AI Chatbots in Ecommerce
How to Build Helpful Virtual Agents for Ecommerce Goals
A provider of full-service ecommerce development since 2003, ScienceSoft creates digital solutions to enhance customer experience throughout the buying journey.
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 33 years in AI and 19 years in ecommerce, ScienceSoft knows how to create a solution that converses with your customers naturally.
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 2022 it’s valued at $106.6 million. 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.
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.
- Handling common questions (e.g., about payment options).
- Booking services.
- Delivery services.
- Customer feedback collection.
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.
- Help in navigation through product/service offering, discounts, purchasing conditions, etc.
- After-sales service (order confirmation, shipping tracking, handling undelivered orders, wrong deliveries, returns).
- 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).
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.
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.
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.
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.
- ScienceSoft is not a product startup that wants to promote a chatbot platform. We are a large software development company, 33 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 19 years, creating solutions to power fast and effective customer support desks.
- We have 700+ employees on board, each with unique skills and knowledge.
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.
We help you understand what functions a chatbot may perform for your exact audience and fully plan its technical implementation.
We fully plan and create both simple transactional and complex conversational chatbots that can support human-like conversations.
Time to Welcome a New Employee That Works Tirelessly 24/7
As you may conclude from our guide, a chatbot can assume not even one but two roles – a customer service agent and a sales rep. And no pay rise requests, sick leaves, or late arrivals. A perfect worker, isn’t it? If you are just as excited at the idea of chatbot deployment as we are, don’t hesitate to reach out to our team (not a bot!) in a live chat with any questions and ideas.