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5 ways AI is helping to improve customer service in e-commerce

AI is transforming e-commerce customer service through chatbots, personalized recommendations, voice assistants, fraud detection and image recognition.

Artificial intelligence (AI) has revolutionized the e-commerce industry in recent years. One of the most significant ways in which AI is impacting e-commerce is by transforming customer service. AI-powered customer service technologies are becoming more common, and they are helping to improve the customer experience in numerous ways.

This article will discuss how AI improves customer service in e-commerce, with examples of companies using these technologies to their advantage.

Chatbots

AI-driven chatbots are assisting online retailers in offering prompt and effective customer support. Without human assistance, chatbots may provide 24/7 customer service. They can assist clients with information about the products, order tracking, returns and refunds, and other services. For instance, H&M uses a chatbot to assist shoppers in finding products and placing orders on its website.

However, chatbots may not always understand complex customer queries, leading to frustration and dissatisfaction.

Product recommendations

AI is capable of analyzing client data and making tailored product recommendations. E-commerce companies can provide customers with products more likely to be of interest by learning about their preferences and past purchases. For instance, using AI, Amazon suggests products based on a customer’s browsing and purchase history.

Personalized recommendations, however, may be perceived negatively by some customers as intrusive or creepy, which is one of the disadvantages of AI.

Fraud detection

AI can help e-commerce businesses detect and prevent fraudulent activity before it happens. By analyzing patterns of fraudulent behavior, AI can identify potential fraudsters and flag suspicious transactions. For example, PayPal uses AI to detect fraudulent transactions and prevent unauthorized account access.

Related: 7 Potential use cases of chatbots in banking

However, these systems may not always accurately distinguish between legitimate and fraudulent, leading to false positives that can inconvenience and frustrate customers.

Voice assistants

With the rise of voice assistants like Amazon’s Alexa and Google Home, e-commerce businesses can use AI to provide a more seamless customer experience. Customers can use their voices to order products, check order status and get answers to questions. For example, Walmart has integrated its shopping service with Google Home, allowing customers to add items to their cart and place orders using voice commands.

Still, voice assistants might not always understand consumer requests correctly, which could cause annoyance and mistakes during the ordering process.

Image recognition

E-commerce companies can enhance their product search and discovery with AI-powered image recognition. AI can make it easier for buyers to find what they want by examining product photos and recognizing characteristics like color, shape and texture. For instance, Wayfair uses image recognition technology to assist clients in locating furniture and home décor items that complement their tastes and aesthetics.

Related: 5 emerging trends in deep learning and artificial intelligence

However, one disadvantage is that image recognition may not always accurately identify products, especially if they are similar in appearance or if the lighting and background in the image are poor. This can lead to frustration and incorrect purchases for the customer.

The future of AI in e-commerce

As e-commerce continues to evolve, AI technology is playing an increasingly important role in the industry. AI is revolutionizing how online retailers conduct business, from personalizing the shopping experience to improving supply chain management. There are several areas where AI is anticipated to significantly affect e-commerce in the future.

Visual search is one such area that enables users to find things by merely submitting a photo. Retailers can use AI to analyze photos and determine product characteristics like color, style and material. Using this technology, a customer’s browsing history can be used to generate product recommendations.

Additionally, it is anticipated that e-commerce will place more emphasis on AI-powered fraud detection. AI can assist merchants in identifying and preventing fraudulent transactions by analyzing trends in client behavior. The risk of stockouts and overstocking can be decreased by using this technology to improve the accuracy of supply chain forecasts and inventory management.

Finally, thanks to its capability to analyze consumer behavior and make real-time price adjustments, AI can assist businesses in optimizing their pricing plans. Additionally, this technology can be used to design customized sales incentives and promotions for specific clients, fostering client loyalty and boosting revenue.

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7 potential use cases of chatbots in banking

chatbots can offer a convenient and accessible way for individuals to manage their personal finances, fraud prevention and more.

Chatbots are computer programs that use artificial intelligence (AI) to simulate conversations with users, providing quick and efficient assistance. In the banking industry, chatbots have the potential to revolutionize the way customers interact with their financial institutions.

Here are seven potential use cases of chatbots in banking:

Customer service

Chatbots are increasingly being used in the banking industry to provide efficient and cost-effective customer service. Customers can interact with chatbots to get answers to their banking-related queries and resolve issues related to their accounts, transactions or products. Chatbots can also be programmed to provide personalized responses to customers, enhancing the customer experience.

Chatbots can provide 24/7 customer support, allowing customers to get assistance at any time of the day or night without the need to wait for a customer service representative. This can significantly reduce wait times and improve customer satisfaction.

Furthermore, chatbots can handle multiple queries simultaneously, enabling them to handle a high volume of customer requests efficiently. This can save banks time and money, as fewer customer service representatives may be needed. For instance, the chatbot of Bank of America’s virtual assistant, Erica, can help customers with a range of tasks, such as checking their account balances, making transfers and even disputing charges.

Personal finance

Chatbots can also be used for personal finance purposes, such as budgeting, financial advice and investment guidance. They can provide personalized recommendations based on a user’s spending habits and financial goals and help users keep track of their expenses and savings. For example, a chatbot could help a user set a budget and remind them when they are approaching their spending limit in a particular category. 

Furthermore, chatbots can assist users in finding the best deals on financial products, such as credit cards, loans and insurance policies. They can compare different options and provide recommendations based on the user’s needs and preferences. For instance, Cleo, a chatbot from Cleo AI, can help users track their spending habits and provide suggestions on how to save money.

Related: How to financially prepare for a recession

Loan applications

Chatbots can be used in loan applications to streamline the process and provide 24/7 support to customers. The chatbot can guide users through the application process, answer questions and provide real-time updates on the status of their application. By automating parts of the loan application process, chatbots can help reduce errors and processing times, leading to a faster turnaround time for loan approvals. Chatbots can also assist in collecting necessary documentation and verifying user information.

Additionally, they can use natural language processing (NLP) to assess the creditworthiness of a user and recommend loan options based on their financial situation. For example, HSBC’s Jade chatbot can help customers apply for personal loans and mortgages by providing assistance and collecting necessary information.

Account management

Chatbots can help customers manage their accounts by providing account balance information, setting up automatic payments and updating personal information. For example, Wells Fargo’s chatbot, named Greenhouse, can help customers manage their accounts by providing balance information, setting up payments and even tracking spending patterns.

Fraud prevention

Chatbots can also be utilized in fraud prevention in banking. Fraudulent activities can lead to significant financial losses for both customers and financial institutions. Chatbots can help prevent fraud by monitoring and analyzing customer behavior and transactions in real-time to detect suspicious activity. Chatbots can also be programmed to send alerts to customers in case of unusual activity or suspicious transactions.

Additionally, chatbots can assist customers in reporting fraudulent activity and provide guidance on the next steps to take. With the help of chatbots, banks can improve their fraud prevention strategies and mitigate financial risks. For instance, the chatbot from Mastercard, named Kai, can help identify suspicious activities and alert customers of potential fraud attempts on their accounts.

Investment assistance

Chatbots can provide investment advice and portfolio management recommendations based on customer preferences, risk appetite and investment goals. For example, the chatbot of Wealthfront can provide investment advice and portfolio management recommendations based on customers’ preferences and risk appetite.

Related: A brief history of digital banking

Marketing and sales

 Chatbots can promote bank products and services and help customers open new accounts or upgrade their existing ones, providing personalized recommendations based on their needs and financial profiles. For instance, Ally Bank’s chatbot, Ally Assist, can provide personalized recommendations and help customers open new accounts or upgrade their existing ones.

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5 real-world applications of natural language processing (NLP)

Chatbots, sentiment analysis, speech recognition, text summarization and machine translation are some examples of real-world applications of NLP.

Natural language processing (NLP) is a field of study that focuses on enabling computers to understand and interpret human language. NLP involves applying machine learning algorithms to analyze and process natural language data, such as text or speech.

NLP has recently been incorporated into a number of practical applications, including sentiment analysis, chatbots and speech recognition. NLP is being used by businesses in a wide range of sectors to automate customer care systems, increase marketing initiatives and improve product offers.

Related: 5 natural language processing (NLP) libraries to use

Specifically, this article looks at sentiment analysis, chatbots, machine translation, text summarization and speech recognition as five instances of NLP in use in the real world. These applications have the potential to revolutionize the way one communicates with technology, making it more natural, intuitive and user-friendly.

Sentiment analysis

NLP can be used to analyze text data to determine the sentiment of the writer toward a particular product, service or brand. This is used in applications such as social media monitoring, customer feedback analysis and market research.

A common use of NLP is sentiment analysis of the stock market, in which investors and traders examine social media sentiment on a particular stock or market. An investor, for instance, can use NLP to examine tweets or news stories about a specific stock to ascertain the general attitude of the market toward that stock. Investors can determine whether these sources are expressing positive or negative opinions about the stock by studying the terminology used in these sources.

By supplying information on market sentiment and enabling investors to modify their strategies as necessary, sentiment research can assist investors in making more educated investment decisions. For instance, if a stock is receiving a lot of positive sentiment, an investor may consider buying more shares, while negative sentiment may prompt them to sell or hold off on buying.

Chatbots

NLP can be used to build conversational interfaces for chatbots that can understand and respond to natural language queries. This is used in customer support systems, virtual assistants and other applications where human-like interaction is required.

A chatbot like ChatGPT that can help consumers with their account questions, transaction histories and other financial questions might be created by a financial institution using NLP. Customers can easily obtain the information they require thanks to the chatbot’s ability to comprehend and respond to natural language questions.

Machine translation

NLP can be used to translate text from one language to another. This is used in applications such as Google Translate, Skype Translator and other language translation services.

Similarly, a multinational corporation may use NLP to translate product descriptions and marketing materials from their original language to the languages of their target markets. This allows them to communicate more effectively with customers in different regions.

Text summarization

NLP can be used to summarize long documents and articles into shorter, concise versions. This is used in applications such as news aggregation services, research paper summaries and other content curation services.

NLP can be used by a news aggregator to condense lengthy news stories into shorter, easier-to-read versions. Without having to read the entire article, readers can immediately receive a summary of the news thanks to text summarization.

Related: 7 artificial intelligence (AI) examples in everyday life

Speech recognition

NLP can be used to convert spoken language into text, allowing for voice-based interfaces and dictation. This is used in applications such as virtual assistants, speech-to-text transcription services and other voice-based applications.

A virtual assistant, such as Alexa from Amazon or Assistant from Google, uses NLP to comprehend spoken instructions and answer questions in natural language. Instead of having to type out commands or inquiries, users may now converse with the assistant by speaking.

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