How Can Chatbots Help E-commerce Businesses?

Today, the word “bot” is no longer associated with science fiction. Now, chatbots allow you to book a ticket or accommodation, order food, or even pay for your order online. 

Chatbots are currently used, not only in e-commerce websites, but also in social networks, like Twitter, Facebook, and, in particular, the Facebook Messenger platform. Why? The reason is simple – instead of calling customer support, over 65% of online shoppers will message a business. Also, 53% of shoppers are more likely to buy from a store they can message. And, Facebook reports on a host of data only proves that trend. Reports show that every month Facebook business pages are exchanging over 2 billion messages with customers.  

But why is it important? Let’s see. 

If you do not want to get left behind but rather be ahead of your competitors in online retail, you should be able to communicate directly with your clients. And, to make such communication even more useful, consider adopting chatbots for your e-commerce business. 

In this article, we will look at the most successful e-commerce chatbot use cases and go through a step-by-step guide on how you can develop a perfect chatbot for your online store. 

But first, let’s see what the benefits are of using chatbots in retail stores. 

Key benefits of using e-commerce chatbots

Around 80% of online businesses are planning to use chatbots by 2020. 

The reasons why this digital trend is up and running lie in the following benefits of chatbots for e-commerce

Improved customer engagement

As we have said, modern customers prefer sending messages to businesses rather than making phone calls. But, to keep customers engaged with your business, you need to respond in time. In fact, according to Harvard Business Review research, a five-minute delay in answering a customer query decreases the customer engagement rate, and a ten-minute delay reduces this metric by 400%. Chatbots for retail businesses respond to potential buyers’ queries immediately, thus increasing the customer engagement rate by 54%

Drive sales 

A chatbot could be integrated into your website or shopping app. By providing customers with immediate responses, shopping chatbots significantly drive sales to your online store. The recent Ubisend report shows that 40% of consumers want offers and deals from chatbots. Also, online shoppers are willing to spend more than $400 on an online store powered by a chatbot. 

Reduce customer acquisition cost

As you may know, Customer Acquisition Cost (CAC) is a metric that shows how much money you spend to attract one customer, and the lower the CAC, the better. Chatbots reduce CAC since they offer engaging and instant conversation on your online store; thus, customers stay longer on your website. Moreover, chatbots for retail and e-commerce sites provide customers with personalized product recommendations and, as a result, increase chances to convert website visitors into customers.  

Decrease customer support costs

If a potential customer is interested in your products, one may ask for a more detailed product description. Statistics show that answering simple questions is relevant to 55% of online shoppers, and 64% of online shoppers prefer 24/7 online support that can not be provided by a real person. Here chatbots are handy. By integrating a simple FAQ chatbot, you can decrease customer support costs by 30%. But to answer complex queries, a chatbot should switch the customer to a human agent. 

Build Loyalty

Customer loyalty, also known as customer retention rate, is another metric that chatbots can improve. To build loyalty among existing customers, consider integrating instant chatbot, and always be in touch with your target audience, provide them with customer service and relevant product recommendations. 

Streamline sales funnel

By using an e-commerce chatbot, customers can identify the product they want, find it in a matter of clicks, and buy more seamlessly. Thus, chatbots have become a new sales channel for online retailers. 

Top benefits of using chatbots for customers

Top 5 most successful e-commerce chatbot use cases

Let’s see the most successful examples that show the use of chatbots in retail by famous brands. 

Order automation 

How many steps do you need to take for ordering, for example, a pizza via a website? The answer is at least five – select a pizza you want, add it to the shopping cart, go to the shopping cart to complete your order, fill in the delivery address, and pay for the order. Chatbots can shorten this process to just two steps – you need to give a chatbot your order details, share your location via GPS, or put it manually. Sounds incredible? That is actually how a Pizza Hut Facebook Messenger chatbot works. Moreover, the company launched a Twitter chatbot that decreases the ordering process to just one action- sending a pizza emoji in direct messages. After the launch of Facebook and Twitter chatbots, Pizza Hut increased digital revenue by 75-80% and continue to receive 50% of its orders from these digital channels. 

Personal product recommendations 

When customers are visiting your online store to buy a particular pair of jeans, they spend some time in the product catalog to find the exact pair they are searching for. Such a buying process may become an irritating experience, especially if your online store has numerous product categories. To avoid this, online retailers integrate chatbots to help buyers in selecting products that perfectly meet their needs, just as Aerie, the clothing and lingerie spin-off of American Eagle Outfitters did. The company integrated a chatbot for Kik messenger, developed on the Pandorabot platform. To learn more about style preferences, the bot shows online shop visitors two images of clothing or lingerie. Then, the bot customizes the item recommendations and offer more relevant products. As a result of such integration, AEO chatbot acquired twice as many users across all social channels. 

Conversational marketing tool

Do you like Nike sneakers? What if you can put any image on sneakers to customize them in the way you want? That is what Nike did in their conversational marketing strategy to promote Nike AirMax Day. The company developed a  Nike StyleBot for Facebook Messenger to enable fans to style their shoe design, using previously uploaded versions for inspiration, and sharing the results with friends via Facebook Messenger. Such a conversational marketing campaign brought the company 12.5 times higher click-through rate (CTR) compared with their other campaigns, and four times higher conversion rate that their average marketing campaign.

Chatbots for shopping assistance 

To receive relevant product recommendations at an online store is a great experience. But what if a chatbot can suggest the whole outfit to you? That idea inspired H&M to create The Official H&M Chatbot. The main goals of the H&M Chatbot are to streamline mobile shopper’s customer experience, help them to search through outfit possibilities, and guide them directly to an online store to complete the purchase. To receive a personalized look, the customer provides the chatbot with gender, style, and the total price for all items. Then, if the shopper likes the outfit, one can save it to archive, buy all items at the H&M online store, or share the look via social networks. By using a chatbot, the company facilitates impulse buys and decreases the shopping cart abandon rate due to streamlined sales funnel. 

Personalized 24/7 customer support 

While a human agent can not be around and answer all the customer’s queries, chatbot nails it. An example is the Twitter DM chatbot created by the Etsy marketplace. The company’s chatbot has clear call-to-action buttons that provide shoppers with more personalized customer service, fixes problems immediately, and helps in finding more information day or night. Despite any issues that may have arisen, in this way, Etsy managed to build a strong relationship with clients which encouraged them to return in the future. 

Common ways customers are using chatbots

How to create a chatbot for an e-commerce store 

If you want to build one for your online store, follow the steps of the chatbot development guide, described below. 

Step 1. Define the bot’s goal 

What do you need a chatbot for? Should it answer user questions, offer discounts, or promote new products? To decide on your chatbot functionality, you will need to communicate with your customer support, sales, and social media teams. To give you an idea of how you can use a chatbot, we have selected the most widely spread use cases of chatbot in online retail:  

  • Send notifications and reminders
  • Work as digital assistants
  • Handle online transactions
  • Collect customer data and user feedback
  • Advertise and broadcast
  • Conduct market research
  • Entertain and educate users
  • Automated customer support
  • Streamlined business processes
  • Give personalized recommendations
  • Provide access to information
  • Upsell products and services, etc.

To build a chatbot for a retail business, we advise you to be selective and wrap your ideas with certain limits, i.e., the number of functions. In the beginning, empower your chatbot with one or two simple tasks and analyze how the bot works for your business. You can always add more sophisticated functions later. 

Step 2. Select the chatbot type

When you are done with the functions of your chatbot, you need to choose its type. Nowadays, there are two main types of chatbots: 

  • Pre-scripted or rule-based chatbots are the simplest type since they work on the basis of predefined answers. Use such a chatbot if you need a solution for solving simple tasks, like customer service bots, to answer users’ questions. 
Scripted chatbots for online retail
  • AI-based chatbots are more advanced than the previous one. By using AI (artificial intelligence) and NLP (natural language processing), such bots can understand user input and answer with relevant non-pre defined answers. You can even empower your bot with an NL (machine learning) algorithm, so the bot can learn from each interaction with a user and remember one’s preferences. Still, AI chatbots require pre-launch training; thus, you need to hire chatbot developers. 
AI chatbots for ecommerce websites

Next, consider where you will use your chatbot since it will impact the technical solution. You can develop a chatbot for the following channels: 

  • E-commerce website 
  • iMessage or SMS 
  • Social networks such as Twitter, or Slack 
  • Messaging apps, including Facebook Messenger, WhatsApp, and Telegram

If you need to use the same chatbot in both your online store and Facebook Messenger, you can create an omnichannel chatbot that will work across several channels. 

When you know your chatbot use case, its type, and channels, you are ready to select the platform to build your chatbot. 

Step 3. Choose your platform

Below we have gathered the most popular platform for building an e-commerce chatbot. 

Unless you want to create a custom e-commerce chatbot, you can select the most suitable platform that varies on the bot’s complexity. 

Best platforms for simple chatbots 



Flow Xo

Target platforms

  • Telegram 
  • Facebook Messenger
  • Facebook Messenger
  •  WhatsApp
  •  Instagram
  • WhatsApp Web
  • Facebook Messenger
  • Slack
  • Telegram 
  • Twilio SMS


  • Free with basic features; 
  • Chatfuel Pro ($30/month) with advanced features is also available
  • Free 
  • Basic ($10/month), 
  • Premium ($30/month)
  • Business ($50/month)
  • Free
  • Standard ($19/month) 


  • Chatfuel chatbots work on pre-defined coded rules and serve clients accordingly.
  • The Chatfuel builder platform has drag-and-drop feature which makes  it the ideal platform for beginners who want a chatbot to perform basic roles. 

  • Botsify provides users with its custom templates for travel, restaurant booking, etc. along with drag-and-drop functionalities.  
  • Anyone can create bots effortlessly without writing a single line of code. 
  • Even the free version comes with 20 templates. 
  • Flow Xo offers templates and tools to create and test the functionalities of your bot with a built-in test console. 
  • The bot can connect with your customers over voice and chat. 

Best platforms for AI chatbots for e-commerce

IBM Watson

Microsoft Bot Framework

Target platform

  • Facebook Messenger
  • WhatsApp
  •  Instagram
  • Website 
  • App
  • Cortana
  • Microsoft Teams
  • Skype 
  • Slack
  • Facebook Messenger
  • Website 
  • App
  • Cortana 
  • Microsoft Teams 
  • Skype
  • Slack 
  • Facebook Messenger


  • Lite (USD 0) 
  • Standard ($0.0025 (USD) per API call) 
  • Premium (price not disclosed)
  • Standard channels (Free)
  • Premium ($0.50 per 1,000 messages)
  • Standard channels (Free)
  • Premium ($0.50 per 1,000 messages)


  • IBM Watson is the platform for AI chatbot that can handle complex conversations. 

  • It can process around four terabytes of data and is hosted on a cluster of ninety IBM Power 750 servers, each of which uses a 3.5 GHz POWER7 eight-core processor.

Chatbot developers can leverage a wide range of technology frameworks, including:

  • Node SDK (Software Development Kit) 
  • Java SDK 
  • Python SDK
  • iOS SDK 
  • Salesforce SDK 
  • Unity SDK

 to make IBM Watson with different platforms.

  • Azure Bot Service provides a scalable, integrated connectivity and development service to help developers create intelligent bots that can engage users across multiple platforms. 

  • The development tools are provisioned with the Microsoft Bot Builder SDK, which .NET and Node.js developers can access and use to create an engaging talking bot.

  •  The SDK also includes an emulator for debugging your bots, as well as a large set of sample bots you can use as building blocks.

The cloud-based service is globally accessible across 141 countries, and bots can communicate in multiple languages including: 

  • English
  • French 
  • Italian 
  • German 
  • Spanish 
  • Japanese 
  • Korean
  • Chinese.
  • This NLP-powered chatbot builder offers resources to create engaging, scalable chatbots to serve different purposes. 
  • learns human language through every interaction and leverages the community to evolve and improve further.

Chatbots with support 50+ languages, and developers have the flexibility to use any of the available SDKs, including: 

  • Android, 
  • iOS, 
  • Cordova, 
  • HTML, 
  • JavaScript, 
  • Node.js, 
  • .NET, 
  • Unity, 
  •  Java

And other. 

When you know what bot-building platform you will choose, it’s time to hire chatbot developers and start building your bot.  

Step 4. Create a chatbot MVP

You can create a simple bot with a DIY platform within several hours. But to develop an AI chatbot is far more difficult. However, to find out whether a chatbot suits your business needs, you do not need to spend a fortune on its development. We recommend launching such projects as MVP (minimum viable product). For chatbot MVP, we suggest only pre-scripted answers. To understand the project scope, check out our estimation of the MVP of a Facebook Messenger bot for e-commerce product recommendation. 


Functions required



12 hours 

Customization for agents

– Logo

– Agent Name

60 hours 

Connection to Data Server API

16 hours 

Switching between a chatbot and human agent

– Switch between Human Agent and Chatbot

32  hours 

Gathering of data from user 

– Type of product

– Color

– Size

– Material 

32  hours 

Data transfering to API, receive results

-The chatbot search for relevant products in your online store database

16  hours 

Search Results

– Show search results

8  hours 

Show more variants

-The chatbot shows more relevant products from the selected category

16 hours 

Chatbot Management

– Define questions and answers

40 hours 


From 232 hours

In our experience, the cost to develop a chatbot MVP varies from $3,500 to $5,000 and takes from 2 to 6 months, depending on the bot’s complexity and the number of integrations. 

Step 5. Launch your chatbot

After your development team has created a bot and ensured it is without errors, it is time to launch it on your e-commerce website. Now you need to pay extra attention to how the chatbot interacts with clients and ask them for feedback. You may also ask users what features they would add to your chatbot. After gathering all user feedback, bring them to your development team to prioritize features to implement during the second e-commerce chatbot development stage.  

In a nutshell

More and more people want to communicate with businesses via messengers, and online retail is one of those industries that can receive a significant profit from this trend. Therefore, to keep the most effective communication with users, increase sales and engagement, and build loyalty, a chatbot is a must. Various use cases and successful e-commerce chatbot examples show that chatbots for the retail industry are a win-win for both sellers and buyers. 

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How eCommerce uses Machine Learning Applications


Ever since eCommerce became a valid buying option for customers in the late 90s – it continues to rapidly grow with 3.45T in projected sales in 2019.  

Ever since eCommerce became a valid buying option for the customers in the late 90s - it continues to rapidly grow with $3.45T in projected sales in 2019.

 The online retail industry adopts all sorts of technological innovations, including big data and machine learning, and apply them in various use cases. The proximity of user data and the variety of use cases contributed a lot to bring these technologies to the level they are today. 

Now, AI in eCommerce (with marketing ad tech) is one of the leading fields that perfect machine learning algorithms for the benefit of superior customer experience.

Such eCommerce machine learning applications as service personalization, sentiment analysis, image classification, and conversational interfaces (chatbots) getting the first experience in the fields of eCommerce marketplaces. 

In this article, we will look at major big data eCommerce artificial intelligence applications and explain how they all improve the flow of business operation.


How to use Machine Learning in eCommerce 

1. Product Feed Recommender Engine

Have you ever thought about why Amazon can guess which products may interest you? It is simple. Amazon has a recommender engine that analyses user search results and proposes relevant recommendations 

Recommender engines work on user data, the Holy Grail of all sorts of consumer insights in big data eCommerce. 

Throughout the numerous sessions of different users, the algorithm gathers the information and clusters patterns. It creates a cohesive picture of what kind of content and products a particular customer segment likes and prefers. 

This information is then clustered and classified by machine learning algorithms into a foundation for further recommendations. For example, if the user is looking for calligraphy kits, his query is matched with the similar from the relevant target audience segment. 


From a technical standpoint, the recommender engine is a combination of:

  • Clustering unsupervised algorithms;
  • Classification supervised algorithms;
  • Predictive algorithm for suggestions;

The methodology of the recommender engine is the following: 

  • Processing user data and extracting preference insights;  
  • Matching insights with the product (or content overall) database;
  • Calculating the probability grid of which kinds of products may be more relevant to a particular user.

As a result, the recommender engine creates an infinite loop in which the user gets content and products that are relatively relevant to its cause and buys even more products. And when the user inputs something new – it is also taken into the equation and subsequently implemented into the recommendation sequence. 

This is how Amazon generates 35% of their revenue. Similarly, Best Buy saw an increase in 23,7% after implementing their recommendation system.

This is how Amazon generates 35% of its revenue. Similarly, Best Buy saw an increase of 23,7% after implementing its recommendation system. Currently, recommender engine features are available for custom use on platforms like Shopify and Magento.    

If you want to know more about recommender engines – check out this article.

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2. Service Personalization / Content Feed Personalization

Automation of the various routines is one of the many benefits of machine learning. 

A great example of this is personalization. The machine learning models for eCommerce can adjust the entire marketplace appearance to meet a particular customer.

The primary motivation behind personalization with AI in eCommerce is user engagement that results in a more attractive and practical customer experience (with more conversions and sales). Marketplaces want users to spend more time and made purchases on their platforms. To make it happen, they reshape some aspects of the website to fit the needs of the particular user. The numbers don’t lie – around 48% of customers appreciate when things are adjusted to their preferences and 74% of online shoppers are disappointed if the online store product feed does not provide them with personalized recommendations. 

Previously, personalization on eCommerce marketplaces required adapting pages and product selection by the context of the particular page or request without using customer data. Now, a couple of algorithms are handling the personalization process. 


From a technical point of view, service personalization is an expanded application of the recommender engine. 

The difference is that instead of slightly adjusting the product feed and related suggestions to the user segment patterns – the entire layout of the marketplace is tailored to the expressed preferences of the particular user. 

The key to successful service personalization is seamless implementation into the user experience. In other words, from the user’s side, personalization comes naturally. 

The foundation of service personalization is within user data patterns. Everything counts for this kind of customization:

  • Product purchases; 
  • Product filtering (color, size, type, etc.) 
  • “For later” and “wishlist” listings;
  • Product searches and Product views;
  • Product rating;
  • Blog views;
  • Comments, product reviews;
  • Interactions with ads;
  • Interactions with “you might also like” and “people also buy” sections;
  • Even cart abandonment says something about the user;

This information is clustered and classified by a combination of supervised and unsupervised machine learning algorithms and later matched with the website’s database to bring to the forth more relevant stuff.


The process includes:

  1. Personalized product feed;
  2. Related suggestions;
  3. Relevant special offers;
  4. Targeted ads; 

Service personalization results in a more focused user experience that avoids possible distractions, cart abandonment, and irrelevant products while emphasizing the stuff that interests the user. 


3. Dynamic Price adjustment – Predictive Analytics

Price adjustment is the field where you can feel the scope of the benefits of machine learning. eCommerce is one of those industries where competition is beyond fierce, especially when it comes to niche consumer segments such as beauty products or hardware. Because of that, it is crucial to get as many advantages as possible to attract and retain customers. 

Enter machine learning.

One of the most effective ways of doing that is by offering more competitive prices for the products of interest. This option is made possible by significant big data eCommerce machine learning price monitoring and adjustment. According to the BigCommerce study, price is one of the major drivers for 47% of the customers in eCommerce. So it makes sense to tweak in the right way.

For example, Amazon uses price adjustment based on external trends and product demand and also internal user data (which is also used for product recommendation). This allows them to subtly make the prices for the products more appealing to the customers interested in them. 

Amazon price adjustment analyses prices on other online stores

Let’s look at another example, Walmart uses price adjustment for customer retention. Their system is all about monitoring the competition and making their own prices look lower in comparison.  

Here’s how the price adjustment system works:

 Walmart uses price adjustment for customer retention.
  • There are three key sources of information:
  1. Marketplace data itself;
  2. General user trends and demands;
  3. A network of competing marketplaces with the related products and target audience segments. 
  • There are regular checks of the prices for the products on the competing marketplaces. The comparison of this information with the prices on your marketplace.
  • Then this information is combined with general user trends and demands. 
  • Then the predictive algorithm calculates the best possible price change for the particular target audience segment. 

In addition to the straightforward competition, the price adjustment is often used to decrease customer churn on a particular online retail shop. 

In this case, the method is more straightforward – it includes the price for the product and user trends. The result is more attractive prices for low demand products that cause the renewal of the customer interest. 


4. Supply and Demand Prediction Using Machine Learning

Supply and demand prediction is the evolution of price adjustment combined with the recommender engine. There are various products the interest for which spikes at a specific time, and this is a perfect reason to take advantage of it. According to Statista, the 2017 winter holidays generated over 8.2 billion worth of eCommerce sales in the United States.

The challenge comes with the management of the product inventory. It is essential to retain smooth processes when the trend is at the peak. The main problems of supply and demand are: 

  • Lack of products that satisfy the specific demand;
  • Insufficient quantity of the products that meet the particular demand.

As a result, companies are losing up to 25% of the monthly revenue due to unpredictable spikes of demands and lacking availability of the product. 

Predictive machine learning algorithms solve both problems. Here’s how it works:

The reaction to product demand variation adds the world outside to the equation. There are general trends and patterns of product demands available in public sources (Google Trends, etc.).

Then there are internal stats of product demand and customer purchase patterns.

This information is combined and laid out on the product inventory. You can see which product supply needs a boost and which products are lacking. 

With this information, you can optimize the process and deliver a satisfying customer experience.


There are two significant types of product demand – seasonal and incidental. 

  • Seasonal demand – like Christmas-related products around Christmas. In this case, you can predict supply and demand prediction in hindsight and then optimize it on the spot.  
  • Incidental demand – Chernobyl-related content because of HBO Mini-series. Barnes and Noble used the interest spike to promote books on radiation-related topics with a revenue increase of up to 15%.

As a result, with the assistance of machine learning, the eCommerce marketplace can easily manage a system of discounts for specific products to satisfy the product demand and attract more customers with more reasonable prices.

5. Machine Learning for Visual Search 

Visual search and image recognition technology had greatly benefitted from the adoption of mobile eCommerce shopping. The reason for its growing popularity is simple. 

Unlike alphanumeric search engines that require specific information to deliver the desired result – all you need for a coherent visual search is an image of a thing the user is searching for. Everything else is handled by an image recognition engine that matches input information with the product database and selects the closest matches. 

Visual search streamlines customer journey towards the purchase, especially for the clothes and make-up segment. For example, had increased its sales by 15% since implementing visual search features.

implementing visual search features, had increased its sales by 15%

Here’s how it works:

  • There is an image recognition algorithm at play. It is used to define an image and describe its surface features. Usually, the process involves a convolutional neural network to recognize an image and a recurrent neural network to describe an image further.
  • Then the image description is combined with the product information.
  • When the search engine processes the image input – it matches the image descriptions and goes to the product information related to it. 

These days, there are two significant proponents of visual search commerce – Amazon and Pinterest. 

While Amazon is using visual search as an additional feature to the core search engine, Pinterest is using it as a core feature with the image coming before the product information. This approach embraces more natural product discovery and as a result, more engaging use of the application.


6. Fraud Detection and Prevention Opportunities for eCommerce

Fraud is one of the eCommerce’s biggest banes. Just last year the eCommerce industry had lost more than billions on various fraud schemes. It is one of the problems that never really goes away – you can find a way to eliminate present threats, and later it will adapt and come back with the new bag of tricks.

Hopefully, with an adoption of AI in eCommerce and implementation of specialized Machine Learning algorithms – predictive analytics are capable of detecting suspicious activity and preventing it from causing damage.

Let’s look at how eCommerce machine learning algorithms handle main fraud threats:

  • Return to Origin fraud. This fraud is an abuse of refund policy. In this case, the fraud detection algorithm analyzes user activity and its patterns and compares it with the frequent cases of refund. Insufficient periods between order and refund usually, expose this trick. 
  • Promo Code Abuse – when scammers create multiple accounts and apply the promo code on orders. In this case, there is anomaly detection and signal source analysis. Usually, this type of scam is performed by low-level criminals without intricate networks of the cover-up. Because of that, similar IP addresses expose this type of fraud. In other cases, promo code abusers are exposed by their behavioral patterns. 
  • Account Takeover. It is one of the more sophisticated types of eCommerce fraud. In this case, external phishing techniques gain access to the user account. The most common method is by installing malware through malicious links. Then fraudster takes over the account and performs purchases as he pleases. Anomaly detection algorithm combined with behavioral patterns combined with additional stages of identity verification (including location, IP, device, etc.) are used to expose and prevent this from happening.

7. Chatbots and conversational interfaces

Chatbots are all the rage right now. In a couple of years, chatbots had managed to evolve from the clumsy ELIZA-styled fancy interfaces to the competent multi-purpose assistants that cover everything from customer support to lead generation. 

With the wide adoption of smartphones and voice-control, the implementation of conversational interfaces to the big data eCommerce marketplaces became a necessity. The key benefits of implementing a conversational interface to the eCommerce store are functional versatility and streamlining of the finding and purchasing of products. In a way, a conversational UI chatbot is the ultimate customer service application. 

The bot can help the user to:

  • Find or suggest relevant products;
  • Compare the qualities of the products;
  • Proceed with the payments;
  • Arrange shopping lists.

At its core, eCommerce machine learning conversational UI use speech recognition algorithms and semantic search natural language processing algorithms. 

  • First, the transcription of the input speech happens. In the case of textual input – it is processed directly.
  • Then the transcribed text is processed and deconstructed to critical elements. Topic modeling, named-entity recognition, and intent analysis algorithms are applied.
  • This process lays the groundwork for determining the request. 
  • Then the algorithm uses available input information and semantic search to find matching credentials in the internal database. The results are arranged by probability and delivered as output. 

If the information is insufficient – a chatbot can ask additional questions regarding aspects of the product or the nature of the query. For example, Nike is using chatbots to simplify finding a fitting product (mixed with special offers and discounts). 

For example, Nike uses a chatbot to improve the product search.

On the other hand, Lego is using a chatbot to make relevant suggestions on gifts.

Another example is Lego chatbot, which makes relevant suggestions on gifts.

If you want to read more about conversational UI – check out this article.

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AI and machine learning for eCommerce seems to be a perfect combination in which both parties benefit from being with each other. 

The role of artificial intelligence in eCommerce is to make the buyers’ journey more comfortable and more efficient with various machine learning algorithms.

eCommerce is an industry where applications of machine learning directly contribute to the quality of the customer experience and business growth. 

Are you ready to apply machine learning in your online store? Fill in the contact form, and we will get in touch.

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