Artificial intelligence (AI)-powered personalization is increasingly becoming prevalent in the ever-expanding digital landscape to increase customer satisfaction and engagement. Businesses and other organizations use AI methods, specifically machine learning (ML), to connect with their customers and offer them a more personalized experience. This article discusses the growing role of AI technology in personalization.
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Importance of AI in Personalization
Although personalization has become crucial for converting a prospect into a customer, deriving and combining the valuable insights from the available data to achieve personalization with innovation and relevance at scale is a significant challenge. AI techniques can be used to overcome this challenge as they can select specific sets of customers based on several criteria and personalize content for those customers with dynamic product recommendations. Thus, AI-powered personalization can significantly improve customer loyalty and satisfaction, resulting in increased engagement.
AI-powered personalization primarily involves collecting and analyzing substantial amounts of customer data, including their purchasing and browsing history, demographic information, and social media interactions, to learn the individual preferences and requirements of every customer and tailor the customer experience based on these preferences.
AI can be used to obtain insights from customer data to determine the content type and the time to send personalized messages to customers. Additionally, AI tools can enable businesses to use performance data to optimize their messaging results before they launch a personalized email campaign.
Specifically, organizations can quickly analyze millions of data points from their most successful campaigns using AI to change layouts, imagery, and messaging for pending emails. AI can also increase the possibilities of purchase by customers by recommending offers and products that are highly relevant to them, which can substantially improve the overall revenue.
Moreover, AI-powered personalization can decrease customer churn rates by offering tailored experiences that meet every customer's requirements, improving customer loyalty. The crucial data insights provided by AI-powered personalization into every customer’s behavior can be used by businesses to understand their customers better and optimize their sales and marketing strategies.
Amazon’s recommendation system is a leading real-world example of AI-powered personalization. The system utilizes an ML to analyze customers' search records, purchase history, and other behavioral data to predict the products in which the customers are interested and recommend such products to them in real-time. These personalized recommendations have played a critical role in increasing sales and customer engagement for Amazon.
Large language model AI tools, such as the generative pre-trained transformer 3 (GPT-3), can provide recommendations for email headlines or subject lines to organizations, which can assist them in focusing more on developing business strategy and optimal decision making.
Implementing AI-powered Personalization
Applying AI techniques for developing an effective personalization strategy requires detailed and careful planning and execution. Initially, the objectives for implementing AI-powered personalization must be defined transparently. For instance, a business can require AI-powered personalization to reduce customer churn, increase revenue, and improve customer satisfaction.
The clarity in personalization objectives is crucial to guide the execution and development of an AI-based strategy to realize the identified objectives. The effectiveness of AI-powered personalization primarily depends on the quantity and quality of the available customer data.
Thus, businesses must develop a mechanism for collecting and storing high-quality customer data that can be utilized to learn the preferences and behavior of customers. Additionally, the personalization strategy must be refined and tested continuously based on the feedback of the customers to improve it and keep it updated.
Businesses must also be transparent about their data usage and collection for personalization purposes to gain the trust of their customers. They can provide information about their privacy policies and explain the mechanism of using customer data to deliver personalized experiences to their customers.
Finally, businesses must integrate personalization across all customer touchpoints, including in-store, social media, and email experiences, to ensure a tailored and consistent customer experience in all channels.
Challenges of Implementing AI in Personalization: The large-scale collection and analysis of customer data for personalization initiatives can increase data security and privacy concerns. Thus, businesses must strictly adhere to existing data privacy regulations, implement effective privacy policies, and adopt strategies to prevent misuse of sensitive customer data.
Additionally, implementing AI-powered personalization can be very expensive for businesses/organizations, specifically for smaller organizations with limited resources. Thus, businesses must consider the overall costs of the technology before implementing it in their operations.
ML Personalization
Recommendations are an extremely effective technique for retaining consumers and increasing sales. ML can be used to develop recommendation engines/recommender systems that can access and combine several data resources, including external corporate data, websites, and clients' private data, enabling businesses to create a tailored, relevant experience for every consumer.
Leading retail industry players increasingly utilize recommendation systems to provide personalized shopping experiences to specific customers. Customers, specifically millennials, typically prefer tailored recommendations as they are more inclined to purchase products promoted in magazines/media.
Recommender systems can be integrated with natural language processing (NLP) and analytical techniques to improve the resultant personalized recommendations and experiences further. ML techniques can be used to predict the future purchases of a consumer using his/her historical purchasing behavior. Thus, ML-powered recommendation engines can make different predictions based on the past orders of consumers and their reactions to the past orders.
ML product recommendations are more relevant and focused than human recommendations as family and friend recommendations are impacted by their individual perception and personal conduct towards the product. Additionally, ML can be utilized to generate personalized suggestions across several stores or shopping channels. For instance, online retail sites can use ML to offer personalized suggestions to their customers and simplify comparison shopping to increase sales.
Real-world Examples
Thread, a fashion company based in the United Kingdom (UK), has employed AI to offer personalized clothing recommendations for every customer. Customers participate in style quizzes to provide their personal style data and receive weekly personalized recommendations that they can vote down or up.
The AI algorithm, Thread utilizes this data to identify patterns in customers’ preferences and customize the recommendations. Specifically, the quality of recommendations improves over time with the increasing amount of customer data.
Similarly, Sesame Street extended personalization to kids by developing the first AI-powered vocabulary learning app that starts observing a child's reading and vocabulary level and then utilizes this data to make personalized learning exercise recommendations. The app continues to update its recommendations as the child progresses in the reading and vocabulary levels.
Hilton Hotels employs a robot concierge, designated as Connie, to make the guests' experience both enjoyable and personal. The two-foot-tall robot is located in the lobby to greet guests and answer their questions. Connie can offer personalized recommendations to guests about restaurants to try and places to visit and learn about them.
Under Armour is using AI to personalize fitness recommendations, with its AI-powered Record app collecting health information on diet, sleep, and physical activity to make personalized recommendations on health goals and workouts. Additionally, the app can analyze workouts to increase their effectiveness.
Diamond website Rare Carat has developed Rocky, the first AI-powered jeweler in the world, which can assist customers intending to purchase diamonds. Rare Carat utilizes AI to compare diamond prices across several retailers to identify the best deal for every customer. Rocky provides customers with an online experience that is similar to the experience offered by brick-and-mortar jewelry stores.
AI has been used in the real world to personalize the in-store experience. For instance, Macy’s utilizes the IBM Watson AI technology in its smartphone-based assistant Macy’s On Call. Customers can start conversing with a digital assistant on their phone while entering the store.
The chatbot then asks customers questions to tailor their shopping experience and provides directions and recommendations for items around the store. Moreover, the bot can alert a human associate to intercept the customer when it senses frustration in the customer.
References and Further Readings
Patel, N., Trivedi, S. (2020). Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty. Empirical Quests for Management Essences, 3(3), 1–24. https://researchberg.com/index.php/eqme/article/view/46
Morgan, B. (2019). The 7 Best Examples Of Artificial Intelligence To Improve Personalization [Online] (Accessed on 26 November 2023)
Josifovsk, V. (2023). The Future Of AI-Powered Personalization: The Potential Of Choices. [Online] (Accessed on 26 November 2023)
Zia, T. (2023). AI-Powered Personalization: How Machine Learning is Transforming Customer Experience. [Online] (Accessed on 26 November 2023)