How can Artificial Intelligence Improve Customer Service

Artificial Intelligence makes e-commerce a lot easier for business owners. AI is used to track the team’s productivity, create smart marketing insights, check inventories, and power supply chains. However, AI’s benefits are perhaps even more game-changing when it comes to customer service and support. For end-users, the use of Artificial Intelligence can become the key improvement of customer experience and be a critical conversion factor. Let’s examine 7 ways in which Artificial Intelligence and chatbots can be used to connect to customers better, drive higher profits, and provide real-time support.

Discovering customer intent

Artificial Intelligence can analyze data on customer on-site interactions as well as viewing and purchase history to predict users' future actions or inquiries. Combined with Machine Learning and big data, it can leverage information to anticipate users’ wants and support them. Machine Learning allows the system to get smarter with each analyzed data byte, automatically improving its analytical and conversational skills. 

For instance, if a customer has a bill that’s soon due, a banking Artificial Intelligence chatbot could send a reminder about an upcoming payment and even prepare a template. This way, customers are reminded to use a bank’s service when it’s relevant for them. 

Anaplan

Anaplan, formerly known as Mintigo, is a digital service that uses Artificial Intelligence and natural language processing to create insights on e-commerce customers and convert them into marketing strategies and business plans. The tool analyzes online visitors’ pathways, detects similar patterns, and establishes relationships between them. The service provides businesses with information on buyers’ intents and their positions on the pipeline and provides suggestions for optimization. 

Image recognition personalizes online fittings

E-commerce stores often struggle to receive relevant information on users’ facial and bodily characteristics, necessary to enable precise try-on tools. For customers, the lack of fitting features is still a major issue, when it comes to online shopping. Artificial Intelligence and Machine learning can solve this issue by providing a neural-network-based image recognition and real-time support. 

To set up such a service, developers train image recognition systems from existing image datasets, such as ImageNet and Coco. These libraries provide free access to millions of images with detailed tags and descriptions. At first, developers need to oversee the accuracy of the AI analysis, but later on, the system uses Machine Learning to evaluate its own conclusions. 

Sephora’s ai-powered customer service

Sephora uses AI to power its Visual Artist instrument, designed to try on brand cosmetics. Women can upload their pictures to the service and try on concealers, foundations, lipsticks, eye makeup. The software automatically recognized their facial features and ensures precise makeup application. 

Along with integrating the AI-powered customer service on the website, the company also integrated AI-based customer support to Facebook Messenger. Women can upload their pictures to the chatbot and get back the image with applied cosmetics, all in one click. 

Customer support with chatbot service

AI chatbots allow brands to connect to their audiences, push relevant product suggestions, help navigate the website, and remind them about checkout. If a chatbot is also powered with deep machine learning, it will become better with each customer interaction. At first, developers should test how chatbots deliver responses to the most common requests and review final outputs, but as services grow, they learn new word structures, abbreviations, and slang independently. 

Claire. AI and NanoRep

An AI-based assistant for fintech service that connects banking customers to companies. Users turn to a chatbot if they require consultation regarding payments, documentation, appointment schedules, HR inquiries, and financial assistance. The chatbot can be integrated as a custom API on the website or connected to companies’ accounts on Facebook, WhatsApp, WeChat. 

A smart AI-chatbot that navigates customers through their online experience. If a website visitor can’t find a feature or page, the software will deliver context-based advice and step-by-step guidance. NanoRep uses machine learning to determine the customer’s device, location, relevant products, and analyze technical issues in real-time. Equipped with Machine Learning, their chatbots aren’t limited to predefined responses but generate new insights on the go. 

Omnichannel experience and quality control

Today, customers no longer expect to be tied to an online web store. They want to access products from mobile devices, messengers, online media pages, and emails. Businesses strive to shorten the path between customers and their purchases, taking e-commerce beyond standard web platforms. This is called omnichannel experience - enabling continuous interaction via multiple channels and seamless transitions between them. Let’s find more details below!

• Artificial Intelligence allows businesses to connect their web platforms, mobile applications, and social media pages, as well as to provide better media experience. 
• AI can remember interactions on the desktop version and let users continue their interactions from a mobile version. 
• Robotic Process Automation enables fast human-like responses on online stores, social media, messengers, allowing users to connect to their favorite brands immediately for guidance and support. 

Domino’s Pizza service

The company created an AI service to encourage its users to evaluate the pizza’s quality via social media and application. The application receives access to a device’s camera and takes pictures of pizza before it’s sold or sent to delivery. Not only can customers verify the ingredients, toppings, crusts, and product’s temperature, but also they get real-time support and feel included in the preparation process. Domino’s Pizza, on the other hand, receives precious insights on customers’ tastes and uses them to improve their services. 

Sales predictions services

Online stores often struggle with forecasting the users’ demand, especially for season deals and during holidays. On special occasions, like Black Friday or Christmas sales, customer behaviors call fall out of typical patterns. Some products go out of stock faster than anticipated whereas others don’t meet the expectations. 

Artificial Intelligence can analyze conversion, online shopping patterns, purchase habits, and market trends and determine what items will be the most popular over the course of the next week, month, quarter, or even year. This makes long-term planning and inventory management much faster, but what’s even more important, leads to a deeper understanding of customers’ tastes and priorities. 

Determine purchase patterns with Tenzo

Businesses can develop custom tools for predictive sales analytics or integrate the existing ones into their CRMs, ERPs, or warehouse management software. One of the best examples of existing AI sales analytical tools is Tenzo - the software that tracks the inventory of online stores and restaurants to determines purchase patterns and predicts the products that will go out of stock soon. 

Moreover, the tool can even order sold out items automatically, which is convenient during busy seasons. Predictive tools for sales management create a win-win situation for businesses and customers: brands make continuous profits and users can always purchase relevant products. 

Understanding user search with an AI service

Existing search algorithms heavily based on keywords - users need to find a precise description of what they are looking for. Reality is much different. Users want to be able to “just look”, similar to offline shopping. The necessity to formulate keywords limits their creativity and spontaneity. 

• Artificial Intelligence makes online shopping more natural, with natural language processing and contextual analysis of long requests, slang, idioms, and descriptive sentences. 
• Users can only describe a vague idea of the sought item, and online stores will be able to offer products that fit the context. 
• By analyzing visitors’ behavior on the website, Artificial Intelligence can understand product preferences and push items that are most likely to interest a user to the top of the page. 

Pinterest image recognition

Pinterest picked up on an interesting shopping pattern: users often want to purchase products, similar to what they saw on bloggers, celebrities, or Facebook friends. They don’t know how to describe the product and have to spend a lot of time researching brands and fashion trends. 

A recently launched AI-based image recognition tool changes the game: now it’s enough to click on any online image and see similar models on Pinterest even without leaving the image search. It’s an intuitive example of how brands can use indirect search methods to help buyers articulate their own preferences and gain a better understanding of their interests. 

Analyzing customer reviews

Fishing out insights from customers’ reviews becomes more confusing with the growing numbers of bots and trolls. A brand’s competitors can fake negative reviews, leaving both the business’s team and buyers confused. In fact, they might use Artificial Intelligence technology as their main weapon that uses language processing to fake human’s manner of speech and writes a review that will be indistinguishable from the real-life one. 

However, AI isn’t only capable of creating the issue with review transparency - it turns out, it’s just as great at solving it. Businesses and development teams can use Artificial Intelligence to detect reviews, written by bots and AI, by utilizing the same Machine Learning principles. Here’s how it works:

1. The neural network analyzes customer reviews and detects recurring patterns. 
2. AI is fed by fake reviews, in which it’s also tasked to detect similar features. 
3. The system compares patterns, detected in real and fake reviews.
4. These insights are used to create a precise detection system that distinguishes fake feedback from the real ones. 

The research by University of Chicago

Scientists had an objective of training AI to detect fake Yelp reviews. To achieve that, the natural language processing system was trained on real feedback. With the help of deep machine learning algorithms, AI was getting better at language analysis with each new review. 

The scientists report that the tool was ultimately able to detect fake reviews with much higher accuracy than an average human buyer. Such services can help customers to receive accurate insights on the products, and brands will be able to distinguish fake feedback from the real one. 

Final thoughts

Artificial Intelligence is already transforming e-commerce and customer support for businesses and customers alike. The technology connects buyers to brands 24/7, raising the bar of responsive user experience even higher. AI introduces personalization into product catalogs, real-time user consults, customer support, analytics and forecasts, and even assists in fraud detection.

Equipped with ML, AI software has endless potential for self-improvement. It’s a long-term investment that can bring profit increases for many years. We’ve already seen this happen to companies like Netflix, whose Artificial Intelligence and Machine Learning suggestion system saves the entreprise $1 billion per year. AI-powered customer support crossed the line of being a future trend and became a profitable long-term instrument.