
All Building AI Chatbots for E-Commerce to Enhance Your Sales Experience
May 31, 202515 min read
AI chatbots are rapidly gaining popularity due to their ability to automate customer interactions and improve customer service. Companies use them to reduce response times, minimize the workload of their employees, and automate repetitive tasks. As a result, human agents have more time to focus on important tasks that require manual effort. But what’s hidden behind modern AI-based chatbots for e-commerce?
Intro
In this article, SapientPro’s team shares their experience working on AI-based projects and chatbots. Our engineers have been creating high-quality solutions since 2017, ensuring that our clients get maximum value from their software. Here’s what you’ll learn:
- Importance of chatbots in e-commerce;
- Benefits of chatbots for e-commerce;
- How to build an e-commerce chatbot;
- Costs to create an e-commerce chatbot.
Interested? Keep on reading our step-by-step guide for more!
Role of Chatbots in Ecommerce: Key Statistics
The e-commerce industry is already leveraging many possible benefits of using AI-based chatbots across all business processes. Let’s take a look at the key statistics based on G2’s research:
- Chatbots and virtual assistants are the most adopted AI tools, with 69% of organizations integrating these into their tech stack;
- Nearly 1.5 million people engaged in at least one chatbot conversation over the past year;
- 82% of customers prefer using chatbots to waiting for a representative, a 20% rise since 2022;
- The global chatbot market is projected to grow from $7.01 billion in 2024 to $20.81 billion by 2029;
- Using chatbots in businesses can save up to 2.5 billion working hours;
These statistics show profound shifts in both business operations and consumer behavior that are reshaping the future of customer engagement. In 2025, eCommerce businesses without chatbots are falling behind compared to eCommerce companies using chatbots.
So, let's dig deeper with clear eCommerce chatbot examples.
What is an eCommerce chatbot?
An eCommerce AI chatbot is software based on artificial intelligence. These eCommerce chatbot solutions don't just follow the communication scenarios, they also learn what the user wants. This is possible thanks to Machine Learning algorithms and Natural Language Processing.

The use of chatbot in eCommerce is diverse: they process orders, help customers navigate the site, solve technical problems, consult them, etc. The main difference between AI chatbots and simple chatbots is analyzing and interpreting complex queries, adapting to new data, and improving.
AI chatbots in eCommerce are tools that can replace employees in the whole department. However, you shouldn't put such responsibilities on virtual assistants. The customer should still have the right to talk to a real person. Communicating with an eCommerce AI chatbot online or over the phone is meant to help customers, not be a barrier to solving complex issues.
Intelligent chatbots rely on a number of modern technologies, each of which plays a key role in their work.
Natural Language Processing (NLP)
NLP (Natural Language Processing) is a technology that allows chatbots to understand and interpret human speech. It includes several key components:
- Tokenization — dividing text into separate words or phrases;
- Syntax analysis — understanding the grammatical structure of a sentence;
- Entity recognition — identifying named objects (e.g., names, dates, place names);
- Intent detection — identifying the main purpose of a request (e.g., requesting information, completing a transaction).
Thanks to NLP, modern chatbots can understand even complex phrases and sentences. They can distinguish synonyms and context, which makes their interaction with users more natural.
Machine learning (ML)
AI-based chatbots use machine learning algorithms to improve the quality of their responses. In the process, they analyze data sets collected during interaction with users and adapt their behavioral models. The following approaches are used in machine learning:
- Supervised learning. Chatbot learns from pre-labeled data. For example, it is given query-response pairs and learns to generate correct answers based on this data.
- Unsupervised learning. Chatbot analyzes large amounts of data and identifies patterns on its own. This approach allows chatbots to improve their performance even if developers have not provided them with explicit instructions.
The best programming languages for machine learning include Python, R, Scala, C++, and Java.
Transformers and models based on GPT
The greatest progress in the chatbot industry was achieved with the advent of transformer architecture and models such as GPT (Generative Pre-trained Transformer). GPT models trained on huge sets of text data can generate meaningful and varied responses that are close to the human style of communication. Transformers have the advantage of being able to handle long dependencies in text and preserve the context of the dialog throughout the entire communication session.
GPT models, such as ChatGPT by OpenAI, have proven to be effective in creating interactive and context-sensitive chatbots. They can engage in conversations on a variety of topics, ranging from simple questions to complex consultations.
Deep Learning
Deep neural networks are used to increase the accuracy of query recognition and improve the quality of the dialog. These networks consist of many layers that analyze different aspects of data, allowing chatbots to better understand human speech. An important feature of deep learning is its ability to learn from large amounts of data and adapt to new use cases over time.
Rule-Based Chatbot vs AI-Based Chatbot
It’s also necessary to understand the difference between rule-based chatbots and AI-based chatbots. These are two completely difference technologies that vary by their use cases. Let’s check it out.
Rule-Based Chatbot
A rule-based chatbot operates based on a predefined set of rules and patterns. It follows a scripted approach to interact with users.
Key features:
- Predefined rules: interactions are governed by fixed if-then conditions or flowcharts;
- Limited flexibility: cannot handle queries outside its programmed scope;
- Deterministic behavior: provides predictable responses based on input matching predefined keywords or patterns.
Pros:
- Easy to design and implement for simple use cases;
- Reliable for structured queries or FAQs;
- Cost-effective for straightforward applications.
Cons:
- Struggles with complex or ambiguous queries;
- Requires manual updates to handle new scenarios;
- Lacks learning ability over time.

AI-Based Chatbot
An AI-based chatbot leverages machine learning, natural language processing, and data-driven models to interact more dynamically and intelligently.
Key Features
- Learning capability: continuously improves by learning from user interactions;
- Context understanding: capable of processing unstructured input and identifying user intent;
- Dynamic responses: generates tailored responses rather than relying on predefined scripts.
Pros
- Versatile in handling diverse and complex queries;
- Can adapt to changing requirements with minimal manual intervention;
- Offers a more conversational and human-like experience.
Cons
- Higher development and maintenance costs;
- Requires significant data for effective training;
- Potential for errors during early learning stages.

Detailed Comparison
Aspect | Rule-Based Chatbots | AI-Based Chatbots |
Technology | Predefined rules and scripts | Machine learning and NLP |
Flexibility | Limited | High |
Learning Ability | None | Continuous |
Use Case Complexity | Simple | Complex |
Response Generation | Fixed | Dynamic |
Development Effort | Lower | Higher |
Business Benefits of AI Chatbots in eCommerce
As there are multiple benefits of eCommerce chatbots, we gathered the most prominent options for businesses. Let’s take a look at them:
- Round-the-clock support. AI chatbots can work 24/7, providing assistance to users at any time.
- Saving time and resources. Automation of routine tasks allows employees to focus on more complex problems.
- Improved service quality. With NLP and machine learning, eCommerce AI chatbots can provide more accurate and personalized responses.
- Adaptability and scalability. AI chatbots can scale with business growth and adapt to new user requests.
SapientPro’s team helps you transform all these benefits into profits for your business. For example, we recently helped a company get AI-based agents for their call center at $1/agent, reducing costs and maximizing their efficiency. There’s an unlimited number of use cases in this industry.

How to Build an eCommerce Chatbot
If you want to build a chatbot for eCommerce, everything begins with analyzing the needs of your business and the target audience. Based on this data, technologies, and tools for eCommerce chatbot development have been selected. Our SapientPro team is skilled at building AI software.

Define Goals and Use Cases
Defining goals and use cases is critical for the success of an AI-based chatbot for e-commerce. Let’s cover this process step-by-step.
Understand business objectives and the primary reasons to use a chatbot
- Enhance customer experience: provide instant support and reduce response times;
- Increase sales: assist in product recommendations, cross-selling, and upselling;
- Reduce operational costs: automate repetitive customer queries and free up human agents;
- Collect customer insights: use chatbot interactions to gather data for marketing and product improvements.
Identify customer pain points
- Difficulty navigating the website or finding products;
- Delayed responses to queries about order status, returns, or delivery;
- Lack of personalized recommendations during shopping;
- Limited engagement or support outside business hours;
Define specific use cases
- Customer support: resolve FAQs about shipping, returns, and product availability;
- Sales assistance: guide users to products based on preferences or past behaviors;
- Order management: allow users to track orders, modify delivery details, or cancel items;
- Marketing and promotions: inform customers about discounts, new arrivals, or seasonal offers;
- Feedback collection: ask for reviews or suggestions at the end of interactions.
Set goals and use cases
- Focus on one or two high-impact use cases, like customer support or product recommendations;
- Expand capabilities over time based on user feedback and performance metrics;
Set measurable goals
- First-response time;
- Resolution rate for customer queries;
- Customer satisfaction score (CSAT);
- Conversion rate from chatbot interactions.
Once you’re done with this stage, you can move on to choosing the best technologies for your solution.
Choose a Platform and Tools
To build a chatbot for eCommerce, you can use ready-made platforms, such as:
- Google Dialogflow. It is an enterprise AI chatbot platform for eCommerce that supports NLP and integrations with various services.
- Microsoft Bot Framework. It is an eCommerce chatbots platform for creating chatbots with machine learning support.
- Rasa. It is an open-source solution for creating AI chatbots that can be customized.
These platforms provide all the necessary tools for integrating NLP, machine learning, and other AI technologies into bots.
Test and Train
The test and train phase is essential for developing an effective e-commerce chatbot. It ensures the chatbot can handle various customer interactions with accuracy and reliability. Below is a more detailed breakdown of the chatbot training stage:
Collect data for training
- Gather conversational data from previous customer interactions, such as emails, live chat logs, or social media messages.
- Use pre-existing datasets to train the chatbot's natural language processing (NLP) model.
- Continuously add new data based on chatbot-user interactions for improvement.
Train the chatbot model
- Intent recognition: train the chatbot to understand user intents, such as product inquiries, order status, or refund requests.
- Entity recognition: ensure the chatbot can identify critical details like product names, order numbers, or dates.
- Context handling: teach the chatbot to maintain context across multi-turn conversations, such as when customers provide additional information.
Test the chatbot
Conduct testing in different scenarios, including:
- Happy paths: ensure the chatbot performs correctly for standard customer queries;
- Edge cases: test how the chatbot responds to incomplete or ambiguous inputs;
- Stress tests: evaluate performance under high query volumes;
Test across multiple devices and platforms (e.g., mobile, desktop, and messaging apps) to ensure consistency.
Optimize based on testing
- Use feedback and analytics to refine responses, intents, and flows.
- Adjust the NLP model if the chatbot fails to understand specific queries or provides irrelevant responses.
- Fine-tune fallback mechanisms to redirect users to human agents when necessary.
Following these steps will allow you to create a high-quality e-commerce chatbot that will meet all your business needs and ensure user satisfaction.
Integrate with External Services
Ecommerce chatbot integration is no less easy than chatbot eCommerce training. Chatbots can integrate with CRM systems, databases, payment systems, and other external services to perform complex tasks such as placing orders, providing real-time information, and analyzing data.
How to Build an E-Commerce Chatbot: Relevant Platforms
Though there are dozens of different platforms out there, we focused on comparing the most prominent and widely adopted eCommerce chatbot solutions. They have already proved their reliability in the e-commerce industry.
Platform | Ease of Use | Integration | Functionality |
Dialogflow | Moderate — requires technical knowledge but has good documentation | Excellent — supports multiple channels and frameworks; native Google services integration | Advanced NLP, intent detection, context management, voice integration |
ManyChat | Very easy — intuitive visual interface | Good — strong Facebook/Instagram integration; basic website integration | Visual flow builder, broadcast features, basic e-commerce tools |
IBM Watson Assistant | Moderate — requires technical background | Excellent — comprehensive API support, multiple channels | Advanced NLP, intent training, voice integration, contextual response |
Microsoft Bot Framework | Moderate — requires development knowledge but has extensive documentation and SDK support | Excellent — seamless integration with Azure services, Microsoft Teams, multiple channels (Web, Teams, Slack, Facebook) | Advanced — AI/ML capabilities via Azure Cognitive Services, LUIS for NLP, QnA Maker, rich dialogs, adaptive cards, authentication |
Chatfuel | Easy — user-friendly interface | Good — strong social media integration | Visual builder, broadcasting, basic AI responses |
Tidio | Very easy — simple setup process | Good — website integration, social media support | Live chat, visual builder, basic automation |
Rasa | Difficult — requires strong technical skills | Excellent — fully customizable, open-source | Advanced NLP, machine learning capabilities, complete customization |
What Impacts the Cost of E-Commerce Chatbot Development
The cost of developing an eCommerce chatbot can vary widely, depending on several interrelated factors. From simple customer service automation to advanced AI-powered shopping assistants, the cost drivers are different. Here is a detailed breakdown of the key elements that influence the cost of e-commerce chatbot development based on SapientPro's experience.
Cost Factor | Impact Level | Description |
Integration Complexity | High | Cost varies based on the number of systems to integrate (CRM, inventory, payment processors) and API complexity |
Conversational Capabilities | Medium to High | More sophisticated natural language processing and understanding increases development costs |
Customization Requirements | Medium to High | Custom features, branding, and unique workflows add to development expenses |
Language Support | Medium | Each additional language requires training data and increases development time |
Security Requirements | High | PCI compliance, data encryption, and user authentication features impact overall costs |
Scalability Needs | Medium | Infrastructure to handle concurrent users and peak traffic affects development investment |
Maintenance & Updates | Medium | Ongoing improvements, bug fixes, and feature updates contribute to total cost |
Summary
Modern AI technologies open new horizons in e-commerce chatbot development. Today's chatbots can conduct meaningful dialogues, adapt to the context, learn from user interactions, and provide high-quality answers. It’s the perfect solution for businesses to increase their profits and reduce expenses.
SapientPro’s team can help you build an AI chatbot that adapts to workflows, uses your company’s tone of voice, integrates with your company’s systems, and takes your business to the next level. Interested? Contact our experts today to get a quote!
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