Are you considering implementing an AI customer experience (CX) but concerned about losing the human touch?
In this article, you’ll learn nine ways you can use AI to create more personalized, efficient CXs that today’s buyers are starting to expect.
What is an AI customer experience?
An AI customer experience (CX) uses artificial intelligence technology to enhance and streamline interactions at each stage of the customer journey.
It involves applying different forms of AI (e.g., machine learning) to more deeply understand and respond to your audience’s needs and preferences.
Brands and business leaders can use these insights to create more personalized, engaging customer touchpoints that make people want to keep interacting with and buying from them.
An AI customer experience can take many forms, depending on your industry and the type of product or service you sell. Here are a few examples:
An AI chatbot that captures initial lead details, answers questions and escalates more complex queries to human agents
An AI-powered search function that allows users to find what they’re looking for faster by uploading images or speaking
AI technology that collects crucial data about each customer to enable more targeted and effective marketing strategies
Before diving into these CX applications, let’s cover the technology that makes it all possible.
What technologies create an AI customer experience?
In simple terms, artificial intelligence technology allows computer systems to perform tasks as a human would. However, using AI is a complex field with many different branches.
Here are four typical AI technologies used to power today’s customer experiences.
1. Natural language processing (NLP)
Natural language processing is a type of conversational AI that helps computers understand and work with human language.
It allows AI to generate text, answer questions and understand the intent behind words and phrases.
Note: Any AI that can create its own content (e.g., text, images, videos or music) is known as “generative AI”.
Natural language processing is used in CX AI solutions like:
AI-powered chatbots and virtual assistants. NLP enables bots and virtual assistants (like ChatGPT) to understand and respond to customer inquiries in natural language
Sentiment analysis. NLP can analyze customer feedback, reviews and social media mentions to gauge customer sentiment and emotions
Here’s a university chatbot using NLP to answer a question about admissions.
The technology allows the AI software to understand the context of the query and respond appropriately with suggested next best steps.
In this case, the chatbot answers the initial question and progresses the conversation by asking the user if they’d like to speak to an admissions counselor.
2. Machine and deep learning
Machine learning and deep learning are both types of AI that allow software to self-learn and improve over time.
Here’s how the two relate:
Machine learning is a branch of AI that allows computers to learn and improve from experience without explicit programming
Deep learning is a more advanced form of machine learning that helps AI handle more complex tasks (e.g., image or speech recognition) using brain-like structures called neural networks
Machine learning analyzes customer data in CX to provide personalized recommendations and improve search results. Deep learning powers advanced features like voice recognition for more natural and accurate customer interactions.
Predictive analytics are machine learning models CX teams use to analyze historical data and predict future interactions.
For example, they can tell you how likely a customer is to churn so you can work to prevent it.
3. Computer vision
Computer vision is a type of AI that enables computers to see, interpret and understand images or videos similarly to humans.
Instead of eyes and a brain, the software uses a sensing device and algorithms to break down images and videos into pixels and recognize patterns. The computer vision software then uses those patterns to determine objects, people or scenes.
Computer vision technology is used for AI customer experiences like:
Visual search. Computer vision allows customers to search for products using images rather than text
Image recognition. Computer vision can identify products, analyze customer-submitted images for support and ensure quality control in physical stores
Both applications can optimize customers’ shopping experiences and make it easier to find desired items online.
4. Augmented reality (AR) and virtual reality (VR)
Augmented and virtual reality technologies create immersive and interactive experiences for individual customers.
Augmented reality overlays digital information onto the real world. It uses a camera and software to blend both views on your device. Virtual reality immerses you in a fully digital environment with a headset that responds to your movements.
For example, potential customers can use AR to “try on” clothes or accessories. Using VR, they can “walk through” properties.
AR and VR aren’t solely powered by artificial intelligence, but they often use AI to enhance the experience. For example, VR headsets can use AI to determine where your hands are, while AR apps use it to recognize a table surface and place a digital object on it.
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9 ways to use AI to enhance your customer experience
Each of the above AI technologies significantly enhances CX by making interactions more efficient, personalized and engaging.
Here are nine ways to experiment with adding AI to your customer experience.
1. Leverage AI-powered CRM data
A successful AI customer experience must start with accurate data. Customer relationship management (CRM) software is crucial for storing all customer information so you can create more personalized and cohesive interactions.
According to Twilio, almost 90% of leaders believe this personalization will be critical to their business’ success in the next three years.
You can use an AI CRM like Pipedrive to centralize information from multiple sources and create a unified view of each customer
Analyzing this database can help you better determine how to reach specific customer groups with content and offers they’ll likely enjoy.
While your team can use your CRM to automate repetitive tasks (e.g., sending follow-up emails or scheduling calls), AI algorithms gain crucial insights into the buyer journey in the background.
You can then access these insights in decision-making to create more targeted and effective customer experiences.
For example, Pipedrive’s AI Sales Assistant continuously analyzes each sales rep’s deals, contacts and emails to pinpoint hot leads and pass on actionable data. Identifying these patterns enables the personal assistant to recommend deals with the highest potential for closing.
AI Sales Assistant will also suggest the most important activities (including automated follow-up reminders) to get these sales over the line.
Thanks to machine learning, the AI learns from and adapts to your team’s workflows, industry and tactics to become smarter and more relevant over time.
You can use these AI-powered CRM insights to make every sales and marketing interaction feel more personal. Over time, this bespoke approach can encourage retention and customer loyalty by mixing the efficiency of AI with human connection and trust.
2. Improve self-service options with an AI chatbot
AI can reduce frustration for customers awaiting support outside business hours by offering round-the-clock customer service. NLP-powered chatbots can kickstart interactions, answer queries and resolve basic issues without human intervention.
When issues become more complex, chatbots can pass the conversation to the most appropriate team member.
For example, tech company IBM’s chatbot offers two routes for new site visitors: support or sales.
The support option leads to various self-service troubleshooting options. The sales option offers a call, email meeting or live chat if reps are online.
AI chatbots can handle a range of queries. However, it’s crucial that the technology can quickly narrow down a customer’s needs and escalate to a human agent when needed.
According to Salesloft research, business-to-business (B2B) live agents have the best chance of booking a meeting when they respond within two minutes of a site visitor engaging with a chatbot.
Leaving people to wait for five minutes increases the risk that they’ll leave your site by 10x. The risk grows to 100x after 10 minutes.
Note: Pipedrive’s Chatbot doesn’t use AI, but you can still use it to start conversations and provide leads with the information they want.
3. Personalize product recommendations
You can use AI to reduce the likelihood of buyers feeling overwhelmed or dissatisfied by irrelevant product suggestions.
Machine learning algorithms can help you analyze customer behavior and preferences to suggest best-fit products. The goal is to boost sales without additional manual effort.
For example, Netflix’s machine-learning capabilities drive its entire personalized customer experience. The software giant uses algorithms to categorize, produce, encode and stream its content, as outlined in the video below.
The AI continuously learns from interactions to improve its accuracy over time. If someone starts watching a new category of shows and movies, the software will drip-feed those types into future recommendations.
4. Provide a modern search experience
AI can provide more intuitive options for customers struggling to find products with your site’s text-only search function.
Augmented search (including computer vision) allows customers to search for products by uploading images, making it easier to find similar items.
For example, Prada allows users to search with visuals instead of trying to type in a long description for a product.
The visual search option allows customers to find specific products when they’re close to buying. Removing friction reduces the chance they’ll give up and abandon the purchase.
Augmented search doesn’t always have to include images. Product discovery platform Syte’s augmented search feature consists of an AI engine that automatically enhances product information.
The feature uses NLP to pinpoint more accurate context and intent to provide more hyper-relevant results. For example, the system understands a user searching for a “dress shirt” wants different types of tailored shirts and blouses rather than dresses.
According to style platform Syte’s research, sessions with this type of augmented search have 48% higher conversion rates than the average. When customers have the tools to quickly find what they’re looking for, they’re more likely to purchase.
5. Speed up email responses
Generative AI can write and inspire sales and marketing email content, preventing writer’s block and speeding up response times.
You can use these tools to automatically respond to common email inquiries and help you create personalized outreach in less time.
For example, Pipedrive’s AI email writer lets you turn natural-language prompts (e.g., “Write me a follow-up email with a discount code”) into professional, high-quality sales emails.
While the first attempt is unlikely to be the final draft, you can use it as a foundation. You can also tweak your prompt with more context or direction to get more relevant outputs.
Choosing the tone and length of the message can also ensure that it feels as relevant to the recipient as possible.
The AI email summarization tool can help you generate concise and actionable summaries for any incoming communication.
Instead of spending time scrolling through threads and potentially missing crucial points, use AI to get a breakdown in seconds.
For example, you can learn who’s satisfied or unhappy, how ready they are to buy and what to do next.
Using these insights, you can write the perfect response to move them along your sales cycle
6. Implement voice-activated assistance
Voice-activated assistance can improve accessibility for site visitors and create a more convenient, hands-free experience for customers on the go.
According to Statista research, almost a quarter of US consumers purchase items using their voice. You can integrate voice recognition technology into your product, app or specific parts of your CX to allow voice commands.
Let’s say you implement AI that allows customers to place orders quickly or reorder using their smartphone’s microphone.
Making the purchasing experience faster and more convenient also opens your brand to the visually impaired.
For example, Bank of America’s customers engage with its real-time virtual assistant, Erica, two million times daily.
Erica handles a wide range of financial services through voice and text. The assistant helps clients with use cases like:
Finding account numbers and transactions
Transferring money
Managing recurring subscriptions
Understanding spending behaviors
Staying informed about refunds and deposits
When customers use Erica as a voice assistant, the system saves conversations for 90 days for the human team to analyze and improve future listening skills.
While it took four years for Erica to reach one billion interactions, the mix of machine learning improvements and 50,000 manual updates helped it achieve a second billion just 18 months later.
7. Analyze sentiment and anticipate customer support
AI can help businesses quickly identify and address negative feedback to improve customer satisfaction. Pinpointing potential issues before they become problems can also prevent frustration.
NLP and sentiment analysis can automatically analyze customer reviews and social media comments to understand how customers feel about your products and services.
For example, AI tools can tell you areas you’re doing well in (e.g., pricing) or where you could improve (e.g., refund processes) at a glance.
Customer experience management software Medallia uses AI to examine customer interactions for feedback and offer insights.
The AI technology analyzes text from interactions to reveal emerging patterns or issues that need attention.
Users can use the feedback to inform future strategies, improve particular product aspects and make all demographics happier.
You can also leverage predictive analytics to anticipate customer needs and issues before they arise. For example, analyzing patterns in customer support interactions can help you identify pain points that precede account cancellations, such as spikes in technical complaints.
If the system detects a user exhibiting similar behaviors, your customer success team can proactively contact the customer with personalized solutions before they decide to leave.
Aside from comments themselves, customer engagement and loyalty metrics can indicate positive or negative sentiment. For example:
Customer retention rate. A high percentage of customers who continue to buy from you can indicate positive sentiment
Customer satisfaction (CSAT). A mediocre score out of 100 (that represents how satisfied customers are with specific processes or interactions) suggests you could improve sentiment
Referral rate. A high percentage of sales from customer referrals can indicate positive sentiment
Customer churn rate. A high percentage of customers who stop buying from you can indicate negative sentiment
You can track key performance indicators (KPIs) like these with dedicated software (e.g., Twilio Engage or Qualtrics).
8. Offer virtual try-ons
Allowing customers to try out products virtually can help them feel more confident about buying online and lower the potential for returns.
Augmented reality filters are a widespread technology because of their low barrier to entry. For example, brands don’t need millions of dollars to create AR experiences. They only need a developer who can use Snapchat’s free software.
The social media platform is driving awareness of its Snap AR platform by partnering with big brands like Amazon.
The e-commerce giant created a filter allowing users to try various eyewear styles virtually.
Customers only need the app and a smartphone to experience the filters.
AR filters are most popular for trying clothes and accessories or virtually “placing” objects (e.g., furniture) in a room to see their impact. However, B2B brands can still get involved in the tech.
For example, HR software Dayforce uses AR filters to enhance its presence with “virtual stands” and 3D objects during conventions to drive brand messaging.
The AR achieved its goal of building brand awareness and encouraging social sharing by providing a more memorable experience for attendees.
9. Transcribe customer calls
You can use AI to reduce customer irritation with specific processes in your call center.
AI-powered speech analytics tools can transcribe and analyze calls to identify common issues and measure agent performance.
Extracting these insights gives you a better understanding of your overall customer service experience so you can improve it.
For example, the bank American Express uses third-party AI software to collect data from its omnichannel call center.
The software collects and analyzes call transcriptions to assess user experiences like account sign-up and credit interactions.
The software can determine customer emotion, how polite the agent dealing with the query was and whether the overall experience was positive or negative.
The brand then applies these “learning points” to enhance products, services and the entire AmEx customer lifecycle.
Final thoughts
By experimenting with the power of AI, you can discover what truly enhances your interactions. However, a focused, incremental approach is crucial for any new tactics to help you track its impact more accurately.
If you’re unsure where to start, consider trying Pipedrive’s 14-day free trial. Using up-to-date CRM data throughout your CX ensures you stay aligned with customer behaviors and preferences to make more informed and impactful decisions.
