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As per a recent Gartner poll, 45% of executive leaders have increased their AI investments due to the popularity of ChatGPT. 70% of these executives confirmed that their organizations are currently exploring Generative Artificial Intelligence. But what makes it so enticing to these decision-makers?

The statistics provide a compelling answer. For 68% of executives, the benefits of Generative AI far outweigh the associated risks. This belief underlines a growing confidence in its capabilities. Generative Artificial Intelligence investment goals are cost optimization (17%) and enhanced user experience (38%).

The trends don’t end here. The 2023 Global Trends in AI Report by S&P Global reveals that 69% of respondents pushed at least one AI deployment into production. The value derived from AI is undeniable. 70% of organizations cite revenue generation as their primary driver.

Moreover, 67.2% of enterprises plan the adoption of LLMs and Generative AI by year-end. And McKinsey & Company’s report on its economic potential projects astounding figures. The technology could contribute $2.6 trillion to $4.4 trillion annually to the global economy.

75% of value created by AI falls within customer operations, marketing, sales, software engineering, and R&D. Industries like banking, high-tech, and life sciences will benefit from it the most. These numbers underline the transformative role of AI across various sectors. We will explore the benefits of Generative AI for business in detail further in the article.

Generative AI Overview

According to McKinsey, Generative AI is “a type of AI that can create new data (text, code, images, video) using patterns it has learned by training on extensive (public) data with machine learning (ML) techniques.” What makes it truly remarkable is its versatility.

It can automate, enhance, and expedite a wide range of tasks across various functions. This includes classifying data, creating content, summarizing information, and answering questions. In essence, it represents a transformative technology with immense potential for companies.

The Mechanics of Generative AI

Generative Artificial Intelligence is powered by neural networks. It analyzes existing data to discover patterns and generate new content. This technology employs different learning methods during training. These include unsupervised or semi-supervised learning.

Two Generative AI models are generative adversarial networks (GANs) and transformer-based models. GANs excel in creating visual and multimedia data. Transformer-based models, such as GPT, specialize in generating text. They can understand the context from internet data.

It relies on machine learning algorithms. ML allows the processing of large volumes of data, often collected from the internet. By learning from a lot of training data, it makes content that fits the patterns it has learned.

Common Generative AI Tools

Within Generative Artificial Intelligence, various powerful tools have emerged with different purposes. Let’s explore some widely used ones:

• ChatGPT: A large language model chatbot for 24/7 customer service and marketing content generation.
• Google Bard: An experimental AI chatbot. It is designed for research, report generation, educational messages creation, and coding tasks.
• Bing Chat: A conversational AI language model. It focuses on information retrieval, task automation, and content creation.
• Midjourney: An AI-powered text-to-image model creating captivating visuals. It can assist in product design and artistic messages.
• GitHub Copilot: An AI code assistant enhancing code writing efficiency. It can also reduce errors and aid in learning new programming languages.
• Dall-E 2: An AI model producing realistic images and art from text descriptions. It is ideal for logos, prototypes, and visual elements.

These diverse tools can reshape operations and consumer interactions. As they evolve, AI use cases promise to further impact innovation and productivity across industries.

Generative AI Benefits for Business

94% of leaders recognize the critical role of AI in the next five years. Generative AI holds immense potential for businesses. As per BCG, it increases productivity, personalized customer experiences, and accelerates R&D. Let’s delve into these benefits of implementing Generative AI further.

Automated Content Production

Generative AI is a powerful tool for enterprises seeking to create content efficiently. It can produce articles, marketing materials, and even code, saving both time and resources. For example, the most common use case for marketers (76%) and sales (82%) specialists is basic text pieces creation and copywriting.

Moreover, more than 50% of business leaders have adopted this new technology specifically for content marketing. By automating information generation, companies can maintain a consistent brand voice and style. Their human resources can focus on more strategic endeavors.

Cost Reduction and Time Savings

One of the most significant Generative AI benefits is its ability to reduce operational costs and save time. By automating repetitive tasks, companies can reallocate resources to more critical areas. This allows to increase efficiency and maintain a competitive edge in the market. According to Deloitte, 82% of leaders believe that AI will improve their employees’ performance.

On average, employees utilizing Generative AI save 1.75 hours daily, a full workday each week. 1/3 of survey respondents report saving 30 minutes to an hour daily with Generative AI-based tools. In a survey of financial services professionals, 36% reported reducing annual costs by over 10%.

Personalization

Generative AI enables enterprises to offer highly personalized experiences to their customers. It powers recommendation engines that suggest products as per individual preferences. This leads to higher sales and clients satisfaction. Additionally, it drives the development of voice assistants enhancing user engagement.

Read also: Exploring the Transformational Potential of Voice Assistants Within an Enterprise

Remarkably, statistics show that 73% of consumers anticipate enhanced personalization. Furthermore, over 75% believe that Generative AI-based applications will elevate their interactions with companies. Additionally, nearly 70% of those who’ve used it are more inclined to buy from companies adopting it.

Routine Task Automation

Generative AI’s automation capabilities extend to routine tasks, liberating employees from these responsibilities. Modern AI can automate tasks that currently take up to 60 to 70% of employees’ time. This, in turn, increases overall productivity and allows staff to focus on higher-value activities. As a result, businesses get a more dynamic and innovative workplace.

Data Analysis and Insights

Generative AI excels in data analysis. So it is especially valuable for companies working with large datasets. It can identify trends, patterns, and anomalies. Such data enables data-driven decision-making and a deeper understanding of operations, customer behavior, and market dynamics.

Customization

AI offers the flexibility to train models on a company’s proprietary data. This ensures that the LLM solutions for business align perfectly with the organization’s specific needs and goals. Therefore, it can be used in various industries and applications.

Improved Customer Experience

Generative AI plays a crucial role in improving the clients experience. It helps to provide quick, accurate, and personalized responses to inquiries. Such features lead to higher levels of consumer satisfaction and loyalty. This enhanced user experience can translate into increased revenue for enterprises.

In a company with 5,000 customer service agents, employing Generative AI-powered solutions raised issue resolution by 14% per hour. It has also reduced issue-handling time by 9% and cut agent attrition and manager requests by 25%. AI in customer care could boost productivity by 30 to 45% of current function costs.

Generative AI vs Conversational AI: Differences in Application

Conversational AI vs Generative AI: these two differ in several ways. These differences include their purpose, interaction style, evaluation metrics, and other characteristics.

Conversational AI is a cutting-edge technology that enables human-like conversations through dialogue-style interactions. It excels at maintaining conversation context. This ensures relevance, satisfaction, and a seamless flow of dialogue. Its adaptability to language, style, and user preferences makes Conversational AI ideal for real-time interactions. It provides users with a natural and engaging experience.

In contrast, Generative AI focuses on creating original content without direct user input. It operates in a one-way communication style, relying less on conversational data and considering a wide range of inputs. This technology prioritizes metrics like perplexity, diversity, novelty, and alignment. This is needed for the generation of high-quality, creative information. However, its predefined patterns and offline nature limit its suitability for real-time interactions.

Maximizing Business Benefits with Generative AI Integration

Benefits of using Generative AI in business contexts hold significant promise. Yet, there are LLM limitations that make it less than ideal for direct use. These include a lack of domain-specific knowledge, privacy and security concerns, LLM hallucinations, etc. One of the solutions to this problem lies in integrating Generative AI with Conversational AI or other existing applications.

Master of Code Global offers Generative AI integration solutions for your specific needs. Our expert team integrates AI into your chosen platforms and applications. We provide integration with your specific data, knowledge base, and backend systems via API connection. The updated systems will create personalized text pieces, summarize information, and elevate user experiences across your ecosystem. Here are two examples of our projects:

• BloomsyBox Chatbot by Master of Code Global and Infobip, integrated Generative AI and Conversational AI, improving the customer experience. During a Mother’s Day campaign, it achieved a 60% quiz completion rate, with 28% of users winning a bouquet. It introduced personalized greeting cards, and 38% chose LLM to generate messages.
• Generative AI Slack Chatbot streamlines knowledge base navigation in a tech company, used by half the staff. It enhances information access, product understanding, and workflows. This boosts team efficiency and knowledge base use.

In summary, Generative AI has its limitations for direct business use. Integrating it into an existing conversational system can unlock its full potential. At Master of Code Global, we have the expertise and innovative solutions to integrate the new technology. This allows companies to enhance conversational interactions and deliver personalized experiences.

Future of Generative AI in Business

To sum it up, Generative AI benefits can transform the economic landscape, paving the way for an exciting future:

• Accenture estimates as much as 40% of all working hours will be supported or augmented by language-based AI;
• Between 2030 and 2060, 50% of today’s work activities could be automated by Generative AI;
• AI is expected to reduce workload by 60% to 70%;
• It can also enable labor productivity growth of 0.1 to 0.6 percent annually through 2040;
• By 2025, it’s projected that 30% of outbound marketing messages from large organizations will be AI-generated, up from less than 2% in 2022;
• Generative AI will enable individuals to have their own personalized AI voice assistant in the coming years;
• Generative AI-powered chatbots are expected to reach human-level performance by 2030. It will have a major impact on knowledge work, benefiting marketing and sales functions across all industries.

Potential benefits of Generative AI include enhanced decision-making, increased productivity, and a transformation of knowledge work. The advancement in AI will undoubtedly reshape the business field in ways we can only begin to imagine.

Don’t miss out on the opportunity to see how Generative AI can revolutionize your customer support and boost your company’s efficiency.

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Benefits of Generative AI for Business: Unlocking Infinite Possibilities was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

 

Enhancing Customer Support on WhatsApp: Building a WhatsApp Chatbot with Human Handover using Dialogflow

In today’s digital age, WhatsApp has become a preferred communication channel for businesses and customers. WhatsApp Business API once limited to a select few, is now within reach of a broader range of businesses, offering new opportunities for providing exceptional customer support and addressing user queries.

One innovative way to leverage the power of WhatsApp API is by integrating it with Dialogflow, a robust natural language processing platform. This integration enables businesses to create a WhatsApp chatbot that can efficiently handle a wide array of customer queries. It is a valuable tool for e-commerce and small-scale businesses seeking to enhance their customer support capabilities.

Real-World Example

To better illustrate the potential of this integration, let’s explore a real-world scenario:

Imagine you run an e-commerce store that sells a variety of electronic gadgets, and you’ve integrated the WhatsApp Business API with Dialogflow to create a sophisticated chatbot.

A customer named Sarah contacted you on WhatsApp business account, inquiring about the availability of the latest smartphone model. The chatbot, powered by Dialogflow, understands her query and provides information about the smartphone’s availability, specifications, and price. It even offers to assist with placing an order.

However, Sarah has specific questions about the smartphone’s camera capabilities and whether it’s compatible with her existing accessories. These queries fall into a more complex category and necessitate a more personalized touch. This is where the power of human intervention comes into play.

Recognizing the complexity of Sarah’s questions, the chatbot seamlessly transfers the conversation to a live human customer support agent, who is also accessible through WhatsApp. The support agent picks up the conversation right where the chatbot left off, addressing Sarah’s concerns in a friendly and knowledgeable manner.

Key Benefits

• The integration of WhatsApp Business API with Dialogflow not only enables businesses to provide automated responses but also ensures a smooth transition to human agents when needed.
• This dynamic interaction enhances the customer experience, instilling confidence in your brand’s commitment to providing excellent service.
• By combining the capabilities of a chatbot with the expertise of human agents, you can meet the diverse needs of your customers efficiently and effectively. This level of responsiveness and personalization sets businesses apart in the competitive landscape of modern customer service.

As we explore the integration of WhatsApp Business API and Dialogflow, we’ll delve deeper into the technical aspects of creating a chatbot with human handover, providing practical insights and steps to empower your business with this powerful tool.

Prerequisite Tutorials

Before you begin this tutorial, it’s essential to review the following prerequisite tutorials:

1. Explore a WhatsApp Business API: Click for Tutorial
2. Learn How to Integrate Dialogflow API: Click for Tutorial

These tutorials provide the foundational knowledge required for successfully implementing the integration discussed in this tutorial.

Technical Implementation

Ensure you have followed the WhatsApp bot tutorial and Dialogflow API integration tutorial. We retrieve user messages from the WhatsApp bot and transmit them to the Dialogflow API. This process involves several key steps.

Step 1: Let’s start by replacing the code with the one specified in Step 17 of the WhatsApp bot tutorial. This code will help you set up the integration seamlessly:

from flask import Flask, request
import requests
import os
import json
from google.cloud import dialogflow
from google.protobuf.json_format import MessageToDict
import datetime
app = Flask(__name__)

os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="<Service_account_key_File_path>"

# Credential Details
MOBILE_NUMBER = '<User_phonenumber>'
ADMIN_NUMBER = '<Admin_number>'
PROJECT_ID="<Project_id>"
language_code="<en-US>"

# Connect with Dialogflow Api and and send response to user
def detect_intent_texts(project_id, session_id, texts, language_code):
    
    session_client = dialogflow.SessionsClient()
    session = session_client.session_path(project_id, session_id)
    print("Session path: {}n".format(session))
    text_input = dialogflow.TextInput(text=texts,   language_code=language_code)
    query_input = dialogflow.QueryInput(text=text_input)
    response = session_client.detect_intent(
       request={"session": session, "query_input": query_input} )

    print("=" * 20)
    final_response = MessageToDict(response._pb)
    print(final_response)
    print("=" * 20)
    final_text =       final_response['queryResult']['fulfillmentMessages'][0]['text']['text'][0]
    intent = final_response['queryResult']['intent']
    action = final_response['queryResult']['action']
    return final_text,action,intent

# connect with the admin and admin direct messages to user
def send_msg_admin(msg):
    headers = {
       'Authorization': f'Bearer {ACCESS_TOKEN}',
   }
    json_data = {
        'messaging_product': 'whatsapp',
        'to': ADMIN_NUMBER ,
        'type': 'text',
        "text": {
            "body": msg
        }
    }
    response=requests.post(f'https://graph.facebook.com/v17.0/{PHONE_NUMBER_ID}/messages', headers=headers, json=json_data)
    return response.text

# store all details in json file
def writedata(human_phone,user_phone,human_interface):
    current_time = datetime.datetime.now()
    dict_data = {'human_phone': [human_phone], 'user_phone': [user_phone],       'human_interface':[human_interface], 'last_updated': [str(current_time)]}
    with open('data.json','w') as json_data:
      json.dump(dict_data,json_data)

#  bot connect with the user send msg
def send_msg(msg,phonenumber):
    headers = {
       'Authorization': f'Bearer {ACCESS_TOKEN}',
   }
    json_data = {
        'messaging_product': 'whatsapp',
        'to': phonenumber,
        'type': 'text',
        "text": {
            "body": msg
        }
    }
    response=requests.post(f'https://graph.facebook.com/v17.0/{PHONE_NUMBER_ID}/messages', headers=headers, json=json_data)
    return response.text 

# Admin  can Send images
def send_image(id,phonenumber):
    headers = {
        'Authorization': f'Bearer {ACCESS_TOKEN}',
    }
    json_data = {
        'messaging_product': 'whatsapp',
        'to': phonenumber,           # example 91<your number>
        'type': 'image',  # Set the message type to 'image'
        "image": {
           "id":id
        }
    }
    response = requests.post(
        f'https://graph.facebook.com/v17.0/{PHONE_NUMBER_ID}/messages', headers=headers, json=json_data)
    print(response.text)



@app.route('/receive_msg', methods=['POST','GET'])
def webhook():
    res = request.get_json()
    print(res)
   
    try:
        ADMIN_NUMBER = "<Admin Number>"
        if res['entry'][0]['changes'][0]['value']['messages'][0]['id']:
            language_code="en"
            session_id = res['entry'][0]['changes'][0]['value']['messages'][0]['from']
            
            if res['entry'][0]['changes'][0]['value']['messages'][0]['from'] == ADMIN_NUMBER:
                with open('data.json','r') as json_data:
                     filtered_rows = json.load(json_data)
                     user_phone = filtered_rows['user_phone'][0]
                try:
                        msg = res['entry'][0]['changes'][0]['value']['messages'][0]['text']['body']
                        send_msg(msg,MOBILE_NUMBER)
                        print(msg)
                        if str(msg).lower() == "bye":
                           human_interface = "False"
                                 writedata(ADMIN_NUMBER,MOBILE_NUMBER,human_interface)
                           msg = "Thank you for connecting with us. We appreciate your time and value your input. If you have any further questions or need assistance in the future, please don't hesitate to reach out. Have a wonderful day ahead!"
                        
                except:
                      img_id=res['entry'][0]['changes'][0]['value']['messages'][0]['image']['id']
                      send_image(img_id,MOBILE_NUMBER)
            else:
                human_phone = ADMIN_NUMBER
                user_phone = session_id
                            
                msg = res['entry'][0]['changes'][0]['value']['messages'][0]['text']['body']
                if not os.path.exists('data.json'):
                    human_interface = 'False'
                    writedata(human_phone,user_phone,human_interface)

                with open('data.json','r') as json_data:
                    filtered_rows = json.load(json_data)
                    
                if filtered_rows['human_interface'][0] == 'False':
                    session_id_1 = f"session_id{session_id}"
                    response, action,intent = detect_intent_texts(PROJECT_ID, session_id_1, msg, language_code)
                  
                    if action== "human_transfer" or "input.unknown":
                        message = f"Request To connect With you and the user number is {session_id}"
                        send_msg_admin(message)
                        print("Send to Admin msg")
                        human_interface = "True"
                        writedata(human_phone,user_phone,human_interface)
                    else:
                        human_interface = "False"
                        writedata(human_phone,user_phone,human_interface)
                        send_msg(response,session_id)
           
                else:
                    send_msg_admin(msg)
                      
    except:
        pass
    return '200 OK HTTPS.'

if __name__ == '__main__':
    app.run()

Step 2: Ensure that you have created the PROJECT_ID as specified in the Dialogflow API Integration Tutorial, which you will need to replace in the provided code.

Step 3: To get GOOGLE_APPLICATION_CREDENTIALS, follow the instructions at this link: https://cloud.google.com/iam/docs/keys-create-delete to generate a JSON service account key. Save the JSON file in the same directory as your Python script.

Step 4: Input values for MOBILE_NUMBER and ADMIN_NUMBER, and ensure that they are different. You can also replace “language_code” as per your requirements, but it should be set to “en” by default.

Now, let’s understand the key functions and their roles in the WhatsApp bot script:

1. detect_intent_texts
   This function demonstrates how Dialogflow sends a request and receives a response via WhatsApp. It sends a specific action to trigger a response, and then it forwards that response back to the user.
2. send_msg_admin
   In this function, When a user wants to connect with a human, this function sends a message to the admin number. The admin directly responds to the user’s request.
3. writedata
   In this function, we store information about the admin number, user number, user’s last update time, and a human interface. Initially, we set the human interface as “false”.
4. send_msg
   In this function, we establish a connection between the bot and the user, and subsequently, we send a response back to the user.
5. send_image
   In this function, when a user inquires about sending images, the admin quickly responds to the user and simplifies the process of sharing images.

When we send messages on WhatsApp we get a response from the dialog flow bot.

In the Flask server terminal, we receive feedback on the sent, read, and delivered messages, allowing us to monitor the status and progress of our message transmissions.

When a user initiates contact with a human agent or bot that doesn’t understand the user query, the Dialogflow action is activated and forwarded to the admin. The admin directly responds to the user’s queries. the Flask app server provides us with responses and updates throughout this communication process.

Here is the Flask app server’s response to initiate a connection with the human agent:

Here is the conversation of the admin directly connecting with the user and responding to the user’s queries.

We also receive responses in the Flask app terminal from the admin during the conversation between the admin and the user. These responses include status updates on sent, read, and delivered messages.

Here is the conversation between the user and the admin when the user asks about the image-related queries.

Admin Send the image to the user:

We can end the conversation with a closing message, like “bye”, or any custom message you’ve chosen to use for concluding the conversation with the admin.

We have the capability to configure a wide range of responses supported by WhatsApp. For more details, please visit: WhatsApp Business API Automation with Flask

We hope that you’ve effectively created a WhatsApp Chatbot using Dialogflow and having fun using it.

Originally published at Enhancing Customer Support On WhatsApp: Building A WhatsApp Chatbot With Human Handover Using Dialogflow on November 9, 2023.

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Enhancing Customer Support on WhatsApp: Building a WhatsApp Chatbot with Human Handover using… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In recent years, as per chatbot statistics the market has experienced remarkable growth. It is fueled by the increasing demand for 24×7 customer services and operational cost reduction. Moreover, there is a rising preference for self-service operations. Businesses are automating sales and support services, enabling timely services at reduced costs. This evolution is underscored by significant chatbot and AI statistics:

• The chatbot market is set to expand at a remarkable 23.3% annually, reaching $15.5 billion by 2028.
• 87.2% of consumers rate their interactions with bots as either neutral or positive.
• 62% of respondents prefer engaging with customer service digital assistants rather than waiting for human agents.
• Chatbots have the potential to automate 30% of tasks performed by today’s contact center staff. This can lead to potential savings of $23 billion in the U.S.
• Bots can manage 30% of live chat communications and 80% of routine tasks.
• Chatbots have remarkably accelerated response times, delivering answers three times faster on average.
• Digital assistants are most frequently employed in sales (41%) and client services (37%). Marketing (17%) is the third most common application.

Now, let’s explore the impact of bots across various industries and their benefits analyzing available chatbot statistics.

MOCG Top Picks

Chatbots User Engagement and Expectations:

• 80% of users have had prior interactions with chatbots.
• Only 9% of consumers oppose companies using digital assistants.
• 73% of users expect websites to feature chatbots for convenient interactions.
• 35% of individuals turn to digital assistants to address complaints or obtain detailed information.
• 74% of internet users prefer using chatbots for simple questions.

Chatbots Business Integration and Benefits:

• 58% of B2B companies integrate chatbots into their websites, compared to 42% in B2C settings.
• Approximately 2/3 of companies express contentment with their bot implementations.
• Digital assistants resolve 58% of returns and cancellations.
• 90% of businesses witnessed faster complaint resolution due to bots.
• 61% of respondents believed chatbots could boost productivity by automating task follow-ups.
• 55% of companies using digital assistants experience an increase in high-quality leads.
• In specific industries, chatbots achieve conversion rates as high as 70%.
• Business leaders have reported a 67% increase in sales through chatbots. Furthermore, 26% of all sales transactions initiate from a bot interaction. Additionally, 35% of business leaders credit digital assistants for closing deals.

Chatbots Industry Impact:

• B2C companies report twice as much satisfaction with chatbots as B2B companies, due to simpler queries. The most content industries include tech (73%), retail (67%), manufacturing (57%), and healthcare (56%).
• Certain industries profit notably from digital assistants, led by real estate (28%), travel (16%), education (14%), healthcare (10%), and finance (5%).

Chatbots Latest Insights

These are the projections for 2023 made in the past that we are yet to evaluate and analyze:

• By 2023, businesses were expected to have saved up to 2.5 billion hours of work.
• Chatbot transactions in eCommerce were anticipated to reach $112 billion by 2023.
• The income generated by the chatbot sector was estimated to be roughly $137.6 million as of 2023.
• 75% of queries would be resolved by chatbots in 2023.
• By 2023, 70% of white-collar workers could have engaged with bots daily.
• The banking industry was projected to witness a success rate of bot interactions reaching over 90% in 2023.
• Artificial intelligence could automate up to 73% of healthcare admin tasks by 2023.
• By 2023, chatbots were expected to save the banking, healthcare, and retail sectors up to $11 billion annually.
• It was predicted that in 2023, the number of voice bots would rise to over 8 billion.
• Another trend for 2023 included the rise of AI-powered GTP-3 digital assistants.
• In 2023, as many as 75% of HR queries globally occurred through HR digital assistants.
• By 2023, over 70% of chatbot conversations were anticipated to be with retail bots.

Chatbot Market Size & Revenue Growth

The global chatbot market experienced remarkable growth in 2022 reaching $4.7 billion. Additionally, it is poised for a robust 23.3% annual expansion from 2023 to 2028, reaching $15.5 billion. In-house bot solutions made up 62.0% of the market. Sales and marketing drove user engagement and held a 39.5% market share.

The finance sector is set for 24.0% growth, reducing costs with chatbots. Retail & e-commerce led with a 30.34% share, followed closely by BFSI in revenue. North America dominated with a 30.72% market share, driven by startups. Asia Pacific followed, boosted by its thriving services industry. This shows the global growth of digital assistants in various sectors and regions.

Chatbot Usage Statistics

Chatbot Adoption Stats:

• There are over 300,000 chatbots in use on Facebook Messenger.
• 1.4 billion people actively use messaging apps. In fact, chatbots experienced a remarkable 92% increase in usage since 2019.
• In 2022, 88% of users engaged in at least one conversation with a chatbot.
• Only 9% of consumers oppose companies using bots.
• 40% of millennials engage with digital assistants daily.
• On average, users pose 4 inquiries to chatbots within one chat session.
• 73% of buyers expect websites to feature digital assistants for convenient interactions.
• Bots contribute to 39% of all chats between businesses and consumers.

Business Integration and Implementation:

• 56% of businesses cite chatbot technology as a transformative force.
• 58% of B2B companies integrate bots into their websites, compared to 42% in B2C settings.
• Small companies (fewer than 250 employees) constitute around 40% of all chatbot-using businesses.
• Among companies utilizing AI bots, 46% employ them for voice-to-text dictation. 26% use them for team collaboration and 24% for employee calendar management. Additionally, 14% use digital assistants for service support, and 13% for IT help desk management.
• 50% of organizations refrain from implementing chatbots due to a lack of applicable use cases. 29% express concerns about security and privacy, and 25% are deterred by cost.
• Approximately 2/3 of companies express contentment with their bot implementations.
• Within companies, 53% utilize AI digital assistants in their IT departments. 23% employ them for administrative tasks.
• Chatbots are most frequently employed in sales (41%) and customer support (37%). marketing (17%) is the third most common application.

Chatbot Customer Use Cases:

• 35% of individuals turn to chatbots to resolve complaints or get detailed information.
• Chatbot usage doubles for tasks like making purchases, scheduling meetings, and signing up for mailing lists.
• 41.3% of buyers turned to digital assistants in 2020 for purchases.
• When seeking information, 54% of respondents would inquire about products via digital assistants. 30% would utilize them for bill payments. Only 23% are willing to delegate dispute resolution to bots.
• 74% of internet users prefer using chatbots for simple questions.
• Digital assistants resolve 58% of returns and cancellations, but only 18% of change in product/service and 17% of billing disputes.

Consumer Preferences and Perceptions:

• 87.2% of consumers rate their interactions with bots as either neutral or positive.
• 62% of respondents prefer engaging with client service digital assistants rather than waiting for human agents.
• 65% of users feel comfortable resolving issues without human intervention.
• 69% of users appreciate digital assistants’ quick reply times. In fact, 59% anticipate a chatbot reply within 5 seconds.
• 48% prioritize bots’ issue-solving abilities over their personalities.
• Globally, 38% of consumers hold a positive view of digital assistants.

Chatbot Benefits for Business

• According to available data, chatbots have the potential to automate 30% of tasks performed by today’s contact center staff. This could lead to potential savings of $23 billion in the U.S.
• Response rates for digital assistants vary between 35–40% for less favorable and 80–90% for the most engaging experiences.
• Approximately 64% of internet users consider 24-hour service a key feature of bots. In fact, 29% of bot interactions occurred outside regular store hours.
• 90% of businesses witnessed faster complaint resolution due to the implementation of digital assistants.
• Chatbots can manage 30% of live chat communications. They can also efficiently handle 80% of routine tasks and client inquiries.
• According to a survey, 61% of respondents believed bots could boost productivity by automating task follow-ups. 57% considered they could facilitate more effective communication within the organization.

Chatbot Stats by Business Function

AI chatbots and assistants are employed across various departments. 53% are utilized in IT, 23% in administration, and 20% in customer care. Additionally, 16% of organizations leverage these technologies in sales and marketing.

Customer Service

• 27% of users were uncertain whether their last client support interaction was with a human or a chatbot. Meanwhile, 62% believed AI could speed up responses while catering to their specific preferences.
• 34% of retail clients expressed comfort in conversing with AI chatbots for service support. Additionally, 64% of businesses anticipated digital assistants enhancing personalized support experiences.
• 67% of individuals utilized bots for client support in the preceding year.
• AI chatbots for business enable organizations to shift 64% of agents’ focus to solving complex issues, compared to 50% without AI.
• Virtual assistants reduce inquiries by 70% across calls, chats, and emails.
• 23% of client service organizations rely on AI digital assistants as their main communication channel.
• Chatbots have remarkably accelerated response times, delivering answers three times faster on average.
• Digital assistants led to a 24% increase in support satisfaction scores.
• According to Statista, 1/3 of consumers found bots ‘very effective’ in resolving queries. 54% deemed them ‘somewhat effective,’ while only 13% considered them ‘not at all effective.’
• 80% of sales and marketing leaders implemented or planned bots integration into customer experience (CX).
• 23% of client service companies actively utilized AI chatbots.
• 70% of consumers in 2020 expressed interest in using digital assistants for basic customer service needs.

Marketing

• 55% of companies using chatbots for marketing experience an increase in high-quality leads.
• In specific industries, chatbots achieve conversion rates as high as 70%.
• Stores see annual revenue surge by 7 to 25% when effectively utilizing bots.
• Digital assistants contribute to upselling in 20% of cases, boosting sales opportunities.
• 53% of buyers are more likely to shop with businesses offering messaging services.
• Chatbots excel in traffic segmentation and targeted product ads, driving 77% of a company’s ROI.
• 36% of companies enhance lead generation using digital assistants, with 62.5% using them for lead qualification.

Sales

• Business leaders have reported a 67% increase in sales through the assistance of chatbots. Furthermore, 26% of all sales transactions initiate from a bot interaction. 35% of business leaders credit digital assistants for closing deals.
• Sales purposes drive 41% of all business chatbot applications.
• 35% of business leaders noted that virtual agents have simplified sales processes.
• 25% of companies utilize bots to recommend products, enhancing personalized user interactions.
• Post-sales and customer service operations benefit from digital assistants. 77% actively engaged in assisting clients after their purchase.
• Upselling opportunities are leveraged in approximately 20% of cases.
• CX profoundly influences buying decisions, with 73% of customers emphasizing its importance. Moreover, 86% of clients are willing to pay a premium of 13%-18% for an enhanced CX.
• 56% of companies recognize conversational bots as industry disruptors. And 43% report competitors’ adoption. Additionally, 57% of businesses recognize chatbots’ substantial ROI and minimal effort requirements.
• 23.7% of inquiries convert into sales without human intervention.
• Sales digital assistants turn over 28% of website visitors into leads.

Chatbot Stats Landscape: Key Industry Statistics

In different sectors, satisfaction with chatbots varies. B2C companies, dealing with simpler queries, tend to be twice as satisfied as B2B companies. The most content industries include tech (73%), retail (67%), manufacturing (57%), and healthcare (56%).

Certain industries profit notably from chatbots. Real estate (28%), travel (16%), education (14%), healthcare (10%), and finance (5%) lead in this regard. When it comes to informational bots, health (64%), communications (59%), and banking (50%) industries embrace them the most.

eCommerce & Retail

• An estimated 70% of bots were projected to be retail-based by 2023. This trend was expected to drive eCommerce transactions via digital assistants, reaching $112 billion by 2023.
• 33% of consumers express a desire to use chatbots to make reservations at hotels or restaurants.
• The willingness to use digital assistants for purchases surged from 17.1% to 41.3% between 2019 and 2020.
• eCommerce stores adopting Facebook Messenger, coupled with abandoned cart chatbots, have boosted revenue by 7–25%.
• The acceptance rate of bots among clients in retail is 34%.
• Predictions for 2024 suggest global consumer retail spending via digital assistants will reach $142 billion.
• Nearly 40% of internet users worldwide prefer chatbot interactions over interactions with virtual agents.
• A significant 47% of consumers are open to making purchases using bots. 71% of Gen Z individuals actively seek products through bot interactions.
• 40% of U.S. consumers have utilized retail digital assistants.
• Among online retailers surveyed, 76% have either implemented or are planning to integrate chatbots into their CX strategies.

Finance & Banking

• 54% of customers favor using a finance chatbot for payment transactions.
• In the finance sector, bots are accepted at a rate of 20%.
• In 2022, over 98 million users (about 37% of the U.S. population) interacted with a bank’s bot. This number is expected to grow to 110.9 million users by 2026.
• All of the top 10 largest commercial banks have integrated digital assistants into their client service strategies.
• Chatbots can improve first-call resolution rates by 20%, increasing it from 50% to 70%, enhancing user satisfaction.
• By 2023, digital assistants were estimated to save banks between $0.50 and $0.70 per interaction, totaling around $7.30 billion in global savings.
• Financial service companies can save more than 4 minutes per inquiry by utilizing chatbots.
• Banks have the potential to automate up to 90% of their customer interactions using digital assistants.
• 80% of financial institutions view bots as a valuable opportunity to enhance their client service.
• Over 43% of customers in the USA prefer using chatbots to resolve their banking issues.
• Banks incorporating digital assistants into their client service can boost their revenue by up to 25%.

Insurance

• The insurance chatbot market’s value is expected to reach $4.5 billion by 2032, displaying rapid growth at a CAGR of 25.6% from 2023 to 2032.
• Within the insurance industry, 83% of customers express satisfaction with chatbot interactions. However, the acceptance rate for digital assistants in insurance remains at 13%.
• In 2018, only 5% of insurance companies utilized AI in the claims submission review process. 70% showed no consideration for its implementation at that time.
• 44% of clients find bots suitable for claims processing. 43% prefer them for insurance applications.
• AI bots can handle over 95% of users’ conversations, reshaping communication in insurance.
• Insurance chatbots can manage 80% of inbound inquiries. Additionally, they redirect the remaining 20% to human agents.

Telecom

• In the telecommunications industry, the acceptance rate for chatbots stands at 25%.
• The IT and telecommunications sectors are at the forefront of machine learning (ML) utilization. 52% of companies extensively employ digital assistants for various purposes.
• Call center chatbots enable 82% of users to access services without enduring long queues.
• AI in the telecommunication market is projected to reach $10 billion by 2028, expanding at a robust CAGR of 37.4%.
• Currently, 63.5% of telecom companies are integrating AI to enhance their network infrastructure.
• 56% of telecom customers opt for self-service to select the best plan. Additionally, 77% of consumers choose self-service channels for bill payments and account recharges.
• Personalization drives significant revenue growth, ranging between 5% and 15% for telecom companies. And digital assistants facilitate personalized interactions.

Healthcare

• The healthcare chatbot market is set to reach $431.47 million by 2028, growing at a rate of 15.20%.
• AI aid bot technology can automate over 70% of administrative tasks in the healthcare industry.
• Currently, 68% of healthcare organizations are incorporating AI.
• Bot interactions in the healthcare sector currently have a success rate of just 12%. However, it’s expected to improve significantly, potentially handling up to 75% of human healthcare queries.
• A national survey in 2021 revealed that 22% of adults had utilized a mental health chatbot, and 47% expressed interest in using it if needed. During the COVID-19 pandemic, nearly 60% of users started utilizing mental health chatbots. 44% exclusively relied on digital assistants without seeking human therapy.
• Consumers acknowledged the benefits of mental healthcare bots (65%) and their importance (74%). However, 86% felt digital assistants lacked understanding or display of human emotions.
• Physicians believe chatbots would be helpful for scheduling doctor appointments (78%), locating health clinics (76%), and providing medication information (71%).

Travel

• 25% of travel and hospitality companies use digital assistants for general inquiries and bookings.
• In restaurants, hotels, and guesthouses, 33% of consumers are embracing web tools powered by chatbots.
• Among the top 5 industries benefiting from bot adoption, the travel sector comprises 16%.
• A significant 33% of users express the desire to use digital assistants for making reservations at hotels or restaurants.
• Two-thirds of respondents find chatbots useful (40%) or very useful (26%) for managing their business and travel arrangements.
• For travel plans and booking comparisons, 37% of users prefer intelligent bots.
• A high demand for digital assistants that save time and money is evident. 87% of users expressed willingness to interact with a travel chatbot offering these benefits.
• 79% of users expressed willingness to ask for help from a bot if it can function as a concierge.
• In late 2017, SITA reported low adoption rates, with only 14% of airlines and 9% of airports utilizing chatbot technology. However, 68% of airlines plan to adopt airline chatbot services in the near future as well as 42% of airports.

Voice Assistants Statistics

• Industry reports show a sharp rise in voice-enabled interfaces, reaching 8.4 billion by 2024.
• 72% of US consumers have interacted with voice interfaces in business settings.
• The voice assistant market is set to grow from USD 4.59 Billion in 2022 to USD 30.72 billion by 2030, with a 31.2% CAGR.
• Over 90% of people aged 18–64 seek information about voice technology, and more than 70% use it at least once.
• 93% of consumers are satisfied with their voice assistants; 50% are very satisfied.
• 80% of buyers shopping via voice assistant are satisfied, and 50% have made purchases.
• 74% of clients use mobile voice assistants at home, with 71% preferring voice queries over typing.
• 51% of voice shoppers research products, 22% make direct purchases, and 17% reorder items. Additionally, 30% track packages, and 20% leave reviews.
• 71% of consumers are satisfied with voice assistants on their mobile devices.
• Food (56%), banking (44%), and retail (35%) are the most popular industries for voice assistant use.

Future of Chatbots Stats

• Larger companies have embraced chatbots at a faster pace, holding over a 46% share in the market. And this trend is expected to continue, with their share growing by 2027.
• Capgemini predicts 70% of consumers will replace physical visits to shops or banks with voice assistants in the next three years.
• Retail consumers are anticipated to spend over $142 billion via bots by 2024. It’s a significant increase from $2.8 billion in 2019, as reported by Insider Intelligence.
• Gartner predicts that by 2027, digital assistants will become the primary channel for client service in 25% of all businesses.
• 1/3 of AI startup founders believe that digital assistants will be the most popular customer technology in the next five years.
• It is projected that AI bots will power 95% of all customer service interactions by 2025.
• With advancements in ML, AI, and natural language processing, chatbots are expected to become more human-like. This is facilitated by the ChatGPT adoption and the development of new AI programs.

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Chatbot Statistics: What Businesses Need to Know About Digital Assistants was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Generative AI is an extremely versatile tool that has found its application in various fields. Therefore, it has the potential to become “general-purpose technology.” Moreover, researchers hope to build artificial general intelligence (AGI). Thus, it can turn into a machine that can perform any task that a human can.

Let’s explore executives’ anticipations about Generative AI trends for 2024 and beyond:

• 77% of businesses expect the largest impact from Generative AI among emerging technologies;
• Over 60% of respondents see it as a chance to gain a competitive edge;
• 73% believe new technology will boost workforce productivity;
• 71% plan to implement it within two years. IT/tech (56%) and operations (56%) are priority areas. They are followed by marketing and sales (42%), and customer service (40%);
• 64% expect it to confer a competitive advantage;
• By 2026, companies focusing on responsible AI could enhance business goal achievement and user acceptance by 50%;
• Artificial intelligence disruption may increase global labor productivity by 1.5%-3.0% annually over the next decade.

In this transformative era, several key trends are shaping the Generative AI landscape:

• AI-driven creativity shaping diverse art forms and cultural expressions;
• Personalized interactions and experiences for heightened engagement;
• Edge computing enabling rapid real-time data processing;
• Intuitive interfaces for seamless human-AI collaboration;
• Multimodal artificial intelligence integrating diverse data types for comprehensive understanding;
• Web3-enabled Generative artificial intelligence for decentralized and secure applications;
• AI-as-a-service providing scalable and accessible solutions;
• Environmentally conscious artificial intelligence promoting sustainable practices;
• Ethical guidelines ensuring responsible and trustworthy deployment.

As businesses navigate new technologies, they face diverse scenarios for Generative AI’s future. It can be a utopian integration into everyday life. Or it can turn to a scenario where regulation hampers progress or public skepticism hinders adoption. The path forward for this technology is both promising and challenging.

Let’s now explore each Generative AI trend in depth, discovering their multifaceted impact.

Generative AI Trends Reshaping Business Landscapes

Text, Image & Video Generation

Generative artificial intelligence tools can produce diverse forms of content. These include written, image, video, audio, and coded materials. Businesses are actively exploring applications across these domains. Specialized applications customized for specific functions will offer greater value than generalized ones.

The trends in the landscape of Generative AI for text generation include:

• Models understanding human psychology and creative processes, leading to better connections with users;
• Creation of emotionally resonant and deeply engaging written content;
• Artificial intelligence adapts content to individual preferences, improving user interactions;
• Autonomy in undertaking complex tasks, in fields such as research and creative brainstorming;
• Use of AI-powered writing assistants for task automation.

In Generative AI for image creation, significant advancements are shaping the industry:

• Move towards realism and creativity, with highly detailed and lifelike images;
• Blurring boundaries between natural and synthetic visuals, transforming design;
• Higher adoption in entertainment and virtual reality industries;
• Empowering designers with tools to prototype and create diverse products.

Generative AI in video generation is poised to transform content creation and consumption:

• Production of high-quality, personalized videos for diverse purposes;
• Integration of AI video generators into existing workflows, targeting specific audiences;
• Higher accuracy of AI algorithms for generation of targeted videos;
• Reduction of time and costs associated with traditional video production methods.

As these trends unfold, innovation continues to push boundaries. It offers unprecedented possibilities for content creation and innovation across various industries.

Music Generation

Generative AI is revolutionizing music creation. These AI models can mimic human voices and generate music. This offers endless possibilities for musicians and composers. In the music world, this new intelligence is becoming a go-to tool for songwriters.

They provide fresh compositions that inspire creativity. It’s not just about creating new music; AI is also shaping the way we experience it. Soon, we might have adaptive soundtracks in video games and live events. This will improve our audiovisual experiences in real time.

AI is on the verge of mastering human-like expression and emotion in voice synthesis. This advancement will open doors to real-time translation, audio dubbing, and automated voice overs. Musicians and audiences expect a future where music becomes an even more immersive and dynamic art form.

NLP Technology and Multimodal AI

Generative AI is also enhancing Natural Language Processing (NLP). Artificial intelligence will become better at understanding texts, speeches, and sentiments with greater depth. This advancement is pivotal for human-like interactions in voice assistants and chatbots. This allows companies to make user experiences more natural and smooth.

Traditionally, such models processed information from single modalities, limiting insights. However, multimodal deep learning allows models to discern relationships between different modalities. They can now translate text to images, images to videos, and vice versa.

This fusion proves highly effective, especially in complex fields like medicine. In healthcare, AI combines textual and visual data for more accurate assessments. In NLP, multimodal models help with language translation, sentiment analysis, and chatbot development.

Generative AI-Powered Chatbots

Generative AI improves conversational abilities and enables personalization and context-awareness. Chatbots powered by Generative AI can continuously learn from user interactions. Such an approach boosts their performance and understanding of consumer needs over time. Advancements in machine learning algorithms are equipping chatbots with emotional intelligence. This way bots can detect and respond to human emotions.

As a result, they provide more compassionate and personalized customer support. Generative AI-powered chatbots will integrate into Virtual and Augmented Reality experiences. This integration will enable users to engage with lifelike virtual characters. This will ensure highly immersive and interactive client interactions. The future holds realistic and engaging virtual environments powered by AI-based technology.

Thinking of incorporating Generative AI into your existing chatbot? Validate your idea with a Proof of Concept before launching. At Master of Code Global, we can seamlessly integrate Generative AI into your current chatbot, train it, and have it ready for you in just two weeks.

Key Trends in Generative AI across Different Functions

According to McKinsey, Generative AI holds immense potential. 75% of its value is concentrated in a few areas. These include customer operations, marketing & sales, and software engineering.

Customer Service

Generative AI in customer service is set to transform operations in the near future. Here are the key trends to be observed:

• Conversational search supporting instant, accurate responses from company knowledge bases;
• Agent assistance through search and summarization, improving conversation quality and trend categorization;
• Call center operational and data optimization. For example, automation of repetitive tasks, summarizing call center data for insights;
• Personalized recommendations through the analysis of the interactions. The result is customized content in preferred formats and tones.

This technology will significantly boost client experience, reducing response time, and increasing sales. Automation of consumer interactions will decrease human-serviced contacts by up to 50%. Such a method will boost productivity and lead to cost savings ranging from 30 to 45% as per recent AI statistics.

Marketing and Sales

Generative AI is transforming marketing and sales operations with the key trends being:

• Hyper-quick sales and marketing content creation;
• Automated repetitive tasks, like keyword research, administrative work, content formatting, and data analysis;
• Sales enablement and custom materials. For example, analyzing client’s profiles and creating tailored materials for enhanced sales success;
• Omnichannel strategy optimization to improve engagement;
• Customer insights with crm data through detection of patterns in user behaviors;
• Adjust training programs with custom training materials, role-play scenarios, and product knowledge sheets.

In sales, AI will identify leads, improve client engagement, and facilitate effective outreach. Such innovations can increase sales productivity by an estimated 3 to 5%. Moreover, Generative AI for marketing will act as a virtual collaborator, accelerating productivity. It will offer insights for knowledge work so the focus is on higher-impact tasks.

Software Engineering

Generative AI can revolutionize software engineering processes. Here is what it can help with:

• Streamline requirements gathering, aligning analyst-customer understanding and minimizing miscommunication risks through quick prototypes;
• Aid in UI template creation and ensure designs meet standards, enhancing application compliance;
• Generate code snippets in various languages. This boosts developer productivity and software quality without extensive programming knowledge;
• Craft diverse test cases and supports Dynamic Code Analysis (DCA) and chaos testing;
• Handle basic client queries, reducing issue resolution times and pressure on service personnel.

This shift will treat programming languages akin to natural languages. This enables its integration into pair programming and augmented coding. The potential impact on software engineering productivity could range from 20 to 45% as per recent Generative AI statistics. As a result, it reduces time spent on activities such as initial code drafts and corrections. Generative artificial intelligence can become a cornerstone of innovation in software engineering.

Generative AI Industries Trends

Retail and eCommerce

Retail and eCommerce are experiencing a surge in Generative AI-powered applications. The innovation transforms customer experiences and operational strategies.

Some of the current and future Generative AI in eCommerce and retail trends include:

• Accelerating consumer research and targeting with synthetic clients and scenario testing;
• Enabling real-time updates on products and preferences through Augmented Reality Support;
• Optimizing inventory management and trend analysis with AI insights;
• Enhancing consumer experience with AI-driven virtual try-ons;
• Streamlining marketing content creation through AI-generated drafts;
• Revolutionizing procurement operations with AI-assisted negotiation playbooks.

Executives’ expectations for Generative AI in retail:

• 66% for customer data analysis;
• 64% for inventory management;
• 62% focus on content generation to upgrade marketing and communication.

Examples:

• Shopify Sidekick bot aiding online store management;
• Stitch Fix’s AI-Based Ads;
• BloomsyBox eCommerce Chatbot;
• Amazon’s recommendation engine;
• Virtual Try On from Google;
• Walmart Vendor Negotiations chatbot;
• Mercari’s Virtual Shopping Assistant Merchat AI.

Healthcare

Healthcare is at the forefront of AI innovation. The areas benefiting the most are drug development, patient monitoring, and telemedicine.

Key trends in Generative AI for healthcare include:

• Streamlining the selection of proteins and molecules for new drug formulation;
• Generating medication instructions, risk notices, and commercial content;
• Preparing scripts for physician interactions;
• Drafting legal documents incorporating specific regulatory requirements;
• Enhancing medical training;
• AI-assisted clinical decision-making.

Executives’ expectations for Generative AI in healthcare:

• 72% for medical records review;
• 70% for medical chatbots;
• 50% focus on image processing applications for surgeries.

Examples:

• Insilico Medicine leading the first Phase II trials for a Generative AI-developed drug with the help of Chemistry42, the Generative AI chemistry engine;
• DiagnaMed’s Brain Health AI Platform CERVAI™;
• Absci’s ML Models for In-Silico Antibody Design.

Financial Services and Banking

Financial services and banking are undergoing transformative changes through AI integration. The areas are fraud detection, risk management, and customer service automation.

Notable Generative AI market trends for financial industry encompass:

• Enhancing fraud detection and security protocols with AI;
• Implementing robust risk management solutions using AI-driven tools;
• Elevating customer service with chatbots and virtual assistants;
• Optimizing legacy code migration with natural-language translation;
• Automating customer emergency responses (e.g., credit card losses) with AI-driven IVR;
• Tailoring retail banking offers with personalized marketing content and A/B testing;
• Ensuring compliance with comprehensive risk model documentation;
• Implementing personalized investment options, fraud detection, and financial recommendations through AI solutions.

Executives’ expectations for Generative AI in banking and financial services:

• 76% for fraud detection;
• 68% for risk management;
• 66% focus on implementing chatbots and virtual assistants.

Examples:

• Morgan Stanley’s Chatbot for financial advisors to manage data;
• ChatGPT-Style CFO Tool from Brex for instant answer for financial questions;
• JPMorgan Chase’s AI Assistant IndexGPT for investment decision-making.

Cybersecurity & Generative AI

Using the power of Generative AI, cybersecurity can enter a new era of heightened defense. AI is capable of:

• Enhancing cybersecurity through AI-driven threat analysis and predictive modeling;
• Improving code security with AI-assisted code reviews and vulnerability assessments;
• Streamlining incident response by automating threat identification and mitigation;
• Enhancing phishing detection and prevention using AI-powered email security solutions;
• Utilizing it for real-time monitoring and analysis of user behavior patterns;
• Enhancing network security with AI-driven intrusion detection and prevention systems;
• Automating malware analysis and simulation for proactive defense strategies;
• Integrating AI into security information and event management for advanced threat intelligence;
• Enabling AI-driven security awareness training for employees to mitigate social engineering threats;
• Utilizing new tools for adaptive and context-aware access control systems.

These advancements can bolster security measures and create a safer digital environment. Therefore, AI-powered tools ensure businesses and individuals stay protected against evolving threats.

Ethics in Generative AI Application

The landscape of Generative AI ethics is evolving rapidly. Intellectual property protection stands as a cornerstone, demanding clear regulations. These include patents and copyrights to safeguard AI-generated creations to avoid legal complications. Simultaneously, the call for robust ethical guidelines grows louder.

The emphasis is on transparency, accountability, fairness, and privacy protection in AI development. This is needed to foster responsible and trustworthy AI deployment. We can expect a surge in AI regulations driven by concerns over deepfakes and misinformation.

Globally, initiatives are underway, with major geopolitical players actively engaging in regulatory discussions. As machine intelligence accelerates, the urgency to address ethical implications mounts. Therefore, we have to face ongoing public debates on the responsible use of innovative tools.

Conclusion

Generative AI is at the forefront of innovation, promising significant advancements for businesses. With its capability, it can revolutionize operations, marketing, sales, and other sectors. Generative AI trends in 2023 and beyond show how powerful and impactful the technology is.

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Generative AI Trends: Transforming Business and Shaping Future was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

There are probably still those software creators who remember the necessity to physically attend meetings to “brainstorm” or report on a project’s progress. However, the COVID pandemic and technological advances sped up the replacement of “old school” methods in software development management with more up-to-date tools. No matter if it goes about the app, some program, or board, the objective is the same: collaborate on a centralized platform to stay on top of every project.

The objective is especially vital in terms of software development outsourcing and offshoring. The global developer population is expected to reach 28.7 million people by 2024, an increase of 3.2 million from the number seen in 2020. The statistics show that the amazing 80% of the world’s top 500 companies leverage offshore teams for their software requirements and daily operations.

No matter whether you carry out a project for one of the top 500 or any other company, the margin for error in software development should near zero. Otherwise, you risk not only your money but your business reputation. Implementing project management into the process of software development is the major key to minimizing errors, making everyone in the team sing the same song, and keeping the project on schedule.

So, we devote our article today to the importance of project management in software development. Let’s start from the basics.

What Does Managing a Software Development Team Mean?

The creation of leading-edge software is not easy. It requires a controlled environment of managed people, time, resources, and outlays to ensure on-time and on-budget delivery.

It’s vital to highlight the difference between basic project management and software development project management. The unique specifics of IT projects make the proper management of a software development team a critical thing.

Daily team supervision and control over the members’ involvement are the aspects that can proficiently help your team cope with the tasks on time and most efficiently.

If you are eager to set your development team up for success, you are in the right place. A development team comprises specialists in different fields. Designers, Developers of the Back and Front End, and QA professionals work on the project. A PM supervises the project’s progress and facilitates communication between specialists and departments to achieve a common goal. Software development project management comprises planning, scheduling, and organization of the development process.

To lead a team of programmers, a PM should be a real professional and a responsible person familiar with the management strategies, approaches, and tools.

So, let’s proceed to the key strategies of project management in software development.

5 Workable Strategies for Managing a Software Developers’ Team

It goes without a doubt that every project manager should know how to motivate and inspire the team working on a particular project. Not less are important the skills of effective leadership and conflict resolution. These skills combined put effective project management in place and let the potential of every team member come to light and boost.

There are multiple strategies for team management. We’ve picked up the 5 most actual in our opinion for software development projects.

Setting clear targets

Any project comprises short-term and long-term goals essential for its successful completion. The mastery of a project manager to set clear goals for a day, a week, or a month determines the project’s success. Make sure every team member understands the goals of the project and the specific timelines they should follow. Project goals, your goals as a manager, and teams’ goals should be linked and brought into line. Make sure the members of the team do not go in opposite or unclear directions. It will help you avoid failing on the deadline. Setting smaller goals is preferable. Eat the elephant one bite at a time, it will help your team cope with the tasks step by step and complete the project gradually.

Establishing effective communication

Software development project management communication strategies are cornerstones that allow the manager to nurture the team’s dynamics and keep the right balance when carrying the project out. Everyone working on the project should understand the roles, responsibilities, expectations, and metrics clearly, even if the work is temporally delegated. It can all be achieved through open communication. Regular team meetings for sharing information and updates should be supplemented by the other channels to discuss project nuances, share concerns, and talk the possible decisions over. It may be email communication, CRM system chats, or video conferences if it’s necessary. There are companies holding daily video check-ins. Though it may seem excessive, such a measure can be a great way to keep members of a remote team connected and engaged.

Keep in mind that a project manager should be in touch with all the members of a development team, regardless of the time zones. However, they should set up and keep to certain communication protocols that may include:

• Limiting messages in chats to the business hours common for all time zones,
• Setting an average response time for messages and e-mails,
• Setting regulations for replies to after-hours emails or texts,
• Making sure everyone’s time is respected.

Making processes transparent

When running a project, a PM cannot control data exchange in full. Yet, it’s vital to make sure that all team members have free and instant access to internal project data and ongoing processes.

Transparency allows to monitor progress and improve accountability on the project. It also helps to make the most of team members’ skill sets. Online collaboration tools such as Slack, Trello, Miro, and much more may help you achieve the necessary transparency, assign tasks, set virtual conversations, and involve everyone in discussions. More than that, tracking employee performance on the project can also be realized with the help of the tools mentioned above.

Encouraging brainstorming and group discussions

“The truth is born in a dispute” are the famous words by Socrates, proven by up-to-date statistics. It shows that brainstorming sessions and group discussions stimulate inventiveness, create unique approaches, and bring forward the best ideas and solutions. Nowadays, most companies adopt the Agile philosophy. The idea that the culture of respect plays a crucial role in creating unique, and competitive products of superior quality.

The project manager’s responsibility in this case is to consider all the ideas and solutions suggested by team members. They should encourage everyone’s active participation in the discussions. By celebrating their successes and project progress, a PM can cultivate a positive and supportive culture so that the team responds with better productivity and efficiency.

Managing internal conflicts

Wherever people work together, there’s always room for conflict. Individuals come with their positive qualities and deficiencies. The key task of a project manager is to resolve tough situations and conflicts. However, it’s better to avoid the latters, without losing objectivity.

If the conflict occurs because of professional issues, it’s reasonable to remind the team about the common goal of the project. Even though the professional views of different people may be different project manager should find a solution to suit everyone. If the conflict is on some personal layer, a PM should be a psychologist to some extent to settle the problem as kindly as possible.

At Stfalcon we are proud to boast our family-like atmosphere and teamwork that allows us to be most efficient in our projects. To mention a few, let’s proceed.

Our Experience

Stfalcon developed a cutting-edge service solution for a Saudi start-up, Hump. It’s concerned with the people who often travel by plane. We launched the solution in just a month.

The key purpose of the startup was to save time by handling all the baggage-related procedures:

• transfer from the traveler’s home to the airport,
• baggage storage,
• transfer back from the airport to the traveler’s home.

The next stage is to scale the project and add the possibility of tracking the baggage along the way. Automated status changing and couriers’ appointments are also considered.

The punch line of the project is not only the ability of the Stfalcon team to carry the project out effectively but maintain communication with foreign clients with different mindsets and culture.

Read the full case study

In 2022 Stfalcon took the challenge to develop a web app to share user experience of disease treatments. Our team had to create a reliable web resource with clear UX, yet inspiring trust in patients. All methods of treatment that have already helped other people are added by the patients, having received prior approval from the platform admin. What’s more, the website now provides alternative treatment methods not acceptable to talk about.

Yet, they have proven to be effective, treatments having been ineffective are also mentioned. So, our team has created a functional user-friendly site with a clear UX, featuring an admin panel for user management and control.

Read the full case study

FAQs

What makes a successful software development team?

Many factors are important for a development team’s success, but effective communication is the key. On the one hand common goals, clear definition of roles and responsibilities as well as team culture should be communicated to each and every member. On the other hand, it’s vital to establish open and effective communication inside the team and with the stakeholders to achieve high-quality results and deliver real value.

Which software development process is most concerned with project management?

The planning stage may by right be considered the one most concerned with project management. At this stage the requirements and scope of work should be correctly evaluated, requirements and metrics accurately defined, and documentation with the outline of how to carry the project out from scratch issued. More than that, the project work should be distributed between the experts skilled and experienced in certain fields for smooth and productive project realization.

What are 3+ core principles for software development team management?

Managing a software development team requires talent, proficiency, and much effort on the part of the project manager. However, if asked to outline the 3 core principles for team management we’d mention the following:

Establish effective leadership

A PM should be a bit more than a manager and a strict boss, but a mentor and a coach to some extent. Accessible managers do not make their team sweep their mistakes under the rug. They’d rather mentor, educate, and guide their team towards their common best result.

Encourage discussions, brainstorming, and communication

As we have mentioned it and not once above, discussions are a critical aspect of any effective team. Everyone should know their input is welcome. Discuss issues together, brainstorm on the issues that set in, look for solutions cooperatively, formulate strategies, and work out policies acceptable for every member. Keeping the whole team on the same page is especially important when experts don’t share the same space physically as most of the teams do now.

Motivate and encourage learning

Great performance grows from good motivation and constant development. Everyone has the innate need to be appreciated. Unfortunately, bosses often forget about it and little praise their staff. Your team should realize, they’ve done a good job. And for doing a good job, especially in IT, one should learn continuously. Let your team have some time in their schedule devoted to learning and professional development.

Conclusion

The importance of project management in software development should never be underestimated. It’s not just about avoiding overdue, or over-budget software projects, but about cutting-edge software solutions, high-quality project results, and real value to users and clients. What’s more, software creation comes with multifaceted risks and challenges. So a Project Manager should overcome all these complexities and create an environment for transparent communication inside and outside the team.

As a company with almost 15 years of successful project realization for clients from different parts of the world, Stfalcon is ready to offer our customers development teams that know how to sing in harmony, creating your best product. Our PMs are experienced orchestrators for dedicated and mixed teams. Сontact us to discuss your variant of our cooperation and let’s start creating your best solution.

Originally published at https://stfalcon.com.

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5 Workable Strategies for Software Development Management was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Without any doubt, the future holds ample space for chatbots for marketing and sales. They make every process way more comfortable.

People have been using chatbots for marketing and sales for quite some time now. But it’s not easy to use them. When not implemented the right way, they know to be intrusive, and the only thing they will achieve is turning your visitors down.

So, how do we do that?

How can we achieve the fantastic results we read about in different case studies by using bots? How can we convert X more leads with them?

This article is the story of how not to use chatbots for marketing, what we learned from it, and how do we convert over 7% of our content readers into hot leads and later into customers.

Let’s get started.

The Problems We Encountered

I work for Userpilot. It is a user onboarding tool. Founded only a couple of years ago, it is one of the product-led growth (PLG) movement pioneers.

Since we’re in a pretty much new market, many of our target users were not educated about the problems we’re solving.

They weren’t aware of the troubles they had; they weren’t sure what exactly Userpilot does.

Keeping this in mind, no matter how great our content was, it just didn’t manage to convert readers into customers, or even worse, into leads.

So we needed to change something. We needed to change our entire approach to the content lead generation game.

Given the rise in usage of chatbots for marketing and sales, we decided to use chatbots as our go-to way of generating customers through content.

We have used chatbots for many content types — playbooks, ebooks, statistics, articles, and other lead magnets. But in this particular article, we’re going to see how we implemented bots on a series of different high-intent blog posts.

High-intent blog posts are the articles searched for and read by the people who are almost immediately ready to buy Userpilot. To better define it in marketing terms — people who are solution-aware.

And how do we know that? — because we are targeting people who are currently using some of our competitors but are looking for alternatives.

Basically, people who are searching for terms like [Product] alternatives. In a nutshell, those articles are targeting these two keywords:

• Appcues alternatives
• Walkme alternatives

If you pay a little attention, you will also see that we did our homework, and we’re currently ranked first on Google for these keywords:

So we had a bunch of people coming to our blog and reading the article. Although the article was pretty in-depth and actionable, they were not converting the way we wanted.

So, we decided to bump up our conversions by implementing bots.

This is the moment where the story begins.

Mistakes to Avoid While Using Chatbots for Marketing and Sales

It was our first attempt at using bots. I am doing to share how we failed and leaned from it to make it better eventually.

So, here we start. Long story short, our first attempt to capture more customers with chatbots went horrible.

We sat down, targeted everyone who was reading one of those two articles, wrote down the copy, and created a flow.

As a result, from more than 200 visitors, we engaged only 0.7% of them and got 0 emails and customers.

When it comes to the targeting, we wanted to engage with everyone who was currently reading one of those two articles, and who lived in the USA or Western Europe.

Mistake #1 — Not Everyone Who Reads Your Content is Ready to Engage

When it comes to the flow itself, we were asking if the user needs more clarification about the differences between Walkme/Appcues and Userpilot.

They could answer only with Yes, please! and No, I’m good for now.

Mistake #2 — Always Offer a Third (Optional) Option to Your Visitors.

If the visitors say Yes, then we ask them the total number of monthly active users (MAU) they have so that we can qualify them better later on.

Mistake #3 — Don’t Add Friction to Your Chatbot Workflow

Every unnecessary step is a waste of time and a chance to lose your customers.

Once the readers answer that question, we push them to schedule a demo, and there lies our third mistake.

Mistake #4 — Don’t be Too Offensive With Your Visitors

Someone who is still isn’t qualified (like the readers in our case), definitely won’t schedule a demo with you. They need a more significant push and more education. When it comes to the final call-to-action (CTA), it should be contextual to their current user journey.

In a nutshell, here’s how it looked like:

Gladly we realized this soon. Armed with this information, we decided to make a better chatbot workflow.

Reforged Chatbot Workflow That Took Our Conversion Rates from 0 to 5%

Every failure is a new chance for success. Without failures, there’s no room for big achievements.

Once we went through fire with the workflow from above, we learned those four lessons.

Now we were ready to go a step further, improve our chatbot workflow, and convert way more visitors into hot leads.

As a result, we were able to engage with almost 8% of the total number of readers and make 64% of them to give their email addresses.

At that point, we improved our targeting, and we were able to narrow down our focus.

Instead of just targeting people in the USA/West Europe and reading one of those two articles, we also targeted people who spent more than 240 seconds (4 minutes) reading the article.

Why?

In the first workflow, we targeted everyone, no matter how long she remains in the article.

But in the second one, our target audience was people who read the content piece itself. This means that they already went through most of the article, and they already have “opinions” regarding our content.

Achievement #1 — More in-depth targeting allowed us to engage with more relevant people and get attention.

Now we approach the workflow itself.

This time, we changed our game a little bit.

Here’s what we changed:

• Instead of just asking if they need help, we now also offered them a touchable lead magnet. Essentially, our initial message was: Heyo! Are you looking for Appcues alternatives? I have a sheet that could save you hours of research.

Achievement #2 — Offer tangible value straight at the beginning; it will intrigue your visitors.

• Besides Yes and No answers, we also offered them a third, custom option, allowing us to immediately jump into the conversation.

Achievement #3 — Not everyone has the same struggles. Someone needs a different kind of help. Offering “custom” answers allow you to engage in more conversations.

• If the customers answer with Yes, we will ask them for an email to send them our comparison sheet.
• Once the customers give us their email addresses, we send them over a sheet in the inbox and leave the chatbot window open to spark further conversations.

Here’s how this flow looks like:

Overall success — from almost 500 people who read our content, we were able to convert over 25 of them into hot leads, allowing our sales team to close them.

Wrapping Up

Chatbots are an excellent tool for leveraging your content for getting leads, but only if you implement them in the right way.

If the implementation is poor, your visitors won’t engage with you and you will lose a bunch of great opportunities.

So, at the end of the day, based on our personal experience, here’s what we learned:

• Always add value to your content, and try to educate your customers even more.
• Your main goal is to engage with your customers and think about how you can do that, no matter what.
• It would help if you experimented a lot for effectively using chatbots for marketing and sales.
• Understand your visitors and the stage of the user journey they’re currently in. Offer the right CTAs for the best engagement.

This post is written by Aazar Ali Shad.

About Aazar: Aazar is the Head of Growth at Userpilot, and has more than 6 years of SaaS experience. He is currently helping 600+ SaaS companies improve user onboarding and increase product adoption. You can connect with him on Twitter or LinkedIn.

This blog was originally posted on kommunicate.io

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Mistakes to Avoid While Using Chatbots for Marketing and Sales was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Automate Code Reviews on Github using a Chatbot

Creating pull requests and reviewing them are two of the most common tasks in a developer’s daily schedule. Most projects have a common guideline which developers need to follow while creating and reviewing the pull requests.

Now it is hard for developers to remember every guideline while making a pull request and even more difficult for reviewers to ensure that every line of code is compliant to the set guidelines.

We faced the same problem with our projects and solved it by automating the major part of the manual work which is rote. This made the lives of our developers and reviewers a lot easier and they spent more time improving code quality and less on common chores.

In this article, I will describe exactly how we did it, what all aspects of the process we automated and the tools we used for this.

How to automate code reviews on GitHub using a chatbot?

Automate Styling Issues

We don’t want our reviewers asking the contributors to add the corresponding Jira issue number and description whenever they make a pull request. Instead, we have deployed a bot that does all the regular checks and helps contributors to follow project guidelines.

Yes, a bot can verify if the description is present or not by checking the body of the pull request and can comment on a pull request if the description is missing.

We can also add a pull request template to get some of the information related to the pull request. But this approach increases the friction required to create a pull request. When we add rules, we need to make sure that the experience of a new developer should be as frictionless as possible and, at the same time, we need to maintain the code quality.

Now let’s look at the steps required in creating such a bot.

‘Danger’ to the rescue

Danger runs during your CI process, and gives teams the chance to automate common code review chores. This provides another logical step in your build and through this Danger can help lint your rote tasks in daily code review. You can use Danger to codify your teams norms, thus leaving humans to think about harder problems. She does this by leaving messages inside your PRs based on rules that you create with the Ruby scripting language. Over time, as rules are adhered to, the message is amended to reflect the current state of the code review.

Danger is used in all sorts of projects: ruby gems, python apps, Xcode projects, blogs, npm websites and modules.

It will give you an abstraction on top of Github’s API to get details related to a pull request and perform the necessary checks. It is created and maintained by Orta and many other awesome contributors. After installation, you need to create a file named Dangerfile which will contain all the rules. This file should be present in the root of your project.

After adding this file you are all set with the rules. Now you need to run Danger every time someone creates a pull request.

Adding it to your CI workflow

We use Bitrise in our mobile SDK projects. It’s a Continuous Integration and Continuous Delivery service for mobile Apps. If you are using a different CI service then, you can check this guide on how you can integrate Danger with that service. There is a detailed blog post on integrating Danger with Bitrise. I will summarise it in five points:

• Install bundler, create a Gemfile and add the Danger gem to the Gemfile.
• Create a Dangerfile for your project.
• Create a bot user on Github and a Personal Access Token for the bot.
• Then add the generated token on Bitrise.
• Add a script step in the project’s workflow. That’s it! ?

Rules which we can Automate

One of the ways to identify what rules we can automate is by looking at Github’s pull request API response. By comparing the API response with our pull request checklist or guideline, we can get an idea of the possibilities that are there. This is how the response looks like:

• It returns almost all the information you see on GitHub’s pull request webpage like title, description, assignee, reviewers, labels etc.
• There’s one more API to fetch a list of changed files. For each file, it will return the name of the file, the number of additions to the file, the number of deletions to the file.
• We don’t have to use this APIs as we will be using Danger which gives us an easy way to interact with this data.

List of Rules we Automated

When we were adding danger to our repository we looked at our requirements and, some of the other projects which were using Danger. Below are some of the checks that we have in our projects.

Bonus: Do you want to build chatbot without any coding?

Learn more about Chatbot Builder

• Warn if it’s a big PR: We tend to make this mistake of pushing a lot of changes in one PR. Reviewing such PRs is a difficult task. We added a warning which shows up when the number of lines updated in a PR is more than 500.
• Encourage pull request descriptions: Sometimes developers think that description is not necessary or we forget to add. Even though you mentioned the issue number, a brief description always helps and gives a context to the pull request. To see if the description is empty or not we can check the body length:

• Check if the tests are missing: We all know tests are important and, we tend to skip this step. Whenever we do any modification in the source code, we should add tests if possible. So, now it warns if there are any changes in the source code and, the tests folder is not modified which means new tests are missing.
• Update Changelog: Added a new feature or fixed a bug — update the Changelog with the details. We made it mandatory to add a Changelog entry if the change is nontrivial. If the Changelog is not updated and pull request is not marked as trivial, then our CI fails the build. Now, we don’t have to keep a track whether the Changelog was not updated.
• Encourage rebase not merge commits: As the project grows it’s always recommended that we should avoid ‘merge’ commits so that the project has a clean history. We prefer using rebase instead of merging different branches. We can add a check for messages of this format: “Merge branch ‘master’” to avoid the merge commits.

Where to go next

For reference, you can check popular open-source projects like React Native or CocoaPods. I discovered while writing this blog post that projects like React Native and React were also using danger. This shows us how this process of automating the checks has become part of the common pull requests workflow.

This blog was originally posted on kommunicate.io

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Automate Code Reviews on Github Using a Chatbot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Amazon Web Services (AWS) is a globally trusted comprehensive cloud platform adopted by major corporations, government agencies, and growing startups. These organizations are adopting this to innovate and become more agile. Amazon Lex is a part of AWS and is gaining popularity among leading companies throughout the world.

What is Amazon Lex?

Amazon Lex is a service by AWS for building conversational interfaces into any application using voice and text. Lex has quickly become popular among chatbot enthusiasts. Notably, popular Amazon products such as Alexa are powered by the same technology as that of Amazon Lex.

In this blog, I will help you to understand the basic concepts of Lex and explain how to create a bot and integrate it into your website.

Bonus: Don’t know to code? Try our codeless bot builder.

Understanding AWS Lex

To understand the Lex platform, let’s consider the conversation below between a flight booking chatbot and a user:

User: Book me a flight to New York from Boston.

Bot: Sure! Which date do you want to fly?

User: I am planning to fly on the 10th of October.

Bot: Ok. Do you want me to book a return ticket as well?

User: Yes.

Bot: Please help me with the date of your return.

User: It is 15th October.

Bot — Alright. Searching for a flight for the 10th of October and returning on the 15th october. Here are the results.

Here, the bot collects the basic information from the user, processes it, and displays the search results to the user. To create this conversation flow using Lex, let’s first discuss the terminology and concepts used in the Lex console. You can create a free account in Amazon Lex to build your chatbot.

SIGNUP NOW

Jump To-

• Intents
• Sample Utterance
• Fulfilling the intent
• Integrating into the website
• Video Tutorial

Intents

An intent represents an action that the user wants to perform. When an end-user interacts with the bot, the user’s query is matched to the best intent available in the bot. Every intent has a set of Sample utterances. You can use the Sample utterance to match the user’s query with an intent. You can configure a bot to support multiple intents. These combined intents can handle a complete conversation.

Sample utterance

This is the collection of possible expressions that an end-user might say. These are basically phrases that mean the same as our defined intent. Let’s go back to our flight booking bot. There are multiple ways to ask a bot to book your flight:

Book me a flight to New York.

Find me a plane to New York.

I want to go on a trip to New York.

Hey, let’s go to New York this winter.

You can provide these expressions while configuring the intent. You can use these expressions to build a model to categorize the user’s queries.

When a user uses any of these sentences, then, this model identifies the intent. If multiple intents are matched, the best match is triggered. Suppose that none of the intents are matched, you can set a message in the Error Handling section to handle such scenarios. If the intent is matched, Lex will check how to fulfill the intent.

Fulfilling the intent

Fulfillments are the responses that the bot sends when an intent is triggered. There are two ways to define fulfillment:

1. Create a Lambda function to fulfill the intent: Amazon recommends creating a Lambda function which will be called when the intent is triggered. Lex will send all details (intent detail and slots), and the Lambda function will perform the action and decide which response to be sent to the user.
2. Lex returns the information(intent detail and slots) to the client application to do the necessary fulfillment.

Slots

Slots are the parameters that are defined as part of the intent configuration. The value of a slot is extracted dynamically at runtime from the user’s query. Slots contain structured data that can easily be used to perform some logic or generate responses.

Each slot has a type that dictates the type of value the slot would contain. Lex provides some inbuilt intents and slot types to extract the basic information i.e, city names, dates, some measurement units, etc.

For example, when the user says:

Book me a flight to New York.

You can configure a slot so that your bot can detect the parameter “DestinationCity” and populate it with the value “New York” or whichever city is present in the user’s query.

Now, let’s identify the other possible slots in the above conversation.

Here is how your Amazon Lex console looks with this configuration.

You can configure fulfillment after finishing the slot configuration.

Configuring Lambda functions as fulfillment

When a user provides all of the slot data required to fulfill the intent, your Lambda function will be invoked if enabled. Lex sends data to the lambda function in a specific format mentioned here. The Lambda function thus performs the business logic.

In our flight booking scenario, our Lambda function calls the flight search APIs to get the result for the user’s query. Then it returns the result to Lex in the aforementioned format. Further, Lex forwards this response to the end user.

This was all about learning the basic functioning of the Amazon Lex platform. Let’s now jump into testing your bot and integrating it into a website.

Build test and publish the bot

After finishing all the configurations, you need to build your bot before you start testing. Lex provides a test window where you can test your bot and see how the bot replies to the user’s queries.

Please note that if you are changing any intent configuration, you must build it again to make the latest changes visible in the test window.

Publishing the bot creates a new version of it. It allows you to create a different version of your bots, and you can control the version which your application use.

Integrating Amazon Lex bot into a website

Lex has inbuilt support to integrate bots with some platforms such as Facebook, Kik, Slack, etc. If you want to integrate your bot with your website or mobile apps, you have two ways:

1. Using AWS SDK: AWS SDK provides APIs to send queries to the bot. This requires a lot of programming knowledge, development, and maintenance efforts.
2. Using Kommunicate, which provides a codeless integration with Amazon Lex.

Integrate Amazon Lex using Kommunicate

Kommunicate is a bot+human hybrid customer support software that provides code-less integration with chatbot builder platforms such as Amazon Lex, Google Dialogflow, etc. Once integrated, your users can chat with your bot using a beautiful and customizable chat widget.

Your Lex bot can be integrated into your website in a few simple steps:

Here is the quick video.

Step 1: Create a free Kommunicate account

You can create a free account in Kommunicate. Head to the signup section to start.

Step 2: Connect your Amazon Lex bot

Post signup, navigate to the bot integration section and select the Amazon Lex platform. Kommunicate requires the below detail to query your bot on your behalf.

You just need to fill in a few details to connect your Lex bot. You can get these details in your AWS Management Console -> Security credentials section.

1. Access key ID & Secret access key: Access key ID and secret access key are required to sign requests sent to your Lex bot. To get your access key, sign into your AWS console as an IAM user having permission to access Lex API. Locate your user name in the upper-right section of the navigation bar. From the drop-down menu, select My Security Credentials. Then create an access key in the Access keys for CLI, SDK, & API access sections. You can find more detail in this blog.
2. Bot name in Lex platform: This is the same name you entered while creating the bot in the Lex platform. If you are not aware of it, you can also get it from the bot list on the Lex home page.
3. Bot alias: A bot alias is a pointer to a specific bot version. The alias is exposed to client applications instead of the version. If you publish a new version of the bot and want kommunicate to connect to the new version, you can simply point the alias to the new version from Lex console without changing anything on Kommunicate Dashboard.
4. Region: AWS region where your Lex service is running. You can find your region in the top-right corner, following the user name in the AWS console.

Once you have the above information follow the below steps, click Save and Proceed.

Step 3: Give your bot an identity

You can give your bot a name and a profile picture. The name and the profile picture will be visible to your users while interacting with your bot. Give your bot a name. This name will be visible to your users who interact with your bot. Click Save and Proceed.

Step 4: Enable/Disable human handoff

Your bot is as smart as you can make it. But at times, it may fail to understand a user’s questions. In that case, you can trigger a chatbot to human handoff. This helps you make the overall user experience better and handle edge cases.

Choose whether to enable or disable this feature and click on Finish bot integration setup.

Step 5: Assign all the incoming conversations to your Lex bot

To let your user chat with the new bot, you need to assign all the conversations to the bot. After finishing the bot setup, click on Let this bot handle all the incoming conversations. Now, all new conversations initiated after the integration will be assigned to this bot, and your bot will start answering them.

You can also enable conversation assignments from the Conversation Rules section.

Step 6: Install the Kommunicate chat widget on your website

The final step is installing the Kommunicate chat widget on your website so your website user can chat with your bot. Copy the installation script from the Install section and paste it into your website. Here are the detailed instructions to install the same. This is how the chat widget looks on a website

Publish your website, and your bot will be ready to chat with your users. Hurray! That was easy, isn’t it?

Suggested Reads: Add Rich Message Button Response in Amazon Lex

Organizations using Amazon Lex successfully

Here are a couple of organizations that have successfully implemented Lex into their business processes to yield significant results.

TransUnion

TransUnion is a worldwide information and insights enterprise that helps businesses transact with their customers using data such as credit scores, credit checks, and credit reports.

Prior to Amazon Lex, this was handled by TransUnion’s contact center, which was high in cost, and also inefficient. TransUnion customers’ time went into navigating the IVR system before they could talk to an agent. By implementing Amazon Lex which is a part of Amazon Connect, to automate the IVR. This change has significantly reduced customer wait times in the contact queue. The customer spends 18 seconds in the IVR as opposed to the previous 2 minutes, with a transfer rate cut by half. This has led to a 40% reduction in the annual costs of the contact center.

ROYBI Inc.

ROYBI Inc. is an enterprise that has its objectives in changing early-childhood education for the better. They mix robotics with AI to make a fun, interactive learning experience that offers more benefits than traditional teaching methods.

This formative period in a child’s life is where they absorb the most information and build a foundation that will carry them through life.” AWS and ROYBI have built a customized platform using the computer vision product Amazon Rekognition to enable learning between children and ROYBI robots. This is where Amazon Lex is the conversational interface that comprehends and engages the child to deliver a better multi-modal learning experience.

Wrapping up the Amazon Lex tutorial

Amazon Lex is a great tool that can be used in conjunction with other suites in AWS to create unique products and services that meet customer needs. You can use Kommunicate to integrate your Amazon Lex chatbot (through this Amazon Lex tutorial) to serve your unique business objectives and serve your customers well.

This blog was originally posted on kommunicate.io

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Create Chatbot Using Amazon Lex (Tutorial) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Product adoption, Engagement and Retention are three key ingredients in growing a business, especially one through the Product-Led Growth Model. If you want to succeed, you need to score high in each of these departments. Chatbots can be pivotal in scoring here, and we are going to tell you how.

In this blog, you will learn:

1. What is Product-Led Growth — Difference between PLG and other Growth Strategies
2. Product Adoption, Engagement and Retention — Why do they matter in a PLG
3. Product Adoption — What is it and how chatbots can help.
4. Chatbots and the role they play in Product Engagement.
5. Product Retention — Essentials and the role of chatbots in it

What is Product- Led Growth?

Product-Led Growth, or PLG, is a relatively new strategy that B2B and SaaS companies use to achieve growth. PLG is when a company’s product is the primary method for customer acquisition, retention and expansion.

PLG is based on the foundational principle that a product is so good, that there is not a lot of requirement of sales or marketing to drive its growth.

Companies like Zoom and Dropbox have been using the PLG approach to phenomenal success.

So, why Product-Led Growth? What’s the Difference between Product-led growth and Sales led and Marketing-led growth?

We have scratched the surface of what Product-led growth is. So Why PLG?

With PLG, your product will drive a majority of user acquisition, which means the cost spent on traditional marketing or sales is reduced. Also, if you offer a good product experience, your customers are more likely to recommend it to their friends and family, which gives a further boost to your brand.

Now, let us see the difference between Product-led growth, compared to the traditional sales led and marketing led growth.

Product-led growth: The focus here is on building a great product that the users will fall in love with. The product will drive customer adoption. These companies may also employ sales and marketing functions, but they may not be the primary driver of growth.

When it comes to Sales Led and Marketing Led Growth strategies, the acquisition of new customers is through sales or marketing efforts respectively. These companies may have dedicated sales or marketing teams, who drive growth.

Product Adoption, Engagement and Retention — Why do they matter in a PLG?

In any business that uses the PLG model, product adoption, engagement and retention are all key metrics that you need to track.

Product adoption is getting users to start using your product. Making them keep using the product on a regular basis is product engagement. Product retention is when they keep using your product over a set period of time.

Sustainability and Scalability are the two hallmarks of a successful growth engine, especially in a PLG company. When these companies focus on Product Adoption, Engagement and Retention, they are addressing these very 2 pillars.

Product adoption, engagement and retention are all interrelated. For instance, if a user does not adopt your product, it is not possible for them to engage with it. Similarly, if you do not keep your customers engaged, there is no way you can retain them for the long run.

Technology comes to the rescue when it comes to driving these 3 metrics, specifically chatbots. Chatbots can drive product adoption by offering personalized recommendations and answering questions related to onboarding. Chatbots can also encourage users to try the product before they buy.

When it comes to product engagement, chatbots can help in real — time assistance and troubleshooting. Chatbots can also gamify the process and also offer rewards to existing customers. And finally, chatbots can help retain existing customers by offering them exclusive content and up-selling and cross-selling your services.

Let’s explore all of these aspects in detail.

Product Adoption — What is it and Why it matters.

When you build a product or a service and the customer uses it for its intended purpose, then this stage of the product’s cycle is called product adoption. Product adoption can also be when a certain percentage of first-time users perform an intended action.

We have talked about awareness before, so how different is it from adoption? Well, for starters, if people do not know about your product or service ( say, an eCommerce website) and then you make them aware, that is the end of the awareness stage.

But when these website visitors turn into paid users, then that is the completion of an intended action. That stage is called product adoption.

Product adoption helps you answer some very important questions, such as:

1. How sticky is my product to new users?
2. Which channels should I spend a majority of my advertising budget on?
3. How are your retention rate and the speed of adoption related?

Metrics such as conversion rate, Time to value and Frequency of purchases all help you drive product adoption. So where do chatbots come into the picture?

How can chatbots drive Product Adoption

Chatbots can be crucial in driving Product Adoption, and some of the ways chatbots can drive it are:

1. Personalized recommendations:

Chatbots can collect user data easily, which companies can then use to make informed decisions. For example, if you are an eCommerce website, a chatbot can recommend products when a visitor is browsing a particular category of products or when they are about to abandon their cart.

Chatbots can also collect data about what users interests, including the articles that they have read or products they have viewed. With this data, your company can create personalized recommendations for products or services that the user is most likely to purchase.

Domino’s did a great job when they implemented an eCommerce chatbot for their Australian market. The chatbot was able to collect orders online. The chatbot was able to re-order a member’s previous orders, and track a user’s order and estimated time of delivery.

2. On boarding assistance.

Chatbots can provide swift responses to customers who may have queries regarding a product, the major features and how to use it. Once the users have this information handy, they can get up and running quickly.

Chatbots can act as virtual guides, walking the users through product features and helping users troubleshoot the problems. For instance, in a clothing eCommerce store, a chatbot can show all the brands of clothes available with their price range, and a user can choose right from within the bot.

All this without navigating through different pages, now that is called enhancing user experience!!

The 24/7 availability of chatbots means that user onboarding can be done at any time of the day or night. This is especially helpful for customers who live in a different country or time zone, or work odd hours.

3. Encouraging Trial and Purchase

In case a customer is in two minds with regards to buying a product, a chatbot can offer a free trial of the product, thereby increasing chances of conversion. “Try before you buy” is an adage that we use in the SaaS industry, and you can use a chatbot to initiate a free trial.

Additionally, chatbots can offer coupons and discounts to customers who are planning to make a purchase. Making the product more affordable and encouraging customers to invest in you, means your implementation of the chatbot was a success.

Many times, chatbots act as an additional FAQ base, where customers can ask common questions without having to navigate through the website to find the FAQ section. With Kommunicate’s Document scanner feature, creating an FAQ chatbot becomes even more simpler.

Finally, chatbots can give social proof to users by displaying testimonials of happy customers. This can help build credibility and trust with the customer, who will then most likely make a purchase.

Product Engagement — What is it?

We have written in detail about product engagement here, but a quick refresher never hurts anyone. Product engagement is a measure of how your intended users interact with your product.

Product engagement helps answer critical questions such as:

1. How often do my customers use my product?
2. Where are they usually stuck while using your product?
3. Which part of my product is the most popular / the users find the most value in?

Metrics such as User Retention Rate, Active User Count, Session length, etc. will all tell you how effective your product engagement strategy is.

So now, let us see how chatbots play a pivotal role in driving product engagement.

Chatbots and the role they play in Product Engagement

1. Real-time assistance and troubleshooting

Chatbots can provide answers to questions about the product, about the various features that the product has to offer, and how to use it. Customers don’t want to wait for days to get answers to their queries, and a chatbot will be there for them even when a customer support agent isn’t.

Troubleshooting of queries is another aspect that chatbots can help in. Chatbots can provide users with tips and tricks on how to use the product, along with redirecting them to specific sections of the website which contain documentation.

2. Offering rewards

We have seen how chatbots can be used to drive personalization at scale. Another area that we find the application of chatbots is in offering personalized rewards, which is based on their interests and behavior. Relevant rewards means that there is a greater chance of the user taking action.

Engaging customers can be taken to a whole new level using chatbots, when you use them to offer rewards in real-time. This can be when a user has completed a certain task or achieved a goal. This comes under the broad category of gamification, which we will cover later.

3. Interactive UX and Gamification

Here are a few ways how chatbots can help in enhancing the User experience during a Digital User Journey, which in turn will help foster product engagement.

1. Progress tracking: Chatbots can keep users motivated by highlighting the major achievements and milestones.
2. Gripping interactions: Chatbots mimic human behavior, and can guide users through a product in a conversational manner.
3. Gamification: “ Learning through play,” the process of making the learning about the product enjoyable through quizzes, can be done using chatbots.
4. Continuous engagement: When people interact with your chatbot regularly, it piques their interest. This helps boost user engagement and reinforces their journey.

Product Retention — What is it and why it matters

When your existing customers continue to buy from your business, then your product retention strategy is working. Some people call it “Customer loyalty,” as well.

You can measure product retention when you answer questions such as:

1. What share of your customers buy more than once from you?
2. What is the average value of a repeat customer’s order?
3. How often do they purchase from you?

Retention matters because a business with a large customer retention rate does not lose a lot of them to the competition. Nor do they get obsolete when a radical new technology is introduced in their space.

Let us now see how chatbots can help drive Product retention.

Product Retention — Essentials and the role of Chatbots

Retention is an important step in the Digital User Journey, and companies that use a PLG model for growth usually focus a lot of their energies in this stage.

Here are a few ways chatbots are helping companies in the retention phase.

1. Offering exclusive content:

Chatbots can offer existing customers early bird access to content, giving them a feeling of exclusivity. For instance, on our own Kommunicate blog, we have deployed a separate chatbot called Adam.

Adam gives visitors to the blog access to new blogs, and also allows them to search various topics that interests them. This helps us retain visitors to the blog, and, after implementing Adam, we have seen a 32% bump in readership.

2. Up-sell and cross-sell products

You can use chatbots to up-sell and cross-sell products if you are running an online store. Getting a visitor to your website is very difficult, as any website owner knows. In fact, the average visitor only spends only about 54 seconds on your website, according to this report from ContentSquare.

To make the most of your website visitors, it is important to keep offering them a collection of products that they are most likely to purchase. This is where chatbots can step in. Chatbots can collect important information about users and then use that information to suggest products that they are most likely to buy.

Up-selling and Cross- selling can thus enhance your business and help you achieve your business objectives through unconventional channels.

3. Garnering positive reviews

Your website visitors are humans too, and every now and then, they would like to leave an honest feedback about their experience. Chatbots to the rescue — again. Before a customer leaves your website, you can prompt them to leave a review about your product, right within the chatbot.

This will give your customers a reason to be loyal to your brand. You can use the positive feedback as testimonials on your website, which can act as powerful social proof.

Major takeaways:

Thanks for reading this far. Here are the key takeaways from this blog post:

1. With PLG, your product will drive a majority of user acquisition, which means the cost spent on traditional marketing or sales is less.
2. Understanding the Digital User Journey is critical to create a product that users will fall in love with, while also meeting their needs.
3. Deploying chatbots to onboard new customers will lead to increased user adoption and satisfaction.

At Kommunicate, we envision a world-beating customer support solution to empower the new era of customer support. We would love to have you on board to have a first-hand experience of Kommunicate. You can signup here and start delighting your customers right away.

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Chatbots Propelling Adoption, Engagement and Retention in Product-Led Growth was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.