How to Create AI-Powered Ecommerce Chatbots

Key Functions Chatbots Perform

AI chatbot in ecommerce is an app that handles customer service conversations using natural language processing. A chatbot must:

Accept a user’s message and understand its context.
Decide what data to get from the knowledge base to answer a user’s request.
Produce an answer in a natural language.
Throughout this process, learn continuously to deal with new situations.

With 35 years in AI and 21 years in ecommerce, ScienceSoft knows how to create a solution that converses with your clients naturally.

A Trend to Reshape Ecommerce Customer Support

We don’t know any cases when companies fully entrusted chatbots with customer support. Indeed, leaving customers without the possibility of human assistance is extremely risky. However, the global chatbot market has been growing consistently, and in 2024 it is estimated at $7.01 billion. This growth confirms that companies are increasingly using chatbots to communicate with customers, which provides benefits for both parties.

Why customers love chatbots

Uninterrupted service anytime – at lunch, at night, on weekends.
Short wait time.
Quick and to-the-point answers, with the possibility of switching to a human agent in complicated cases.
A preferred language option (in multilingual chatbots).

Why support agents love chatbots

Escaping simple repetitive inquiries and dedicating time to challenging situations where the whole customer experience is at stake.
The agents’ balanced workload, even in case of colleagues’ vacations or sick leaves.
All customer service KPIs are improving: first response time, resolutions rate, etc.

2 Types of Chatbots for Different Ecommerce Tasks

As ecommerce customers contact the support with various problems, you should consider two types of chatbots that handle different tasks well and choose which one suits your company’s needs better.

Transactional chatbots

Transactional chatbots must understand the request context but don’t need to simulate a human-like response – they return predefined answers or a set of options.

Implementation specifics: NLP + rule-based algorithms.

Best for:

Handling common questions (e.g., about payment options).
Booking services.
Delivery services.
Customer feedback collection.

Conversational chatbots

Conversational chatbots must understand the context and the conversational sentiment of customers’ messages, and respond in a human-like manner.

Implementation specifics: NLP + machine learning + access to the knowledge base.

Best for:

Help in navigation through product/service offering, discounts, purchasing conditions, etc.
After-sales service (order confirmation, shipping tracking, handling undelivered orders, wrong deliveries, returns).

How to Build an Ecommerce Chatbot: Sample Architecture

We take an advanced type of chatbots – conversational – and show you the functional components that constitute it.

A chat client. An app that provides a user interface for live messaging.
Natural Language Understanding (NLU). A part of an NLP engine that analyzes the incoming message and extracts intents and entities from it. Intent is what a user wants and entities are keywords (objects, places, time, etc.). NLU relies on:
- Lexical analysis to identify the structure of words.
- Syntactic analysis to understand the dictionary meaning of words in the sentence.
- Semantic analysis to understand the context of the sentence.
- Pragmatic analysis to add real-world knowledge to understand the sentence (e.g., apple as a fruit vs. Apple as a technology).
- Discourse integration to define how a preceding sentence affects the interpretation of the next sentence.
Natural Language Generation (NLG). A part of an NLP engine that generates sentences in a natural language and applies grammatical rules to them. NLG relies on:
- Text planning to select the content needed for the response.
- Sentence planning to choose the right words.
- Text realization to map the whole sentence.
Dialog state tracking. A part of a dialogue manager that tracks what has previously happened in the dialogue.
Policy learning. A decision-making mechanism that chooses an applicable conversational policy and directs how the dialogue proceeds.
Knowledge database. An organized collection of ready-to-use info to answer FAQs.
Enterprise systems. Systems that provide user-specific data (e.g., order details based on a customer’s order tracking number).

Chatbot Platforms and Other Techs You Will Need

There are two approaches to chatbot creation: you can use a ready platform for a simple solution or a development framework to build a sophisticated chatbot from scratch. Chatbot frameworks include all the components needed to program a chatbot’s ML-powered core. That is why we don’t mention any particular tech stack for each module (e.g., NLP, NLG) separately.

Where Ecommerce Chatbots Can Be Placed

In our projects, we always analyze our client’s engagement with the target audience in different digital touchpoints to understand where a chatbot can bring the most value and what tasks it may fulfill. There are three channels where we (and now you) may integrate a chatbot:

An ecommerce website

A chatbot may be focused on pre-sale assistance: inform the customers about current promotions and best offers, help understand payment, delivery and return terms.

Messengers

Chatbots in messengers are convenient for post-sale assistance. They can handle delivery issues and product returns, collect customer feedback, offer maintenance and repair services.

Social media

A chatbot can help convert your social media followers into buyers when it’s integrated as a pop-up window on a relevant social media page, in an ad or messages.

Companies That Already Talk to Their Customers via a Chatbot

Facebook bot by Domino’s Pizza

A food delivery service offers their Facebook subscribers to quickly order pizza. Not intended for multitasking, the bot processes only limited types of requests but copes with this task flawlessly.

Website chatbot by H&M

H&M’s virtual assistant helps online shoppers deal with the most common situations or offers to connect them to a human agent. The bot is good at understanding message intent and navigating to possible scenarios of further conversation.

WhatsApp chatbot by KFC

KFC launched its ecommerce bot as a quick way to place an order. With just a few clicks, customers can choose their meals on the go. The chatbot is transactional. It doesn’t support a conversation, but rather offers to go step by step through the ordering process.

Why Does ScienceSoft Talk So Confidently about Chatbots?

ScienceSoft is not a product startup that wants to promote a chatbot platform. We are a large software development company, 35 years on the market, with expertise in different software, including chatbots.
We are experts in data-driven solutions, and the development of AI-powered software is our thing.
We have been working with ecommerce companies for 21 years, creating solutions to power fast and effective customer support desks.
We have 750+ employees on board, each with unique skills and knowledge.

What makes ScienceSoft different

We achieve project success no matter what

ScienceSoft does not pass off mere project administration for project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.

See how we do that

What Help ScienceSoft Can Offer to You

Though certainly important, our programming competence and experience in AI is not all you can benefit from. We are value-focused consultants who can guarantee the business feasibility and high return of your chatbot investment.

Chatbot consulting

We help you understand what functions a chatbot may perform for your exact audience and fully plan its technical implementation.

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Chatbot development

We fully plan and create both simple transactional and complex conversational chatbots that can support human-like conversations.