Customer experience (CX) refers to customers’ impressions of a company or brand based on their interactions. CX encompasses all the customer’s touchpoints with the business. It includes purchasing points, service interactions, advertising impacts, and brand reputation.
Artificial Intelligence involves computers performing tasks that typically require human intelligence. Examples include learning, problem-solving, decision-making, and pattern recognition. AI does these tasks faster and better compared to humans, and in the process improves customer experience metrics.
Artificial Intelligence finds various applications to improve customer experience.
Chatbots simulate conversation with human users online. These computer-programs answer customer queries and resolves routine issues.
Developers design a set of rules or a script that the chatbot will follow to determine how to respond to users’ input. These rules or scripts depend on several factors, such as:
- The specific words or phrases that users type
- The context of the conversation
- The user’s past interactions with the chatbot
The first-generation chatbots provide the user with a pre-written response and process only basic questions. The latest chatbots co-opt Artificial Intelligence and generate responses using natural language processing (NLP) techniques. These chatbots handle dynamic solutions and enable users to interact with websites or apps more naturally. Users can engage with bots using spoken or written language rather than predefined programming commands.
AI-powered chatbots improve customer experience in several ways:
- Quick response: Chatbots provide immediate responses to customer inquiries, reducing the time customers have to wait for a response.
- 24/7 availability: Chatbots operate around the clock. Customers get assistance or information anytime, even outside business hours or in the dead of night. They get the same consistent level of service regardless of the hour or the day.
- Personalization: Developers can program chatbots to provide personalized responses to customer inquiries or requests based on the customer’s previous interactions with the company.
- Efficient resolution of issues: Programming chatbots to troubleshoot and resolve common customer issues save time and effort. AI-powered tools monitor the performance of chatbots in real-time and flag developers if the chatbot gets struck at some point.
- Multilingual support: programming chatbots to support multiple languages help companies with customers who speak languages other than the primary language of the business. Geographically dispersed customers can respond in the local language, forging a better connection with the local customers.
Chatbots also promote self-service in customer support, at a time when 71% of customers want to solve customer service issues by themselves.
The most prominent AI applications are to personalize customer experiences. Delivering a personalized customer experience involves moving away from the conventional approach of segmenting customers into “types” and understanding the individual customer’s tastes and preferences.” Personalization improves customer service metrics in 2023. Today, 80% of customers expect personalized offers from retailers.
AI helps businesses better understand and anticipate their customer’s needs and preferences and provide more personalized and relevant experiences. Machine learning (ML) models analyze data about customers’ preferences, behaviors, and interactions with a company. These models scour through purchase history, browser history, page clicks, time spent on a page, and other factors to decipher customer interests and preferences in real-time.
Such insights enable:
- Personalized recommendations: AI makes personalized product or service recommendations to customers based on their past purchases and interactions with the company.
- Targeted promotions. The AI software identifies customers interested in particular products or services and sends targeted promotions or offers.
- Customized content: AI tailors the content of emails, websites, and other communications to each customer’s preferences.
- Dynamic content: AI-powered content marketing solutions tailor websites and app content, offers, and recommendations based on each customer. The best example is Netflix’s recommendation engine, which predicts what each user will want to watch, down to the images that best engage the user.
- Pre-empting churn: AI and ML models use data on customer behavior to evaluate the likelihood of the customer or prospect leaving or remaining with the business. Marketers can reengage users at risk of churn with personalized marketing, special offers, and other personalized interventions.
3. Predictive analysis
Predictive analytics entails using algorithms to analyze data about past events and make predictions about future outcomes.
Using Artificial Intelligence for this purpose involves training a predictive model on a large dataset of past events and their corresponding outcomes and then using the model to make predictions about future events.
To make predictions, the model looks for patterns and trends in the data indicative of specific outcomes. For example, a predictive model trained to analyze customer data can predict which customers will likely purchase shortly.
As the model receives new data and feedback, the accuracy improves. For example, if a model predicts customers likely to churn, updating it with data about which customers did churn and why will improve its predictions in the future.
AI-powered predictive analytics improve customer experience in several ways:
- Proactive problem solving: Predictive analytics help businesses anticipate potential issues or problems and address them before they occur. For example, a predictive model might analyze customer data and identify patterns indicative of customer churn. The business may reach out to the customer and address any issues that may lead to churn.
- Personalized recommendations: A predictive model analyzes a customer’s purchases and recommends similar products.
- Improved efficiency: Predictive analytics identifies patterns and trends in data that indicate inefficiencies. The business may use such insights to optimize processes and allocate resources more effectively. Optimizing the workflow and speeding up things improves customer experiences.
4. Voice recognition
AI-powered voice recognition technology enables customers to interact with businesses through voice commands.
Developers start building a voice recognition system by collecting a large dataset of audio recordings of people speaking different languages and accents. They then use machine learning algorithms to analyze this data and learn to recognize patterns in the audio corresponding to different words and sounds.
A trained system transcribes spoken words into written text and understands spoken commands. The system improves over time as it receives new data and feedback. For example, updating a system trained to recognize English with an American accent with other accents enhances the system’s effectiveness.
AI-powered voice recognition tools:
- Improve convenience: Voice recognition technology enables customers to interact with businesses conveniently. Customers may use their voice to access information and services, which is far easier and more convenient than typing commands.
- Efficient resolution of issues: Voice recognition enables customers to speak to a virtual assistant to troubleshoot and resolve issues without waiting for the arrival of a human agent. This quick and efficient way to get assistance improves the customer experience manifold.
- Multilingual support: Programming voice recognition to support multiple languages help companies with customers who speak languages other than the primary language of the business.
AI-powered voice recognition technology enables businesses to provide more convenient, efficient, and personalized customer experiences. Customers can access information and services easier, without having to type in everything.
5. Image recognition
AI-powered image recognition technology enables customers to search for products or information by taking pictures or uploading images.
An important use case of Artificial Intelligence in 2023 is categorizing images based on characteristics such as color, shape, and type of objects. AI-powered image recognition technology enables computers to analyze and understand the content of images. The algorithm identifies scenes, people, things, and other elements within a picture and classifies images based on content. For instance, the AI tool may recognize all photos with cars and assign a specific label. AI and ML models analyze masses of such images in real-time and reverse engineer image inputs to find images with similar categorizations.
These options enable image-based search, improving user convenience. Users may, for example, click on a piece of clothing and search for stores selling the same or similar items. Brands such as Amazon, Google, and eBay have started adopting image-based search. In 2023, image-based searches will become more popular.
To build an image recognition system, developers start by collecting a large dataset of images labeled with the objects or elements they contain. They then use machine learning algorithms to analyze this data and learn to recognize patterns in the pictures that correspond to different things or factors.
A trained AI system analyzes new images and identifies the objects or elements. The technology:
- Improves convenience: Image recognition technology enables customers to access information and services. They can search or interact with a company using images rather than text or other input methods. Customers who have difficulty typing find image search options handy.
- Personalize experiences: An image recognition system may analyze the images customers upload or share to make personalized product or service recommendations.
- Efficient resolution of issues: Customers may avail of self-service troubleshooting options by taking a picture of the problem and uploading it to a virtual assistant. This quick way to get assistance saves time and improves the customer experience.
6. Streamlined field service scheduling
Artificial Intelligence streamlines scheduling and makes field service more efficient.
Automated agent workforce scheduling distributes field service work among field agents without overworking agents. The algorithm considers factors such as vacation time applied for, work hours, work already assigned, and other considerations. It also reconciles customer preferences and draws up an optimal schedule that assigns the most competent field agent to a job while ensuring minimal wait time for the customer. The automated suite harness the power of AI and ML models to analyze historical data to forecast the time it takes to complete one job and the commute time before scheduling the next appointment.
Optimized scheduling distributes workloads among field agents. It also reduces over-staffing during lean periods and ensures field agents do not have too much on their plate during busier periods.
Such optimized scheduling delivers huge efficiency improvements and costs savings. Reduced wait times and more focused field agents improve customer experience metrics.
7. End-to-end process efficiency
Artificial Intelligence optimizes processes and allocates resources more effectively, leading to faster resolution of issues, shorter wait times for service, and better work quality.
AI-powered engines track and map user interactions with a business across multiple channels. AI and ML models analyze such data masses in real-time to identify critical moments that influence customer experience metrics. Such moments are include when customers make the purchase, engage with a service or leave a provider. These insights help enterprises reinforce crucial points in the customer journey or steer away from negative paths.
AI models also enable intelligent contact routing. The model identifies the contact’s needs and routes them to the correct department or resource person. AI does away with annoying and frustrating messages such as “For sales, select 1; for customer service, select 2….” The AI-powered system analyzes voice and text to automatically decipher the user’s intent and route the user through to the best-fit service agent. Customer experience metrics shoot up.
8. Sentiment Analysis
Marketers and business analysts use sentiment analysis to gauge public opinion on the company’s products or services and make timely fixes.
Sentiment analysis involves analyzing customer feedback and other data to determine the underlying sentiment. Combining Natural Language Processing and Machine Learning makes it possible to analyze masses of text and speech conversations and interpret human language.
Such an analysis is far more effective than traditional feedback, which may lack completeness or accuracy. Since customer sentiments change quickly, it renders traditional feedback forms obsolete.
There are several ways to use AI to perform sentiment analysis:
- Text classification: AI models classify text as positive, negative, or neutral based on the words and phrases used. Training AI models using supervised learning techniques and a large dataset of labeled text data enable such classification.
- Machine learning algorithms: Machine learning algorithms analyze text data and identify patterns and trends indicative of positive or negative sentiment. In unsupervised learning techniques, data scientists do not provide the AI system with labeled data. Instead, the system identifies patterns on its own.
- Deep learning techniques: A machine learning algorithm processes large amounts of data and learns complex patterns to identify deep patterns.
9. Training customer support agents
Customer experience often depends on customer support agents’ performance and efficacy. Customer support agents and direct sales staff is often the only contact points between a company and its customer. Trainers can use artificial intelligence to coach customer service agents in several ways:
- Identifying areas for improvement: AI-powered algorithms analyze customer interactions and identify areas where agents could improve their performance. For example, an AI system might identify instances where agents were slow to respond or provided incomplete or inaccurate information. Managers can use such insights to identify areas where agents need training and provide targeted coaching to improve agent performance.
- Providing personalized feedback: AI models provide personalized feedback to agents based on their performance. For example, an AI system might analyze an agent’s interactions with customers and provide feedback on their responses’ tone, language, and content. Agents improve their communication skills and provide a better customer experience.
- Offering training and support: AI-powered tools provide real-time training and support to agents. For example, an AI system provides agents with relevant information or guidance in real time when they interact with customers. Agents become empowered to handle challenging or complex customer inquiries.
- Monitoring performance: AI tools monitor agent performance in real-time and provide feedback on areas where they excel or could improve. Managers can identify top performers and provide additional training or support to those who need it.
- AI solutions driven by NLP and Natural Language Understanding (NLU) simulate conversations with training operators before they get on calls with live users. Such interventions improve customer service agents’ confidence, reduce stress, and aid them with complex customer problems.
10. User Interface (UI) Optimization
Optimizing the user interface involves improving elements of the website or app to make navigation easy for users. Improving UI makes it easier for users to get the needed information. Users spend more time on the interface, translating to more sales, leads, or conversions.
Web developers use AI to analyze how users interact with a website or app and identify areas for improvement. For example, an AI system might identify popular pages or features. Developers could make changes to the UI to make such resources more intuitive and user-friendly. The tool also identifies spots where users spend too long, indicating something wrong with the UI.
AI-powered tools analyze user behavior in real-time to identify patterns and potential issues. Designers can make changes to the UI to improve the user experience.
As competition gets tough and technology marches on relentlessly in 2023, AI is becoming indispensable for companies to improve customer experience metrics. Customers who do not have a good experience soon leave. Conversely, satisfied customers become loyal brand advocates.