MyBuys Personalized Web Recommendations
Personalized Recommendations Drive Conversion and Revenue
According to a 2013 survey conducted by the e-tailing group, 77% of online shoppers make additional purchases when e-commerce sites present them with personalized product recommendations.
This makes perfect sense – featuring those products that are the most attractive to a particular consumer, individually, in real-time, is a merchandiser’s dream – and real data from hundreds of e-commerce sites shows that 1:1 personalization like this increases revenue by up to 10% and can double average order value. The challenge is that trying to do such extreme personalization manually is virtually impossible.
Enter MyBuys Personalized Web Recommendations, which automatically creates the completely personalized experience that marketers crave and consumers increasingly expect.
MyBuys dynamically merchandises your e-commerce site for each individual consumer. We compile behavioral data on nearly 250 million individuals, and our portfolio of powerful algorithms sifts through nearly a petabyte of information to create a unique profile for every one of them. When combined with our best practices for online merchandising, our big data analytics make the optimal recommendations for each individual consumer, in real-time.
With Every Click, MyBuys Leads to the Most Relevant Choices
MyBuys uniquely understands your catalog assortment, brands, affinities, pricing, and purchasing history. We learn from every view and every click and track and remember every facet of your shoppers’ behavior. We know consumers’ stated and implicit preferences, their browsing habits, the offers they accept and reject and the products they actually purchase. We use this to create predictive behavioral profiles that shape individual recommendations across your site and that evolve over time.
We combine data we glean from consumer behavior on your e-commerce site with behavior we see across email and display advertising – and all for every device used by that particular individual.
The results? A more engaging and effective shopping experience for your shoppers. Better business results for you: higher conversion rates, higher average order value, and more margin and revenue.
Ultimate Flexibility and Power
MyBuys Web Recommendations can deliver a mix of cross-sells and up-sells, top sellers, featured, and new or on-sale items across categories and brands. It can rank potential recommendations based on each shopper’s preferences, price points, and favorite brands. It can automatically offer new products or prices based on current shopping activities and changes in your catalog.
MyBuys Web Recommendations supports a wide variety of promotional, cross-selling and up-selling techniques including sale announcements, new product introductions, featured items, top selling brands or products, pre-arrival announcements, product life cycle changes and much much more.
MyBuys Web Recommendations works with all major e-commerce platforms, and consumers can interact with it using all major web browsers on their desktop, laptop, tablet and mobile devices.
Rapid, proven results
In use today across hundreds of leading e-commerce sites, MyBuys Personalized Web Recommendations immediately deliver incremental lift, higher conversion rates and dramatically increased average order value – and the results keep getting better over time as we learn more and more about your audience, and as you engage across more and more channels.
MyBuys Personalized Web Recommendations is part of the MyBuys Customer-Centric Marketing Suite. It works terrifically well on it’s own, but if you choose to adopt an omni-channel strategy it also automatically coordinates with MyBuys Personalized Email and Display Advertising to completely delight your customers and deliver the highest possible return for your business.
You can start today – and in as little as 30 days begin captivating your customers while reaping the benefits of the world’s leading customer-centric marketing suite.
MyBuys Personalized Web Recommendation










