Businesses increasingly focus on personalized marketing as a leading share of consumers currently expect personalization as the standard. However, businesses are required to collect and analyze a massive amount of data to understand the requirements of their customers and identify the most effective ways of engaging them to deliver a personalized experience, which is a significant challenge.
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Artificial intelligence (AI) technologies, specifically machine learning (ML), can enable businesses to personalize marketing messages at scale and utilize real-time data to quickly optimize marketing campaigns and predict future consumer behaviors. This article discusses the role of AI in personalized marketing and several real-world examples in this field.
Importance of AI in Personalized Marketing
Personalized marketing refers to the marketing strategy that primarily targets individual customers with tailored recommendations, offers, and messages based on their demographics, behavior, and interests. The approach involves using customer data to create a personalized experience for every customer to achieve higher customer satisfaction, engagement, and conversion rates. Currently, marketing personalization has become crucial for businesses to generate revenue and gain a competitive edge in the market as consumers increasingly prefer to purchase from brands offering personalized experiences.
In personalized marketing, AI methods such as ML, natural language processing (NLP), semantic search, neural networks, and sentiment analysis can be utilized to gain insights into customer preferences and behavior and effectively create customized experiences using substantial amounts of customer data, including social media activity, browsing behavior, purchase history, and demographic information.
Specifically, AI algorithms can detect patterns in the behavior of customers, such as the product types in which they are interested, the times of day when they typically make purchases, and channels they prefer for communication, by analyzing customer data.
AI also maximizes the effectiveness of personalized marketing by allowing businesses to optimize their marketing strategy during a personalized marketing campaign/in real time. In customer analysis, AI can create detailed profiles of every customer by combining data from various sources, such as social media activity.
These profiles contain information such as age, interests, location, purchase history, and gender, which can be utilized to create a more personalized marketing experience. AI techniques can predict future customer behavior, such as the probability of the customer making a purchase and products they are expected to purchase, by analyzing different data, including the customers' past behavior.
Moreover, AI can be utilized to scale and automate personalized marketing efforts. Marketers can simultaneously create personalized experiences for millions of customers using AI-powered tools without manual intervention, saving resources and time and improving customer experience. Thus, using AI in personalized marketing can create valuable customer experiences that can assist businesses in building stronger relationships with their customers, improve customer retention, and increase customer loyalty and satisfaction.
Several industries use AI in personalized marketing, including automotive, finance, travel and hospitality, healthcare, and e-commerce. Online retailers use AI to create a personalized shopping experience for customers, while hotels and travel companies utilize this technology to personalize the travel experiences of their customers.
AI Applications in Personalized Marketing
Messaging and Emails: AI/ML allows marketers to send personalized messages and emails by effectively analyzing customer preferences. AI can predict the content type that can engage particular individuals under various conditions and create an appealing message for each individual automatically.
AI can ensure that messages and emails are sent at an appropriate time of the day for each customer using real-time and historic data-based automation rules. The technology can also send targeted messages responding to specific customer behaviors, such as the current location or online activity of the customer. These targeted messages primarily include push notifications that grab users' attention by appearing on their mobile screens and making suggestions about products. Such personalized text leads to a higher click-through rate.
Dynamic Websites: AI can ensure that every customer views a customized version of the brand’s website using past purchases or browsing history and live behavioral signals. Such dynamic websites can adapt to individual users and show them a custom layout and various products.
Moreover, ML can assist businesses in customizing content using different data, such as user location, common searches, and trending topics. Several sites also utilize local weather conditions to recommend related products, such as sunscreen ads in a heatwave.
Tailored Recommendations: AI models can analyze user behaviors and site traffic data to assist businesses in engaging with their customers by making specific recommendations about products/services that customers seek. For instance, companies such as Amazon and Spotify have developed recommendation engines that can suggest specific recommendations dynamically to customers. Collaborative filtering and content-based filtering are utilized to recommend items depending on their characteristics.
Additionally, AI can make personalized recommendations to retail website visitors. For instance, a chatbot can offer a selection of items based on the keywords in previous shopping history and conversation and then make recommendations for complementary items and loyalty-based discounts when a consumer decides to purchase an item.
Assistive Search: Advancements in natural language programming have increased the focus on assistive search, where a search engine primarily guides users through the search process and displays results that match the personal behaviors and preferences of users to improve buying prospects.
Smart search provides customized autosuggest and even adjusts misspellings by recognizing context. Additionally, machine vision/image recognition technology allows customers to capture an image of an item they intend to purchase and feed the image into a search engine to identify similar-looking items.
Forecasting: AI, specifically ML, can accurately predict customer behaviors in the future, and the prediction accuracy of ML engines increases over time with the increasing amount of data collected by these engines. This data also enables marketing teams to optimize individual customer journeys and effectively adjust to the next action of the customer.
Moreover, predictive marketing allows businesses to make better forecasts about products with good sales prospects, which is crucial for budget allocation and improving return on investments.
Language and Sentiment: An AI model can scan customer reviews and listen to messages and conversations to determine whether the expressed sentiment is negative or positive. This AI-based approach is extremely beneficial for businesses relying on voice communications, such as a telemarketing company or a cloud-based contact center.
Marketers can also use AI to evaluate customer reactions to a service, product, or advertisement, and use the information to improve their personalized marketing campaign. For instance, the loyalty of a wavering customer can be regained with a personalized special offer on his/her favorite product.
Moreover, chatbots can be programmed using more natural language to provide human-like interactions with customers by enabling these bots to respond to specific phrases or words with predefined actions, such as mitigating a problem or making recommendations.
Successful Real-world Examples
Spotify employs AI algorithms to analyze users' listening habits and create personalized playlists depending on their preferences. The personalized playlists of Spotify have increased user engagement significantly and enabled the company to retain its position as one of the leading music streaming platforms.
Similarly, Amazon utilizes AI algorithms for customer behavior analysis and recommends products relevant to customers' requirements and interests. This approach has resulted in a substantial rise in sales and helped Amazon to become one of the biggest e-commerce platforms globally.
Netflix uses AI techniques to analyze users' viewing habits and recommend content that matches the interests of each user. The AI-based content recommendations increased user engagement significantly and enabled the company to become one of the major streaming platforms in the world.
Sephora employs AI-powered virtual try-on technology to assist its customers in identifying the proper makeup products based on their skin type and tone. This virtual try-on technology has substantially increased customer satisfaction and helped Sephora maintain its leading position in beauty retail.
References and Further Reading
Raitaluoto, T. (2023).The role of artificial intelligence in personalized marketing. [Online] (Accessed on 20 November 2023)
Lin, P. (2022). AI-Based Marketing Personalization: How Machines Analyze Your Audience. [Online] (Accessed on 20 November 2023)
Bhatasana, M. Artificial Intelligence in Personalized Marketing: Revolutionizing The Industry. [Online] (Accessed on 20 November 2023)
Artificial Intelligence (AI) Personalised Marketing Campaigns. [Online] Available at https://kps.com/global/en/company/blog/ai-personalized-marketing.html (Accessed on 20 November 2023)
Kumar, V., Rajan, B., Venkatesan, R., Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155. https://doi.org/10.1177/0008125619859317