Category: Chat

From the random hi-hello’s to the formal work meetings, SPACIUM CHAT is there for all your virtual-connection requirements. Download the SPACIUM CHAT App today! ​ https://preview.redd.it/itfvdfvgwot61.jpg?width=1080&format=pjpg&auto=webp&s=0e4fc2fb208c70f22d93386d4a0e14f1f7b6063d submitted by /u/Spaciumindia [link] [comments]

AI(Artificial Intelligence) & NLP(Natural Language Processing) are the biggest player in this evolving technology, CHATBOT. No doubt it’s evolving as the backbone for the customer service industry, But surprisingly it moves diversity lead the Tech future into automation in every factor. Brands & Companies realizes sales bot is an incredibly feasible and versatile tool for automation business process. Chatbot Marketing is a way to promote products and services via a bot. The key things expect from chatbots are: Get prompt solutions. Considering their queries instantly. Guidance and a better knowledge base throughout the process. A seamless user experience. https://preview.redd.it/bi39qgtbwot61.png?width=1600&format=png&auto=webp&s=cbc7c9723379151c2acd2368aa4943d246f5ff6d AI bots have already touched businesses, from banks and hospitals to real estate and e-stores, and all types and sizes of units. They aren’t just benefiting businesses but also providing support to non-profit organizations and educational institutions. With the chatbots’ innovation, the burden of time-consuming tasks was quickly transferred to an automated system, allowing businesses to serve their prospects better. It is one way to stay competitive in modern-day commerce. With the right choice of the chatbot development team, you can save lots of money and raise revenue and overall customer support services. The adoption rate of such marketing ways is rising immensely following the dynamic market. Nowadays, customers are quite smart and intelligent and get annoyed with old CSS ways. Thus, it is quite essential to introduce better and intelligent ways to connect with the audience. For more details submitted by /u/botpenguin1 [link] [comments]

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This post is written by Viraj Anchan, Full Stack Engineer at Haptik.

Haptik is a chat application that connects users to their digital personal assistants in real time. When chatting with your personal assistant, you want as little delay as possible in order to get things done quickly.

When I joined Haptik after graduating from college in May 2015, my first technical challenge was learning the messaging architecture. In July 2015, I suggested to our CTO that we shift from XMPP to MQTT. Our mobile apps (Android & iOS) and Athena (web chat tool used by our assistants) used XMPP for real-time messaging. We were facing a lot of issues with XMPP. To point out a few, XMPP lacks built-in Quality of Service (QoS). QoS is an agreement between the sender and receiver of a message regarding the guarantees of delivering a message. Since XMPP lacked the built-in QoS, we had to build our own custom solution to ensure message delivery. Along with that XMPP session is one big long XML document and every client has to use an event-driven XML parser. All in all, XMPP was proving to be a lot of overhead and maintenance for us and we needed a better more scalable solution. In January 2016, we finally decided to shift from XMPP to MQTT.

MQTT is a lightweight machine-to-machine/”Internet of things” connectivity protocol. MQTT stands for MQ Telemetry Transport. MQTT is an extremely simple and lightweight publish/subscribe messaging protocol. It is designed for constrained devices, high latency or unreliable networks. The design principles are to minimize network bandwidth and device resource requirements whilst also attempting to ensure reliability and some degree of assurance of delivery. These principles make MQTT ideal for IOT and mobile apps where bandwidth and battery power are at a premium.

5 reasons why we shifted from XMPP to MQTT

1. Less overhead and lightweight
2. Supports QoS (fire and forget, at least once and exactly once)
3. Low keep-alive traffic
4. Pub/Sub Mechanism
5. Low power usage

MQTT provides 3 types of QoS for delivering messages:

QoS 0 (fire and forget) — The message is delivered at most once. QoS 0 messages are not stored. Therefore QoS 0 messages are lost if client disconnects. In QoS 0, delivery of message is not acknowledged. It is the fastest mode of transfer.

QoS 1 (at least once) — The message is delivered at least once. Messages might be delivered multiple times if sender does not receive an acknowledgement from the receiver. The messages must be stored at the sender’s end, until sender receives a confirmation from the receiver.

QoS 2 (Exactly once) — The message is delivered exactly once. QoS 2 ensures that the message is received exactly once by the receiver. The messages must be stored at the sender’s end, until sender receives a confirmation from the receiver. It is the most reliable mode of transfer. It is also the slowest mode of transfer since it uses an advanced acknowledgement sequence as compared to QoS 1.

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4. An expert system: Conversational AI Vs Chatbots

MQTT uses a pattern called publish/subscribe. Multiple clients connect to the MQTT broker. Clients can either publish or subscribe to a topic. Topics are used by the broker to decide who will receive a message. The broker and MQTT act as a simple, common interface for everything to connect to.

Our MQTT server is powered by Mosquitto. Mosquitto is an open source message broker that implements the MQTT. It is written in C. Mosquitto is easy to install and deploy. It supports TLS, Websockets and provides authentication either via username/password, pre-shared keys or TLS client certificates. It also supports ACL. Using ACL, you can configure which clients can access which topics. We decided to use Paho Python library for the backend. Our backend maintains an MQTT connection and routes messages through our chat pipeline.

Installation (mosquitto on ubuntu)
sudo apt-get install mosquitto

Installation (paho-python)
pip install paho-python

Here is a simple client that subscribes to a topic (sports/cricket/india) and prints out the resulting messages.

<i><span style=”font-weight: 400;”>import paho.mqtt.client as mqtt</span></i>

<i><span style=”font-weight: 400;”># The callback for when the client receives a CONNACK response from the server.
</span></i><i><span style=”font-weight: 400;”>def on_connect(client, userdata, flags, rc):
</span></i><i><span style=”font-weight: 400;”>&nbsp; &nbsp; print(“Connected with result code ” + str(rc))
</span></i><i><span style=”font-weight: 400;”>&nbsp; &nbsp; client.subscribe(“sports/cricket/india”)</span></i>

<i><span style=”font-weight: 400;”># The callback for when a PUBLISH message is received from the server.
</span></i><i><span style=”font-weight: 400;”>def on_message(client, userdata, msg):
</span></i><i><span style=”font-weight: 400;”>&nbsp; &nbsp; print(msg.topic +” ” + str(msg.payload))
</span></i><i><span style=”font-weight: 400;”>&nbsp; &nbsp; client = mqtt.Client()</span></i><i>
</i>

<i><span style=”font-weight: 400;”>client.on_connect = on_connect
</span></i><i><span style=”font-weight: 400;”>client.on_message = on_message</span></i>

<i><span style=”font-weight: 400;”>client.connect(“localhost”, 1883, 60)</span></i>

<i><span style=”font-weight: 400;”>client.loop_forever() &nbsp;</span></i>

Here is a simple client that publishes to a topic (sports/cricket/india)

<i><span style=”font-weight: 400;”>import paho.mqtt.client as mqtt</span></i>

<i><span style=”font-weight: 400;”>client = mqtt.Client()
</span></i><i><span style=”font-weight: 400;”>client.connect(“localhost”, 1883, 60)
</span></i><i><span style=”font-weight: 400;”>client.publish(“sports/cricket/india”, “India wins Asia Cup 2016!”)
</span></i><i><span style=”font-weight: 400;”>client.loop(2)</span></i>

MQTT has helped to make our application lightweight and ensure real-time reliable message delivery. MQTT is an amazing protocol which has lots applications in mobile, IOT and M2M communications. If you want a lightweight and reliable messaging protocol, then you should definitely consider MQTT.

Wish to be a part of the amazing things we build? Look no further! Reach out to us at hello@haptik.co

Want to develop an Intelligent Virtual Assistant solution for your brand?

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Real Time Messaging Using Mqtt was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chatbots have changed how businesses in the eCommerce industry connect with their customers with instant, affordable, and highly customizable support. Over the years, companies have been innovating with chatbots and coming up with unique implementations that help achieve different business objectives. In this article, we’ll take a closer look at the top 8 examples of chatbots in the eCommerce industry in 2020.

SnapTravel

Nike StyleBot

With a clever campaign during the launch of their AirMax Day shoes, Nike increased its average CTR by 12.5 times and the conversions by 4 times. How did they do it? With a chatbot called StyleBot.

The StyleBot is an AI chatbot that allows enthusiasts to find shoes based on their preferences through product recommendations. However, StyleBot’s party trick was giving users the ability to create their own personalized shoe designs. After designing their own shoes, customers had the option to share it (or save) or even buy it.

Aerie

A lot of eCommerce chatbots have limited functionality, with most of them simply being “store-finders”. Not Aerie’s chatbot though. This chatbot by US-based clothing and lingerie brand has taken a fresh approach to product recommendations. Instead of simply sending the user links to different products, Aerie sends over a side-by-side comparison of two products with two captions: This and That. Users reply with either “This” or “That” to indicate their preferences.

Whole Foods

In 2016, Whole Foods launched its chatbot on Facebook Messenger and it did everything a Whole Foods’ chatbot should do — it helped customers find recipes, ingredients, locate nearby stores, recommend different recipes, and also teach how to make them.

However, Whole Foods’ chatbot was different in one way — customers could use emojis to talk to it. Now not everyone wants to talk using emojis but customer engagement sure increased because people want to see what a chatbot would recommend if you send it an emoji of what’s in your fridge.

Lidl’s Winebot

Lidl’s Winebot Margot is an AI chatbot that recommends different wines to users by catching keywords in their messages, everything from price and grape to taste and region. Margot has a friendly tone and educates users on various types of wines, what they go well with, the price, and quite a few other details — basically everything most people need to quickly pick up a new wine.

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2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

The chatbot is created by Lidl UK and operates on Facebook Messenger.

Sephora Facebook Messenger Chatbots

Sephora has two AI chatbots on Facebook Messenger. The first is the Sephora Reservation Assistant which helps customers make a booking at Sephora quickly. Since its launch, the chatbot has resulted in an 11 percent increase in conversions.

The second chatbot is called Sephora Virtual Artist and is a big step in chatbot innovation. Virtual Artist is a shade matching bot that allows customers to try on different shades of lipstick by uploading a picture. Virtual Artist can also be used to find different shades of lipstick.

Dom Juan (Domino’s Pizza)

In 2016, Domino’s introduced Dom, the Pizza Bot, a chatbot that could take your orders — through voice as well. It’s a great chatbot that works with Facebook Messenger, Slack, WhatsApp, Apple Watch, and a few other platforms. However, it’s not the chatbot we have on our list.

Meet Dom Juan, Domino’s latest chatbot that servers romantics on Tinder “cheesy” messages. It was more of a marketing stunt than actual customer service but it was a great stunt, nonetheless and since we’re giving points for innovation, Dom Juan had to be on this list of top eCommerce chatbots.

Subway (Google RCS)

Google RCS (Rich Communication Services) is a new messaging protocol with endless use cases for business messaging. It also supports advanced chatbots.

However, Subway did not create a chatbot for Google RCS. Instead, they used the service natively to send deals and promotional offers to customers in an interactive and rich-media format. The reason we’re including this in our list of chatbots is because Google RCS will soon become a must-have for business messaging. When Subway used RCS during its limited release phase, it still managed to increase conversions on sandwiches by 140% and by 51% on meal deals. With RCS soon launching on all major networks, this effectiveness will only increase.

In conclusion…

These were just eight examples of how eCommerce businesses can leverage conversational technology and chatbots, in particular, to provide an interactive customer experience. If you’re interested in learning more about how chatbots can help you or how you can create a chatbot for your own business, reach out to Master of Code Global today for a free consultation!

CONTACT US FOR A FREE CONSULTATION

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Top 8 Examples of Chatbots in the eCommerce Industry in 2020 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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Chatbots are the talk of the town. They’re one of the hottest topics in the technology world today.

Like any new technology, much of the fact and fiction gets jumbled up. This leads to a number of myths and misconceptions that sway popular opinion, particularly among decision makers.

We set the record straight on 5 of the most common myths about chatbots so you can be confident of deploying one in your business today.

Myth #1: Implementing a chatbot requires too much time and resource

A common misconception is that implementing a chatbot requires the availability of a large IT department and highly qualified AI & machine learning experts, solution architects, and data scientists on the company’s side.

And this is true… if you are custom building your own chatbot (more on this later).

If you partner with a solution provider who knows their stuff, implementing a chatbot actually requires minimal time and resources, and can be easily deployed in a matter of weeks.

Here’s some proof:

Noel Leeming, New Zealand’s largest electronics retailer, implemented their web-based chatbot in the middle of the 2020 COVID-19 pandemic in just 12 days.

That’s right — a fully functioning AI-powered chatbot with integrations into existing business systems in less than two weeks.

→ Read the full case study here

Advanced solution providers essentially provide a platform that has everything set up and ready to go.

Most of what is required during the implementation is the configuration of the conversations to match the decided use case(s). There is very little to no coding effort required, meaning IT and development resources are minimal.

Easy to use, intuitive tools (think ‘drag and drop’ style functionality) make it easy for users to design, build and test conversations quickly. Teams can move fast to get their chatbot up and running without any technical expertise.

The key is also to start with a single use case, get your chatbot live, collect data, and iterate as you go (we’ll touch on this in more detail a bit later).

Myth #2: It’s better to build your own chatbot than to buy

Aside from the exorbitant costs associated with building from scratch, here’s why a platform is your best bet.

a) Custom-built solutions are slow to develop and deploy
Just thinking of what goes into it is exhausting — trust us, we know. A platform is cost-effective and faster to deploy. Low code platforms, like Ambit, require minimal development and allow you to implement a fully functioning chatbot quickly, that delivers better bang for your buck, and accelerated time-to-value.

b) Custom-built chatbots require ongoing resources, time and money
It’s not just the initial build you have to think about. Don’t forget the requirements to maintain and improve the platform, minimise technical debt, fix bugs, run QA and testing, and ensure it scales with your business. A solution provider will take care of this for you.

c) Specialised components
Then there are the specialised components that go into developing a platform from the ground up. This is often overlooked by businesses that choose to build.

Capabilities such as artificial intelligence, machine learning algorithms, Natural Language Processing/Understanding, neural linguistics, integrations with key business systems, the list goes on. Acquiring the knowledge and skills to develop these is expensive and time-consuming. A platform has these capabilities built-in already.

d) Constant improvements
A best-in-class platform constantly improves over time — and not at your own expense.

Access to new features at a regular cadence based on extensive R&D is guaranteed. This free’s your team up to focus on other projects. All the technical aspects are looked after for you (you’re welcome), and you also get access to an industry-leading team of product specialists who are responsible for ensuring you get the most out of the solution as your business grows.

Unless you have time, skills and cash to burn to build a custom chatbot, a best-in-class platform will return a better ROI every single time. It’s like ERP systems; sooner rather than later, those who build their own end up crawling back to a best-of-breed solution. Save yourself the headaches!

Trending Bot Articles:

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Myth #3: Chatbots don’t generate a return on investment

This is the biggest misconception of all.

Any lapse in ROI is often down to how the technology has been implemented, rather than the limitations of the technology itself.

Businesses that utilise the technology to its full potential are generating incredible returns. Here are a few examples:

• Chatbots generates 2.2X more ecommerce upsells
• Chatbots can cut customer service costs by 30%
• Noel Leeming sees 16X ROI from their chatbot, Nola
• Vector’s chatbot deflects ~2,000 customer service calls per month
• Retailer drops cost to serve to $0.47

And there are so many more (if you need further proof, let’s chat).

So how do businesses ensure their chatbot generates a return on investment?

One of the mistakes businesses make when implementing a chatbot is they clamber anything and everything in at the beginning of the project. They pour time and money into developing the ‘perfect’ chatbot that can do everything under the sun, before setting it live and forgetting about it.

This approach is slow, neglectful, and costly.

It’s important to start small, get your chatbot up early, gather & analyse user data, learn fast, and iterate on the fly.

Why? Rather than sinking time into building the entire chat experience up front and making a host of assumptions, your chatbot is live faster, shortening the time-to-value, and uses data to drive decision making on the areas your chatbot should develop next. ROI continues to rise as your chatbots’ skill set develops over time.

Conversation design also plays an important role in the value a consumer and business receives from its chatbot.

What use cases does your chatbot specialise in, how are its conversations designed, how deep do they go, do they exist to support existing customers or convert new customers? Do they align with your use case and goals? The list goes on.

It’s a specialised skill to design chatbot conversations that achieve businesses goals and expectations. That’s why we at Ambit have a dedicated team of experts to advise our customers on designing conversations that drive a measurable return on investment.

Note: If you’d like to know what goes into creating great digital conversations let us know, we’d be happy to share our experiences, or download our Conversation Design eBook here.

Myth #4: The technology isn’t up to scratch

Like any new innovation, it’s fair to say the early instances of chatbots left a lot to be desired.

As developers and businesses navigated this new technology, consumers were often left with underwhelming experiences, and businesses looking to point the blame.

Much of this is historical and not a reflection on the quality standards of modern chatbots. It also comes down to the use cases and expectations decided by the business, and how the technology is utilised to deliver on these.

Through AI and machine learning, chatbots have evolved exponentially since their inception. Today they’re smarter than ever, more refined, and able to provide value to an array of use cases on demand.

Sophisticated data processing means today’s chatbots are more accurate and reliable, learning from each and every interaction to grow their capabilities faster.

The most advanced chatbots utilise NLP/NLU to better interpret and understand the way humans speak (let’s face it, even us humans have trouble understanding each other at times!).

Today’s chatbots understand the complex nuances of human language, such as tone, sentiment, context, and intent to respond appropriately, solve more intricate enquiries, and provide better customer experiences.

Note: We wrote an eBook on the different types of chatbots available, from simple FAQ bots to AI-powered Digital Employees. You can find out which type of chatbot suits your business here.

Myth #5: Chatbots prevent access to human agents

The last myth is that by using chatbots, consumers no longer have access to human agents.

By integrating a chatbot with a live chat system businesses provide a fully seamless handover from chatbot to a human when the situation requires it. This also extends to email support and call-back functions.

Many chatbots utilise AI and machine learning technology. They’re smart enough to detect enquiries that are more complicated and best solved with human interaction.

The chatbot calls on a human agent standing by to take over the conversation and provides them with all the context and information gathered. This brings the human agent up to speed on the enquiry immediately, enabling them to assist in real-time.

Chatbots exist to augment humans, not displace them. By taking care of high volume routine tasks, they free up human agents to focus on higher priorities to add more value. Working hand-in-hand they are the perfect combination to deliver customer service at scale.

So there you have it — the truth about chatbots

Their popularity as a customer service solution has grown exponentially — and there’s good reason for it.

By automating customer conversations, chatbots have proven themselves to free up human agents to be more productive, reduce support costs, and act as an additional channel to increase sales.

Of course, if you need further proof, we’re only a conversation away.

Don’t forget to give us your 👏 !

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5 Common Chatbot Myths Explained was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.