How to create a Swiggy-like chatbot using Nected?

Creating a customer-facing chatbot can revolutionize the way businesses interact with their customers. Companies like Swiggy and Zomato leverage these chatbots to enhance user engagement, streamline customer service, and drive sales. But why should you consider implementing a customer-facing chatbot for your business?

A well-designed chatbot can provide instant responses, handle multiple inquiries simultaneously, and operate 24/7, offering a seamless customer experience. This not only improves customer satisfaction but also reduces operational costs. By automating routine tasks, chatbots free up human agents to focus on more complex issues, boosting overall efficiency.

Moreover, chatbots can collect valuable data on customer preferences and behavior, offering insights that can inform marketing strategies and product development. This data-driven approach ensures that your business stays ahead of the competition by continually adapting to customer needs.

In this blog, we will guide you through the process of creating a Swiggy-like chatbot using Nected, covering everything from designing the chatbot flow to building the logic behind it. Whether you're a developer or a business owner looking to enhance customer interaction, this comprehensive guide will provide you with the tools and knowledge needed to succeed.

How are Companies Benefiting from Their Customer-Facing Chatbots?

Swiggy and Zomato have set benchmarks in the food delivery industry by effectively utilizing customer-facing chatbots. These chatbots are not just tools for answering queries but powerful assets that drive significant business benefits through a data-driven approach.

1. Enhanced Customer Engagement: Customer-facing chatbots offer real-time interaction, ensuring users receive instant responses to their queries. According to industry reports, Swiggy's chatbot handles over 60% of customer interactions without human intervention, significantly reducing response times and improving user satisfaction. Zomato's chatbot has contributed to a 40% increase in customer retention by providing timely order updates and personalized recommendations.
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2. Operational Efficiency: By automating routine customer service tasks, chatbots help Swiggy and Zomato reduce the workload on human agents. This automation has resulted in a 30% reduction in operational costs for Swiggy. Zomato's chatbot system has enabled the company to manage peak order times more efficiently, leading to a 25% decrease in average resolution time for customer issues.
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3. Data-Driven Insights: One of the most significant advantages of using chatbots is the ability to collect and analyze customer data. Swiggy's chatbot collects data from over 1 million interactions per month, providing insights into customer preferences and behavior. This data has enabled Swiggy to increase its order conversion rate by 15% through targeted marketing strategies. Zomato utilizes chatbot data to analyze trends and feedback, leading to a 20% improvement in menu optimization and service offerings.
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4. Personalization: Chatbots enable a high level of personalization by using the data they collect to tailor interactions. Swiggy's chatbot uses machine learning algorithms to recommend dishes based on past orders, leading to a 12% increase in average order value. Zomato's chatbot offers personalized discounts and suggestions, which have contributed to a 10% rise in repeat orders.
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5. Scalability: As businesses grow, the volume of customer interactions increases. Chatbots provide a scalable solution that can handle a large number of interactions simultaneously without compromising on service quality. During peak hours, Swiggy's chatbot manages up to 70% of interactions, ensuring that customers receive timely assistance. Zomato's chatbot has been instrumental in scaling customer support, maintaining service quality during high-demand periods, and supporting its expansion into new markets.

In summary, customer-facing chatbots offer Swiggy and Zomato enhanced customer engagement, operational efficiency, valuable data insights, personalized experiences, and scalability. These benefits make chatbots an indispensable part of their customer service strategy, driving both customer satisfaction and business growth.

Designing the Chatbot Flow

Designing an effective chatbot flow is crucial for providing a seamless customer experience. The Swiggy chatbot flow is designed to guide users through various stages of their interaction, from initial queries to order completion. Below, we outline the key components of the Swiggy chatbot flow, which we will replicate using Nected.

1. Greetings and Initial Query:
   • The chatbot starts with a friendly greeting and asks the user how it can assist them today.
   • Example: "Hi! Welcome to Swiggy. How can I help you today? Are you looking to place an order, track your delivery, or need help with something else?"
2. Identifying the User’s Intent:
   • The chatbot uses natural language processing (NLP) to understand the user's intent.
   • Common intents include placing an order, tracking an order, customer support, and account management.
   • Example: If a user wants to track their order, the chatbot will ask for the order ID or registered phone number.
3. Providing Relevant Options:
   • Based on the user's intent, the chatbot provides relevant options.
   • For placing an order, it might ask for cuisine preferences or recommend popular dishes.
   • For tracking an order, it will provide the current status and estimated delivery time.
   • Example: "Sure, I can help you with that. Are you in the mood for Indian, Chinese, or Italian today?"
4. Collecting Necessary Information:
   • The chatbot collects additional information required to complete the task.
   • For orders, it might ask for delivery address, payment method, and any special instructions.
   • Example: "Great choice! Please provide your delivery address and select a payment method."
5. Confirmation and Summary:
   • The chatbot confirms the details provided by the user and summarizes the information.
   • It ensures that all details are correct before proceeding.
   • Example: "Your order for Chicken Biryani has been placed. It will be delivered to 123 Main Street, and you will pay via credit card. Is everything correct?"
6. Order Completion and Feedback:
   • Once the order is confirmed, the chatbot provides an order confirmation number and estimated delivery time.
   • It also asks for user feedback to improve future interactions.
   • Example: "Thank you for your order! Your order number is #12345, and it will be delivered in approximately 30 minutes. Can you please rate your experience with us today?"

By following this structured flow, the Swiggy chatbot ensures a smooth and efficient interaction, providing users with a reliable and pleasant experience.

Challenges Companies Face While Building a Swiggy-Like Chatbot

Building a chatbot as sophisticated as Swiggy's requires tackling multiple challenges, ranging from managing complex conversational logic to ensuring seamless scalability. These challenges often arise due to the intricacies of integrating real-time data, maintaining flexibility, and achieving high performance. Here’s a breakdown of the common hurdles and how low-code/no-code platforms like Nected can help overcome them.

1. Complexity in Conversational Logic: Chatbots need to handle diverse user queries such as order tracking, placing new orders, and resolving issues. Managing such complex conversational flows with traditional hard-coded solutions can become overwhelming. According to industry reports, over 50% of chatbot projects fail due to poorly managed conversational logic.
   Nected’s visual workflow editor enables businesses to map out chatbot flows intuitively. Instead of coding every interaction, users can define and update conversation paths visually, reducing complexity and improving logic clarity.
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2. Integration with Real-Time Systems: A Swiggy-like chatbot relies on real-time data, such as order statuses, delivery timelines, and inventory updates. Integrating multiple systems and ensuring consistent data flow can take months of development effort.
   With seamless API and database integrations, Nected allows businesses to fetch and process real-time data effortlessly. For instance, attributes like order_id or delivery_status can be directly linked to external systems, reducing integration time by up to 70%.
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3. High Development Costs and Time: Developing a chatbot backend from scratch requires significant resources and expertise. Studies indicate that custom chatbot solutions can take up to 4–6 months to build, with development costs often exceeding $100,000.
   Nected’s low-code platform drastically reduces development time by up to 80%. Users can design chatbot workflows and decision tables without writing extensive code, enabling faster deployment and lower costs.
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4. Scalability Issues with Traditional Solutions: As the user base grows, chatbots need to handle a higher volume of interactions without compromising on performance. Scaling hard-coded solutions often involves reengineering the entire backend, which can be expensive and time-consuming.
   Nected is built for scalability, allowing businesses to add new intents, workflows, or integrations effortlessly. Its architecture ensures smooth performance even during peak loads, such as high-demand periods or festive seasons.
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5. Adapting to Changing Requirements: Chatbot logic must evolve to handle new features, services, or customer expectations. Updating traditional systems involves lengthy code changes and deployments, leading to delays and lost opportunities.
   With Nected’s Decision Tables and workflows, updates can be made instantly without requiring redeployment. Businesses can modify rules or add new functionalities dynamically, ensuring they remain agile and responsive.
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6. Dependence on Developers for Maintenance: Hard-coded chatbot systems require continuous developer involvement for updates, bug fixes, and optimization. This dependency can slow down innovation and strain IT resources.
   Nected’s user-friendly interface empowers non-technical users to manage and maintain chatbots independently. This reduces reliance on developers, freeing up technical teams to focus on strategic initiatives.

By addressing these challenges with a platform like Nected, companies can build Swiggy-like chatbots that are not only efficient and scalable but also flexible enough to adapt to evolving customer needs. With reduced development time, seamless integrations, and easy scalability, Nected empowers businesses to deliver exceptional chatbot experiences with minimal effort.

Building the Frontend:

Building the front end for your Swiggy-like chatbot focuses on creating an interface where users can interact smoothly with the chatbot. Although the exact implementation depends on your chosen technology stack, here’s a simplified approach.

Start with a basic chat interface design. This includes a text input field at the bottom for users to type their messages and a scrollable message display area above to show the conversation history. You can achieve this using any frontend framework like React, Angular, or Vue.js, or even plain HTML, CSS, and JavaScript.

When the user types a message and presses enter, capture this input and send it to the backend. For instance, you can use an event listener in JavaScript to handle the 'Enter' key press. The backend processes the input and returns a response, which you then display in the conversation history area.

Here’s a brief example of how this can be structured:

<div class="chat-container">
    <div class="chat-history" id="chatHistory"></div>
    <input type="text" id="userInput" placeholder="Type your message here..." onkeypress="sendMessage(event)">
</div>

<script>
    function sendMessage(event) {
        if (event.key === 'Enter') {
            let userInput = event.target.value;
            // Code to send userInput to backend and display response
        }
    }
</script>

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This example highlights the essential components: capturing user input and displaying chatbot responses. Depending on your requirements, you can enhance this with features like message timestamps, user avatars, and more.

By focusing on these basics, you create a responsive and user-friendly frontend that supports effective interaction with your chatbot. Now, let’s see how you can build the backend logic for this chatbot in the next section.

Building the Backend Part:

Developing the backend for a Swiggy-like chatbot involves creating a system that processes user inputs, identifies their intent, and generates appropriate responses. You can either opt for a traditional hard-coded method or utilize a more efficient and scalable solution like Nected. Below, we explore both approaches to highlight their strengths and limitations.

1. Using the Hard-Coded Method

The hard-coded method requires manually defining every aspect of the chatbot's conversational flow, including input handling and response generation. This approach is built entirely using a programming language such as Python, JavaScript, or Node.js. While it offers full control, it often proves resource-intensive and challenging to maintain, especially for chatbots with complex flows.

Consider implementing a basic conversational flow for order tracking. Here’s how the hard-coded backend logic might look:

from flask import Flask, request, jsonify

app = Flask(__name__)

# Simulated database for orders
orders_db = {
    "12345": {"status": "Out for delivery", "eta": "30 minutes"},
    "67890": {"status": "Order confirmed", "eta": "45 minutes"}
}

def get_order_status(order_id):
    if order_id in orders_db:
        return f"Your order with ID {order_id} is {orders_db[order_id]['status']} and will arrive in {orders_db[order_id]['eta']}."
    else:
        return "Order ID not found. Please check and try again."

def handle_user_input(user_message):
    # Define intents and responses
    if "track order" in user_message.lower():
        return "Please provide your order ID to track the order."
    elif "place order" in user_message.lower():
        return "What would you like to order today? Please provide your cuisine preference."
    elif "customer support" in user_message.lower():
        return "How can we assist you today? Please specify your issue."
    else:
        return "I'm sorry, I didn't understand that. Can you rephrase?"

@app.route('/chatbot', methods=['POST'])
def chatbot():
    user_message = request.json.get('message')
    context = request.json.get('context', {})
    
    # Handle user input and maintain context
    if "track order" in context.get('intent', ''):
        order_id = user_message.strip()
        return jsonify({"response": get_order_status(order_id)})
    
    response = handle_user_input(user_message)
    context['intent'] = "track order" if "track order" in user_message.lower() else ""
    return jsonify({"response": response, "context": context})

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

Challenges of the Hard-Coded Method:

• Static and Inflexible: Expanding the chatbot's capabilities (e.g., adding a new intent like "cancel order") requires significant code changes and redeployment.
• Time-Consuming Maintenance: Adjusting responses or refining logic involves modifying and testing large portions of code.
• Error-Prone: Managing multiple nested conditions for complex flows increases the likelihood of bugs.
• Lack of Scalability: Adding advanced features such as personalized responses or API integrations becomes increasingly complex.

While this approach may work for simple chatbots, it becomes inefficient and cumbersome as complexity grows.

Hard Code vs. Nected

Using Nected, you can define the chatbot's conversational flow and backend logic through a visual editor. This involves setting up triggers, defining rules, and integrating with external systems and APIs. Nected’s platform streamlines these processes, enabling rapid development and easy maintenance of complex chatbot workflows.

Creating the backend logic using Nected:

Creating the backend logic for your Swiggy-like chatbot using Nected involves a series of steps that utilize Nected's visual workflow editor. This process allows you to efficiently define how the chatbot processes and responds to various user inputs. Here's a comprehensive guide to setting up this backend logic.

By the way, this is how the flow looks like for creating the chatbot:

Step 1: Define the Conversational Flow

Start by mapping out the chatbot's conversational flow, outlining the various interactions users will have with the chatbot, including greetings, order placement, order tracking, customer support, and account management.

1. Create a New Flow:
   • Log in to your Nected account and navigate to the dashboard.
   • Click on "Create New Flow" to start defining your chatbot's conversational logic.

Step 2: Set Up Initial Triggers

Define the initial trigger for the chatbot, such as a user message. This trigger node captures incoming messages from users and initiates the chatbot workflow.

1. Add Trigger Node:
   • In the Nected visual editor, add a trigger node. Configure it to listen for user inputs.
   • Set up conditions for the trigger, such as capturing any text input from the user.

Step 3: Fetch User Data with REST API Node

Fetching user data using a REST API node to personalize responses and understand user context.

1. Add REST API Node:
   • After the trigger node, add a REST API node to fetch user data.
   • Configure the REST API node with the endpoint that retrieves user data, such as order history or profile details.
   • Ensure the API call includes necessary authentication and parameters to fetch relevant user information.

Step 4: Create the Switch Block

The switch block is crucial for routing the workflow based on specific conditions, such as the type of user query. This allows the chatbot to handle different user queries efficiently.

1. Add Switch Block:
   • Add a switch block after the REST API node. This block will evaluate the user data fetched from the API and determine the next steps.
   • Define multiple conditions within the switch block to handle different types of user queries.
2. Configuring the Switch Block:
   • Inside the switch block, create conditions for various user intents like "Track my order," "Place an order," "Customer support," and "Account management."
   • Each condition should route to a different branch of the workflow, guiding the chatbot to respond appropriately to the user query.

Step 5: Nested Switch Blocks for Detailed Query Handling

For more complex queries, create nested switch blocks within the initial switch block. This provides granular control over the chatbot's decision-making process.

1. Track Order Workflow:
   • Condition: If the query is "Track my order," route to a nested switch block to handle different order statuses.
   • Nested Switch Block: Create further conditions to check the order status (e.g., "Order placed," "Order shipped," "Order delivered").
   • Responses: Provide relevant responses based on the order status, such as estimated delivery time, tracking information, or delivery confirmation.
2. Place Order Workflow:
   • Condition: If the query is "Place an order," guide the user through the order placement process.
   • Workflow Steps: Ask for cuisine preferences, collect order details, choose payment methods, confirm order details, and provide order confirmation and ETA.
3. Customer Support Workflow:
   • Condition: If the query is "Customer support," present various support options.
   • Nested Switch Block: Create further conditions to handle specific support queries like "Report an issue" or "General inquiry."
   • Responses: Collect issue details, provide resolutions, or escalate to human agents as needed.
4. Account Management Workflow:
   • Condition: If the query is "Account management," route to specific account management tasks.
   • Workflow Steps: Allow users to update account details or view order history, and confirm changes or display requested information.

Step 6: Integrate with External Systems

To provide a seamless experience, integrate your chatbot with external systems and databases.

1. Database Integration:
   • Use Nected's database integration features to connect your chatbot to your order management system.
   • This allows the chatbot to fetch and update order details in real-time, providing accurate information to users.
2. API Integrations:
   • Integrate third-party APIs for functionalities such as payment processing, location services, and customer feedback collection.
   • Add API call nodes within your workflows to interact with these external services.

Step 7: Testing and Deployment

Before going live, thoroughly test your chatbot to ensure all workflows and integrations function correctly.

1. Simulate Conversations:
   • Use Nected's testing environment to simulate user interactions and validate the chatbot's responses.
   • Test various scenarios to ensure the chatbot handles all possible user inputs accurately.
2. Refine and Optimize:
   • Based on testing feedback, refine the conversational flows and make necessary adjustments.
   • Optimize the workflows for better performance and user experience.
3. Deploy:
   • Once testing is complete, deploy the chatbot to your desired platform, whether it’s a website, mobile app, or messaging service.
   • Monitor the chatbot's performance and user interactions to continually improve its functionality.

By following these steps, you can create a robust and efficient backend logic for your Swiggy-like chatbot using Nected. This approach not only simplifies the development process but also ensures that your chatbot is scalable, maintainable, and capable of handling complex user interactions.

Conclusion

So, in this guide, we have explored the process of creating a Swiggy-like chatbot using Nected. From setting up your Nected account and creating a new workflow to defining the chatbot's conversational flow and integrating the API within the code, we have covered all the essential steps.By following these steps, you can create a sophisticated and engaging chatbot that replicates the success of Swiggy's renowned conversational assistant. Remember to thoroughly test and optimize your chatbot to ensure it meets user expectations and provides an exceptional conversational experience.For further details and to dive deeper into the technical aspects, refer to the provided code file and the Nected documentation.

FAQs:

1. What is a Swiggy-like chatbot, and why is it useful?

A Swiggy-like chatbot is a conversational AI tool designed to automate customer interactions for food delivery or similar services. It can handle tasks like order placement, tracking, and customer support, improving user experience by providing instant responses and reducing operational costs.

2. How does Nected simplify chatbot development?

Nected offers a low-code platform with tools like Decision Tables and workflows, allowing users to visually define chatbot logic without extensive coding. It integrates seamlessly with APIs and databases, enabling businesses to build scalable and efficient chatbots quickly.

3. Can Nected support advanced chatbot features like NLP?

Yes, Nected can integrate with external NLP services via APIs, enhancing the chatbot's ability to understand and respond to user inputs contextually. This makes it suitable for handling complex queries and providing personalized experiences.

4. How does Nected compare to hard-coded chatbot solutions?

Nected reduces development time by up to 70% and simplifies maintenance with its intuitive interface. Unlike hard-coded solutions that require extensive updates for changes, Nected allows non-technical users to manage and update chatbot workflows effortlessly.

5. Can I scale a chatbot built with Nected as my business grows?

Absolutely. Nected’s platform is designed to handle high volumes of interactions and supports advanced workflows for scalability. You can easily add new features, intents, and integrations as your business needs evolve.