Challenges faced by online retailers:
Online retailers face several challenges when it comes to increasing sales and improving customer engagement. One of the major challenges is to provide personalized product recommendations to customers. As customers' expectations continue to rise, they demand tailored recommendations that cater to their unique needs and preferences. Another challenge is to deal with the vast amount of data generated by customers, making it difficult to make sense of customer behavior and predict their future needs.
How XStak's helps overcome those challenges:
XStak's AI-based product recommendation engine offers a solution to these challenges. It provides personalized recommendations to customers by leveraging machine learning algorithms that analyze customer data to predict their preferences. XStak's platform uses advanced data analysis techniques to identify patterns in customer behavior, purchase history, and other data points to make relevant recommendations.
The platform is also designed to be scalable, meaning it can handle large amounts of customer data and provide real-time recommendations to customers. Additionally, XStak's platform offers a range of customization options that allow retailers to tailor the recommendations to their specific needs.
How XStak’s Recommendation Engine Works?
Let's look at what type of filters XStak’s Product Recommendation Engine uses to recommend products.
Based on the Similarity of the Product:
XStak’s recommendation engine recommends products that have similar tags/attributes. This doesn’t involve, for example, a user looking at a polo shirt - the customer sees polo shirts of different colors and designs.
Based on Similarity of the Customer:
People who have purchased an item in the past are likely to be interested in similar items. We use this strategy to suggest products for purchase. The prediction is based on past likes/actions of a similar customer and not on product features.
Hot Selling Products
These recommendations are based on the products that are being recently most purchased by overall visitors. This helps to improve the discoverability of the most selling products.
Retailers experiencing growth while using XStak's product recommendation engine:
Several retailers have reported significant growth in revenue and ROI after implementing XStak's AI-based product recommendation engine. Here are some examples:
Bonanza, Pakistan top fashion retail brand, reported a 261x ROI and a 13% increase in overall revenue after implementing XStak's platform.
b) Alkaram Studio
A fashion retailer, reported a 94x ROI after implementing XStak's platform. The platform helped Alkaram Studio provide personalized recommendations to its customers, resulting in a significant increase in sales.
An online retailer of makeup and beauty products, reported a 41x ROI and a 10% increase in overall transactions after implementing XStak's platform. The platform helped MEME identify customer behavior patterns and make personalized recommendations, resulting in increased customer engagement and loyalty.
In conclusion, XStak's AI-based product recommendation engine offers a solution to some of the key challenges faced by online retailers. It provides personalized recommendations to customers, helping retailers increase sales and improve customer engagement. The platform is also scalable and customizable, making it suitable for a wide range of retailers. Several retailers have reported significant growth in revenue and ROI after implementing XStak's platform, making it a valuable tool for online retailers looking to improve their sales and customer engagement.