Year: 2021

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 👏 !

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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 👏 !

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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.

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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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Chatbot based robo-advisors — The best AI can do currently to help retail investors?

In my previous post I wrote about robo-advisors. I have explained in detail about the reason for the success of companies offering robo-advisory platforms in the US and discussed about some challenges similar companies face in India. Read the article to understand the current state of robo-advisors better. The link to my previous post can be found here.

Just like stocks now, a few years ago the world of AI made me excited . My current project at Virtusa revolves around optimising the NLP and enhancing the features of chatbots for a consumer banking client. While researching about robo-advisors I learnt that a chatbot based robo-advisor is the intersection of two things I am currently in love with, investing and chatbots.

While studying an Analytics course at University of Hyderabad I built a stock prediction model which had very poor back testing results. After a bit of reading I realised that it is hard to build a good stock prediction model as it is difficult to understand and figure out the causation of things in the market. You can maybe just dump a lot of data and build a deep learning model with amazing back testing results but again it becomes difficult to explain the model and quantify risk. Explainability and risk quantification are very important when you build such financial models. So the best thing AI could do currently to enhance investing for a retail investor is to build an AI based conversational agent that will help them in making their investment decisions. These kind of conversational agents are called chatbot based robo-advisors.

You can look at chatbot based robo-advisors through the eyes of consulting companies that have wealth management clients or mutual fund companies that are planning to offer investment advisory services.

Consulting firms :

Since I work in a B2B consulting company, I will first give my perspective on chatbot based robo-advisory solutions with respect to such companies. Any AI/ML solution built by a B2B provider is going to either reduce costs or increase revenue for any client. Chatbot based robo-advisors fall under this unique category of achieving both the objectives for your clients in the area of wealth management.

1. Firstly, they save costs because clients don’t have to hire multiple human fund advisors to manage their customers, a single chatbot can mange all the customers.
2. Secondly, if you build a very good chatbot with a nice user experience and algorithms within the bot are good enough to recommend the right funds to the customers, current set of customers of your client are more likely to recommend the platform to other new people and in turn also help increase the revenue for the client.

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 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

Therefore, it makes sense for a B2B provider looking to increase their foot print in the AI space to focus on helping their wealth management clients build good chatbots that give investment advise.

I feel many wealth management companies in the future are going to have their own version of a chatbot as a robo-advisor because chatbots offer more interactivity with the customer and can make the overall user experience way better. Some big banks already have such chatbots but they are not sophisticated enough. Good machine learning based chatbots are still in a nascent stage and so much more can be done to make the interaction with chatbots more human like.

Mutual Fund Investment Platforms :

I believe robo-advisors in the form of a chatbot is the right way even for companies that let you invest in mutual funds in India to offer robo-advisory solutions. As I mentioned in my previous post many mutual fund platforms in India are offering many services for free. One obvious way such companies would generate cash in the future is by providing investment advisory solutions. The cheapest way for them to provide personalised investment advise to their customers with a good user experience is through such chatbots that act as robo-advisors. The data that such companies collect about the customers can be used to train the chatbot to have more personalised and interesting conversations with customers. This could turn out to be a big competitive advantage for mutual fund investment platforms.

Features we can build using chatbot platforms currently available in the market –

I would like to mention some good to have features of a chatbot that helps the customers, of a wealth management client of B2B consulting companies and any fin-tech start up or mutual fund investment platform that wants to offer investment advisory services, in making investment decisions.

• The chatbot should gauge the risk appetite of customers using the platform and understand their financial goals.
• Do asset allocation and construct a portfolio based on the customers risk appetite and financial goals.
• Rebalance the portfolio when the financial goals of the customer change or the macro economic conditions change.
• Answer questions around the customers retirement planning and other financial goals like child education etc. For example it should give answers to question like ‘how much should I save every month to accumulate x amount during my retirement’.
• One feature I would love to see is that the chatbot should educate the customer and allow him to modify his portfolio based on the knowledge he has gained. The thrill of knowing why you are investing in something is much more than a bot just recommending something without letting you know the ‘why’. Even if the customer doesn’t want to modify his portfolio as an investor he would feel safer and more comfortable to use the platform if he knows why a bot is investing in a particular fund.

Most of the current set of AI based chatbots have an intent classifier that tries to identify the intent of the customer and an NER ( Named entity recognition) model to identify entities that are required to fulfil a particular task. The dialogue management is mostly rule based. All the good to have features mentioned above can be implemented using such chatbots. Looking at the pace at which NLP developments are happening, I have a feeling that we would find better ways to use AI to build more powerful chatbots very soon and the features chatbot based robo-advisors will have are only going to get better.

Don’t forget to give us your 👏 !

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Chatbot based robo-advisors — The best AI can do currently to help retail investors? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

One question we hear a lot is “What’s the best way to start testing a chatbot ?” The answer is surprisingly simple: Build up knowledge by educating yourself, and you will be able to answer this question yourself. This post will walk you through the major milestones that test automation engineers have to take for building this knowledge. We will suggest readings and other material that you might find helpful as well.

Step One: Build a First Sample Chatbot

The best way to get going is to actually build yourself a sample chatbot. The reason is that some things like natural language processing (NLP) might appear like magic if you are new to the topic. They aren’t. I suggest to take one of these tutorials — don’t worry, all of them have free plans available:

• Quickstart: Build an agent (Google Dialogflow)
• Getting started with Watson Assistant (IBM Watson Assistant)

Go the whole way from starting the project to publishing a chatbot on a preview website — everything you need is included in those cloud platforms.

I do not recommend to start with a free, self-hosted stack like Rasa, as it might distract from the journey to become a chatbot tester by tearing your focus more towards infrastructure topics and actual chatbot development.

Step Two: Understand Machine Learning Concepts and Metrics

When dealing with non-trivial chatbots, there is always a machine learning component for natural language processing (NLP) involved. You do not have to get an expert in machine learning to efficiently test them, but you definitely have to understand the taxonomy used in machine learning projects. The central concepts to learn are:

• Intents, Entities, Utterances
• Accuracy, Sensitivity, Specificity, Precision, Recall, F1-Score
• Confidence and Confidence Threshold
• Confusion Matrix

Here are some links to introductory articles:

• Upskilling for test automation engineers — Testing Conversational AI
• Quality Metrics for NLU/Chatbot Training Data (Part 2)
• Upskilling Test Engineers for Chatbot Projects

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 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

Step Three: Get Comfortable with BotiumScript

You learned how to test websites with Selenium. You learned how to test smartphone apps with Appium. You might be tempted to think A chatbot is basically a website answering questions, why not using Selenium or Appium for testing a chatbot ? Chatbots are a new kind of apps that require new kind of tools to perform effective and high-coverage testing — a goal that cannot be reached with slow, real-time, flaky end-2-end tests.

Wrote about this conflict in one of my previous blog posts.

So invest some days to educate yourself with BotiumScript, an easy-to-learn scripting language for describing chatbot test cases. You can start at the official Botium Docs or watch Shama Ugale in one of her awesome talks:

Step Four: Get Your Toolstack In Place

In kitchen one of the most important phrases is mise en place — it means to put everything into place before getting started with cooking. Now that you have a good understanding of the basics, it is time to get your tool stack in place you can use for your chatbot testing projects.

• Subscribe to a free-forever Botium Box Mini instance to optimize your learning curve — Botium Box includes everything to get you started, all you need is a web browser
• For command line afficienados, install Botium CLI, the swiss-army-knife for chatbot testing
• If you are used to test runners like Mocha, Jest or Jasmine, you will love Botium Bindings

To get started in Botium Box, read through the Botium Box Wiki with tutorials, howtos, technical background and reference docs.

Step Five: Understand the Challenges of Chatbot Testing

There are some important differences when testing a chatbot as compared to websites or smartphone apps. What will strike experienced test automation engineers most is that a user can ask a chatbot literally anything (and the users will do as soon as the chatbot is live, believe me). Dealing with non-determinism is a critical topic in software testing. Due to the nature of human language it is impossible for software tests to cover all possible situations.

You can read more in one of my previous blog posts.

Because of this it is even more important to understand the required test levels to meet at least the minimum quality standards in software projects.

• Regression Testing on API level— Identify flaws in the conversation flow before going to production
• NLP Testing — Improve your chatbot understanding
• E2E Testing — Verifying the end-user experience
• Voice Testing — Understand your users on voice channels
• Performance Testing — Ensure your chatbot is responsive under high load
• Security Testing — Making your chatbot secure
• Monitoring — Get notified when problems arise

Read about some important pitfulls here: 4 DO’s and 3 DON’Ts for Chatbot Testing Strategies

The good news is that you can shine and partially re-use your test automation skills gathered in your previous projects in chatbot testing as well.

Up and Beyond

Wherever you are in your journey, from building your first sample chatbot to running a full-blown continuous chatbot testing pipeline, we’re delighted to have you as part of our Botium community. Please feel free to ask questions on our Botium forum. We look forward to helping you in your journey to become a chatbot testing expert!

Don’t forget to give us your 👏 !

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The 5 Step Journey to Becoming a Chatbot Tester 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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