Published: 5 August 2024
Contributors: Molly Hayes, Amanda Downie
AI personalization refers to the use of artificial intelligence (AI) to tailor messaging, product recommendations and services to individual users. By analyzing data and learning from user behavior, AI-powered tools can create highly personalized encounters that enhance customer experiences and increase customer engagement.
Recent advancements in AI technology, such as generative AI, have enhanced marketing practices by generating personalized experiences in close to real time. These advancements are ushering in an era of omnichannel hyper-personalization—a customized and seamless customer experience across platforms that responds to customer behavior immediately.
As AI-powered personalization has become more nuanced and powerful, consumers have come to expect these tailored experiences. A recent report from the IBM Institute for Business Value found that three in five consumers would like to use AI applications as they shop. And according to the consultancy McKinsey, 71% of consumers expect companies to deliver personalized content. 67% of those customers say they are frustrated when their interactions with businesses aren’t tailored to their needs.1 Personalization has also been shown to drive expansion. The same report found that fast-growing organizations drive 40% more revenue from personalization than their more slowly moving counterparts.
In today’s landscape, AI personalization is used across industries to create relevant product recommendations and contextually appropriate experiences at scale. These tactics apply whether a target user is a single online shopper, a procurement specialist in a business-to-business (B2B) organization or an employee receiving personalized communications.
Some industry-specific applications for AI personalization include:
Ecommerce: In ecommerce, AI surfaces recommendations based on browsing and purchase history, suggesting products based on a user’s specific preferences and needs. It can also produce custom emails or other messages for consumers, facilitating personalized marketing campaigns.
Entertainment: Customized content suggestions on streaming services are typically powered by AI personalization. These recommendation engines surface playlists, movies or other content tailored to individual preferences.
Training and education: Adaptive learning systems—whether in the workplace or elsewhere—offer tailored educational content and resources. Using AI, they provide personalized feedback and progress tracking.
Finance: AI personalization offers customized financial advice and investment recommendations based on a user’s goals and broader market conditions.
Marketing: AI personalization drives several marketing strategies, including custom email marketing campaigns or online advertisements targeted toward specific consumer groups.
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AI tools can interact with consumers at every touchpoint in the customer journey, from browsing an online marketplace to receiving follow-up messages about a product or service.
Deployed effectively, AI personalization significantly impacts revenue. A survey of hundreds of leading CEOs from the IBM Institute for Business Value found organizations prioritizing customer experience (CX) stood to see three times the revenue growth of their peers. 86% of those leaders considered personalization an essential part of their CX campaigns.
Some of the major benefits of AI personalization include:
Tailored experiences positively impact customer satisfaction and loyalty, providing a digital experience that’s contextually appropriate and likely to result in positive relationships with a brand.
Content personalization keeps users engaged for longer by providing them with information that they are most likely to need.
Relevant recommendations increase the likelihood of a purchase, leading to a higher number of sales.
With AI, organizations use automation to create vast numbers of marketing campaigns, product recommendations or customer service experiences, freeing up resources to deploy in other areas. Some studies have suggested that a personalization program reduces customer acquisition costs by as much as 50%.2
Providing personalized experiences based on customer preferences can provide a significant competitive advantage. Some brands rely heavily on AI personalization for their fundamental business model, such as subscription companies providing curated goods to their customers.
As AI personalization surfaces granular data about an organization’s users, the technology can be used to gain insights into current and future customer behavior, allowing for more informed decision-making. With detailed data on users, businesses also glean valuable information on their most high-value customers, allowing them to iterate intelligently and move quickly.
AI-driven personalization typically deploys some combination of machine learning (ML), natural language processing (NLP) and generative AI. Generally, the process works by collecting customer data about user behavior, preferences and interactions—along with contextual data like location, time of day and device used. Often, this data-collection involves merging organizational data with third-party datasets.
This data is then analyzed by AI algorithms, which identify patterns and trends in user behavior. Typically, the AI will also group users into segments based on similar characteristics and behaviors in a process known as audience segmentation. By analyzing these segments and user behaviors, the AI then recommends products, services or content that aligns with user preferences and demographics. It can also display specific content on a website or app to different users based on their unique profiles.
As the AI continues to “learn” from users over time, it further optimizes its personalization process, adapting continuously to refine its recommendations and responses.
AI personalization enhances user engagement by providing specific recommendations and delivering unique content based on an individual’s preferences and needs. Some key AI personalization applications include:
AI analyzes user data, including browsing history, social media interactions, purchase patterns and preferences to suggest products that align with individual tastes. This technique is widely used in ecommerce platforms like Amazon and Netflix, helping to increase sales and improve customer experience by showing the most relevant items. The more data that an AI has throughout the customer journey—for example, items purchased or viewed during certain times of the year—the more refined and accurate its recommendations are.
AI-powered chatbots and virtual assistants provide personalized interactions in conversational language by "reading" and understanding user queries, then offering tailored responses. These chatbots can handle customer service, provide product recommendations, and assist in troubleshooting, creating a more efficient and personalized user experience. Available at all hours of the day, these chatbots also collect valuable insights into consumer buying patterns and engagement habits, driving efficiency.
Content personalization involves using AI to deliver tailored emails, articles, product descriptions, videos, text messages or other media to users based on their interest and behavior. By using content personalization, organizations can deliver high-quality and engaging assets that resonate with target audiences while saving time and resources.
Dynamic pricing is an AI-driven strategy where prices are adjusted in real-time based on various factors such as demand, supply, consumer behavior and market conditions. Though historically used most often by hospitality and travel organizations, dynamic pricing is now used in various industries to optimize pricing to maximize revenue and provide lower rates to consumers during off-peak periods.
Predictive personalization uses AI to anticipate user needs and preferences before they explicitly express them. By analyzing historical data, AI can predict what products or content a user might be interested next, enhancing the overall user experience. For example, Starbucks started a predictive personalization program powered by machine learning algorithms that offered specific drinks to app users based on their purchase history. Predictions about what consumers would order based on the time of day or weather were also integrated into the brand’s inventory management system.
Generative AI and other advancements in AI technologies have deeply impacted the practice and deployment of personalization in commerce settings and the business world. Increasingly, AI technologies have the capacity to create specific content for individual users, or to forecast customer needs. Some recent advancements in AI personalization include:
Hyper-personalization advances the practice of personalization by using real-time data and AI to deliver highly customized experiences. Where segmentation groups customers together, this process enables organizations to speak directly to individual consumers. This can include real-time product recommendations, dynamic website content that responds to user navigation, and personalized marketing campaigns that adapt based on user interactions. With a deep understanding of individual consumers and how they interact with a business, organizations are able to deliver contextually relevant information on the correct channel at exactly the right time.
Omnichannel personalization, or channel-less personalization, ensures a consistent and personalized experience across all customer touchpoints including websites, mobile apps, social media and in-store buying. AI can integrate data from multiple channels to create a seamless and cohesive user journey: For example, the beauty retailer Sephora has been effective in its omnichannel personalization strategy by offering a companion app that helps consumers find items. The app unifies data points such as previous purchases and brands tried on at the counter in-store.
Generative AI can create marketing copy, articles, and even creative assets based on user preferences and behavior. This enables brands to produce a large volume of relevant content efficiently, and create far more content based on individual preferences than in the past. For example, generative AI might create specific advertisements for an individual consumer based on the time of day or how close an app user is to a particular store.
While many AI personalization use cases apply to external marketing, similar tactics are also deployed internally. AI-driven personalization in HR helps in identifying and nurturing talent by tailoring training programs, career development plans and employee engagement strategies to specific users. This ensures that employees receive the right support and opportunities to grow, leading to better employee retention and job satisfaction. Virtual agents and virtual assistants also provide personalized communications to employees related to their day-to-day responsibility, reducing errors and increasing efficiency.
Personalization efforts are transforming how businesses interact with customers and employees, but scalable and successful campaigns tend to start with a strong data foundation and routinely audit internal practices.
Some common best practices for deploying AI personalization include:
Effective and agile AI systems are built on a strong data foundation. Capturing and cleaning this data—both internal data and third-party information—often requires significant investment.
This might also mean hiring engineers and acquiring the computing power necessary to host an AI system.
Even as today’s consumers desire personalization, the average user remains concerned over data privacy. Effective AI personalization programs strive to provide consumers with information they can use—without unnecessarily mining personal data they might be uncomfortable sharing.
Good data governance can also require an organization to implement robust security protocols to safeguard data from breaches.
Using an AI to personalize user experience typically requires clear communication in which users are informed how their data is being used.
Clear expectations around data use and management can also ensure that AI models are trained on diverse data to prevent biases and discrimination.
Organizations typically see better results when they carefully audit the model used to train and tune their personalization AI system. By choosing an AI model that’s well suited to business cases and personalization tasks, brands can facilitate better-performing products. Successful models are also typically updated regularly and retrained on new data to improve accuracy.
Successful campaigns typically involve significant planning before the training of an AI system. Creating a roadmap to align personalization strategies with overall business objectives can help ensure the eventual product drives growth and profitability.
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Footnotes
1 The value of getting personalization right—or wrong—is multiplying (link resides outside ibm.com), McKinsey, 12 November 2021.
2 What is personalization? (link resides outside ibm.com), McKinsey, 30 May 2023.