Global e-commerce sales are likely to soar to $4.9 trillion by 2025, according to eMarketer's report. However, with the ever-expanding options available to consumers, selecting the ideal product can prove overwhelming, leading to decision fatigue. The abundance of choices can make it challenging for customers to distinguish between products, resulting in diminished website engagement.
Employing an AI-driven strategy presents a highly effective solution to address this predicament. AI can learn from experiences, adapt to new inputs, and perform tasks like those driven by human intelligence. This blog looks at how product recommendation engines, visual recognition technology, and chatbots can help e-commerce ensure a seamless customer experience.
Product Recommendation Engines
Product recommendation engines use AI to comprehend shoppers' behavioral patterns. They analyze the user's purchases and streaming behavior to offer customized product recommendations and promotional offers.
As more and more activities of our daily life move online, product recommendation engines have several applications. For example, companies can use these engines to curate targeted movies or web series on an OTT platform or generate a personalized playlist based on a user's specific interests.
Let's look at how recommendation engines operate and the different strategies they follow.
Content-based: Content-based recommendation system suggests items to users based on the characteristics or features of the items. It analyzes the properties of the items and matches them to the preferences or profiles of users to make recommendations. For example, if anyone is continuously watching and liking thriller web series, the system must prioritize recommending thriller series to the user.
Implementing a content-based recommendation system involves the following steps:
- Data Collection: Building a content-based recommendation engine begins with collecting data such as descriptions, metadata, and user ratings on the product catalog and recommendations.
- Preprocessing: Once the data collection is complete, it is then cleaned and preprocessed to eliminate noise, standardize formats, and extract useful information. Preprocessing often involves text normalization, tokenization, identification, and removal of the predefined stop words (for example, articles and connecting words) that do not carry any weight in prioritization.
- Feature Extraction: Data collection and preprocessing are followed by the identification of key features or descriptive attributes like keywords, metadata, or any other relevant information. The raw data is then transformed into a feature representation, understood by the recommendation algorithm.
- User Profile Creation: The profile can be a vector summarizing the user's preferences or a set of weighted features reflecting their interests.
Similarity calculation involves establishing the similarity between the recommendation items, the user profile, and their past purchases. Organizations can use similarity metrics such as cosine similarity, Jaccard similarity, or Euclidean distance, depending on the type of features and data available.
- Ranking & recommendation: The items with the highest similarity scores are considered the most relevant and are recommended to the user. E-commerce companies can set a threshold to filter out less relevant items or use a top-k approach where K represents the number of items to recommend from the large pool of data set. For example, If K=5, then it means the system will recommend the top 5 items of the user's choice.
- Evaluation & Iteration: Evaluating the performance of a recommendation system involves working with appropriate evaluation metrics such as precision, mean average precision, or recall. Companies must continuously collect user feedback on their recommendation systems to identify loopholes and improve their accuracy and relevance.
Collaboration-based recommendation systems: Collaborative filtering utilizes similarities between users and items concurrently to provide recommendations. For example, user X is interested in purchasing athlete shoes and likes the Nike brand; like this user, there is another user, Y, who has also shown interest in Nike brands and has also purchased Air Jordan shoes, so the likelihood of recommending AJ to User X will be more.
This example illustrates user-based collaborative filtering that identifies similar users based on their past interactions and generates recommendations by considering items preferred by users with similar tastes. It relies on users' interactions with products, such as browsing, clicking, and rating. E-commerce companies can use metrics like cosine similarity or Pearson correlation to identify users with similar preferences.
In contrast, item-based collaborative filtering identifies similar items based on user interactions and generates recommendations by considering items that interest the user. Similarity metrics are computed based on the user-item matrix that enables the system to identify similarities between users or items based on their interactions. For example, consider a user who gives five stars to one shoe brand and two stars to another. This information is relevant to creating a user-item matrix based on the user's preferences. Once you have identified similar items, the system recommends those items to the user.
Both user-based and item-based collaborative filtering have their pros and cons. User-based filtering is more effective when the user-item matrix is sparse (i.e., there are few interactions per user) or when users' preferences remain relatively stable over time. Item-based filtering is more suitable with a large and stable system that efficiently computes item similarities.
Hybrid approach: Hybrid recommendations bring the best of both worlds by combining multiple approaches to offer more accurate and diverse recommendations. By combining multiple approaches, hybrid systems strive to overcome limitations and enhance the overall performance of the recommendations.
Image recognition technology
Businesses can also leverage the power of visual information to enhance decision-making, gain deeper insights into the products and deliver more personalized and visually appealing experiences to customers using another AI technology, "Image Recognition."
It is a technology that involves the analysis and understanding of digital images or visual data. It enables machines to identify and categorize objects/patterns in images, like human brains.
There are vast applications of image recognition techniques in the e-commerce domain, from visual search to visual quality control. Virtually trials are the most recent trial trend in the e-commerce domain.
They help the customer better judge a product before making a purchase. The user needs to upload the image that serves as a base for this process; then, in the next step, selected products are superimposed on the images using image processing techniques and algorithms.
Rendering techniques are also used to enhance the realism of the virtual try-on. This entire process of virtually trying on the products can increase customer satisfaction and reduce the percentage of products returned.
Chatbots or virtual assistants
Chatbots are a great way to reduce the turnaround time and enhance the post-order experience for customers. They can eliminate language barriers and increase the overall efficacy of the customer service processes.
Keyword recognition-based chatbots: Unlike menu-based chatbots, keyword recognition-based chatbots can observe the user inputs and respond accordingly. These chatbots utilize customizable keywords and Natural Language Processing (NLP) to determine the best response for a customer.
AI/ML-based chatbots: These deploy machine learning to remember the context. For example, if a user enquired about returns on the e-commerce website, these bots could set up the right context by showing the last three recently delivered orders to the users, as users would most likely like to return the most recent orders.
Similarly, sharing other relevant information, such as the return policy, can provide more insight. Thus, by providing contextual responses, businesses can increase their sales and deliver an elevated user experience.
Voice bots: With the help of voice chatbots, organizations can use voice commands and queries to interact with customers. They can offer personalized assistance by accessing user-specific information or preferences.
For example, they can retrieve personalized recommendations, access calendar events, or provide tailored responses based on user profiles.
Utilizing AI for e-commerce
AI has substantially transformed the e-commerce industry by enhancing customer engagement and satisfaction through personalized products and services. For the organizations, it has increased conversion rates and fostered customer loyalty.
The advent of AI-driven chatbots has revolutionized customer support by offering prompt and precise responses, thereby reducing response times and enabling human agents to concentrate on more intricate and groundbreaking endeavors.
However, the successful integration of AI with a company’s e-commerce strategy depends on multiple factors, such as the availability of high-quality data, skilled resources, and a pre-defined AI roadmap with set goals and objectives.
Once the AI strategy and roadmap have been identified, companies need to proactively identify and address challenges related to data security, privacy concerns, and ethical considerations.
A comprehensive approach is key to ensuring optimal growth, innovation, and business opportunities, and a skilled technology partner with experience in delivering similar projects in the e-commerce space can significantly increase the success rate and RoI.