Category: Chat

General Artificial Intelligence is still out of reach, but we already managed to produce a nice amount of Machine Learning models. They are widely adopted in many areas of our lives. For this reason, understanding decisions of blackbox models becomes critical and we are seeing increasing interest and number of tools in the field of eXplainable AI (XAI).

Don’t forget about human!

Yes, we should keep in mind that we explain models to humans. For that reason we build explanations which are visual or textual —it is more readable and informative than the raw numbers. But how do we know what should be explained?

At the moment we are given just a set of static tools. We need to make the process interactive. Imagine yourself trying to understand the decision made by a human expert. What would you do? Talk to them and ask questions! We need to apply the same pattern for predictive models. If we want to understand what people want to know we need to let them ask questions. Only having these questions we can think of the ways of answering them.

Human, please meet an ML model, ML model this is human

We build a conversational system (xaibot — chatbot for XAI) with a human on one end and the ML model on the other. This xaibot was named dr Ant (after doctorant — PhD student). It allows talking about Random Forest model predicting survival on Titanic. You can see an example conversation below.

DrAnt understands and responds to several groups of queries:

• Supplying information about the user (passenger), e.g. specifying age or gender. Alternatively, you can start as one of the main characters from the movie Titanic.
• Inference — asking about the probability of survival.
• Dialogue support queries — listing variables, restarting etc.

Trending Bot Articles:

1. The Messenger Rules for European Facebook Pages Are Changing. Here’s What You Need to Know

2. This Is Why Chatbot Business Are Dying

3. Facebook acquires Kustomer: an end for chatbots businesses?

4. The Five P’s of successful chatbots

Xaibot uses visual explanations from DALEX toolset. It answers what-if questions with Ceteris Paribus profiles and feature contributions queries with iBreakDown.

So, what people ask about?

They say Gentlemen do not read each other’s mail. I’m very sorry drAnt — we make an exception this time. We shared xaibot in this post and collected over 1000 human-model conversations.

There are certain repeating patterns in user queries. Here are the most frequent:

• why — general explanation queries, such as ”why”,
  ”explain it to me”, ”how was that derived/calculated”.
• what-if — alternative scenario queries. Frequent exam-
  ples: what if I’m older, what if I travelled in the 1st
  class.
• what do you know about me
• data-related questions — e.g. feature histogram, distribution, dataset size
• local feature importance — How does age influence my
  survival, What makes me more likely to survive
• global feature importance — How does age influence sur-
  vival across all passengers
• how to improve — actionable queries for maximizing
  the prediction, e.g. what should I do to survive, how
  can I increase my chances.

Full results of the analysis might be found in this paper:

What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations

It’s just a beginning of a human-model interaction

We see people engaging in the conversation with the Machine Learning model. It lets them understand more than a single decision and model metrics such as accuracy. And for researchers in the field of eXplainable AI this method provides insight into human needs to be addressed with the explanation methods.

People say talking to plants helps them grow. Talking to Machine Learning models makes those blackboxes more transparent and that is a very desirable trait.

PS drAnt has its own phone number! Yes, it is possible to call the Machine Learning model! Not the most practical thing in this particular case though.

PS2 Let’s not disclose this number. We have already read drAnt’s messages. After all, even the bot deserves some privacy.

Don’t forget to give us your 👏 !

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What would you ask the Machine Learning model? 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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There has been an exponential growth in the use of Conversational AI in the retail industry. Global retailers and eCommerce players have realized the power of Messaging to enhance CX, drive revenue, and reduce costs.

Today, messaging apps have over 5 billion monthly active users, as per a study by Hubspot.

According to Gartner, $8 billion in business expenses will be saved by chatbots by 2022.

Consumers today expect a personalized shopping experience. A Virtual Shopping Assistant can guide the user through the buying journey and provide highly personalized product recommendations. AI-powered chatbots or virtual assistants can replicate the in-store shopping experience by acting as virtual sales agents.

The advancement of artificial intelligence in retail has revolutionized the broken Customer Support experience. You can deflect traffic from costlier channels to your chatbot, provide 24*7 support, and at the same time enable live chat support for complex queries.

Let’s look at some of the top advantages of deploying an eCommerce Virtual Assistant:

A . 24/7 customer service — AI-powered Intelligent Virtual Assistants allow your consumers to engage with your brand 24/7. IVAs are redefining the way companies handle customer queries by providing consistent service anytime, anywhere.

Trending Bot Articles:

1. The Messenger Rules for European Facebook Pages Are Changing. Here’s What You Need to Know

2. This Is Why Chatbot Business Are Dying

3. Facebook acquires Kustomer: an end for chatbots businesses?

4. The Five P’s of successful chatbots

B . No Abandoned Carts — Cart abandonment is one of the most challenging issues that the e-commerce industry faces today. With the help of a retail chatbot, you can send customers prompts, notifications, reminders, etc. to facilitate cart recovery.

C . Omnichannel Experience — The modern customer wants the ease and convenience of shopping. An Omnichannel virtual assistant facilitates smoother communication and offers a consistent experience across multiple channels.

D . Order tracking — eCommerce chatbot can assist customers in keeping track of their purchases, order status, billing info, shipping info, etc.

E . Product Recommendations — Virtual assistants can provide highly personalized recommendations and expert-like guidance — effectively serving as an expert shopping assistant for your users.

The future of Virtual Assistants in Retail and eCommerce Industry

1 . WhatsApp

WhatsApp Commerce:

The growing adoption of WhatsApp by businesses is a testament to the power of WhatsApp as a platform to help businesses achieve massive scale at low cost. From SMBs to large scale enterprises, WhatsApp has emerged as the primary tool of communication, commerce, and support.

The future of eCommerce is Conversational Commerce and WhatsApp is projected to be the largest player in this market. The decision-makers in eCommerce and Retail brands have integrated WhatsApp into their overall Digital Transformation strategy.

[Webinar] WhatsApp Commerce For Brands: All You Need to Know

WATCH WEBINAR

WhatsApp allows you to share personalized user-specific product recommendations, important alerts, discounts, and sell seamlessly, all while saving time, money, and human resources. You can also prompt customers to share their real-time location to enhance delivery.

After a long wait, the National Payments Corporation of India finally gave a green signal to WhatsApp Pay! This will significantly help the retailers to transact with consumers with utmost ease and convenience!

Here are some of the WhatsApp chatbot use cases in retail for Conversational Commerce.

Order tracking and refunds & cancellations are two of the most common support use cases, and both of these can be handled over WhatsApp swiftly and in a completely frictionless manner.

WhatsApp Support The response time on WhatsApp is 9X faster than a phone call.

70% of customers will choose to message rather than call.

It is not only convenient for users to message directly on WhatsApp but also a cost-saving mechanism for businesses.

WhatsApp enjoys an open-rate of around 70%. You can seamlessly reroute high volumes of incoming support queries from call centers to WhatsApp. Chat with your prospects and customers and resolve queries 24*7, significantly bringing down operational costs.

[Webinar] How JioMart Handles 40% Customer Support on WhatsApp

WATCH WEBINAR

2. VOICE

48% of search queries are now Voice Powered. $40 Billion is the projected voice shopping market by 2022. According to a study by ComScore, 50% of online searches will be done through voice by 2020.

Voice makes it easy and convenient for buyers to shop online. Instead of navigating through the application, they can simply ask what they’re looking for and the AI-powered engine will find it for them.
Voice assistants are unleashing their potential to revolutionize the retail industry as customers are increasingly leaning towards virtual assistants.

With an The three key features of a Voice powered AI Shopping Assistant are Product Recommendations, Quick and Easy Ordering, and Contextual Cross-selling. increase in the adoption of Virtual Assistants in the retail and eCommerce industry, it is safe to say that the Future of Online Shopping is VOICE!

Actionable insights to improve engagement, increase cart recovery, and drive sales across the customer journey

Download ebook

3. Google Business Messages (GBM)

35% of product searches happen on Google. 42% of mobile-driven brand interactions involve Google search.

Google Business Messages (GBM) enables you to directly talk to your users right at the beginning of their journey through Google Search & Google Maps.

GBM eliminates the friction of calls, emails, and app downloads and empowers users to get in touch with businesses directly via messaging from mobile entry points such as Google Maps and Search.

You can leverage the combined power of AI and human intervention with a Seamless AI to Agent Handoff.

Haptik worked with Netmeds, India’s largest online pharmacy, to help them get started with their GBM journey.

To know more about how Google Business Messages can help your brand deliver an exceptional customer experience, improve conversions, and reduce customer support costs, visit here.

How are brands leveraging Haptik’s eCommerce Virtual Assistants?

Learn exactly how IVAs are helping real-world eCommerce businesses!1. HealthKart

Business Goals:

1. HealthKart wanted to make support more self-serve and asynchronous to handle the surge of user queries during COVID-19

2. They wanted to proactively educate users and recommend relevant products based on their lifestyle and fitness goals

Solution

1. They activated on-demand support for routine queries such as order tracking, payments, bill queries, refund, and replacement.

2. HealthKart also shared personalized diet and workout plans based on the users’ goals, eating habits, current weight, lifestyle, etc.

You can read the full case study here.

2. Flo Mattress

Business Goals:

After the launch, Flo saw a massive jump in their sales leading to a 50x rise in customer support queries. Flo wanted a solution that could scale rapidly without constraints.

Solution

1. Guiding website visitors with product information and FAQs

2. Answering support queries around usage & delivery

You can read the full case study here.

Haptik’s Smart Skills have played a huge role in delivering exceptional ROI for retailers and eCommerce players globally. You can build virtual agents by importing from our comprehensive library of Smart Skills derived from key learnings & best practices, custom-made for the retail industry. Know more about Smart Skills here.

To Sum Up…In the wake of COVID-19, chatbots for the retail industry have come to the rescue for major retailers and eCommerce brands. With the growing usage of AI in retail, IVAs are playing a key role in transforming the broken, costly, and inefficient traditional systems of handling customer support. Retailers are now realizing the power of Conversational Commerce and including it in their overall digital transformation strategy.

At Haptik, we’ve had the experience of solving complex business problems for some of the largest brands in the world. If you want to provide an exceptional Customer Experience, generate more sales, and reduce Customer Support costs, let’s grab a cup of coffee (virtually, of course!) and discuss how Haptik can help you meet your business goals!

Here’s a guide on eCommerce Chatbots and how to Drive Sales and Customer Retention

Don’t forget to give us your 👏 !

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3 Virtual Assistant Trends for Retail and eCommerce Brands was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Analyse Tweets from Twitter Streaming with Node.js & free NLP API

Intro

According to WordStream article from 04.2020 everyday Twitter users send circa 500 million tweets. In this huge sea of information, it is hard to manually find and analyse tweets about your brand or product. Fortunately Twitter API gives us a possibility to follow a number of keywords automatically.

In this article I would like to present you how to easily create a Node.js application to follow provided keywords on Twitter in real time. Next, we will analyse them using a free NLP tool from ThadeusAI (fyi: you could use any other AI). Our selected keyword will be Cyberpunk. Eventually we will be able to build a simple bot for gathering tweets about Cyberpunk 2077, analyse them with a taught NLP and enable auto answering.

Twitter App Creation & Streaming Setup

Let’s start by creating a Twitter app. Go to Twitter Developer Portal and create a new app. If you didn’t have developer access before, firstly you have to fill a form about your use cases and Twitter staff has to accept your developer application. Twitter approval usually works pretty fast, so it should not be a big problem.

When you create a new app, generate:
a) access token & secret,
b) API key & secret.
and save them somewhere.

For Twitter streaming connection we will use a NPM package called “twitter”. Let’s move to testing Twitter streaming.

require('dotenv').config();
   const Twitter = require('twitter');
   const client = new Twitter({
   consumer_key: process.env.TWITTER_CONSUMER_KEY,
   consumer_secret: process.env.TWITTER_CONSUMER_SECRET,           
   access_token_key: process.env.TWITTER_ACCESS_TOKEN_KEY,
   access_token_secret: process.env.TWITTER_ACCESS_TOKEN_SECRET
});

const stream = client.stream('statuses/filter', {track: 'cyberpunk', tweet_mode: 'extended', language: 'en'});

stream.on('data', (data) => {
   if (!data.retweeted_status) {
      const tweetText = data?.extended_tweet?.full_text || data.text;
      console.log(tweetText);
   }
});

stream.on('error', (error) => {
   throw error;
});

So what has happened here:

• I am loading Twitter credentials from .env file with “dotenv” package
• I am initialising “Twitter” package with my previously generated app credentials
• I am starting a stream with options to track tweets with “cyberpunk” keyword only, using an extended mode (by default longer tweets are cut) and filtering tweets to only english
• If a new tweet has been posted, a “data” listener will run.
• By checking “retweeted_status” we are ignoring re-tweets.

Trending Bot Articles:

1. The Messenger Rules for European Facebook Pages Are Changing. Here’s What You Need to Know

2. This Is Why Chatbot Business Are Dying

3. Facebook acquires Kustomer: an end for chatbots businesses?

4. The Five P’s of successful chatbots

Feel free to run it and test on your keyword. My “Cyberpunk” stream worked like that:

NLP Model Setup & usage of Thadeus NPM package

First step is behind us, now let’s create an account in Thadeus AI platform and teach our NLP model.

For now let us pause the development part in order to prepare and teach our model. Please let me quickly explain how ThadeusAi works.

Go to Thadeus AI, create an account and login in a dashboard. After logging into the dashboard, you have to create a “Workspace” — it’s a single NLP model. In your workspace management page, you create intents with at least 5 examples. You can think about intents like they were message categories. For example, you can create an intent “love” and provide many tweets (in our case) which are saying that Cyberpunk 2077 is a great game. Hopefully the below screenshots of my trained model will help you to imagine how it works.

And examples used in my intent:

Of course, the more examples in intents, the better the model works, so 5 is an absolute minimum. After a new intent is added, remember to re-train your AI. Note that you can also use “Test Talk” to test your model anytime.

You can also add examples with Thadeus API. For instance, you can create intents examples from a CSV file instead of manually adding them in the dashboard.

What is more, the team from ThadeusAI also prepared an NPM package so it’s really simple to integrate with your Node.js application.

Once you have your model prepared and taught, let’s move back to our application and add intent recognition with our trained model.

const { Thadeus } = require('thadeus');

const thadeus = new Thadeus({
  apiSecret: process.env.THADEUS_API_SECRET,
  apiKey: process.env.THADEUS_API_KEY,
  workspaceId: process.env.THADEUS_WORKSPACE_ID,
});

Initialise the Thadeus client with your workspace credentials and id (generated in ThadeusAI dashboard).

const stream = client.stream('statuses/filter', {track: 'cyberpunk', tweet_mode: 'extended', language: 'en'});

stream.on('data', async (data) => {
  if (!data.retweeted_status) {
    const tweetText = data?.extended_tweet?.full_text || data.text;
    console.log('tweet', tweetText);
    const predictions = await thadeus.predictIntents(tweetText);
    console.log('predictions', predictions);
    console.log('n===============n');
  }
});

I have updated our previous code with the predict intents method. Now every tweet will be sent to our NLP model and it will return predictions about intents. ThadeusAI also offers entity recognition, but for now it only works with predefined ones for every workspace. Some time in the near future, Thadeus is likely to add custom entities support which should be working similarly to intents.

What comes next? How to handle ingested data?

Now it’s up to you how you would like to handle this data. In this tutorial I will add one more functionality to have a real Twitter bot.

I want to auto-answer every tweet with a “refund” intent which has probability over 70%. (CDProjektRed, do not worry, I will send you the code later 😀 )

Updated “data” listener with answering on tweet:

stream.on('data', async (data) => {
  if (!data.retweeted_status) {
    const tweetText = data?.extended_tweet?.full_text || data.text;
    const predictions = await thadeus.predictIntents(tweetText);
    if (predictions.some(({ intent, probability }) => 
intent === 'Refund' && probability * 100 > 70 )) {
        await client.post( 'statuses/update', {
          status: 'Hi, we are sorry to hear about your problems. To get a refund, please send an email to refunds@refunds.com',
          in_reply_to_status_id: data.id,
        })
    }
  }
});

That’s all. We have created a Twitter bot for CDProjektRed for handling users issues automatically.

ThadeusAI is still in development. The team is currently working on custom entities and sentiment analysis. Please leave a comment/note if you found this article interesting, if so, once Thadeus adds any new features I will prepare a quick guide how to utilise them.

Feel free to ask if you have any problems with this code or if you have any questions about Thadeus.

Don’t forget to give us your 👏 !

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Analyse Twitter Streaming data with Node.js & free NLP API 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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