With online shopping, self-service kiosks in stores, and online chat support, the customer today can buy whatever they seek without leaving their home or talking to another person. The convenience appeals, and the abundance of choice can lead to cost savings. Yet many contemporary consumers want one thing back: personalization.
Ironically, AI-driven customer engagement is helping businesses provide brand interactions that connect on an individual level. Leveraging vast amounts of customer data to provide seamless interactions across channels and drive meaningful engagement, artificial intelligence can revolutionize relationship building to maximize conversions.
Why Personalized Customer Experiences Matter
Personalization drives performance and better customer outcomes, according to McKinsey’s Next in Personalization 2021 Report. The consulting firm reported consumers expect personalized interactions (71% of them) and more than three-quarters of them get frustrated when that doesn’t happen.
Additionally, McKinsey found, companies effectively leveraging personalization generated 40% more revenue than their competitors. In fact, they suggested, “Across US industries, shifting to top-quartile performance in personalization would generate over $1 trillion in value.”
The study, and others like it, suggest personalized customer experience is now table stakes for business success.
Nevertheless,according to Forrester , customer experience has declined for the past three years. Recognizing customer experience benefits customer satisfaction, retention and lifetime value, many companies claim to be “customer-centric.” Yet they don’t have the understanding needed to effectively bridge the gap between wanting to connect with consumers and providing them with a personalized customer experience.
Customer satisfaction is also declining. In the latestAmerican Customer Satisfaction Index, consumer sentiment faltered 0.4% in the first quarter of 2025 and by 1.3% for the past year to 77. This contraction represented the only time the Index has remained flat or dropped for four consecutive quarters since the pandemic.
“The reality is customers are providing all kinds of signals. Companies just need to learn how to listen,” according to a Harvard Business Review Analytics Services study of customer service success in digital interactions.
Listening gets easier with AI. In near real-time, businesses can collect and analyze relevant behavioral, transactional and lifestyle data about consumers and shift away from generic messaging in favor of more individualized journeys.
How AI Powers Personalization Across the Customer
Journey
Artificial intelligence — whether it’s through machine learning, natural language processing or generative AI — can powerpersonalized customer experiences in several ways. Before diving into specific examples in the next section, let’s first consider potential applications at a broader level.
With AI, a business can deliver personalized experiences by:
- Segmenting customers on a granular level (e.g., an online retailer shows its cares by reaching out with a communication targeting their customers in neighborhoods affected by the severe weather events)
- Analyzing consumer behavior to identify the specific life cycle stage (e.g., reviewing data to filter out loyal customers who might be open to an upsell or tracking customer acquisition dates to recognize an anniversary with the brand)
- Generating coupon codes with individualized rates based on the customers’ past purchase history (e.g., rewarding those who typically buy more with higher discounts or providing promotional rates for brands the individual regularly purchases)
- Reviewing data at scale to determine the best timing for communications with customers or the appropriate channel (e.g., looking at historic purchase data and using forecast modeling to predict when people who download a physical fitness app typically churn, then tailoring an outreach to keep them engaged)
- Recommending the best cadence for customer communication and crafting messaging personalized to the individual yet based on what works best for that business (e.g., a real estate brokerage diving into the data to determine the number of touches needed to keep its business in the mind of the homeowner who might put their home on the market)
- Creating an extensive database of common customer questions and concerns from which AI tools can generate FAQs, scripts for customer service representatives, or directly provide chatbot support (e.g., by connecting backend systems to customer data such as purchase history or preferences AI chatbots can provide 24/7 support while handling multiple conversations at once in a personalized way. Large language models (LLM) enable these chatbots to engage in natural, engaging conversations that are human-like for the consumer)
Use Cases That Link AI Personalization to
Conversions
Many consumers expect high levels of personalization because they’re already benefiting from AI-powered hyper-personalization on sites such as Netflix or Amazon.
Netflix asks users to give content they watch thumbs up or thumbs down. This helps guide the AI algorithm to suggest other content the viewer might enjoy based on analysis of the company’s vast database of viewers with similar streaming habits and preferences correlated with all the platform’s programming.
Similarly, Amazon makes product recommendations based on the site visitors’ browsing history. It also uses data insights to upsell and cross-sell with the “customers who bought this item also bought” feature.
Furniture and home goods retailer Wayfair uses its AI-powered product recommendation engines to boost average order value. Analyzing the customer’s browsing history (e.g., how long they linger on a particular sofa or what product reviews they interact with), color preferences, price sensitivity, and past purchases, the company suggests items that “Complete the Look” to encourage browsing customers to build a room setup rather than purchase a single item.
Cosmetics retailer Sephora is another brand that leans into AI to improve customer experience with personalization. It uses AI to analyze the customers’ skin tone from a picture they upload to the application. The technology then recommends the most compatible shade in Sephora’s inventory, which improves customer engagement and reduces product return rates.
But AI in customer experience isn’t exclusive to retail. Take Wells Fargo’s new feature in which its AI engine analyzes the individual’s financial transaction behavior and account activity to forewarn you of upcoming expenses. Other banking institutions use AI tools to deliver spending trend alerts and cash flow predictions based on the individual clients’ account activity and recurring bills.
In the hospitality sphere,Hilton uses artificial intelligence to predict guest needs and a generative AI coaching experience to train its guest services team and improve how staff recognize the most loyal customers.
Tools and Platforms for AI-Driven Customer
Personalization
The variety of AI-driven tools and platforms available to support personalized CX continues to grow. Several leading solutions already have mainstream momentum, such as:
- HubSpot, which partners customer relationship management with AI personalization of email, web content, and workflows
- MailChimp, an all-in-one marketing platform providing AI-driven behavioral targeting and smart content
- Salesforce, which offers enterprise-grade AI to enable its users to streamline sales, manage customer relationships, and improve customer service
- Adobe Sensei, which uses AI and machine learning to tag and categorize images and videos, optimize creative content, and help with customer segmentation
- Dynamic Yield, which integrates with other business systems to deliver consistent, personalized digital customer experiences across multiple channels
- LimeSpot, which powers personalized product recommendations and upselling and cross selling for ecommerce sites using Shopify
- MonkeyLearn, which provides no-code text analysis and data visualization tools to simplify classifying customer feedback sentiment and develop predictive analysis
Integrating these leading tools with other systems to gain broader access to business data can also enhance personalization efforts while streamlining processes and saving resources.
Challenges in Implementing AI for CX
AI applications in personalized marketing and improving customer experience will continue to grow as the technology evolves. Still, there are certain challenges to consider when looking to implement these tools. These include needing to address:
- Data quality issues as inaccurate or inconsistent data input into the AI can lead to biased decisions, irrelevant recommendations, or incorrect personalization
- Lack of in-house AI expertise which can make it more difficult for CX teams to align their goals with the capabilities of the technology
- Need for data privacy and compliance since the machine learning models and data analysis often rely on troves of sensitive customer data
- Erosion of trust if customers have to interact with tone-deaf chatbots or get recommendations that don’t reflect their needs or pain points
Best Practices to Transform CX with AI Personalization
One best practice remains constant across any area of business: establish objectives and develop key performance metrics to evaluate how well those goals are being met. Without a clear idea of AI personalization, the organization risks wasting time and money pursuing technological innovation that doesn’t move the needle.
Another best practice involves data governance. “Garbage in, garbage out” is an oft-repeated phrase in data analytics. Any AI initiative’s success relies on effective integration of accurate, quality data that has consistency and bridges organizational silos for the most comprehensive view of the customer’s journey.
It is also advisable to view AI as a partner to human talent, rather than as a replacement for human talent. While some predict AI will replace humans in many areas of work, that future isn’t here yet. It is important to retain human oversight of AI to confirm accuracy and appropriateness.
Guarding data privacy and security is essential too. Communicating with customers about what data will be used and how to avoid backlash. Transparency about data handling and a robust data security policy also protect the business from compliance issues and help manage risk.
Incorporating AI into the Customer Journey
Today’s customers expect a personalized journey with a business, whatever the industry. With personalization an imperative for customer experience, more organizations are turning to AI technology to enable and scale personalization efforts with a specificity and speed difficult for a human team to match on their own.
Research indicates that brands that embrace AI for strategic personalized marketing and improve customer experience across all channels outperform their peers. Fortunately, this technology has become more accessible to all — from deep-pocketed enterprises to smaller businesses.
By heeding the challenges identified in this article and adhering to its suggested best practices, any organization can begin taking small, data-driven steps to interact with their customers in a more personalized way. A personalized CX can benefit the bottom line by improving customer satisfaction, reducing churn, driving conversions and increased revenue.
FAQs
1. What is AI-powered personalization in customer experience?
A. AI-powered personalization in customer experience is the use of artificial intelligence to adapt dynamically to the individual customer’s preferences, behaviors, and needs. AI can help businesses tailor interactions, content, and services to deliver relevant recommendations, targeted messages, and real-time support.
2. How does AI improve the customer journey?
A. AI improves the customer journey by delivering personalized, seamless experiences at every touchpoint. By analyzing consumer behavior and preferences, the business can better anticipate customer needs, offer tailored product recommendations, automate support through chatbots, and optimize communication timing and channels.
3. What are some examples of AI tools for customer personalization?
A. Examples of AI tools for customer personalization include MailChimp’s automated product recommendations, Sprout Social’s sentiment analysis, Optimizely for dynamic content creation, and HubSpot’s customer relationship management which uses AI to score and segment leads. Using generative AI like Claude or ChatGPT can also simplify the creation of responsive, tailored content.
4. Are there any risks in using AI for personalization?
A. There are some risks in using AI for personalization. Watch out for inaccurate and inconsistent data. Plus, beware of potential compliance issues and data privacy, and security concerns. Mitigate risk by incorporating human oversight of the AI tools.
5. How can small businesses use AI for personalized CX?
A. Small businesses can use AI for personalized customer experiences in powerful ways. A small business might leverage AI for personalized recommendations, chatbots and virtual assistants, predictive analytics, dynamic website content, sentiment analysis, review management and tailoring loyalty programs.
