Category: Chat

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In the effort to automate as many processes as possible, companies resort to various solutions powered by modern technology. We’ve written about Enterprise Resource Planning systems and automated contact centers lately. Internet bots, or simply bots, may be the best-known form of automation in customer communications.

Gartner once predicted that by 2020, more than 50% of medium to large enterprises would have deployed product chatbots. By 2021, over 50% should be spending more on creating various types of bots than traditional mobile app development.

(We should clarify one thing now. We understand chatbots as programs which carry on conversations with humans by automatically engaging with received messages. The terms may be used interchangeably, but chatbots are in fact a category in a rather arbitrary classification of bots. Along with search engine spiders, transactional bots in the area of robotic business process automation, video game bots, and other helpful applications, chatbots can be classified as ‘good bots.’ Forum and comment spammers, hacker bots, scrapers, Twitter impersonators, and similar social bots are naturally ‘bad/malicious.’)

As chatbot developers, we are primarily interested in raising awareness of helpful bots. The first chapter of this post is classifying them by different criteria. The second offers advice to business people that wish to employ chatbot technology for different purposes.

Types of Chatbots Classified by…

Deployment/Medium

The easiest classification is to divide chatbots into:

1. bots integrated within social media and custom instant messaging apps like Facebook Messenger, Slack, Telegram, Twitter, WhatsApp, etc.;
2. standalone applications (e.g., Amazon Alexa, Google Assistant or Siri).

Standalone chatbots may leverage various mediums to receive and respond to messages, such as voice, SMS, or website chat windows.

Social messaging chatbots need not be installed separately and are easy to access. They also feel natural for app users: they interact with the bot like with their friends. Additionally, bot owners take the instant advantage of the messengers’ impressive adoption rate.

Expertise

It’s pretty simple to distinguish between generalist and specialist bots. Alexa, Google Assistant, Cortana, and Siri belong to the first class. Generalist chatbots can understand what a user is asking but usually can’t complete a task on their own. Often, the interaction starting with “OK Google” is only the first step in a process. The chatbot would route the request, but subsequent steps will depend on specialist bots that ‘do the job’ for the user. They possess the domain-specific expertise necessary to accomplish tasks.

Another common sense rule is that digital giants mostly build generalist bots. Enterprises and individual chatbot developers concentrate on a specific job-to-be-done. For example, 1–800-Flowers chatbot was one of the first available on the Messenger platform. It enabled customers to order flowers or speak with support. In June 2016, soon after launch, 70% of customers ordering through the chatbot were reportedly new customers. It’s predicted that companies will be increasingly looking to develop chatbots to improve communication and increase productivity.

Purpose

From a business perspective, the programs should fall into three most common groups:

1. Support/action chatbots

Such bots should master a single domain, e.g., know everything about a company, so that they can walk users through any business process or answer various questions. Support chatbots must be easy to navigate, fast in completing tasks, as well as possess a personality, multi-turn capability, and context awareness. Speech is an optional feature.

Such chatbots have the ability to simplify the way businesses interact with customers. 24×7 customer service is probably the most appropriate application for them in any industry. The bots help automate frequently asked questions and can differentiate between the issues they can manage and questions they should refer to a human.

Read Also: Automating a tourism and hospitality enterprise

The function is not limited to customer support, however. For example, the Messenger chatbot of the travel search engine Hipmunk promises to answer questions, search for flights and hotels, and provide recommendations to travelers. In its welcome message, Hipmunk suggests that customers should start by indicating their location. Action chatbots may be asking for relevant data from the user to take action or complete a task, e.g., check flight status, book cost, etc.

In the case of e-commerce, support/action chatbots can help businesses by:

• adding interactivity
• building customer relationships on a more personal level
• solving the abandoned cart issue
• substituting for emails
• managing sales funnels

2. Skills chatbots

These are typically more single-turn-type bots. They have set commands like “Open the garage door” or “Play me the song of an acorn woodpecker,” which they should perform quickly. Custom Alexa skills are an obvious example.

Speech functionality is desirable for this type of applications, so there’s no need to press any buttons. It’s also important to focus on integration, especially when controlling smart home devices. Conversely, contextual awareness can be limited, unless you need a particularly advanced assistant.

3. Assistant chatbots

Assistants are a middle ground between the above types of chatbots. They can respond to questions on any topic and are conversational and entertaining. Siri is a great example. Such bots may eventually become powerful marketing platforms and navigators of all other bots that are currently out there.

Bot Intelligence

Programmers also differentiate bots that are built with and without machine learning (ML) and artificial intelligence (AI). Basically, some bots learn, and some don’t (that much). We can distinguish two groups based on the bot intelligence:

1) Scripted chatbots

These are also called quick reply bots. They are designed to respond to specific commands and answer specifically phrased questions. The interaction is based on a “script” which determines what can and can’t be done. The script is either a set of questions which a user is anticipated to ask or a rule-based model where each action by the user triggers an action or response by the chatbot. Often, the user doesn’t even have to type anything in, selecting from an available list of questions or commands instead.

The bot’s domain is necessarily limited, e.g., for a customer service chatbot. It might feel restrictive, but by being explicit about the limits of the bot’s domain and grammar of acceptable responses, you can keep the interaction directed and the quality of the user experience pretty high. When users go off the script, the bot can always transfer the communication to a human customer service agent.

Scripted bots are the simplest, cheapest, and businesses’ likeliest first choice. A brand chatbot on a messaging app can be limited in its capabilities but still communicate with personality and style. A single chatbot can seamlessly blend the brand marketing discourse with fun content and helpful service. It can successfully engage people into the brand, create an element of interactive communication, and even upsell and cross-sell to customers.

Still, some scenarios require more advanced bots. They can be programmed to respond the same way each time, to react differently to messages containing specific keywords, and even to adapt their responses to the situation. Some script bots use Natural Language Processing — an AI technology — on the front end of the interaction. NLP allows mapping user’s text or voice to an intent by parsing out words that may match an answer in the script.

2) Smart bots

A robust server-side processing component allows these bots to access to massive computing power in understanding and responding to queries. It’s often combined with the open-sourcing of ML libraries like TensorFlow.

Chatbots using ML can understand questions and commands the way people phrase them and can learn and develop over time. Elements of ML and AI are required if they have to process complex requests and manage dynamic outputs. This is beneficial if you’re looking to offer a human-like experience. However, smart bots need not necessarily be that ‘smart.’ Sometimes, an ML-powered application’s only task is to recognize the breeds of dogs in photos.

Many chatbots leverage AI for the first response mechanism. If the interaction takes a turn that the AI can’t handle, the system falls back on a human agent to sort things out. The “AI + Human Agent” model is also suitable for customer service applications.

Complexity

The quality of user experience delivered by a bot often correlates with its technical complexity and ability to leverage conversation contexts. In this aspect, chatbots come in three distinct flavors.

1) Menu/button-based chatbots

This is the most basic type today, largely coinciding with the scripted bots category. In most cases, these chatbots are decision trees presented to the user in the form of quick reply buttons, very similar to the automated phone menus. Each response/selected button takes the user down a specific path, which opens up a predetermined set of possibilities towards the ultimate answer or task.

These applications are sufficient for guiding new users or answering the FAQs that make up 80% of support queries. They’re also pretty efficient for on-demand services. For example, messenger bots designed by Domino’s Pizza and Pizza Hut are presenting information about the latest promotions and allowing customers to place orders with a single tap.

However, menu-based chatbots are not suitable for more advanced scenarios in which there are too many variables, or it’s too difficult to predict how users should get to specific answers.

2) Keyword recognition-based chatbots

These bots can listen to what users say or type. They utilize customizable keywords and AI to determine how to respond appropriately.

It is becoming popular to build chatbots that are both keyword recognition- and button-based. The users can first try to ask their questions directly. If the result is inadequate or the user requires some guidance, they can use the chatbot’s menu buttons.

3) Contextual chatbots

These are the most advanced of the three types of chatbots. Alexa, Google Assistant, and Siri are some of the examples of context-enabled chatbots. They utilize ML and AI to remember conversations with specific users to learn over time. They’re smart enough to learn from past experiences, i.e. what users are asking for, how they’re asking it, and so on.

For example, a contextual chatbot for pizza orders will store a customer’s data from each conversation. When the customer uses the application next time, it will recall their most common order, delivery address, and payment information. Then, the bot will ask if the customer would like to repeat the order. Instead of typing and having to respond to many questions, the customer just has to answer with ‘Yes.’

Chatbot Technology

Technology-wise, there are two types of chatbots:

1. drag-and-drop / do-it-yourself bots;
2. code-based chatbots.

Drag-and-drop bots are created with the help of bot building platforms, e.g., Chatfuel, Chattypeople, Motion.ai, etc., and other broadly accessible tools. There are usually off-the-shelf templates suitable for specific businesses (e.g., customer support, e-commerce, surveys, etc.). Even non-technical users can build a chatbot with a predefined functionality and only add the features they need. It’s a piece of cake in the case of Alexa skills: Blueprints service lets ordinary users endow their Echo devices with unique abilities without any programming.

Code-based bots typically include built-in AI technology and are made using software frameworks like Facebook Bot Engine with Wi.ai NLP service or Microsoft Bot Framework with luis.ai NLP service. These tools work well for basic customer engagement requirements but may fall short for over-the-top functional integration. In such cases, businesses should seek custom chatbot development services.

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Types of Chatbots: An Overview for Business People was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Conversational AI bots that provide customer support have exploded in scope recently. The market is set to grow at 22.6% CAGR till 2030 and reach $44.38 billion.

84% of companies believe that AI chatbots will be central to customer communication in the near future. The potential for ROI and the improvement in AI technology have created an enormous demand for chatbots and influenced multiple companies to enter the foray. A recent article states that well over 200 companies are currently selling chatbots.

One of the critical startups in this area is Ada, which offers extensive integrations and great chatbots to many businesses.

Ada: A Review

Ada’s customer service chatbots integrate with multiple LLMs and provide answers to frequently asked questions (FAQs). It is used by enterprises and startups alike, with companies like Air Asia and Meta using the company to improve their CX. Let’s explore the product by reviewing its pros and cons.

Pros

1. Several customers have achieved high CSAT scores and high containment rates with Ada’s chatbots.
2. The weekly chatbot reports are thorough and provide people with insights into the conversations that have happened.
3. The tool for building the chatbot is easy to use.

Cons

According to customer reviews in G2:

1. Implementing the software is hard
2. Training for the end-customer is lacking
3. Integrations with Zendesk are complex and challenging to implement
4. Some of the UX is not very intuitive

To ensure people can cut through these issues, Here are 10 Ada alternatives that businesses can use instead. We’ve chosen these products because they:

1. Are integration-friendly
2. Offer a robust customer service experience
3. Have extensive integrations
4. Have easy workflows for implementation and deployment

So, let’s get started!

Also Read: Ada.CX Vs. Kommunicate- A Detailed Comparison

1. Kommunicate

We admit we’re relatively biased regarding our product, but Kommunicate has received rave reviews from thousands of customers in the past two years. The reason is simple: our product is built to be plug-and-play, we have easy workflows and algorithms, and we provide a human-handoff system that’s the best in the industry.

We are a leading Ada alternative that integrates with every leading LLM provider in the industry so you can serve the best CX to your customers every day.

To help you make this decision, we’ve compiled a list of Pros and Cons for our product.

Pros

1. Integrations — We offer omnichannel integrations to everything from WhatsApp to Zendesk, so you can go live within a few minutes.
2. 24/7 Customer Support — We offer proactive customer support to each client. We also build new integrations and features for you on a need basis.
3. Pricing — We offer a competitive pricing advantage to our larger clients so that they can enjoy cost savings at scale. We offer straightforward, competitive pricing for smaller businesses as well.
4. Security Compliant– The platform offers HIPAA, GDPR, and SOC2 compliance at no extra cost.
5. Best-in-Class Bot-to-Human Handover System — Our systems are built to maximize the “human + AI” strategy for customer support. Our bot-to-human handoff system is one of the leading ones in the industry.

Cons

1. Flow Design — Designing a conversation on the platform is time-consuming. However, advanced plans are completely easy, and our team handles much of the conversation design.
2. Some Tech-Heavy Integrations — While most of the integrations on the platform are built for non-developers, some integrations (including those for Android apps) require hands-on development.
3. Fewer Customizations — The chatbot has some limitations when it comes to UI customizations, so it might be difficult to match it to your brand’s design language.

2. Intercom

Intercom is an enterprise business-ready customer service platform. They’ve also released Fin, a new AI chatbot that uses the latest LLM technology to answer your questions. Since Intercom is one of the older companies, it also has a fully-powered customer service suite that will be sufficient for your entire business. The pros and cons of the Fin chatbot are:

Pros

1. Omnichannel integrations — Fin integrates with many channels, including WhatsApp, Facebook, and Line.
2. Deep app ecosystem — Implementation is simple because Intercom has a buzzing app ecosystem with 400+ integrations across different platforms.
3. Customizable — Fin is very customizable and can be trained to give custom responses using a flow designer.

Cons

1. Seat-based pricing — With Fin, you’re paying around $99 for each agent on your platform, making it very expensive for any mid-sized business.
2. HIPAA Compliance Comes at Extra Cost — While the base plan for Fin is pervasive, you will need to increase your costs to be HIPAA compliant.
3. Customer Service is Slow — Since it is a larger company, support and training can be slow to start. However, customers report that all their problems get resolved eventually.

3. Drift

Drift is a rather unique product in this category. While most of the chatbot providers here work to help the customer support teams, Drift is also well-suited to lead qualification and other sales needs. Their chatbot has improved dramatically in the past few years, and it’s used across multiple enterprises.

Pros

1. Lead Qualification — Since Drift is also used for sales, their chatbot can identify high-intent customers while chatting.
2. Personalization — Drift’s chatbot offers personalized responses, and you can also use APIs to feed important customer information. This will allow you to communicate better with your customers.
3. Calendar Integration — Drift integrates directly with your mail and calendar clients to book a meeting on the customer’s behalf.

Cons

1. High Prices — Drift’s introductory pricing starts at $2500/month, making it one of the most expensive products on the market.
2. Qualification doesn’t Always Work — Several customers have complained that even unqualified people can book a meeting with Drift customer support representatives, which costs them time.
3. Learning Curve — Drift has a good UI overall, but the initial number of options makes the learning curve quite steep. While training material is available, it can be cumbersome.

Also Read: Intercom vs Drift vs Kommunicate — Which is best?

4. Amazon Lex

Amazon has built a robust chatbot-builder on its cloud platform, AWS. Amazon Lex challenges Ada by carrying all the basic conversational AI and Automatic Speech Recognition capabilities. Since Amazon also created Alexa, Lex’s natural language understanding and speech recognition capabilities have advanced.

Pros

1. Natural language IVR — Lex’s Alexa-like natural language recognition and understanding capabilities make voice chatbots built on the platform very robust.
2. Scalability — Since the model is based on AWS, it also is scaled easily. Extra messages can easily be handled through the AWS cloud infrastructure.
3. Both text and voice — Amazon Lex is one of the few services that provides both text and voice capabilities in their chatbots.

Cons

1. Integration with Other Services is Poor — While you can integrate Amazon Lex with AWS without hindrance, other integrations are tricky. Customers complain that any migration is complex.
2. Poor Documentation — Several users have complained that Lex has inadequate documentation, which often includes conflicting information.
3. Limited Language Support — While Amazon Lex is multilingual, popular languages like Hindi are still missing from the system.

5. IBM WatsonX

IBM was the first company ever to build a conversational AI. So, it’s no surprise that their recent IBM WatsonX bot has become quite popular with enterprises. It has highly augmented learning capabilities, can understand customer needs, and improves complaint resolution rates.

Pros

1. Integrations — IBM offers excellent plug-and-play capabilities, and its integrations are easy to use.
2. Scalability — WatsonX can be scaled using cloud services, and the chatbot is built to handle large volumes of conversations simultaneously.
3. Easier workflow — Compared to similar enterprise-class products like DIalogueFlow and Amazon Lex, WatsonX is more accessible to implement and has a better GUI.

Cons

1. Initial Learning Curve — The product requires some knowledge of fine-tuning and data wrangling, which the average user might not be comfortable with.
2. Pricing — As it is an enterprise model, the prices of WatsonX can often go higher than $1050/month. Customers complain that the token-based pricing makes budgeting difficult.

6. Yellow.AI

A newer alternative to Ada, Yellow.AI has been making waves with its multi-LLM-capable platform. They primarily serve enterprises in the APAC market and offer customized chatbots for customer support.

Pros

1. Ease-of-Use — Yellow.ai’s product has a good UI and is easy to use, even for beginners.
2. Multi-LLM Architecture — Yellow.AI’s infrastructure allows you to use various proprietary and open-source LLMs to customize customer responses.
3. Integrations — Because of their robust ecosystem, you can deploy Yellow.AI’s chatbots on various channels.

Cons

1. Steep Learning Curve — Adjusting to the host of options and the conversation designer is difficult.
2. Some Analytics Problems — A few customers have complained that the platform could have more robust analytics.
3. Some Integrations are Hard to Implement — While documentation exists for most integrations, some integrations to CRMs and ticketing platforms can be difficult to implement.

7. DialogFlow CX

DialogFlow CX is Google’s answer to Amazon Lex, and it is an excellent platform to implement a customer support chatbot. Google’s chatbot builder is one of the cheapest ways to implement a chatbot for your business, making it a viable Ada alternative.

All DialogFlow integrations are easier with Kommunicate.

Pros

1. State-of-the-art NLP — As expected from Google, their chatbot builder comes pre-built with advanced NLP features, making chatbot building easier overall.
2. Omnichannel Presence — DialogFlow CX can be connected to almost every marketing channel.
3. Pricing — DialogFlow ES is free for 180 messages/minute, making it a great choice for beginners and small businesses.

Cons

1. Difficulty in Collecting Information on Prospects — DialogFlow ES makes it hard to collect information from prospects because of its tech-heavy integration processes.
2. Limited Customization — While basic customization options exist, much customization is unavailable on the platform.
3. Integrations are Tech-Heavy — Despite an omnichannel presence, the platform’s integrations require comprehensive technological knowledge.

8. Kore.AI

Kore AI has been an excellent software for teams wanting to implement conversational AI into their CX workflows. The platform features an intuitive drag-and-drop UX. Enterprises across South Asia, South East Asia, and Europe use Kore for CX.

Pros

1. Easy-to-Use — The product is easy to use and has a drag-and-drop interface. You can easily design conversations on the platform.
2. Detailed Training Guides — The platform features robust documentation that covers all use cases. It also has video tutorials for a lot of integrations.
3. Customizable Dashboards — The product also comes with a robust analytics platform where you can customize your dashboards to focus on what you need for your day-to-day.

Cons

1. Building Delays — A few customers have complained about increased wait times while training their chatbots.
2. Slower updates — The platform rolls out new features often, but the process is frequently time-intensive.

9. AISera

A leading provider of conversational AI chatbots for domain-specific customer experience purposes. The platform offers a stable and user-friendly interface. It’s also customizable and can be scaled and pushed onto different channels.

Pros

1. Easy to Learn — The product has a user-friendly interface, which is easy to pick up even for people without tech expertise.
2. Quick Resolutions — AISera’s support team is proactive and quickly resolves support queries.
3. Integrations — AISera comes armed with many integrations that make it easy to deploy it on multiple channels.

Cons

1. Removing Articles from Training Data is Hard — Though the chatbot needs to be retrained for optimal functionality, the UI for removing out-of-date articles is not straightforward.
2. Impersonal responses — The AI chatbot can sometimes be impersonal, affecting the overall CX.
3. Staging is Hard — A few customers have complained about the difficulty of testing the app because the staging environment is hard to use.

10. Freshchat by Freshworks

Freshworks has been a constant presence in the customer service apps department, so their chatbot has been quickly adopted as an Ada alternative. The CRM and customer support ticketing platform has integrated an AI that can act as a FAQ chatbot and tackle complex queries as well.

Pros

1. Robust Notifications — Request handling is streamlined because Freshchat has a great notification system that alerts agents and managers immediately.
2. Integration Ecosystem — Since Freshworks is an old company, it already has a lot of integrations. This makes it possible to implement your chatbot everywhere.
3. Workflows are Easy to Implement — The workflows in Freshchat are user-friendly and easy to implement.

Cons

1. Slow Customer Service — Several customers have reported low custom support at Freshchat.
2. Pricing Structure — While Freshchat was initially built for small businesses, it now has a complicated pricing structure that is difficult for them.
3. Too Many Tools — Freshworks employs many tools in the ecosystem, making choosing the right tools to implement in a workflow challenging.

Also Read: 11 AI Tools For Your Customer Support Team In 2024

Also Read: 10 Best AI Customer Service Chatbots for Businesses in 2024

Review Summary

Conversational chatbots have seen incredible growth in the past few years. The launch of LLMs in late 2022 and the veritable AI arms race have created several customer service chatbot companies.

One of the winners is Ada, which has created a robust ecosystem with its chatbots, which are being adopted across many enterprises.

However, the Ada chatbot has some problems, such as a lack of customer training, a complex UX, and tech-heavy integrations. So, we have given you a list of possible Ada alternatives that you can use. Feel free to use the alternative platforms and decide based on their pros and cons.

And if you want to try the #1 Ada alternative Kommunicate, we’re just a call away.

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Top 10 Ada Alternatives in 2024 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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