【2020 Solutions】 Not Just Attractiveness, But Capturing Consumer Attention: Rosetta.ai Personalized Recommendation System
When consumers don't know what to buy or are specifically interested in certain types of products, their first thought might be to shop online. Why? Because it's fast, convenient, offers 24-hour delivery, and they don't have a preference for specific brands, allowing them to find products from various sellers on a single platform.
While browsing online, you've surely seen product recommendation features like 'You may also like...' or 'Others also viewed...'. These not only help you find the products you need more quickly but also allow for fast comparison of similar items in terms of specs and price. It's a very useful feature for consumers. For merchants, this data helps understand customers' habits and preferences.
'Recommendation systems' basically ineffective?
Amazon and the globally known streaming platform Netflix see significant revenue boosts from their recommendation systems. Coupled with articles teaching how to increase conversion rates, e-commerce business owners might have quietly thought, 'I've tried using similar recommendation systems, but they haven't shown significant results.' Perhaps, aside from focusing on targeted marketing, we should consider each consumer as an individual with unique preferences, behaviors, and needs, creating a personalized shopping experience just for them.
Traditional methods, like recommending more fans after one buys a fan, no longer meet the current users' needs.
Modern networks use AI algorithms to dynamically calculate user behavior and update website merchandise, promptly adjusting the recommendations on sites and e-commerce platforms, such as recommended content and the content viewed, to better align with what users are seeking.
Why not analyze consumer profiles, allowing customers to have their 'personalized website'?
Data shows that e-commerce worldwide spends a lot on advertising to drive traffic, yet the actual conversion rates average only 2 - 3%. Why is this? Rosetta.ai has learned from over five years of e-commerce experience that two main factors affect customer conversion and retention:
★Inability to grasp customer purchase preferences, motivations, and situations, including personal factors
★E-commerce often involves wearing many hats with insufficient resources and budget to adopt innovative technology
Thus, Rosetta.ai has always focused on two critical business factors: 'Customer Acquisition' and 'Customer Retention', emphasizing integration of all touchpoints throughout the consumer's online and offline journey, rather than just being an online recommendation tool. Its personalized recommendation system suggests products that best suit each customer's taste, analyzing preferences to help brands more accurately understand their consumer profiles and recommend styles to potential customers.
Rosetta.ai also designs recommendation systems to suit different pages and KPI stages based on consumer shopping processes. Users can select scenarios based on their website's needs; the system also provides real-time feedback and automated engines. Now, with over 30 unified API-ready free modules and 14 basic scenario combinations, your e-commerce site will no longer be just another site. With AI and deep learning, the concept of homepage will be redefined, tailoring product recommendations and homepage design to meet individual consumer demands.
From another angle, creating a precision recommendation service for e-commerce, allowing the use of a personalized recommendation system, not only prevents customer churn and increases average order value but also enhances turnover and provides a novel shopping experience for modern consumers, resulting in a mutually beneficial cycle.
「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」