Forem: AskSid AI

What are the transformer networks?

AskSid AI — Mon, 21 Sep 2020 05:10:13 +0000

Transformer networks have become very popular ever since Google BERT achieved state of the art results in various NLP benchmark tasks. It was first mentioned in the paper ‘Attention is all you need’ (https://arxiv.org/abs/1706.03762) in 2017.

So what are transformer networks actually? Are they different from neural networks?

Transformer networks are actually a kind of neural networks architecture. Other kinds of architectures include CNNs (Convolutional neural networks) and RNNs (Recurrent neural networks).
Different architectures are better suited to certain tasks. For example, CNN’s are better suited for image-related tasks, and RNNs are better suited for text and sequence tasks. Transformer networks are best suited for the sequence to sequence tasks involving text. They overcome a few weaknesses of other networks such as RNNs.

What is the structure of a transformer network?

Transformers consist of encoders and decoders which are stacked on top of each other. This means that each encoder’s input is the output of the previous encoder and so on. The input of the first encoder is word embeddings for each word in the input sentence.
But first, let’s look at what encoders and decoders do. Encoders transform an input sentence into an embedding. An embedding is just a list of numbers. This embedding is a meaningful representation of the input sentence in the vector space.
Decoders transform an embedding generated by an encoder into an output sequence. In the above diagram, the sentence in one language is being translated into another language.
The word embeddings used for the encoders inputs can be any recognized word embeddings like word2vec, Glove, etc. These techniques already transform words into meaningful vectors.
Encoders and decoders in a transformer also use self-attention.

What is Self Attention?
Self Attention is a concept used by encoders and decoders to gain a better understanding of the context of each word. In natural language, when looking at the words in a sentence, the meaning of each word is decided by the words around it. More importantly, the meaning is decided by specific words around it and not all the words.

For example, let’s consider the below sentence,

The animal didn’t cross the street because it was too tired

In the above sentence, the word ‘it’ refers to the animal. This is obvious to humans but not so to machines. Self-attention allows the network to pay attention to specific words that are connected to each word in the sentence.
Transformer networks use multi-headed self-attention which allows for multiple independent representations of attention to be considered for each input word.

What are the benefits of transformer networks?
Transformer networks outperform regular RNNs and LSTMs easily on NLP benchmark tasks. They also provide some more benefits,

The encoder-decoder architecture allows transformer networks to train in a parallel fashion. This means that with enough resources, these networks can be trained much faster than RNNs or LSTMs
The Self Attention mechanism allows words that are further apart in a sentence to be associated with each other. They do not have a sliding window like RNNs.
What are the applications of transformer networks?
Machine Translation – It has already been used extensively for machine translation and has achieved state of the art results.
Text Summarization – This task generates an abstract summary of a longer piece of text.
Sequence to Sequence tasks – It can be used in any text seq2seq task like question generation, paraphrasing, etc
Classification and Clustering – The encoder’s outputs (embeddings) can be used as sentence embeddings for any classification or clustering tasks
Conclusions
We have looked at the successor of RNNs and LSTMs. Transformer networks improve upon sequence to sequence tasks in both accuracy and speed.
However, research is already ongoing to replace or enhance transformer networks to handle even longer pieces of text. Google’s BigBird aims to do exactly that (https://arxiv.org/abs/2007.14062).
The NLP area has been improving leaps and bounds over the last few years and it looks like this is only going to continue.

Article Source: https://www.asksid.ai/resources/what-are-the-transformer-networks/

Customers = Heroes! Here’s saluting our heroes, Customer Service Agents and Marketers.

AskSid AI — Tue, 15 Sep 2020 09:05:10 +0000

Have we as an industry ever stopped to consider, that there is a specialized set of professionals filling the gap between the product and the end customer, working day and night to ensure just one simple thing – happy customers?

Say hello to the Customer Service Agents and Marketers of the world.
Not only do they ensure that even the smallest issue faced by their customers is solved, but they do it with a smile. All of this stems from a pure passion that drives these superheroes to overcome any hurdle between their customers and their happiness. This is especially pertinent right now, when the world is facing a pandemic, with lockdowns being imposed and businesses struggling to survive. To help others is one’s greatest achievement, but to do so in challenging circumstances like Covid 19, is only driven by the passion to serve. This article is dedicated to all those Customer Service Agents and Marketers, who set a shining example of what it takes to truly create happy customers.

At AskSid.ai, our value system as an organization stems from the foundation of helping our end customers, i.e. Customer Service Agents and Marketers, achieve the pinnacle of professional success every single day, through the support of our solutions. Think of AskSid.ai as the pillar of support for your company’s customer experience function, because that’s how we plan to revolutionize the way your end customers perceive and respond to your brand!

Just like your customer service agents and marketers are passionate to improve their customers’ lives, so are we. The inspiration we gain from their passion and drive, only leads to continual improvement from our end. Because, we’re ultimately in this, to ensure the happiness and success of every Customer Service Agent and Marketer out there, through our innovative AI solutions.

Both these groups of ours are undeniably selfless, working day and night with their teams, creating plans and strategies to solve real problems for real people. Customer Service Agents, as we all know, are first in the line of support and service when it comes to customers, ensuring that their callers get all the answers and resolutions they need. And whenever it comes to customer engagement and higher level problem solving, it’s the Marketers to the rescue. And right behind them, supporting them every step of the way, is AskSid.ai.

We truly believe our core values and principles are what tie us so closely with these two groups. Customer service agents are best at selling and that is what they should do when a customer calls in. Our #AI solution handles all the repetitive and transactional questions from consumers such as order status, delivery delays etc while connecting the ‘high intent’ buyers straight to the agent so that he can make a sale. Same goes for marketers, where our intelligent AI technology supports everything they could possibly need to get deeper insights and a thorough understanding of their end customers.

Conclusion
Ultimately, Customer Service Agents, Marketers and AskSid.ai echo one value and one goal – customer happiness. Whether it is them solving real-time end customer problems, or us at AskSid.ai supporting them in this pursuit with our technological offerings. These are professionals that need to be celebrated and recognized for their relentless efforts and motivation to do whatever it takes, to make that one customer smile. But the question is, how do we celebrate them?

Well, we have a few ideas. Stay tuned to know what we at AskSid.ai have in store for our heroes – the Customer Service Agents and Marketers of the world.

Build the Digital Avatar of your best salesperson NOW

AskSid AI — Wed, 05 Aug 2020 06:44:59 +0000

Every Brand is in the business of making & selling a ‘Product’. Assuming you have made a ‘good product’ that meets a need in the market, what is that one fundamental core competency that you (as a brand) need, to sell more of your products? I believe, it is your ability to HUMANIZE the shopping experience of your customers and answer all the questions she has at that zero moment of truth. This is even more relevant in the current times when social distancing and contactless shopping is the new normal and probably something that will stay for a long time.

Think about the last time you (as a shopper) bought something from your favorite brand. You went into the brand’s store (physical or web), you looked around the assortment on display, found some relevant choices (with some struggle) and then while evaluating which one to buy, you had so many questions on these products – Questions that are about the product, the varieties and possibilities that you as a consumer can enjoy by using the product and questions that can be answered only by a product expert from the brand. Was it easy for you to find those answers? If you were shopping at the store, probably it was relatively easier – you would have asked your questions to the store associate and hopefully (although not always) she knew the answer. What about when you were buying online?

The challenge of appeasing your customers and addressing their questions anytime and anywhere has just got bigger in 2020 amidst the social distancing and lockdowns. Your consumers have shifted to digital and even if some of them are entering your stores, they will prefer contactless shopping. Agree?

Let’s take my example from last weekend. I needed to buy a new trimmer, I go to my favorite brand’s web-shop and I was welcomed with hundreds of options, loads of images and text information 90% of which I either did not need or I didn’t care. All I cared for was a trimmer that works for both head and beard, has the 2 specific attachments that I am used to and is waterproof. That, it was a struggle to get answers to my questions while I was wading through the different options, is an obvious understatement. And till today I have not bought my trimmer!

At AskSid.ai we help brands solve this specific problem. Our vision is help you build the digital avatar of your best sales guy! With several global brands as our customers and after going live in 20 countries across multiple channels, it is clear that consumers want their questions answered immediately and without the answers, they will simply not buy. And therefore, at AskSid.ai we firmly believe that every brand (regardless of what product you make) needs a digital product expert who understands the domain, is available online across channels & 24×7 addressing questions of your consumers. Not just our customers alone, the a recent example is none other than BMW who launched their digital product expert last week as an in-car personal assistant helping BMW drivers with any questions on their car. If you find this interesting, I recommend you to read this blog post on how AI for retail solutions can help you deliver delightful and exceptional customer service

I am excited at the pace in which this new trend is fast emerging. On one side I see global brands embracing this opportunity not just to improve conversions or save customer service operations cost but also to discover precision marketing insights from customer questions. On the other side Vertical AI technology is finding a new meaning and thanks to the investment and focus from biggies such as Microsoft in laying out the foundation technology layer, Vertical AI in its truest sense is already becoming a new normal.

If you would be interested to learn more about what we do or share some of your perspective on the above, I welcome you to reach me via our website www.asksid.ai or my email sanjoy.roy@asksid.ai

What is a neural network?

AskSid AI — Mon, 27 Jul 2020 07:28:54 +0000

In previous blog posts, we have discussed Machine Learning and Deep Learning. We spoke about how they are not very different. They only differ in the algorithms and models used to solve problems.If you have heard the term ‘Deep Learning’, you would also come across ‘Neural Network’. Woah sounds ominous. In this blog post, let’s unpack the term ‘Neural Network’ and take a peek inside the closet. Is it really a monster hiding inside? Or a harmless cat?

Neural Networks in Human Brains
The term ‘neural network’ comes from the human brain. The human brain contains billions of neurons which pass signals. A huge network of these neurons passing signals is called a neural network.

The neurons used in deep learning are inspired by the human brain. You can consider them to be a simple form of the same concept.

Neural Networks and Deep Learning
As we have seen in previous posts, Machine Learning models are function approximators. They attempt to learn the relationships between inputs and outputs and create an approximation of the function between them.

As an example consider the below table

X. Y.

1 3

2 6

3 9

Here Y = 3X represents the function or relationship between the input X and output Y. In deep learning, neural networks attempt to do the same thing.

In this figure, we see how a single neuron acts as a function between inputs and outputs. In deep learning, we create a network of these neurons all working together to do the same. This increases the capability of the neural network to learn more complex relationships.

How does a neural network graph make predictions?

When it comes down to the fundamentals, neural network predictions are just maths, not magic. So let’s walk through a simple prediction. Some concepts are simplified for understanding.

The input to a neural network is a vector. A vector is just a series of numerical inputs. Therefore any data (such as text, images, audio etc) you want the neural network to process must first be converted to numerical vector form before the neural network can process it.

Let’s say the input is [x1, x2, x3….]

Each layer in the neural network has a weight matrix associated with it. [w1, w2, w3…..]

As the input passes through each layer, it is multiplied with the respective weight matrix and in the process it is transformed into something different.

After all the layers are processed in this way, we are left with the output vector [y1, y2, y3…..]

This is the output of the neural network.

If the task is to predict whether the input image is a cat or a dog, the output vector could look like this,

[ycat, ydog]

[1, 0] is a cat

[0, 1] is a dog

How does a neural network model learn?
We saw how neural networks make predictions, but how do they learn the weights for each layer in the first place?

Neural networks learn their weights using a process called ‘Training’.

Training happens when we feed a lot of data where the inputs and outputs are known, also called ‘Training Data’.

The network is initialized with random weights
It is then asked to make predictions on training data
Wherever it makes wrong predictions, The weights are changed slightly in the direction of correct predictions. This step is also called ‘neural network backpropagation’
This process is repeated over and over again till the weights hardly change, also called ‘Convergence’
The network is then asked to predict on new data which it has never seen before (Test Data). The accuracy of predictions is measured.
Conclusion
A Neural network is an advanced algorithm which is capable of learning complex relationships in the data and make accurate predictions. I like to compare them with ‘Play Doh’. A clay which children play with which can be moulded and stretched to be anything you want them to be.

If you have any feedback or you wish to know more on the AI and ML, you can also read our articles here. Or else just drop us an email at curiousmuch@asksid.ai or visit our website www.asksid.ai to start a conversation!

Wondering what AI really means?

AskSid AI — Fri, 24 Jul 2020 06:25:52 +0000

AI Artificial Intelligence as a concept has taken the world by storm, but has also left millions confused about its true definition. With over 400+ million hits on Google, it’s a natural assumption that there is a lot of curiosity about AI and all the technologies it comprises. Through this blog post, you can discover and understand AI effectively, thanks to a few real life use cases that elaborate on its implementation across industries. And hopefully, you will understand it well enough to spread the knowledge among your peers!

AI Definition – Breaking it down.
Artificial Intelligence has been defined in several ways, but the one we find most precise is – “Intelligence that’s been constructed by humans through the usage of machines to mimic their behavior’. Before we delve into how data scientists have reconstructed these behaviors, let’s take a look at the different aspects of the human mind.

Observe-observe-observe!
On a daily basis, humans are exposed to heaps of data, making observation a key trait. A study even says that the human brain processes 50 to 70 thousand thoughts a day, which it then processes to create a memory. Examples of this include something as simplistic as observing a no parking signboard, to something as complex as taking in data from a teacher on a complex topic and processing it.
It’s all about the learning
Observations provide humans with a plethora of signals, direct or derived, that allow us to build appropriate actions or responses to these signals in the future. Now, a direct signal allows a person to gauge a response based on a previous experience, such as getting an electric shock prepares you for being careful the next time you are around an exposed wire or outlet. On the other hand, a derived signal gives the human mind cues based on an observed second hand experience, like seeing someone get electrocuted, also makes a person more careful when presented with similar situations in the future.
The power of prediction: Now when an event takes place that the human mind has been exposed to before, it brings up the memory of the processed observations, correlates it to the learning gained from that experience and helps predict the smartest course of action. Going back to our earlier example, human cognition predicts danger when it sees an exposed electrical wire.
Getting into action mode
The final outcome of the previous behavioral stages, action mode is when the human reacts in tandem with the prediction signal that’s being relayed. Meaning, the human stays away from the electrical wire, based on the previous observations, learning and prediction.
HOW DOES AI ML FACTOR IN WITH HUMAN BEHAVIOUR?
So far, we’ve gone through the different human behavioral aspects that AI draws from. In AI, what happens is data scientists train machines to artificially reconstruct intelligence that can draw on the human responses to an event. Let’s take a look at how the human intelligence building process takes place, step by step.

AI AND OBSERVATION:
The process begins with feeding as much relevant data, direct or derived, as possible into machines, more the better in fact. Bigger the data pool fed in, more the observations

AI AND LEARNING:
AI responds to different types of stimuli, depending on what you train it to do, these can be both direct and derived. In the case of direct signals supplied by data scientists, machines are able to create connections and learn the meaning immediately. This is also known as supervised learning and is the first step in discovering the world of AI. For example, if the data provided says 6=2 X 3, and 16=4 X 4, the machine learns that N=A X B.

On the other hand, when the data supplied consists of derived signals, the machine does everything it can to derive learning – also known as unsupervised learning. Meaning if the machine is provided visual data of different types of shirts, it eventually learns to identify a shirt, which constitutes the second step in understanding AI.

AI AND PREDICTION:
AI is built around the paradigm of a technology’s capability to predict outcomes based on data used to build its intelligence quotient. So when a machine has undergone training with either the supervised or unsupervised methods, it has essentially learnt to predict outcomes for both known and unknown data. Meaning if shown an unseen image of different garments, the machine would be able to predict if it consists of a shirt or no shirt.

AI AND ACTION:
Once AI has been used to generate predictions, the same are used to configure and implement probable actions based on the scenario. For instance, on command the machine would be able to pick out shirts based on various parameters like color, pattern, fit, etc.

HOW FAR CAN AI TECH GO?
Now that we have a grasp on what makes up AI tech, let’s move on to its innumerable implementations in the digital age. AI tech can essentially augment or replace any type of human behavior. However, the complexities in implementation would vary scenario to scenario. One thing we can say for sure is that the sky is the limit when it comes to making machines human-like in behavior and response, but it sure is a long road to get there.

Owing to its immense popularity and acceptance in tech and business, AI has grown to become all pervasive. From our phones, smart home devices, to healthcare and human-like chatbots, the reality we are witnessing is most exciting.

Popular use cases:
Trend prediction: Supply chain, sales, etc.
AI for Chatbots and conversational apps: Customer service automation, product search, product questions and answers, recommendations etc.
Voice interactions: Automated IVRs, Alexa, Google Assistant, new automobile experiences like ‘Hello Mercedes’.
Computer vision: Automated traffic violation tickets, face detection and recognition etc.
Healthcare: Correlating diagnosis reports and predicting probable health risks, patient care management and predicting risks etc.
AI IN FUTURE
We’ve learnt how AI constructs human intelligence through machines to replicate human behavior, allowing it to automate and repeat structured tasks. A lot of work is still required in reducing the challenges that arise with human cognitive involvement in making tasks happen accurately. To take it to the next level, AI will have to grow and morph to automate human-like reasoning, analyze sensitive and complex data and diagnose problems in real-time.

To know more on the differences between AI and ML, you can also read the article ‘AI vs machine learning’. Curious much? Drop us an email at curiousmuch@asksid.ai or visit our website www.asksid.ai to start a conversation!

Can I know if this dress suits pregnant women?

AskSid AI — Fri, 24 Jul 2020 06:20:54 +0000

Building #ai #Chatbots that can answer FAQ and transactional questions is easy. Building a #chatbot that can answer complex customer questions is HARD.

You need much more than a chatbot - you need a full stack vertical #ai solution that can build out a #retail #knowledgegraph from the available data you have and then start generating its own data from the usage (akin a data flywheel).

Reach us to learn more how vertical #ai can help you delight your customers and improve #ecommerce conversions

Multilingual Conversational AI Chatbot

AskSid AI — Wed, 22 Jul 2020 05:33:36 +0000

Greetings! When we go to a physical store and talk to attendants, we tend to use our regional language rather than an official language even when we know the language, due to the ease of expression. After looking at some chatbot conversations, I have come to realize that it’s the same case when users chat with a chatbot too. However, most chatbots fail to support different languages and use the default English language. In this blog, I’ll walk you through steps to train your own conversational AI chatbot in any language you want.

Basics first!
No matter which language you are working with, certain tasks like tokenization, parsing, entity recognition, etc are something you carry out anyways. A lot of modules offer these tasks for a lot of languages. You would want to refer to my blog – ‘NLP with Python’ where I have listed several modules (some being only Python modules) and their support for other languages. There are also several tools specially created to work on a particular language like JapaneseTokenizer, Chinese-tokenizer, etc. However, for many languages, there may not be any pre-trained models available. In that case, tools like Spacy let you train NLP models for your own language.

Multilingual Language Models
When you are training your own deep learning NLP models, choosing the best embedding model for your task is important. It is ok if the language you choose to build your bot in does not have any pre-trained language models available. Most of the algorithms for language models out there are unsupervised and let you train state-of-the-art models in any language/set of languages you want. Once you have the language model ready for your language, you can finetune your model to perform various tasks like text classification, next sentence prediction, answer generation, etc.

Building the conversational AI chatbot
Ok, now that you have prerequisites ready, let’s see if you can build your chatbot. In my blog – ‘How are chatbots trained’, I have listed down the steps to train a chatbot. Do refer to the blog to get more details about the below steps.

Know the context
Identify the intent
Identify entities
Select an action
Retrieve results/reply
Here, keeping track of the current context and selecting actions are language agnostic. The other 3 tasks are what we are interested in.

Identify the intent
Intent classification requires a multi-class and/or multi-label text classification model which can classify your text into one or more intents (maybe based on context). We have our language model which can either be finetuned to do this task or can be used to retrieve embeddings which can be used as features to build classification models using other machine learning algorithms/frameworks.

Identify entities
An entity recognition system could be rule-based or built using deep learning techniques. To build rule-based entity systems, you will need POS tags or dependency parser tags on which you can build your entity regex rules. Or you can just use regex rules on queries. However, people are moving towards deep learning techniques to build NER systems. For all the above techniques, you can use tools like Spacy and Stanford CoreNLP about which we discussed in the earlier section.
Retrieve results/replies
We have discussed in our other blog that we need a Knowledge Graph which we can use for query expansion to retrieve results. This knowledge graph contains entities linked to one another based on some relation. How do we build this? Use all your entity extraction or information extraction techniques on domain data to build out a simple Knowledge Graph. When a user queries the bot, match the identified entities from the query to your knowledge graph by leveraging vector similarity techniques – Here comes your embeddings again! A recent advance in Knowledge Graph frameworks has brought in the concept of Knowledge Graph Embeddings which could make this task much easier.

Conclusion
After going through the above steps, it’s clear that we can create a Conversational AI chatbot in any language we want. The key is to know what tools do you have in hand which can handle your language or help you create NLP models for the language desired, have a language model for your language, and you are good to go!

CHATBOTS FOR CUSTOMER SERVICE

AskSid AI — Mon, 20 Jul 2020 05:43:18 +0000

A few days ago I had a terrible shopping experience while browsing for an Ethnic wear on India’s biggest clothing website. I was looking for a “Lehenga” of a specific type. There were so many options with no proper product description. I tried to contact customer support, ten minutes passed by, and even after navigating through multi IVR options, I didn’t get connected to any agent. After waiting for a pretty long time, I logged out. With lockdown and social distancing as the new normal, I can’t even visit the store and I just wonder – why is it so difficult to get answers to my questions from my favorite brand?

ENTER AI CUSTOMER SUPPORT CHATBOTS!!
Before i explain further on how AI Chatbots could have made a significant difference to my shopping journey above, let’s first understand some basics about this technology

What is a chatbot?
A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. The chatbot market size is expected to grow from USD 2.6 billion in 2019 to USD 9.4 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 29.7% during the forecast period.

How does it work?

Analyze the user’s request to understand the intent and extract relevant information (also called NLP)

Respond to user with an appropriate answer which could be either of the following

      i) a predefined text message or a helpful link

      ii) relevant information from a knowledge base

In this article, let’s dig more into how chatbots in customer service are helpful and why every retail brand should have customer service chatbots that can simplify the shopper’s journey and her buying experience. According to research by Gartner, by 2022, 70% of white-collar workers will interact with conversational AI platforms on a daily basis. There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years,” says Van Baker, VP Analyst at Gartner. “This increase has been driven by customer service, knowledge management and user support.”

What are AI customer service chatbots? Do businesses really need it?
The main goal of the best customer service chatbots is to help your business make more money and save more money. Period! How can it achieve it’s goal? By providing exceptional customer service across the customer’s ‘consideration’ journey, ‘during-sale’ journey and also add value in the ‘post-sale’ journey.

Now let’s break the buying journey further and understand the typical pain points customers face where an intelligent customer service bot could help.

Product Discovery: As i narrated above, i am looking at a specific type of Lehenga and i am finding it difficult to find out the right one from the vast assortment available in the catalog. Instead of having me spend endless hours trying to locate the right product, an AI chatbot for retail that is knowledgeable of the products on offer could make my product discovery journey much more easier and simpler.
Answering my questions about the brand and about the products: I like the black Lehenga with a golden lace and I want to know what is the material used for the inner lining. To my dismay, the answer is not available on the product description. I search some more but still no answer. I call the call center but the agent is clueless. Should it be really this difficult for me to get the answer? I am sure the brand knows everything about this Lehenga, then why is it so difficult for me to find the answer to this simple question? An AI chatbot in retail could help here too by giving me the answer instantly. A study conducted by PwC revealed nearly 80% of American consumers feel that speed, convenience, knowledgeable help, and friendly service are the most important elements of a positive customer experience.
Connect me with in-house experts seamlessly: Often time “buying” is an emotive experience. It is about knowing the variety and possibilities we as consumers can derive from the products we intend to buy and who better than the brand itself to guide us on this journey. The best customer service chatbots can help here too – not just answer my factual questions instantly but by seamlessly connecting me to the brand’s in-house experts who can help me not only with choosing the right Lehenga but might also be able to recommend what tops and looks i can buy from the same brand.
What outcomes can chatbots for customer service deliver?
Often chatbots in customer service are equated to cost savings as its primary business outcome. The logic is quite simple – 70% of your customer queries to your call center agents are repetitive in nature. Examples being “where is my order”, “how to return”, “when will i get my refund” etc. For answering these questions, why do you need a human agent behind the phone? AI for chatbots that integrate with your order management system should be able to handle the job at 30% the cost of an agent. Indeed that is true and that is one of the primary use-case for existing customer service chatbots. Infact Chatbots will be responsible for saving over $8 billion annually by 2022, up from $20 million in 2017 (Juniper Research). Business Insider Intelligence estimates that up to 73% of healthcare admin tasks could be automated by AI, and the adoption of chatbots could save the healthcare, banking, and retail sectors $11 billion annually by 2023.

But if you head customer service of a brand, you probably care for 2 metrics

Cost-per-case
Revenue-per-case
Increasing your Revenue-per-case while reducing your Cost-per-case is a difficult one and is what differentiates best customer service chatbots from the rest. Only those AI for retail offerings that are vertical specific (also known as #VerticalAI) and focused on your specific industry can deliver on both the metrics i mentioned above and during your planning phase make sure that you are choosing the right one. To learn more about how these specialized domain specific chatbot offerings work i invite you to read my colleague’s blog “Every brand needs a digital product expert”

Chatbots in Customer Service – Conclusion
Chatbots for customer service can be a real game changer for your business- especially during these times when digital shopping is expected to increase exponentially in the post Covid-19 era. Not just help you keep pace with the increasing call volumes at fraction of a cost, an intelligent Vertical AI based chatbot for retail can actually help you sell more by filtering out high-intent buyers from the rest and connecting them to your human agents to close a sale. You can read about one such success story of a leading Europe based fashion brand here.

So, if you are running the customer service for this ethinic brand in my story or maybe any such brand, here is why you should have intelligent customer service chatbots to add some serious firepower to your existing customer service team.

It will help you accelerate your conversions by simplifying the shopper’s journey
It will help you save on your customer service operations cost by automating the repetitive queries of your customers
It will help you get to know your customers a lot more intimately by engaging them in 1:1 personalized conversations at scale.