Machine learning and AI-powered algorithms are revolutionizing and changing how marketers and business people analyze, evaluate, and interpret their data. In many cases, traditional manual approaches often have limited scope and are time-consuming. Artificial intelligence tools process data swiftly in large volumes, extracting valuable business insights, identifying trends, and understanding customer preferences and needs that may help a firm increase sales and offer a better customer experience. Firm must know their target audience to offer the better services and product according to their needs. It is impossible for an e-commerce platform to attain its good without comprehending the best offers to users. There are variety of Artificial Intelligence and machine learning (ML) solution to help businesses thrive in the market.
In this case, Horizon is a large e-commerce platform looking forward to increasing its sales and enhancing customer experience by implementing a personalized recommendation system. Horizon has an inventory of millions of goods across multiple categories and wants to leverage the benefits of machine learning algorithms in predicting user preferences and tests and recommend different tailored products meeting each customer’s or user’s interests. The Horizon must indicate whether users will buy particular products or not. The platform should also know the likelihood of the target audience to purchase a specific item. The ML model for Horizon must also allow it to provide its users with various small personalized goods recommendations based on top user-item matchings. Therefore, this project proposal aims to develop machine learning solutions to analyze demographic information, browsing patterns, and user behavior to predict user preferences and deliver personalized recommendations in real-time.
Despite being a large e-commerce platform, Horizon needs a better user experience. It has a vast inventory of millions of goods, but its sales still need to grow as the stakeholders expect to meet set goals. Horizon still needs to determine whether its users will buy their particular products. It is also experiencing a problem assessing the likelihood of customers purchasing a specific item (Hwangbo et al., 2018, p. 94). The e-commerce platforms must give users a list of personalized goods recommendations based on top user-product similarities and matchings.
Horizon requires reasonable AI solutions to address its needs and reduce its current limitations of being unable to meet its target sales and poor user experience. This platform has various needs to manage, and the first one is increasing its sales within three months by 20%. The second need is improving user experience through the new AI solution, allowing Horizon to understand user preferences and needs. There are also some goals to meet, including the ability to make a successful prediction on whether users will buy a specific firm’s product. Such information is essential to help Horizon minimize costs by selling only the customers’ preferred goods. The next goal is to predict customers’ possibility of successfully buying specifics (Jiang et al., 2019, p. 3023). The final goal is to provide users with a good list of personalized product recommendations for most user-item matchings (Hwangbo et al., 2018, p. 95). The proposed AI solutions (collaborative and content-based filtering techniques) will help Horizon meet the above needs and goals. For example, it increases sales and predicts successful users’ possibilities to buy particular products.
There are a variety of resources that Horizon requires to implement the AI solutions successfully. The platform needs a minimum of five experienced data analysts. These people will quickly analyze user data to understand customer preferences, behaviors, and trends. At least the first software engineers and three information technology personnel are needed. Horizon requires a good office with tables, chairs, and at least ten quality computers for the professionals above. The firm will also require three support staff with technical experience in data analysis and information technology. There is a need for strong Wi-Fi to use during analysis.
There is a need for various categories of data, including user interaction and related data, such as information on user preferences, interactions, and behavior. For example, reviews, purchase ratings, clicks, and views. It will also include information on the demographics the users express. The other is product data, a structured dataset with information regarding products and items in the Horizon e-commerce platform, including prices, product descriptions, brands, categories, and attributes. It is possible to implement two AI solutions: collaborative and content filtering. Customer history data will help determine user changes to make a personalized purchase for specific items. Knowing customers’ frequent purchases may allow an e-commerce platform to decide what prospects will buy next. Reviewing and viewing data is also essential for the Horizon e-commerce platform. Customer data will aid in understanding the appropriate machine learning system.
They are affordable regarding resources, and Horizon, a large e-commerce platform, will implement them. It is also possible to access relevant data for the two techniques. Collaborative filtering is a personalized recommendation system that will use product data like clicks, likes, comments, and dislikes. Content-based filtering will give customized recommendations by using specific items’ attributes and determining some similarities.
The three use cases determine the proper machine learning (ML) technique or model. The first is predicting whether users will buy a particular platform’s product. The next step is determining the likelihood of the business’ audience purchasing a specific item. The third use case provides users with a personalized item recommendations list based on user matchings (highest). Appropriate ML techniques for addressing the three specific use cases are content-based and collaborative customized recommendation systems.
The content-based algorithms will analyze and evaluate individual target customers’ browsing behavior, preferences, demographic information, tests, and purchasing history. Horizon, the AI solution, generates personalized item recommendations for every target customer using insights from the data above. A content-based filtering system will help Horizon provide its users with a good list of customized item recommendations based on the (highest) leading user-good matchings (AltexSoft, 2021, p. 3). For example, if one of its customers frequently buys shoes for games, the AI system will suggest more related goods like workout accessories. The e-commerce platform, with the content-based algorithm, will analyze the interests of its customers in various item categories like home decor goods and then showcase a collection of similar item offerings. It can also provide a personalized discount for multiple related items.
A content-based filtering system will also allow Horizon to predict the possibilities (likelihood) of its customers purchasing particular items. Horizon should go through content products and past customer behavior to make it possible to determine whether a customer may buy a specific good. The e-commerce platform, through this AI solution, will create customer profiles to help get various personalized suggestions (AltexSoft, 2021, p. 2). For example, from the customer’s history, if one frequently buys sports shoes from Horizon, there is a likelihood of purchasing the latest offering of the same product. The platform will also determine the chance of a particular user to acquire a specific good. For instance, if users in the past liked a particular item, availing a similar good with favorable reviews will make them purchase the product.
The collaborative filtering algorithm compares the target items with all the users to make various recommendations. User-product interaction is very crucial for this model. User-item or product interactions include clicks, likes, comments, views, reviews, and dislikes. This recommender system first collects past information on user behavior and makes an informed decision on products to display to active users of the item with similar preferences (Necula, 2023, p. 439). It can be either ads clicked by the users or products put into the cart.
The CF technique will help Horizon predict the likelihood of a given user buying a specific item. It will also help determine whether a user will purchase a particular good. This AI algorithm will analyze and evaluate individual target customers with user interactions. For example, if Horizon’s customer A purchases and likes products X, Z, and Y, and user B buys items X, Y, and M, there is a good chance for B to like Z and A to prefer M. Here, the e-commerce platform can predict the likelihood of selling a specific good to a given target customer. With the CF technique, Horizon will provide its customers with a list of recommendations for personalized goods based on the highest user matchings for a product. The firm uses product ratings and reviews as part of its recommendation algorithm for this case. The e-commerce platform will analyze customer reviews and texts effectively to identify critical sentiments and phrases and use the same information to recommend an item. For instance, if a user writes a positive and impressive review about the durability of a product, the algorithm will recommend a list of similar durable items.
Content-based and collaborative filtering algorithms are functional personalized recommendation systems for the Horizon e-commerce platform. Creating a customized user interface and tailoring products to meet specific customers’ preferences is essential. Availing of personalized customer experience will help Horizon grow its business, increase sales by 20%, and improve customer engagement. The platform should use customer data and create an effective personalized AI solution in this case. Content-based and collaborative filtering systems will show content and items relevant to individual users based on purchasing and browsing history (Necula, 2023, p. 439). A recommendation algorithm integration is better for Horizon to match its content, services, and products to the right customers. When a firm approach it appropriately, it will help to better user engagement. Machine learning is the right way for e-commerce platforms to improve customer experience. Appropriate use of data and ML algorithms will allow Horizon to implement intelligent technologies or models like content-based and collaborative systems.
Horizon’s e-commerce platform used a variety of relevant analyses to determine the proper machine learning technique to use and increase sales, improve user experience, and make predictions, such as whether particular target customers will buy their products or not. One type of data was interaction user data. It entails information on customer interactions, preferences, and behaviors, including reviews, clicks, ratings, views, and purchases. Product data is another category involving information on items available in the e-commerce platform, such as product types, prices, descriptions, and attributes. For instance, predictor variables help to estimate and forecast your target variable. In this case, the predictor variable included customer history, preferences, tests, comments, and views (Wang et al., 2018, p.2). The target variable for this organization was the three use cases where the predictors would influence them—for example, using customer purchasing history (predictor) to predict whether a particular consumer will buy a specific good (target variable). Data on user reviews helped determine other possible product options for the customer (Necula, 2023, p. 439). For instance, if a buyer comments positively on specific sports shoes, then the ML algorithm avails a variety of similar offerings.
Machine learning and AI-powered algorithms are revolutionizing and improving how advertisers, marketers, and entrepreneurs analyze, evaluate, and interpret their data. Artificial intelligence tools process data faster in large quantities, extracting valuable business insights and identifying trends to understand customer preferences and needs that may help a firm increase sales and offer better customer experience. Horizon is a large e-commerce platform that wants to boost sales and improve customer experience and engagement via a personalized recommendation system. Horizon intends to take advantage of the benefits of machine learning algorithms in predicting target customer preferences and recommendations of carefully tailored products. Horizon must make informed predictions of whether users will buy particular products. The e-commerce platform should implement a collaborative and content-based filtering system to meet these needs. A content-based system will help the firm evaluate a target user’s browsing behavior, attitudes, tests, demographic information, and purchasing history.
The solution will generate personalized item recommendations for all target audiences. The collaborative filtering algorithm helps to compare the target goods with all the users to make multiple recommendations. User-product interaction is very critical for this system. User-item or product interactions include clicks, likes, comments, views, reviews, and dislikes. By giving a personalized consumer experience, an e-commerce platform will increase customer satisfaction and attain its goals.
AltexSoft. (2021). Recommender Systems: Behind the Scenes of Machine Learning-Based Personalization. [online] Available at: https://www.altexsoft.com/blog/recommender-system-personalization/.
Hwangbo, H., Kim, Y.S. and Cha, K.J., 2018. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, pp.94-101.
Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H. and Wang, X., 2019. A trust-based collaborative filtering algorithm for E-commerce recommendation system. Journal of ambient intelligence and humanized computing, 10, pp.3023-3034.
Necula, S.C., 2023. Exploring the Impact of Time Spent Reading Product Information on E-commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior. Behavioral Sciences, 13(6), p.439.
Wang, D., Liang, Y., Xu, D., Feng, X. and Guan, R., 2018. A content-based recommender system for computer science publications. Knowledge-Based Systems, 157, pp.1-9.