Category: Chat

Humans have long been fascinated by the concept of machine thinking and working like a human, at least intellectually. Though there might be potentially devastating consequences of a machine becoming as intelligent as humans, we simply cannot deny that they could be of great help and improve our daily lives.

Highlights:

• What is Cognition?
• How can we test the Cognition of a Software?
• Cognitive Capabilities of a Cognitive Virtual Assistant
• Memory Retention
• Understanding spell mistakes and Paraphrases
• Understanding long-form Sentences

Cognitive AI has been powering virtual assistant services throughout their existence. The capabilities of virtual assistants are increasing year by year and With various virtual personal assistants showing very advanced and high intelligence capabilities, it is common for anyone to get curious as to what is a virtual assistant ai? and to what extent this “cognition” they exhibit would go.

In this article, let us try to understand exactly that. However, before that, we must understand what cognition means and what to think of when software exhibits cognition.

What is Cognition?

Basically, cognition is a state of awareness of an entity of its surroundings and an ability to evaluate it and give an intelligent response to the stimuli. To define more formally, it is a mental action or a process of a being in which it acquires understanding through thinking, experience, and senses which it uses to communicate with the world.

Other than being able to “sense” the world, the cognitive being must have the intelligence to understand and react to it and also have a working memory.

Humans have intuitive cognition which is developed through a lot of means. However, a machine exhibiting cognition means that it is also “aware” of its surroundings and gives intelligent responses if we interact with them.

How can we test the Cognition of a Software?

In humans, cognitive capabilities are tested by making them do various activities that require a certain level of cognition. So, a similar form can be used to test and assess the level of cognition of software.

This actually can be seen as the core idea behind the Turing test, which is a thought experiment that goes like this: a computer is hidden behind the doors of a room and a man would be communicating with that computer by sending them written notes. If the computer manages to answer all queries satisfactorily without a man getting doubt, the computer wins!

Artificial Intelligence is actually divided in general into weak AI and strong AI. The former one being Artificial intelligence that works only for a subset of problems and requires a lot of training material to solve a new set of problems and the latter is a general AI that can solve almost all problems that are given to it. Strong AI has not been developed fully yet, so much of the development is still ongoing.

So, this is the first limitation we see with today’s AI. Which is that they can exhibit only finite cognition in limited fields onto which they are trained on. There is another general problem associated with the present state of AI — — black-box nature.

That is, much of all machine learning models used in practice are black boxes and we do not easily know how exactly a particular decision is made and so, we cannot look at “process” and come to a conclusion whether the machine is “thinking” and going through “right steps” of intelligence in giving an answer.

So, what can we do? One easy way is to look at various examples in which virtual assistants give intelligent responses and analyze how intelligently they handled the situation.

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?

Cognitive Capabilities of a Cognitive Virtual Assistant

Here, let us see various examples of how an intelligent virtual assistant would respond to a query and analyze it from various viewpoints of cognition.

Memory Retention

When using memory, we can understand that a virtual assistant would have an exceptional memory where it is able to access an appropriate memory when there is particular use.

Consider you are talking to a virtual assistant regarding going to a place for a vacation. Think that you told it a very while ago that you like to go to a particular place in the winter season. Now, when you ask a virtual assistant to suggest a place to visit, the virtual assistant would check that the current season is winter and from an understanding of your interests, it may suggest to you places that really suit you.

Not only this long-form memory retention, virtual assistants can also retain current context across various previous messages and thus can give intelligent answers based on previous conversations.

Understanding Spell Mistakes and Paraphrases

The ability to understand spelling mistakes and paraphrases signifies that a virtual assistant is cognizant enough not to take for granted that what you type to be correct and includes complete intent. Even though you give an incorrect input or do not specifically specify intent, the virtual assistant would extract the correct intent and proceed with the next actions.

For example, let us think you have given an input when my event is scheduled? Then, it might understand that you have asked when your event is scheduled. Also, you did not specify what event you have asked for. The virtual assistant tries to guess the most appropriate event that you might ask about and would provide a response based on that.

Understanding long-form Sentences

Understanding long-form sentences signify that a virtual assistant is able to break sentences down and can still understand the overall intent and meaning of a sentence.

For example, if you provide Hi I am John Doe. Currently, I have a mid-tier plan. I Am not satisfied with it. Either solve my problem or get me a higher plan. Then, the virtual assistant would understand that you are somewhat angry and tailor its response. Also, it first asks about the problem and if the problem is not solvable easily, then provides information regarding higher plans. If you are still not satisfied, then your request, with all appropriate information, would be forwarded to a support representative.

Conclusion

From the above analysis, we can come to a conclusion that virtual assistants, with help of cognitive AI technologies, exhibit exceptionally good cognitive capabilities. The capabilities would exhibit intelligence in discerning the intent of the user and also remember and understand the context.

So the limit of a virtual assistant to exhibit cognitive capabilities cannot be specified as such, except for the fact that it’s limit is just to our imaginations. The existing attributes and level of intelligence is certainly growing with a drastic pace.

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To what extent does a virtual Assistant exhibit Cognition? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the previous article, I wrote about why good chatbots need context instead of tree-based flows. The benefits of introducing context are that users can engage in a more natural dialogue with your chatbot, get direct replies and change information without restarting the conversation.

I’ll be using Dialogflow and Cloud Functions for Firebase to describe and explain the implementation. The ticket price inquiry example is based on the scenario described in my previous article, so take a look if you have not.

Concept

1. Instead of one intent with the required slot filling parameters, create that intent followed by one intent for each parameter. (See purple boxes above)

2. In those intents with a single slot filling parameter, set it optional.

3. Put all entities extracted from any intent into the conversation context programmatically. (See the blue box above)

4. Make a functional response for a group of related intents (see the orange box above), so that you’re making a chatbot to reply based on the user’s intent and information (either mentioned or referred from context), instead of intent without information.

Let’s take a closer look at the code.

Intent mapping

Start by creating a map of intents. Let the agent (a webhook client) use the intent map to handle incoming messages.

Remember to create those intents in Dialogflow and turn on the webhook fulfilment.

Intent fulfilment

Next, for each intent that acts on the customer’s query (such as the ticket price inquiry in this case), the chatbot will reply based on the number of participants (children, adults and seniors), the site they wish to visit, and the citizenship.

1. Extract both the slot filling values (parameters) and the context parameters found in the request body.
2. If the customer has explicitly mentioned the site, the number of people and/or their citizenship, use that. If not, recall from the context in the current conversation session.
3. Make a functional reply using those parameters, such as replyTicketPrice(agent, citizenship, participants, site) .
4. Finally, keep all (and new) parameters mentioned in the context.
   agent.context.set({name, lifespan, parameters: { …currentParams, …newParams}}) This is helpful when the bot goes back to step 1 and 2, preventing the user from repeating the details.
5. In many cases, a chatbot is designed to fulfil a wide range of customer requests, so a good idea is to assign a topic name in the context to keep the current conversation relevant (be it about ticket price inquiry, membership registration or something else) in case the user informs the chatbot about a different site or a different number of visitors.

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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?

So how should the bot exactly reply? There are 8 possible ways to reply, based on what parameters were given, and it’s up to your conversation design.

If the customer comes with a short and sweet message like “ticket prices”, the chatbot will ask for one of the required parameters. Or if the customer says “ticket prices to the cloud forest”, there’s an equal chance that the bot will ask for the number of the participants or the citizenship. All bot replies are in the form of questions, which will elicit a relevant answer from the user, otherwise, the bot will tell the customer the ticket prices right away if all are known.

What happens when the user says “2 adults, 1 child”, “cloud forest”, or “tourist”? The respective parameter-based intent is triggered, not the ticket price intent. In this situation, the chatbot will invoke the replyTicketPrice response based on whatever information is passed to it each time. There is also the possibility that the customer starts talking to the chatbot that they are “interested to visit” without a specific purpose like inquiring about the ticket prices, so the bot may ask “Which site?”.

The fulfilment of this parameter-based intent (site) shares the same design pattern for the other two (participants and citizenship).

Conclusion

Context is a great way to carry important details from intent to intent, especially if the customer changes information, interrupts the flow by going off-topic, or wants the bot to complete multiple requests.

You’ve probably looked up local weather info using digital assistants, and then asked “what about (this city) instead?” and still get the weather forecast. That’s context at work. Can you think of other use cases too? Are you thinking of re-designing your bot dialogues? Or do you have different ways to accomplish context using other bot frameworks?

Opinions expressed are solely my own and do not express the views or opinions of my employer. If you enjoyed this, subscribe to my updates or connect with me over LinkedIn.

Don’t forget to give us your 👏 !

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Making chatbots reply smarter with context using Dialogflow Fulfillment 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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Among the various ways you can improve customer satisfaction, chatbots are a powerful solution to help the customer base. Chatbots are affordable, help scale your business, fully customizable, help your customers find the right products/services, and help build trust for your business. To prove this I’ll go through following content:

1. What is a machine learning chatbot?
2. Why chatbots are important in different business spheres?
3. Build you own NLP based chatbot using PyTorch.
4. Deploy chatbot in Javascript and Flask.

What is a machine learning chatbot?

A chatbot (Conversational AI) is an automated program that simulates human conversation through text messages, voice chats, or both. It learns to do that based on a lot of inputs, and Natural Language Processing (NLP).

For the sake of semantics, chatbots and conversational assistants will be used interchangeably in this article, they sort of mean the same thing.

Why chatbots are important in different business spheres?

Business Insider reported that the global chatbot market was expected to grow from $2.6 billion in 2019 to $9.4 billion in 2024, forecasting a compound annual growth rate of 29.7%. The same report also suggested that the highest growth in chatbot implementation would be in the retail and ecommerce industries, due to the increasing demand of providing customers with seamless omnichannel experiences.

That alone should be enough to convince you that chatbots are the way to handle customer relationships moving forward, but they will also continue to grow as internal tools for enterprise tools and nearly every industry will adopt the technology if it hasn’t already.

Below are the key reasons why more and more businesses are adopting the chatbot strategy and how they are a win-win formula to acquire & retain customers.

• Reduce customer waiting time — 21% of consumers see chatbots as the easiest way to contact a business. Bots are a smarter way to ensure that customers receive the immediate response that they are looking for without making them wait in a queue.
• 24×7 availability — Bots are always available to engage customers with immediate answers to the common questions asked by them. The top potential benefit of using chatbots is 24-hour customer service.
• Better customer engagement — Conversational bots can engage customers round the clock by starting proactive conservation and offering personalized recommendations that boost customer experience.
• Save customer service costs — Chatbots will help businesses save more than $8 billion per year. Bots can be easily scaled which saves customer support costs of hiring more resources, infrastructure costs, etc.
• Automate lead qualification & sales — You can automate your sales funnel with chatbots to prequalify leads and direct them to the right team for further nurturing. Being able to engage customers instantly increases the number of leads and conversion rates.

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?

Build you own NLP based chatbot using PyTorch

There are many platform where developers, data scientists, and machine learning engineers can create and maintain chatbots like Dialogflow and Amazon Lex. But my goal in this article to show you how to create a chatbot from scratch to help you understand concepts of Feed-Forward Networks for Natural Language Processing.

Let’s get started!

You can easily find a complete code in my GitHub repo.

Here is a short plan that I want to follow to build a model.

1. Theory + NLP concepts (Stemming, Tokenization, bag of words)
2. Create training data
3. PyTorch model and training
4. Save/load model and implement the chat

We will build chatbot for Coffee and Tea Supplier needs to handle simple questions about hours of operation, reservation options and so on.

A chatbot framework needs a structure in which conversational intents are defined. One clean way to do this is with a JSON file, like this.

Each conversational intent contains:

• a tag (a unique name)
• patterns (sentence patterns for our neural network text classifier)
• responses (one will be used as a response)

So our NLP pipeline looks like this

• Tokenize
• Lower + stem
• Exclude punctuation characters
• Bag of Words

We create a list of documents (sentences), each sentence is a list of stemmedwords and each document is associated with an intent (a class). Full code is in this file.

Then we need to set a training data and hyperparameters.

After all needed preprocessing steps we create a model.py file to define FeedForward Neural Network.

Feedforward neural networks are artificial neural networks where the connections between units do not form a cycle. Feedforward neural networks were the first type of artificial neural network invented and are simpler than their counterpart, recurrent neural networks. They are called feedforward because information only travels forward in the network (no loops), first through the input nodes, then through the hidden nodes (if present), and finally through the output nodes.

Be careful! In the end we don’t need an activation function because later we will use cross-entropy loss and it automatically apply an activation function for us.

Why we use ReLU?

They are simple, fast to compute, and don’t suffer from vanishing gradients, like sigmoid functions (logistic, tanh, erf, and similar). The simplicity of implementation makes them suitable for use on GPUs, which are very common today due to being optimised for matrix operations (which are also needed for 3D graphics).

After defining a CrossEntropy Loss and Adam we implement backward and optimizer step.

What do all these lines mean?

We set zero_grad() to optimizer because in PyTorch, for every mini-batch during the training phase, we need to explicitly set the gradients to zero before starting to do backpropragation (i.e., updation of Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes.

Calling .backward() mutiple times accumulates the gradient (by addition) for each parameter. This is why you should call optimizer.zero_grad() after each .step() call. Note that following the first .backward call, a second call is only possible after you have performed another forward pass.

optimizer.step is performs a parameter update based on the current gradient (stored in .grad attribute of a parameter) and the update rule.

Finally, after running train.py script what a wonderful result we got!

And in the last part we need to save our model. Here the way I did it easily.

Chatbot Deployment with Flask and JavaScript

I decided to go further and create this amazing visualization of ChatBot.

All my HTML, CSS and JavaScript scripts you will find in my GitHub repo.

Enjoy!

Conclusion

Now, as you are aware of what a chatbot is and how important bot technology is for any kinds of business. You will certainly agree that bots have drastically changed the way businesses interact with their customers.

Chatbot technologies will become a vital part of customer engagement strategy going forward. Near to future bots will advance to enhance human capabilities and human agents to be more innovative, in handling strategic activities.

Don’t forget to give us your 👏 !

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NLP based Chatbot in PyTorch. Bonus Flask and JavaScript deployment was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

Have you ever wondered how techies build a chatbot? With the development of the python language, it has become simple with just a few lines of code. I’m trying to give some idea about how to build a simple chatbot using python and NLP which we come across in our daily interaction with any app/website.

Recently, the human interaction for most kinds of initial customer support is being handled by these chatbots which reduces an enormous amount of logs or complaints coming in the day-to-day lives for any product or service that we buy. The simple and repetitive queries are very well handled and it’s good to leave these tasks to robots and focus our human energy on more efficient work that adds value to the company.

There are two types of chatbots that we generally come across,

1. Rule-Based Chatbot: It’s a decision tree-bots and use series of defined rules. The structures and answers are all pre-defined you are in control of the conversation.
2. Artificial Intelligence Chatbot: It is powered by NLP(Natural Language Processing). It is more suited for complex kinds and a large number of queries.

Some applications of Chatbots:

Without wasting much time, let’s dive into the jupyter notebook and get our hands dirty with coding.

1. Import Required Libraries

Building a chatbot requires only three important libraries as follows,
nltk — Natural Language Tool Kit for natural language processing.
strings — To process strings in python
random — To randomly select the words or responses

2. Import the Corpus

Courpus in simple terms means collection of texts(strings, words, sentences etc). It is the training data required for chatbot to learn. The corpus plays a very important role in deciding the responses. So whatever you want your chatbot to learn and respond has to put in a txt file and save it.

The NLTK data package includes pre-trained punkt tokenizer for english language so it is preferred over other tokenizers such as tweepy, RegEx etc.

Wordnet is a semantically-oriented dictionary of English included in NLTK library.

Corpus is the core of our chatbot from which we proceed further to data preprocessing where we handle text case and convert all the data input to either lower or upper case to avoid misinterpretation of words.

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. Tokenization

Tokenization is a process of converting a sentence into individual collection of words as shown below. The punkt tokenizer is used for this purpose.

Once we separate the sentences and words using tokenizer, let’s check if it is done correctly with the following codes.

4. Text Pre-processing

Lemmetization or Stemming is a process of finding similarity of words which are having same root(lemma) words. For example:

5. Generating Bag Of Words (BOW)

The process of converting words into numbers by generating vector embeddings from the tokens generated in the previous steps. For example

On top of this vector embeddings, one hot encoding is applied through which all the words are converted in 0’s and 1’s which will be used as input for the ML algorithms.

6. Defining the Greeting function

7. Response Generation function

Import two libraries that are important at this stage as follows:

Tfidf — Term frequency(tf) is used to count the frequeny of occurance of words in the corpus & how rare is the occurance of the word is identified by inverse document frequency(idf).

Once we have bag of words(BOW) converted into 0’s and 1’s the cosine_similarity function is used to produce normalized output so that machine can understand.

Next, we write a function for response generation so that after we provide certain data (corpus) to it and ask some questions, we get an answer. But in case if the user asks something which the chatbot don’t understand meaning tfidf==0 then the machine should respond accordingly as mentioned in the message.

8. Defining conversation start/end protocols

In this section, as soon as the user greets, the chatbot will be able to respond back with the message & when the user says bye, it will quit. In between any questions related to our corpus will be responded back to the user which makes this chatbot interesting.

9. Chatbot queries and responses

Now, our simple chatbot is ready to respond to the user. Type in some inputs saying hi and it will respond with any of the greeting input by randomising it. When we ask any question and it will respond back to user with the related words and sentences which makes sense. Sample Chatbot interactions are shown below

Note: Since it is a simple chatbot, it will not answer some of the direct questions like what is data science and stuffs like that.

10. Conclusion

This is one of the most simple chatbots you can build with very few lines of code. Of course, if you want a more sophisticated chatbot then it all depends on the scale & vastness of the corpus which we give for training & the complexity of the code which helps the chatbot to learn and respond to the user questions.

Hope these baby steps helps my fellow friends and Data Science aspirants to dig deeper and build more complicated chatbots as per the requirement.

Final code for this project can be found at my GitHub repository.

Happy Learning!

11. References

1. https://www.nltk.org/_modules/nltk/tokenize/punkt.html
2. https://www.esparkinfo.com/in-depth-guide-of-chatbot.html
3. https://en.wikipedia.org/wiki/Data_science
4. https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html
5. https://www.greatlearning.in/academy/courses/261323/52827#?utm_source=share_with_friends

Don’t forget to give us your 👏 !

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Building a simple Chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why should you focus on increasing your customer retention?

Most companies focus more on acquisition than on retention. For a lot of them, that’s a mistake. Unless you’re just starting out, you should focus on retaining existing customers.

Various studies have shown that retaining customers could be anywhere from 5 to 25 times cheaper than acquiring new customers. To make this sweeter, customer retention actually helps you with customer acquisition since loyal customers are 4 times more likely than new customers to refer you to their friends and family. These loyal customers are also 7 times more likely to try your new offerings.‍

Which customer retention metrics should you focus on for your Shopify store?

There are many metrics that you could track to measure customer retention. Here are the most important ones:

1. Repeat purchase rate

This refers to the percentage of customers who made more than one purchase from your Shopify store during a specific time period out of the total number of customers who made purchases from your Shopify store during that time period.

You can use this formula to calculate your repeat purchase rate:

Repeat purchase rate = (Number of customers who made multiple purchases / total number of unique customers) x 100

2. Purchase frequency

The purchase frequency helps you figure out how often your customers make purchases from your store. Your purchase frequency could vary depending on your industry and the type of products that your store sells. For example, stores selling fast-moving consumer goods would have a very high purchase frequency while stores that sell consumer durables would have a low purchase frequency.

To calculate your average purchase frequency for a specific period, you can use this formula:

Average purchase frequency = Total number of purchases / Total number of unique customers‍

3. Average order value

Your average order value is the amount of money that your customers spend on every purchase that they make at your Shopify store. Your average order value could increase because of your customers buying more expensive items or even because of them buying multiple products (or many of one product) at one time.

A high average order value could be an indicator of good levels of customer retention because loyal customers would be the ones most open to spending a lot of money in a single order (they trust you) and new customers would be less likely to make very expensive purchases the very first time.

To calculate your average order value, divide your total revenue for a certain period by the number of unique orders you had during that period.

Average order value = Total revenue / Total number of orders‍

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?

4. Customer lifetime value

Your customer lifetime value is the amount of revenue that you will earn from a customer over the entire amount of time that they do business with you. If you increase your customer retention, you will end up increasing your customer lifetime value. There are many complex formulae that you could use to calculate your customer lifetime value, but let’s make it simple. Here’s the easiest, most straightforward formula that you could use.

Customer lifetime value = Average order value x Average purchase frequency x Average customer lifespan

Quick tip: Read this article to learn how to increase your customer lifetime value.‍

How to improve your customer retention on Shopify?

1. Respond to customer queries quickly

According to Forrester Research, 53% of customers end up abandoning their carts if they don’t get their questions answered instantly. And this isn’t just limited to new customers. Even if your customers have made purchases from your store earlier and they came back, they’re liable to abandon their cart and even stop doing business with you if you don’t answer their questions quickly.

That’s why you need to deploy an intelligent chatbot on your Shopify store. If you use an AI-powered Shopify chatbot from Engati, you can answer around 80% of your customer queries instantly, passing only the most complex questions on to live agents.

Now, speaking of those complex questions; you don’t want your customers to have to get on a phone call or start an email conversation to get the answers they’re looking for. Use Engati Live Chat to let your customers converse with your agents and have their questions answered over the same platform.

A Forrester study has shown that 44% of online shoppers believe that live chat is the best feature that an eCommerce website could have.‍

2. Have a better return policy

Making your return policy more lenient would cause your customers to trust you more. It’ll make them feel like your offerings must be high quality if you have such a great return policy. That trust will cause them to stick with you and keep doin business with you for a long time.

And if you’re afraid that such a return policy would cause customers to purchase your offerings, use them once and return them, thus making you actually lose money on shipping, you don’t need to worry about that.

What actually tends to happen is that such a policy would cause your customers to make more purchases, thinking that they can return them if they don’t like them. But, now the Endowment Effect kicks in, making them value these products more because they belong to them. The result? They just don’t end up returning their purchases. A more lenient return policy could thus help you increase your sales as well as your customer retention.‍

3. Use Shopify Analytics

Shopify analytics helps you understand your sales trends and shows you who your recurring customers are and what they are buying. It even shows you what items they are buying together, which could allow you to bundle those items and offer discounts on them.

It could help you understand your customers’ interests and offer products that match what they are looking for, thus keeping them coming back for more.‍

4. Personalize product recommendations

You can improve your customer experience by displaying the products that your customers would be most interested in across your Shopify store. Displaying product recommendations based on your customers’ preferences would make it seem like your catalog matches their needs and tastes. It also creates a better customer experience because they don’t have to spend their time hunting for the products that they are interested in.

You could even set up product recommendations that are based on how they have historically browsed through your store. Using personalized recommendations makes life easier for your customers and shows that you have what they need, so they won’t need to look in other places and they’ll keep coming back to your store.

5. Engage your customers in their language

If you can’t engage and support your customers in their language, they aren’t going to stick with you. They just won’t trust you as much if you only provide support in English while they speak other languages. They’ll look at you as an outsider and that’s not good for customer retention.

Deploying an Engati chatbot on your Shopify store allows you to engage your customers across multiple regions in 50+ languages, helping you make all your customers feel comfortable doing business with you and more inclined to stay loyal.

6. Leverage customer accounts

Creating an account would subconsciously make your customers think that they are going to make more purchases from your store in the future. That’s essentially them justifying the act of making an account, even if they didn’t initially intend to make future purchases from you.

Creating an account would certainly make the buying process much easier for your customers, but you might not want to force customers to make one, that could just be annoying and cause new customers to abandon purchases.

You want to offer guest checkout options but still incentivize customers to create accounts on your store.‍

7. Create a loyalty program

A loyalty program is a time-tested way to increase your customer retention. The Harvard Business Review even shows that companies with strong loyalty programs tend to grow their revenues 2.5x faster than their competitors.

You can find apps to build loyalty programs on the Shopify App Store, and you can even use your Engati chatbot to run your loyalty program.‍

8. Allow customers to subscribe to products

If you sell products that your customers would typically need to purchase at regular intervals, you don’t want to give them the opportunity to discover your competitors the next time they need to make a purchase.

Allow them to subscribe to those products and give them a discount for doing that. This enables you to earn recurring revenue and eliminates the effort your customers have to make to reorder, along with the chance of them selecting a competitor.‍

9. Create a feedback loop

After your customers make a purchase, you want to lay the foundation for a good relationship by asking them for feedback and even requesting a review. Asking for feedback (and acting on it) shows your customers that you care about their opinions and makes them want to stick with you longer.

Requesting a review is very effective because when they do leave a positive review they are essentially getting conditioned to think positively about your store. Thinking negatively would create dissonance in their mind and it just wouldn’t feel right. They’ll essentially be justifying the act of leaving a positive review by telling themselves that they do like your store and your offerings.‍

10. Go omnichannel

An omnichannel approach makes life easier for your customers and allows you to reach them on all the channels they’re hanging out on. Deploying an omnichannel Engati chatbot for your Shopify store even helps you reduce customer effort because they’ll be able to get support, check their order status, and do much more directly over WhatsApp, Facebook Messenger, Telegram, Instagram, and 10+ other channels instead of having to visit and navigate through your website.‍

11. Wish them on occasions

If you collected information about your customers like their date of birth when they set up an account with you, you could send them a message to wish them, along with a gift card or even a small gift for your most loyal, high-value customers.

Even if you don’t have that information, you could still wish them on festive occasions and send them gift cards or gifts, thus making them feel better about you.‍

12. Improve your user experience

A bad user experience would cause you to lose customers. Your store should be easy to use and should require as little effort as possible for your customers to make purchases. Look at analytics and heat maps to identify issues with your store and fix them.

When you design your Shopify store, make sure not to stray too far away from the commonly accepted web conventions, doing that could make your website feel less intuitive. If certain buttons and other features are not where your customers would intuitively expect them to be, your customers might get confused and frustrated. You don’t want that to happen.

Another way to improve your user experience is to deploy an Engati chatbot on your Shopify store so that your customers don’t have to hunt through your entire website to find the information they need. They can get all the information they’re looking for — from other details to store policies — in one place, by simply asking a question.

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This article about “How to retain customers on your Shopify store” was originally published in Engati blogs.

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How to retain customers on your Shopify store was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Speech Recognition Engineer Armin Saeb brings you the fifth installment of our “Lessons from Our Voice Engine” series, featuring high-level insights from our Engineering and Speech Tech teams on how our voice engine works.

What is an acoustic model (AM)?

Acoustic Models (AM) are key components of any speech recognition engine. An AM describes the statistical properties of sound events and connects the acoustic information with phonemes or other linguistic units. Hidden Markov Models (HMM) are one of the most common types of AMs. Other acoustic models include Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN).

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 AMs important for SoapBox?

AMs play an even more critical role at SoapBox than in normal, adult-focused voice engines because recognizing children’s speech is much more challenging. Kids have smaller vocal tracts and slower and more variable speech patterns. They inhabit noisy environments and use a lot of spontaneous speech, imaginative words, ungrammatical phrases, and incorrect pronunciations!

At SoapBox we work hard to design and train AMs that can cater to all of this complexity and do the heavy lifting of accurately converting children’s speech to text.

Catch up on our previous “Lessons from Our Voice Engine”:

• #1: Natural Language Processing (NLP)
• #2: Custom Language Models (CLMs)
• #3: Deep Learning
• #4: Debiasing

While you’re here, check out our latest insights on voice tech for kids.

Don’t forget to give us your 👏 !

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What Are Acoustic Models and Why Are They Needed in Speech Recognition for Kids? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Black Friday and Cyber Monday are the two single days that concentrate more sales of the whole year. Christmas sales are bigger but they are spread out over a much longer period of time. So, is your online shop ready for Black Friday / Cyber Monday? If you’re not sure you should definitely go over one of the many checklists posts advising you on how to make sure you test your website and checkout process, make sure it scales, etc. Also, this podcast collects a great set of experiences and Insights for Black Friday and Cyber Monday Sales.

But these checklists focus a lot on the technical part and tend to forget that a spike in visitors directly translates into a spike of questions to answer. If you’re too busy to answer those questions immediately, visitors will just bounce to another shop. And with that, your sales will go down.

Unless you know how to clone yourself, the best option to make sure all your visitors get the attention they need is to make sure you add a chatbot to your eCommerce site. Among all the benefits a chatbot brings to eCommerce, they can:

• Help customers find the products they want
• Recommend them the products, including those you want to push during these two days (e.g. as you’re running out of stock for some products you may want to redirect visitors to products with a larger remaining stock)
• Answer any type of FAQ question
• Up-sell and cross-sell products to maximize the sales per client
• And do all this in the native language of the client (something that not even your clones would be able to do 🙂 )

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?

But not any bot will do, you need a bot that

1. Understands your shop catalog. Products, payment methods, orders,… The bot needs to be fully aware of all the shop data.
2. Dynamically configures itself to use the shop data to provide personalized answers
3. It lets users to add products to the cart from the bot to speed up the shopping process

Keep in mind that you won’t have the time to use an external bot creation tool to update the conversations every time you want to, for instance, feature a different product or update the availability of the different shipping options. You need a bot that can work out-of-the-box thanks to deep integration with the eCommerce platform you’re on. For instance, like Xatkit does with WooCommerce (for now, expanding to other platforms in the future!).

And you shouldn’t wait until the last minute if you want to get the most out of the chatbot. Even with automatic bots with plenty of built-in conversations you always have the option to add custom FAQs that are very specific to your shop. Get them ready before the D-day arrives. Also, chatbots are a great tool to discover what your visitors are looking for. Most chatbot tools come with a monitoring dashboard where you can see what are the most common questions and, even more importantly, all the questions you were not prepared for. Collect this data and advance and use it to make sure all the information is updated and available in the shop.

Start TODAY preparing your shop for Black Friday with our chatbot, the perfect chatbot for WooCommerce (and it’s not us who say so!)

Don’t forget to give us your 👏 !

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Chatbots are your secret Black Friday weapon was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.