How AI is Transforming Customer Experience Through Personalization Strategies
Personalization tools are changing with such blinding speed. It seems there’s a new tool, capability, or feature available daily. Thanks to the promise of artificial intelligence (AI), machine learning (ML), and the availability of a nearly endless stream of consumer data, personalization can be applied much more easily across channels. It can also be done with much more precision.
AI uses data to contribute to more personalized experiences in a few different ways. But, data is central to the way AI-powered tools improve customer experience.
Capabilities that include making predictions (e.g., predictive analytics), understanding and processing human language (e.g., natural language processing), and creating original text, graphics, and other content (e.g. generative AI) work together to enable and facilitate customer experience personalization.
AI-powered personalization strategies are also much more scalable than ever before. They make it possible to increase relevance while reducing the effort involved in providing hyper-personalized individualized experiences. They ultimately improve customer experience metrics like customer satisfaction (CSAT) and customer effort scores (CES).
What is AI-driven customer experience?
AI-driven customer experience is the sophisticated use of artificial intelligence technologies to elevate every facet of a customer’s interaction with a brand. Key AI technologies involved include natural language processing (NLP), text analysis, and sentiment analysis. These tools serve a dual role: they replace manual, time-consuming processes and simultaneously offer deep analytics capabilities.
In the sphere of AI customer experience, the application of AI is far-reaching, encompassing everything from customer support and sales to marketing initiatives. AI algorithms are designed to automate repetitive tasks, allowing human employees to focus on more complex, value-added activities. Furthermore, these algorithms sift through large datasets, extracting actionable insights that would be nearly impossible to identify manually.
What sets AI-driven customer experience apart is the ability to analyze unstructured data, such as customer reviews, social media chatter, and even voice recordings from customer service interactions. By evaluating this data, businesses can identify customer needs, preferences, and pain points more precisely. This newfound intelligence empowers teams to make data-driven decisions that result in more personalized, efficient, and satisfying customer experiences.
How AI Can Improve the Customer Experience Today
Just about everything we do online creates a trail of data. Interacting on social media, streaming online games and movies, browsing the internet, and searching for recipes on your smartphone results in data.
The data keeps piling up and there’s more of it every year. According to Statista, the world created about 79 zettabytes of data in 2021 and that’s predicted to jump to 181 zettabytes by 2025.
A single zettabyte of data is nearly unfathomable for the human mind to comprehend. Put it this way: if you needed to store a single zettabyte of data on a bunch of 1 terabyte hard drives, you’d need 1 billion hard drives to do it.
The strength of AI-powered customer engagement comes from its ability to manage data in several ways including:
- Data collection – AI tools collect data on customer behavior and preferences, categorize it, and store it in ways that make it actionable.
- Data analysis – AI automates many data analysis functions including data processing, anomaly detection, and reporting.
- Personalization – AI creates personalized experiences from data by identifying patterns and behaviors gleaned from large datasets and creating audience segments from this information. It can then recommend products, content, and messaging to the appropriate segments across a variety of different digital experiences (e.g., your app vs. your website vs. your newsletter).
Examples of AI Supporting Customer Experiences
An example of AI-powered data analysis in action is Monetate’s automated personalization platform which uses data to automatically deliver the most relevant experience to each individual website visitor. Monetate uses a combination of 3rd party and customer data to create both custom and out-of-the-box behavioral targets which it uses to personalize customer experiences.
Examples of AI supporting customer experiences include:
- Reebok’s home page customization which delivered unique content to visitors based on their past shopping behavior.
- Nespresso’s personalized content that educates and guides visitors with product recommendations and relevant information like recipes matched to their past behaviors and purchases.
The use of AI in shaping customer experiences is more than just a trend; it’s a transformative approach that’s making a significant impact across various industries. From automated personalization platforms like Monetate to innovative homepage customizations by companies like Reebok and Nespresso, AI is proving to be an invaluable tool. It not only provides a customized and engaging journey for the consumer but also optimizes efficiency, allowing businesses to deliver higher-quality service.
Top 4 Ways Marketers Are Using AI-Powered Personalization
AI-powered personalization is already part of most marketer’s toolbox. It’s being widely used by marketers to enhance customer experience personalization in some important and ubiquitous ways.
The best way to jump on board the personalization train is to use tools that are already integrating AI-powered features into their offerings. Below, we’ve listed some examples of what that looks like now.
1. Segmenting and Targeting with AI Predictive Analytics
Machine learning is a subset of AI that’s critical to segmenting and targeting. ML algorithms use data analysis to identify micro-segments of users. The micro-segments are derived from subtle patterns in user behavior. This is what becomes the foundation for hyper targeted messages and offers.
This precision can only be achieved with AI since the datasets being analyzed are huge and data ingestion is continuous. Think of it as a one-to-one personalized service on a massive scale. Companies like Reebok and Pandora use this technology to effectively scale personalization to very large and diverse customer and user audiences.
2. Recommending More Personalized Content
AI provides precisely relevant product recommendations based on purchase history and browsing. Again, this is done by using customer data, session-based information, shopping history, and third-party data to create segments.
This informs the personalized content recommendations created by a personalization engine. These recommendations appear on your website (e.g., recommending similar products, product bundles, or new products that compliment a past purchase). Recommendations can extend to emails, ads, and AI-enabled chatbots that act as shopping assistants.
3. Improving Personalizing Experiences Across Channels
Omnichannel shopping behaviors are now the norm with both B2B and B2C consumers. This necessitates a need for seamless omnichannel personalized experiences. AI-powered data integration makes this possible.
AI-powered tools understand and analyze previous interactions and channel engagement. They then deliver contextual messaging to each customer. This consistency across channels is exactly how AI can improve customer experience.
4. Enabling Hyper-Personalization at Scale
Hyper-personalization is the process of customizing content, products, or services to individual users based on their preferences, behaviors, and real-time data. This level of personalization is essential to deliver the best possible customer experience.
With the aid of AI, hyper-personalization can provide a uniquely custom experience for every customer. The challenge is in scaling this level of personalization without overwhelming or distancing customers. AI can help achieve this too, by ensuring that personalized content remains dynamic, adaptive, and in tune with changing preferences and behaviors.
Using Chatbots for AI-Based Customer Support
AI is an extremely powerful customer service and support tool. AI-powered Chatbots, in particular, have become integral to leveling up support experiences. Chatbots powered by natural language processing provide immediate help to customers in a way that feels intuitive and natural.
Chatbots can be programmed with algorithms that provide relevant answers to customer questions. They can also be used as a frontline support tool that ultimately directs customers to a human agent. One of the key benefits of chatbots is that they’re always on, ensuring 24/7 automated support, consistent engagement, and multi-language support regardless of where a customer is located.
Is AI the Future of Customer Experience?
AI is rapidly becoming a cornerstone in the evolution of customer experience. By analyzing data from a customer’s purchase history, preferences, and feedback, AI enables businesses to offer highly personalized interactions, from product recommendations to customer service touchpoints. This advanced level of personalization serves to enrich the overall AI customer experience journey, leading to higher customer satisfaction and loyalty.
Contrary to the notion that AI could replace human customer service agents, the current landscape suggests otherwise. Due to ongoing talent shortages and budget limitations in contact centers, AI serves as a complementary tool that allows businesses to do more with less. While it optimizes efficiency, it also frees up human agents to focus on tasks that require emotional intelligence and complex decision-making.
Measuring the Impact of AI Customer Experiences
To truly understand the value of AI-powered personalization in business, it’s crucial to benchmark your marketing initiatives and measure performance. Defining clear success metrics up front is the first step in doing this effectively.
As we covered above, AI offers capabilities like predictive analytics, natural language processing, and audience segmentation. This makes personalization more scalable and precise, but data analysis backed by AI-driven insights helps with planning and optimization.
This is exactly why data-driven insights should be the foundation of your AI-based customer support and marketing strategy. Use this information to understand what’s working and what isn’t, then adjust and refine your approach over time.