Abstract
This chapter delves into the captivating intersection of AI and wishlists, exploring how e-commerce undergoes a transformative shift with innovative strategies and enhanced consumer experiences. A critical examination of existing literature unveils a multifaceted relationship between AI and wishlists, presenting a myriad of opportunities that redefine their function and shape consumer behavior. From personalized recommendations to predictive analytics, this chapter illuminates the profound impact AI integration has on consumer satisfaction and engagement. It also addresses challenges, emphasizing issues like data privacy and security. Serving as a comprehensive guide, this chapter navigates the intricate terrain of AI-infused wishlists, providing insights to revolutionize the e-commerce industry. By ensuring a robust, personalized, and secure shopping experience, the integration of AI in wishlists emerges as a pivotal force in reshaping consumer interactions.
TopIntroduction
Wishlist marketing is a business tactic for accumulating future sales by putting online wish into websites. It creates a distinct liminal area between ownership and non-ownership, allowing customers to interact with products (what they kept in wishlist) without any need to buy them. This creates a sense of psychological ownership and increases the probability of buying. However, the effectiveness of wishlist marketing is influenced by two factors, first, the social context of the wishlist and second, duration of product has been on the wishlist.
The advantages of executing wishlist marketing include improved sales and capability to strategically manage online wishlists to increase their potential. Retailers may create a personalized experience for customers and modify their marketing efforts accordingly by utilizing wishlists (Popovich & Hamilton, 2021). Retailers may utilize strategies like completing customers’ requests promptly and perfectly, giving exclusively customized suggestions, reminding people of needs and goals and envisaging what people want and delivering it without being asked to execute wishlist marketing successfully. These connected strategies can boost customer experiences, increase operational efficiencies, and create a competitive edge. Wishlist campaigns that focus on promoting innovative technologies, target high-tech and niche markets, and invest in capacity, production and employee training are successful examples. Additionally, ads that simplify customers’ decision-making processes by showcasing products in successive pages and empowering them to create wishlists have proven successful (Groening, Wiggins, & Raoofpanah, 2021).
How Wishlist Marketing Works?
In the e-commerce world, wishlist marketing is a dynamic approach. It all starts with customer engagement, with customers curating wishlists that represent their tastes and prospective future purchases (Oberlo, 2023). This collection of requested products is a useful archive of the customer intent. Businesses then use this data to make sensible choices about popular items, emerging trends, and individual preferences (BigCommerce, 2022). Personalization and retargeting are the next stages that reveal the actual potential of wishlist marketing. Marketers adapt incentives, mailings, and adverts to each customer’s specific tastes using data from wishlists. Furthermore, the data gathered and analysed enables businesses to carefully retarget customers who have shown their interest but yet to purchase, urging them toward conversion (SalesMango, 2015). Besides, from individual consumer contacts, wishlist marketing is critical for inventory management, enabling business in optimizing their product offers based on real-time demand signals received from wishlists. Wishlist marketing, in essence, turns passive lists into active resources, enhancing engagement, customization, and, eventually, the chance of successful conversions (Sheppard, 2023).
Key Terms in this Chapter
Cognitive System: A cognitive system is an AI-powered system that imitates the human thought processes as well as cognitive abilities, for instance, reasoning, learning and problem-solving. Cognitive systems, in context of e-commerce wish lists, can analyse user preferences, behaviour and feedback to logically acclimate and personalize the wishlist experience, enhancing relevance and user engagement.
Explainable AI: The concept of explainable AI denotes the capability of AI to offer understandable explanations or justifications for their decisions and recommendations. Explainable AI algorithms, with reference to e-commerce wish lists, can help users to understand why specific products are suggested or prioritized in their wish lists, increasing trust and transparency in the recommendation process.
Retargeting: Retargeting is a marketing technique used to re-engage users who have previously interacted with a website or product but did not complete a desired action, such as making a purchase. Retargeting strategies, in the context of e-commerce wish lists, can be implemented to recall users of products they have saved in their wishlists, encourage them to revisit e-commerce platform and eventually drive conversion and sales.
Wishlist: In the context of E-commerce, wishlist refers to a curated list of anticipated items or products that a user keeps for future reference or purchase. Normally, wishlists allow users to bookmark items or products they are interested in and facilitating easier navigation and decision-making during their shopping journey.
Blockchain: In the context of e-commerce wish lists, blockchain technology can be used to safeguard the security and transparency of wish list data. By providing a decentralized and absolute ledger, blockchain can help to avert unauthorized access, tampering or manipulation of wish list information, thereby enhancing trust and data integrity for both users and retailers.
Personalization in E-commerce Wishlist: Personalization in e-commerce wish lists encompasses tailoring the content and recommendations within a wishlist to match the individual preferences and interests of each user. By leveraging AI algorithms and user data, personalized wish lists can offer relevant product suggestions, promotions and content, thereby enhancing the shopping experience and increasing user engagement.
Customer Journey: The customer journey denotes the series of interactions and touchpoints that a user experiences when engaging with an e-commerce platform, from initial discovery to post-purchase support. Understanding the customer journey. in the context of wish lists, enable retailers to optimize the wishlist experience at each stage, from product discovery and consideration to conversion and retention.
AI-Enhanced Wish Lists: AI-enhanced wish lists leverage AI algorithms to advance the functionality and personalization of traditional wish lists. By accurately analysing user preferences, behaviour and browsing history, AI can offer personalized recommendations, correctly predict future purchases, and enhance wishlist experience for users, thus enhancing engagement and satisfaction.
Advanced Natural Language Processing (NLP): Advanced NLP techniques allow AI to understand and process human language with better accuracy and nuance. Advanced NLP algorithms, in context of e-commerce wish lists, can analyse user-generated content (UGC) like reviews and ratings of the product, feedback and wishlist descriptions to abstract valuable insights and improve recommendation accuracy.