepted internal submodule that the Drupal consultants would assist you with is chatbot_api_ai. You have to use this submodule with the Dialogflow Webhook module.

Define Dialogflow

Brands and developers use interactive SAAS tool Dialogflow to create custom chatbots for social media and website marketing. These chatbots can work with platforms like Twitter, Facebook, Skype, and Telegram.

Here, the tool handles the NLP (Natural Language Processing) logic; i.e., the translation of human command into computing language. Brands can access this data from their backend logs. It works with multiple server-side languages. Plus, developers can import or export the chatbot data easily in the JSON data format via Dialogflow.

What is Dialogflow (Api.AI) Webhook?

Before creating a marketing chatbot with Dialogflow and Drupal 8, it is important to know how it works. Essentially, through the Dialogflow Api.AI Webhook, the module merges with the Drupal website. Therefore, brands using this technology would get the service of Dialogflow agents. These agents interact with the brand website, fill slots, and handle Intent (user-side prompt for action) requests.

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Tips on creating the marketing chatbots with Drupal 8 + Dialogflow

To generate successful marketing chatbots with the support of Drupal 8 and Dialogflow, particular configuration steps are essential. To note, professionals handling this task should carry out the steps in a sequential manner.

• Creating the agent

The first point to keep in mind for developers is logging into Dialogflow. Since the Dialogflow tools work with Google, you can log through your Google account. There you will find a visible ‘Create Agent’ option in the console. Clicking on that would portray the main API conversational app interface.

• Focus on intent

The Intent is a crucial element to focus on while creating the marketing chatbot. This is the main interface that connects the agent and the end-user. Thus, developers should do this step carefully.

The Intent takes the user’s input and manages the response that is delivered back. Select the + icon beside ‘Intents’ in the left sidebar to add the menu and save it.

• Responses and Training Phrases

Developers can add ‘training phrases’, which are the expected inputs from users. The technology allows the developer to set corresponding answers or responses for potential intent requests. These are effective when the users do not give a response in time; the tool automatically substitutes with an appropriate response. You can easily add particular responses under the Response category.

Here, the tip is to test out responses after the Intent is delivered. This testing would ensure that the Intent is effectively working. For real-time responses, you can use the web callback option from the Dialogflow webhook.

• Install Webhook packages and modules

At this step, add the Dialogflow (Api.AI) Webhook and Chatbot API modules. This installation is necessary for you to write the custom integration logic without errors. Here, the Chatbot API modules work to develop a Drupal content-oriented common layer. This can work with multiple chatbot frameworks like Alexa and Dialogflow accurately.

Here, the Dialogflow (Api.AI) Webhook module works to integrate with the Drupal website. As a result, the tool can properly handle responses to the intent requests of the end-users.

Also, keep in mind to install the iboldurev/dialogue package. This PHP SDK is an important configuration for Dialogflow API.

• Configure the Dialogflow Agent with webhook

After completing the module installation steps, you would notice seamless responses for the Dialogflow intent requests. The path all the Intents take is “api.ai/webhook”.

First, configure the “api.ai/webhook” path into the Dialogflow agent. You would find the Fulfilment section in the Dialogflow agent. Activate the webhook choice and add the webhook URL. Then, save the data.

The agent would focus on getting the responses directly from webhook calls when the user adds the input. In case the user does not provide a response, one of the static response phrases you set beforehand would activate.

• Get the webhook responses from the Drupal site

If you are using the Drupal website, you would need an intent response for continuing with the chatbot set-up. Here, generate a Chatbot Intent Plugin. Use the same intent name you added previously into the agent as the ID.

For example, you are creating a chatbot_intent model. Here, add the intent plugin class for the website into the src/Plugin/chatbot/Intent module directory. Use the process() abstract method here; make sure the class extension is accurately entered. The response set you put into the abstract method would carry out the Dialogflow intent. Later, activate the webhook from the Intent Fulfillment section.

After completing all of the steps, check that the responses you are getting are accurate and functional. If so, the marketing chatbot is effectively integrated and in working condition. Following this, brands can engage in their customer engagement strategies via the chatbot.

Conclusion

All in all, for creating a marketing chatbot, it is important to integrate both Drupal 8 and Dialogflow API modules carefully. The main point to keep in mind is to follow the creation steps accurately and conduct tests. This would save time and error potential for brands when they are creating their marketing chatbot.

Don’t forget to give us your 👏 !

6 Tips To Create A Marketing Chatbot Using DialogFlow & Drupal 8 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How Rasa NLU is moving past Intents

Isn’t it about time we get past intents?

If you have ever developed a Conversational AI agent using NLU, you know how often users don’t follow the happy path.

They may say or type responses that make perfect sense however their responses still fall outside of any intent.

For example, if a user asks about a refund, by typing just their order number, what happens?

What is the intent of that message?

Obviously, the order number is an entity but since there isn’t a clear intent that it’s mapped to, it will trigger a retrieval action that combines all of your intents into a single FAQ and in this way we have already moved past intents and right into context!

RASA is taking this insight to the next level and on May 25th, Alan Nickole, the co-founder and CTO or Rasa will share how RASA is moving beyond intents and using context!

Featured Speaker

Alan Nickole, Co-founder & CTO @ Rasa

NLU: Going Beyond Intents & Entities

In this talk, Alan will share how RASA is going beyond Intents and Entities.

Very exciting talk as we are seeing NLP/NLU going through a major revolution.

Develop your own AI Agent in our Certified NLU Workshop

Join our NLU Workshop on May 27th you can create you own Conversational Agent in our full day workshop and get certified in Conversational AI Development.

How Rasa NLU is moving past Intents 🚀🚀🚀 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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