Long gone are the days when customers used to wait 20-30 minutes to get on call with a care executive. Now, they will abandon their call in 2 minutes 36 seconds.

Thanks to AI, businesses can now deliver real-time responses and personalize engagement across multiple touch points. 

If you are a business looking to explore AI in customer experience, this guide will help you understand AI's utility in depth. You’ll also learn how to choose and implement the right AI tool.

What is artificial intelligence in customer experience? 

Artificial intelligence in customer experience involves implementing AI technologies to enhance interaction between businesses and their customers. The goal is to deliver fast, efficient, and personalized experiences that allow businesses to work more productively and save costs.

Integrating AI into customer experience helps businesses understand what their customers want even without asking. It utilizes advanced algorithms to analyze customer data, predict preferences and improve response times, enhancing the overall customer experience. 

Benefits of artificial intelligence in customer experience

With the rise of AI, there is a question that people are desperately trying to figure out: Will AI replace human agents? The simple answer is no. 

Although Goldman Sachs says AI could replace the equivalent of 300 million full-time jobs, most experts believe that human efforts in customer service will be augmented with AI-driven automation and not replaced.

Let’s find out how:

Delivering faster responses with immediate chatbot assistance

In 2023, 44% of customers appreciated chatbots for helping them find relevant product information before making a purchase, and another 35% were keen on using chatbots available on brands’ websites.

One of the primary reasons customers love chatbots is that they can provide instant responses. Additionally, chatbots can also support agents with pre-written responses to customers based on the conversation’s context.

Forecasting customer needs using predictive analytics

58% of customers are willing to pay more for better customer service. AI for CX can analyze past interactions and behaviors to engage customers proactively and address potential concerns before they arise.

This allows businesses to identify signs of dissatisfaction, provide quality assurance, and discover up-selling and cross-selling opportunities, improving the overall customer experience. 

Maintaining employee stability through proactive retention analytics

AI can identify at-risk employees and address their concerns before they decide to leave. This is done by analyzing patterns in employee behavior, performance metrics, and engagement levels. 

For example, a company might notice that employees with prolonged project deadlines and fewer growth opportunities are more likely to leave. HR teams can then intervene by offering professional development programs, mentorship or improved incentives to retain top talent.

Delivering tailored experiences to build lasting relationships

According to a report by Deloitte, brands that are focused on delivering personalized experiences are :

• 48% more likely to exceed their revenue targets 
• 71% more likely to improve customer loyalty

AI can tap into customer behavior data to reveal preferences, helping businesses craft customized interactions and recommend products and services that align with individual tastes and needs.

Efficient resource allocation with intelligent lead scoring

Companies can leverage AI to implement smart lead-scoring systems that prioritize customers based on various factors, including engagement levels and industry relevance. 

This allows sales teams to focus their efforts on leads most likely to convert, resulting in optimized resource allocation. 

Amplifying business outcomes through sales prediction

AI-powered sales prediction tools can efficiently analyze historical data, customer interactions and external market factors to generate accurate forecasts. These insights help the sales team to make data-driven decisions and refine strategies. 

By leveraging AI, businesses can achieve new levels of growth, optimize resource allocation and ultimately amplify their success.

Examples: Uses of artificial intelligence in customer experience

Meeting customers' demands is one of the biggest challenges businesses have faced. But with AI, things are changing for the better. Gartner predicts that by 2025, 80% of customer service organizations will utilize AI technology in some form to improve customer experience and agent productivity. 

Let’s understand how some brands are excelling in meeting customers’ demands with the following AI customer experience examples :

Plivo CX’s self-service chatbot

The cloud-based omnichannel customer service platform Plivo CX comes with an advanced AI-powered self-service chatbot driven by OpenAI’s latest model. Once trained by a custom dataset of the company, the chatbot is designed to answer common queries swiftly and accurately. 

Plus, the chatbot is available 24/7. Even if the customers put the conversation on hold, chatbots can pick up where they left off. Plus, for complicated issues requiring human touch, the chatbots can hand them over to relevant agents, along with detailed notes and summaries.

Some of the other notable features of Plivo CX include:

• A unified agent desktop for omnichannel support helps agents quickly switch between channels with complete visibility of customers’ issues
• Integrations with top marketing platforms such as HubSpot, Help Scout, Zendesk, and other home-grown systems
• Visual workflow builder for creating automatic workflows
• Live coaching so that agents always remain knowledgeable about the process
• Metrics and Reporting to analyze customer interactions and gather feedback in real-time

Shopify’s advanced AI implementation

Shopify utilizes AI to analyze customer data such as browsing history, purchase patterns and demographic information to tailor product recommendations for each shopper.

Unlike traditional recommendations, AI product recommendations on Shopify shift based on real-time insights and user interactions.

Shopify’s advanced AI also helps in :

• Predictive inventory management to minimize overstocking and stockouts
• Fraud detection and prevention to provide a secure and trustworthy shopping environment
• Enhanced supply chain management by improving logistics and delivery processes

Amazon’s product recommendation system

Amazon takes product recommendations one step further by personalizing product descriptions that are more relevant to customers.

For example, instead of using generic phrases like “users also search for” or “more like this,” they provide more specific product descriptions such as “Cookie boxes for Christmas” or “Curling rod for straight hair.”

It also uses super-specific product descriptions to make it easy for customers to quickly locate a product based on the attributes they are looking for, such as “medium-sized bed for small dogs.”

eBay’s Gen AI seller function

Earlier in 2024, eBay launched a tool allowing marketplace sellers to remove the background around the product on a photo and replace it with a more professional-looking background created by Gen AI.

This helps small-range sellers who cannot invest in studio equipment create a professional shoot that appeals to more customers.

How to evaluate an AI customer experience solution

With many AI customer experience solutions in the market, it is extremely difficult to determine the best option. Moreover, most vendors glorify and present their AI capabilities in different ways to stand out from the crowd. Evaluating the right solution requires a strategic approach to ensure it aligns with your business needs and goals.

Consider these key factors when assessing an AI customer experience solution:

Feature sets

Look for platforms that offer an extensive range of features to support diverse needs. Consider features such as omnichannel routing, advanced personalization and real-time data analytics to empower your team to test and refine customer experiences.

Moreover, choose a platform that can handle basic and advanced scenarios to help you scale your efforts as needed.

User-friendly interface

An intuitive and user-friendly tool enables your team to set up, execute and analyze experiments without extensive training. A well-designed interface can minimize the learning curve and empower your team to focus on driving results rather than learning how to use a tool.

Advanced AI and machine learning capabilities

Opt for platforms with sophisticated algorithms that can analyze user behavior, predict trends, and adapt to customer preferences in real time. This allows for more precise targeting, personalized experiences and continuous optimization.

Robust security and compliance measures

Protecting customer privacy is non-negotiable for building trust and loyalty. Ensure the platform adheres to stringent security standards and complies with relevant data protection regulations, such as GDPR, to avoid legal complications and maintain customer trust.

Cost-effectiveness

Examine the platform’s pricing model to ensure it complements your budget and functionality. Beyond the initial costs, consider long-term expenses, such as feature upgrades, additional integrations, and ongoing support.

Proven track record

Look for platforms with a history of success with similar businesses. Evaluate case studies, customer testimonials and independent reviews to gauge the platform’s reliability and performance. A strong track record means the platform can deliver the required results and ensures confidence in your investment.

Steps to implement an artificial intelligence customer experience solution

Implementing AI requires a clear understanding of customer needs, seamless integration with existing systems, and proper team training. Here are the steps to help you get started:

1. Set your objectives

Identify the specific customer experience goals you want to achieve. Whether you are creating a more personalized customer journey, accelerating response times or automating repetitive tasks, defining your objectives will help you pick the right AI tool that fits your needs perfectly.

2. Select the right tool

Pick a tool that caters to your identified needs and objectives. Ensure the tool is scalable and adaptable to changing business needs. Consider other factors such as pricing, long-term viability, and support for these tools. 

3. Train and fine-tune the AI model

Engineers typically perform this step, which involves adjusting the model’s parameters to ensure accuracy. The process involves:

• Gathering relevant data from chat logs, email exchanges, and call transcripts
• Cleaning and categorizing the data before feeding it into the AI model
• Continuous fine-tuning by incorporating new data and feedback from customer interactions
• Testing AI responses before deployment to test for accuracy, relevance, and tone

4. Integrate with CX channels

Integrate the AI solution into your current systems, including your CRM, live chat or email platforms. The IT team typically does this, which involves API integrations, data syncing, and testing to ensure there are no errors. 

5. Ensure compliance with data protection laws

Maintain strict compliance with relevant data privacy laws and regulations, such as :

• General Data Protection Regulation (GDPR)
• Health Insurance Portability and Accountability Act (HIPAA)
• Payment Card Industry Data Security Standard (PCI DSS)

Also, regularly review and update your privacy policies to evolve with changing regulations.

6. Collect feedback on the new processes

Once done, collect feedback from agents and customers to identify areas for improvement and optimize where needed. Address any concerns promptly to maintain trust and satisfaction.

7. Train your team

To fully utilize the power of AI, train your customer service team on how to use the tools effectively. The training should cover more technical aspects, understand limitations, and effectively handle an escalated customer.

8. Measure return on investment (ROI)

Evaluate the ROI by assessing metrics such as customer satisfaction scores and response times. Makes adjustments where necessary based on performance data and evolving customer needs.

Accelerate response times with Plivo CX’s AI customer experience solution

Plivo CX is built as an all-in-one, AI-driven customer service solution for the modern era— agile and adaptive.  It features an AI-powered self-service chatbot, developed and powered by OpenAI, to provide exceptional omnichannel customer service for businesses of all sizes. 

Here is what it has to offer:

• Super-fast responses: AI chatbots efficiently handle customer inquiries, including exchanges, information updates, and order tracking in real-time
• Train as you need: Integrate with custom knowledge base and train AI chatbots to make informed decisions based on your company’s policies and brand guidelines
• Easy to update: Whether promoting offers, launching new products, or communicating policy changes, update the chatbots as needed. It is a simple and cost-effective process 
• Seamless escalation: For more complex issues that require human intervention, the chatbot can hand over the problem to the most suitable agent with complete context
• Keep it on track: Set boundaries to align your chatbot with your company’s guidelines, policies, and processes

As noted before, Plivo CX also features the unified agent desktop, which facilitates seamless communication across channels—email, SMS, chat, WhatsApp, and voice—to empower support teams to operate more efficiently and effectively than ever before.

To try Plivo CX today, book a demo.