Author: Franz Malten Buemann

How Chatbots Can Reduce Product Return — An Extensive Guide

A recent survey suggests that almost 30% of all purchases that are made online are returned. For startups and even for stable businesses, this is too much of a percentage.¹

If your business is facing conduct of shopper behavior that indicates a return is likely, a variety of chatbots can effectively and preemptively intervene in this transaction and can prevent a return from happening.

Introduction

There are multiple ways in which chatbots can help you to reduce your product returns, and this article will take a look at some of the significant ways in which chatbots can help businesses. However, it is important to understand here that chatbot efforts must be met with equal efforts from the business operators to ensure that chatbot is able to give its maximum.

· Helping Customers in Choosing the Suitable Products

This works in multiple ways. One of the basic ways, for example, is if a buyer has viewed and checked the size guide listed on your store and has wrongfully added two of the same items in the cart. Here, a chatbot can make its intervention and help the customer to select the products of the right size.

This ensures that not only the customers are being helped to avoid the hassle but also makes sure that the returns on products remain minimal. After all, 70% of the product returns are because of the initial wrong order placed by the customers.

Furthermore, chatbots can help potential buyers in making the right choices for the products. For example, a customer has searched for sportswear from your site for football, but since the search results will show all the sportswear listed on your website, a chatbot can then help the customers by intimating them about the particular product that they are actually looking for.²

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?

· Guiding Customers about Usage and Installation of the Products

One of the major reasons for product return is when customers are unable to understand how to effectively use or install the product that they have bought. Amazon says that 40% returns of its products related to the niche of electrical accessories are because of the users’ ignorance to effectively use and/or install them.

However, with chatbots, you can easily solve this problem. Almost all the modern chatbots are providing installation guidelines with the help of video tutorials and the written guidebooks. It is a big factor that can significantly reduce the return of your products, especially if you are dealing with products that need installation or the ones that require tutorials to make the most of their usage.

· Making the Return/Exchange Process more Efficient

It has to be probably one of the most underrated functions of chatbots, and it needs to be talked about more. Even the era of human live chat support couldn’t cater to the return and exchange process more efficiently than how chatbots are doing right now.

Nowadays, if a customer wants to exchange a particular product, chatbots will quickly and efficiently solve the query of the customer. This way, your store will get an exchange instead of a return, which would normally have happened without the presence of chatbots. Even the customers that come looking for returns can be effectively guided by the chatbots to rather go an exchange.

Furthermore, when the return is inevitable, chatbots make sure that it happens as soon as possible and with the least of a hassle for the customer. It ensures that not only the product is returned quickly and ready to be sold again, but also that the customer enjoys a positive support experience and considers you for future orders.

Conclusion

Apart from the reasons listed above, there are multiple other ways in which chatbots can help boom your business and reduce your returns. Modern AI-based chatbots are able to track the user data and then use the data for future references and preferences to the particular users.³

You must have experienced it yourself when you left an online cart without processing it, and you received a mail reminding you to finalize and process your order. This is an AI chatbot at its best. With the development of AI, which seems unstoppable, there will be more features added in the chatbots that would surely make them more user-friendly, and that would help customers in catering for their problems regarding orders’ returns and exchanges.

Communicating Knowledge. Saltlux

Home – Saltlux

1. https://searchcustomerexperience.techtarget.com/feature/Customer-service-chatbots-help-reduce-product-returns

2. https://www.bigcommerce.com/blog/chatbots/

3. https://chatbotsmagazine.com/how-with-the-help-of-chatbots-customer-service-costs-could-be-reduced-up-to-30-b9266a369945

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How Chatbots Can Reduce Product Return — An Extensive Guide 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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Each second countless amount of data is created by social media users on the internet, for instance, since 2013, the number of Tweets each minute has increased 58 % to more than 474000 Tweets per minute in 2019. Instagram users upload over 100 million photos and videos everyday. Due to this constant flow of data the internet turns out to be the best data source that can be encountered, thus being the object of numerous analysis that can be performed using artificial intelligence.

One of the most famous platforms used these days to share media content is YouTube. YouTube is the preferred way that people use to share content, on this platform it can be encounter videos about endless topics so each video can reach millions of people that can react in a variety of ways. The purpose of this post is truly worth it. since, we will learn how to use data analysis, machine learning, and data mining techniques to analyze videos on YouTube.

Knowing the inside of a website.

First things first. Before performing any analysis, we need to collect the data of interest. Performing analysis over websites can be a bit challenging since in these cases there is not a data set or formal database that can be used to perform such analysis. To perform these studies we need to extract the data directly from the website delving into the deepest parts of it, the process to navigate into the website is called web scraping.

A brief introduction to Selenium.

Selenium is a web scraping Python library that allows us to interact with websites and extract data from them using Python code. Selenium serves as an interface between Python and the website using a web browser like Firefox or Chrome as a web engine. Let’s see a simple example to get an idea about all the potential this library can unfold.

The website https://quotes.toscrape.com/ will be our target to explore the different methods we can use to extract data from this page. The page looks like this:

The goal is to obtain quotes from the website, to do that we need to understand what is behind this website, thus we should dive into the html code that structures every website on the internet. Using the developer tools that most browsers have, we can uncover the HTML and CSS code, said that, the inner structure of our website looks like this:

In the image above it is shown part of the HTML code within the website, it can be observed a kind of tree structure where each node is represented by a HTML tag. HTML tags are like keywords that define how the web browser will format and display the content. With the help of tags, a web browser can distinguish between HTML content and simple content.

The text we want to obtain is within a span tag, which has a class named “text”, the tag span and therefore the “text” class are within a div section with a class named “quote”. We can see that the class “quote” is a container for each quote on the page. Known this, we can easily infer that each quote is within a tag named “text”, which in turn is inside a div tag with a class named “quote”. Let’s use this to get the first quote with Selenium. The code is shown below.

Selenium has a class WebDriver that will allow us to interact with websites. in this particular case, we are going to use Firefox as an interface between Python and the website. By default, when you use the WebDriver class, it opens up your browser.

Preventing the browser from loading

Since we are not interested in opening the browser when the WebDriver is executed, the lines the code 4 and 5 disable this event creating an option that is passed as an argument of the WebDriver instantiation done at the line of code 7.

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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?

The first quote

The driver.get method will then navigate to the page given by the URL. WebDriver will wait until the page is fully loaded before returning control to the script. Finally, WebDriver offers a number of ways to find elements by using one of the find_element_by_* methods.

Since we already know the name of the class that contains the quote, we will use it using the method find_element_by_class_name(class_name) using the class name as a parameter. Then we extract the text property from the object returned by the method to obtain the quote.

“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”

Getting the rest of the quotes.

This method obtains the first quote by default, however, we are getting the first quote just because it appears in the first element with the text class. if we want to obtain all the quotes on the first page, the driver object provides a set of methods that extract all the elements on the website with the class name indicated. In this scenario, the ways to find elements would be done using the find_elements_by_* methods. Notice the plural on the word “elements”.

If we want to obtain all the quotes from the first page, it is enough to implement a for loop using the method mentioned before. For example, the code shown below will extract all the quotes from the first page.

Diving into a YouTube page video.

Try to open a YouTube video and you will notice that the website does not load the comments until you scroll down over the page. Therefore, if we try to use the previous script we will have nothing but the data referring to the video player, which is loaded when the website starts.

Using Selenium to execute JavaScript code.

Fortunately, one of the advantages of selenium is that it allows us to navigate into the website using Python code in the same way as we would with the web browser. Using the following script we can scroll down over the web page.

The key to moving through the web page is the ability of Selenium to execute JavaScript code by using the method execute_script . Consequently, we can take advantage of all the dynamic properties within a web page. Thus, the script shown above gets information about the web page, specifically, the scrollHeight property, which indicates the height of the document element.

Then, it is time to scroll down over the web page using the method window.scrollTo(0, height) . Finally, the while loop will continue scrolling down to the point where it is impossible to continue doing this, that is, the property scrollHeight will not change anymore, indicating the page is fully loaded.

The comment section.

If we look at the HTML code that generates the comment section we will see the following structure.

It can be observed that the comments are inside a structure which starts with the id “comments”, then if we continue unfolding the structure, we realize that all the comments are within a div tag that contains the id “contents”, hence each comment is identified with the classes style-scope and ytd-comment-thread-renderer

Within this section we encounter two subsections that store data about the comment. The first section is identified with the id: “comment”, the second section identified with the id “replies” stores information about the replies made on that specific comment. Since the interest holds just above the text written about the video, let us concentrate on the first section.

As we unfolded the comment section we see two new sections, the first is identified with the id “paid-comment-images” and the other with id “body”, the last one contains the information that we are looking for, so unfolding this section we encounter three subsections with ids “author-thumbnail”, “main”, and “action-menu”. The first two contain the desired information, which for this case, will be the author of the comment, its URL channel, and the comment.

Now that we have an idea about the tags that contain the information we are looking for, we can use the WebDriver object to extract the data from those tags, the following methods can be used to achieve this.

The first thing we should do is to scroll down on the website, so we the comment section can fully be loaded, then we use the method find_elements_by_id to retrieve all the elements with the id “comment”. Next, we use a for loop to iterate over each comment. Finally, we use the find_element_by_* methods to move through the HTML structure and retrieve the information that we are looking for.

Conclusions.

In this post I explore only the surface of one of the applications for which we can use Selenium, since what, unlike other frameworks for web scrapping that only allows to load the static content, Selenium can be used to interact with websites using Python code similarly to the way we would do it using a browser.

In this case I used Selenium to execute JavaScript code to move through the page and load the comment section of a YouTube video. and extract the text content from the comments. This is a very naive implementation and the script I developed was just to show the potential of this library, but the wait times and the way we extract the comment can be optimized to use for real applications.

In future posts I will show how an entire application can be developed to not only extract the text from the comments but to analyze them using natural language processing (NLP). The script can be found in the following GitHub repository.

If you want to keep in contact with me and know more about this kind of content. I invite you to follow me on Medium and check my profile on LinkedIn

References

How Much Data is Created on the Internet Each Day?

YouTube Demographics & Data to Know in 2021

Selenium with Python

Don’t forget to give us your 👏 !

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YouTube comment analysis. Part I. 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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Lecturers, professors, teachers, and educational institutions have been coming up with new ways to conduct lessons since the pandemic began. 

There are a lot of factors that go into creating a conducive learning environment, especially with Covid. There are numerous teaching styles as there is no one-size-fits-all approach to learning. 

In recent months, Noodle Factory has talked about various teaching methods and concepts including universal design for learning, blended learning design, and AI intelligent tutoring systems. 

If you have yet to find a suitable solution for your classroom, the flipped classroom could be the answer to your problems.

11 Chatbot Best Practices You Should Follow to Create a Powerful Chatbot — EmpathyBots

If you are reading this guide, then probably you are creating a chatbot or about to create one.

And now you are wondering, What are the traits of a good chatbot?

“The Chatbot Best Practices!”

Isn’t it?

Then, you are at the right place!

Because in this guide, I’m going to share all the golden rules you need to know to create a powerful chatbot.

Just creating and adding a chatbot into your business is not going to work anymore.

It has to drive the expected results for your business as well.

And to achieve that, you need to follow these chatbot best practices, which will help you to create an effective chatbot strategy and make the most out of it.

Suggested Guide: How to Create a Chatbot from Scratch in 2021 (The Ultimate Guide)

11 Chatbot Best Practices to Create a Powerful Chatbot

1. Set Expectations

What do you expect from your chatbot?

The reason it exists?

Its goals?

It can be anything like, you want to generate leads for your business, answer frequently asked questions, improve the customer support system, or just have funny conversations.

For example, the purpose of the restaurant bot will be completely different than the insurance bot.

A restaurant bot can be used to view the menu, order food, book a table, make payments, and many more.

And on the other hand, an insurance bot can be used to find the right policy, manage claims and renewals, premium payments, and many more.

The overall point is, set the purpose of your chatbot because only then you can measure the efficiency of your chatbot.

Like, if your goal is to collect leads then you can track the number of collected leads and so on.

And, It is very important to track your chatbot’s performance and analyze it so that you can optimize it for better results.

Pro Tip:

Don’t rush for a lot of expectations at once as you can’t expect it from a brand new chatbot. Take one goal at a time and add more with time.

2. Choose the Right Development Platform

After setting the expectations from your chatbot, the next task is to choose the right development platform to fulfill those expectations.

There are two types of development platforms,

1. No-code Development Platforms
2. And, Development Frameworks.

The no-code development platforms allow you to create a chatbot with a simple drag-and-drop flow builder.

And, development frameworks allow you to leverage its NLP engine to create awesome conversational experiences.

Now, you might be wondering how to choose between them?

The formula is very simple,

Expectations + Type of Chatbot + Features = Chatbot Platform

Now, you already knew about expectations, so I’m not going to tell you again.

Next, you need to look at which type of chatbot you want to create, like Rule-based or AI-based.

Refer to this guide to know more about the types of chatbot.

Then, look at which features you want in your chatbot.

And finally, choose the platform which marks all of these three checkboxes.

Below is the list of some no-code platforms that I personally use,

1. Chatfuel
2. ManyChat

And, some widely used development frameworks,

1. Dialogflow
2. Wit.ai
3. IBM Watson

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?

3. Decide the Bot Functionality Upfront

It’s a good practice to decide the chatbot’s functionality before you actually start to create one.

It is different than setting expectations.

In expectations, you theoretically decide “what” problems are you trying to solve or what goals you are trying to achieve.

But in bot functionality, you decide “how” to solve those problems or achieve those goals. You can call it the technical side of setting expectations.

For example, if your goal is to build a FAQ bot, then you need to decide whether it will be the prefixed question set that users can select or they can ask any question they want.

4. Design Chatbot’s Personality

It’s very important to give a unique personality to your chatbot and be consistent with it.

The chatbot is just the digital extension of you or your brand and hence your chatbot’s personality should also match with it.

Define the tone and voice of your chatbot according to your target audience.

And, decide whether it should be formal and keep the conversation professional or informal and keep the conversation more friendly.

Whatever it be, just keep it consistent throughout the conversation.

Pro Tip:

Be transparent. Tell your users that they are talking with a chatbot by giving such kind of name to it or telling it in the greeting message.

Like I gave the name EmpathyBot to my chatbot and send a greeting message as,

“Hey there! I’m EmpathyBot! How can I help you?”

By doing so, you will not mislead your users and also earn their trust.

5. Make Conversations Human

How do you chat with anyone?

Give an answer to yourself.

Yes, you are creating a chat “bot” but still, it is chatting with a human.

So your conversations should also be designed in such a way that humans can understand and relate.

Don’t write long paragraphs, cut them into small chunks of messages as most of us do.

Use emojis, photos, videos, GIFs, etc. Just bring the personality into it.

Pro Tip:

Just be natural while designing the conversations as if you are talking to a real human being and read it aloud so that you can improve it.

6. Close-ended Conversations

Always remember to design close-ended conversations.

Especially, when you are creating an AI-based chatbot.

But, What is close-ended?

For example, asking people, “Tell me about yourself?”

The answer will be unpredictable and not have a fixed endpoint.

It is an “open-ended” conversation.

But if you asked, “Tell me your name?”

Then, the answer will be predictable and fixed for everyone.

It is called a “close-ended” conversation.

Then the question arises, Why to design close-ended conversations?

And the answer is, because you are creating a chatbot to achieve some business goals.

For example, if you are creating a lead generation bot, then to generate a lead you need to design a funnel that consists of some conversational steps.

And to go from one step to another, you need to have specific and fixed responses on each step.

But open-ended conversations do not have fixed endpoints, hence the chatbot will not understand what next step to take, which will lead to the conversation failure.

That is the reason you should design close-ended conversations.

7. Repairing the Conversation Failure

In the previous point, you learned how conversations can fail in chatbots.

And it is quite normal in real-life communication as well.

Remember, how sometimes you get out of track when talking about some specific topic and your friend brings you on track again.

This same process is implemented in chatbots as well to repair the conversation failure.

It is called fallback intent or message.

If a user asked something out of the chatbot’s scope, then the chatbot will reply with a fallback message and try to bring the conversation on track again.

Pro Tip:

Don’t use the generic fallback message like “Sorry, I don’t understand!”.

Show the available options with buttons or ask if they want to talk with a live agent instead.

8. Keep it Simple and Clean

Would you like to use an ugly-looking complex app and spend time on it?

Definitely not! (I’m guessing…)

The more simple it is, the better it is!

Don’t make it too complex.

Both for you and your chatbot’s users.

It should be easy for users to navigate through its different functions.

Because it’s all about user experience.

9. Ability to Easy Exit and Unsubscribe

Another key point in improving its user experience is the ability to easy exit and unsubscribe at any time from the chatbot.

You should not force them to be in a conversation or a subscriber if they don’t want to.

By saying the ability to exit, I mean that they should have the option to exit from the ongoing conversation and return back to the main menu or start a new conversation.

And also the ability to leave the conversation at any time and unsubscribe to the bot.

Pro Tip:

You can achieve it by either giving an option button at the bottom or notifying them about a keyword to do so at the beginning of a conversation.

10. Create Different Prototypes

You cannot create Siri or Alexa in the first attempt.

You have to create different conversational flows, design multiple conversational scripts, and create different versions of chatbot.

Then finally select the one which is better at meeting your expectations and giving a top-notch user experience.

11. Measure & Improve Your Performance

And finally, the most common thing you need to practice to be successful in any kind of work, measuring the results and improving for better performance.

This part comes after you successfully launch your chatbot.

You have to track that whether it’s meeting your KPI’s (goals) or not.

Then, improve it by observing your users, trends, how they are interacting with a chatbot, the most common topic they discussed, and so on.

Wrapping up the Chatbot Best Practices

Creating a chatbot takes planning and effective implementation of that plan.

You have to spend time creating and optimizing your chatbot.

It’s possible to not get the expected results from it in the initial days, but it doesn’t mean that there is a problem with the entire chatbot.

It is just that your chatbot is missing one of these important chatbot best practices and need to identify and correct it.

I hope that these 11 chatbot best practices will help you to create a powerful chatbot for your business.

Liked this story? Consider following me to read more stories like this.

You can also follow me on Twitter and LinkedIn.

Don’t forget to give us your 👏 !

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11 Chatbot Best Practices You Should Follow to Create a Powerful Chatbot — EmpathyBots 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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Building an AI Chatbot has never been easier

Microsoft Bot Framework is a framework for building enterprise-grade conversational AI experiences. It is hosted on Microsoft Azure’s cloud infrastructure. With this framework, we have launched a few virtual assistants such as Meva for PETRONAS Dagangan Berhad’s MyMesra, Skye for Kuala Lumpur Convention Centre, and Nadia for Affin Hwang Asset Management. In this article, I’m going to share why you should consider Microsoft Bot Framework for your next AI Chatbot as a developer.

Easy to start

First, this bot framework is very easy to start. We can have a simple Q&A bot using QnA Maker in less than an hour without coding at all. You can upload an excel file of questions and answers or provide a webpage. You can refer to this link to get your first bot up and running. Some bots may continue as a simple QnA bot, and some may evolve to be a more complex bot.

Flexibility & Complexity

A bot may need to be very flexible, or complex based on clients’ business requirements. Here, we can use the Microsoft Bot Framework SDK to build our complex bot dialogs and integrations. We can program complex nested dialogs to run product recommender, for example. You can also program the bot to guide users to give feedback or make an appointment.

Within the bot dialogs, we can also integrate our bot with Microsoft LUIS which is a Cognitive Service solution. LUIS allows us to get a user’s intents and entities to make our bot smarter. For example, if a user’s intent is to submit a complain, we can use LUIS to get their name, email, and phone numbers accurately. LUIS also can get dates, measurements and also custom entities using regular expressions such as your countries’ identity number.

Since the bot can be hand coded, we can also connect to any REST API. For example, we can connect the Azure Text Translator to translate Malay language into English to be processed by LUIS. This is because there are languages that are not supported by LUIS natively. We can also connect to our other apps to push or get data.

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?

Programming Languages

Even though this bot framework is built by Microsoft, we can use 4 types of different programming languages ranging from C#, Java, JavaScript to Python to build the bot. We do not need to learn another language if we already know one of those programming languages. There are also bot project templates for Visual Studio to help us to get started. The templates come with samples on how to do certain tasks in a chatbot.

Bot Testing

For testing the bot, we can use Bot Framework Emulator which is installed onto our machine. This tool is a conversation interface to debug the bot locally with our development tool. It is fully integrated with Visual Studio, and it makes testing and debugging work a lot easier. For example, we can set breakpoints in the code and do live debugging.

Ready Integration with XTOPIA

Microsoft Bot Framework is also ready for integration with XTOPIA. By integrating with XTOPIA, your end users can update the bot’s responses easily and enhance your website with bot interaction. The end users can also make your bot better with XTOPIA codeless workflow by getting data from internal and external sources.

That’s all for now. We will share more about Microsoft LUIS and bot development in the future. You can find out more about Microsoft Bot Framework here and XTOPIA platform here.

XIMNET is a digital solutions provider with two decades of track records specialising in web application development, AI Chatbot and system integration.

XIMNET is introducing a brand new way of building AI Chatbot with XYAN. Head over to our website to experience it and sign up for a trial today.

Don’t forget to give us your 👏 !

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Why you should consider Microsoft Bot Framework for your next AI Chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.