AI Recommendations: How To Win And Sustain Your Customers’ Attention

Recommendations Studio gives every marketer the power of AI to drive intelligent recommendations. For example, retailers can design customer journeys with highly relevant content and recommendations in real time based on the shopper’s affinities, search and browse activity, and purchase history. Streaming service providers can use advanced AI to precisely match millions of viewers with hundreds of thousands of media content catalog items and engage viewers with daily content feeds that are auto optimized based on prior activity. 

With the expanded Studio, marketers can quickly get started designing recommendations with a library of 100+ pre-built AI Recommendations Recipes and insert product and content recommendations into emails and other notifications using drag-and-drop functionality. And then test the incremental lift of those campaigns using A/B tests or holdout tests. 

Let’s dig a little deeper into what you can expect in the expanded Recommendations Studio.

We’re launching the library of 100+ pre-built AI marketing recipes, preloaded with configurations for common campaigns, like abandoned carts, price drops, newsletter feeds based on affinities, and cross-merchandising based on the wisdom of the crowds. Each recipe comes with helpful information on suggested campaigns and user touchpoints and you can easily configure each of the recipes to get the recommendations just right for your campaign. 

Whether it’s engaging an active user on your site or in-app, or re-engaging someone who is fading away or winning back their attention with fresh content you will find a recipe tailored and optimized to that audience.

Once you’ve built the recommendation scheme using our out-of-the-box recommendation recipes, you can add these to your emails, SMS, push notifications, in-app messages, and other notifications using an easy drag-and-drop interface.

For better engagement results, you can easily suppress repetitive recommendations. You can also include seasonal and business promotions alongside predictive content.

The success of organic recommendations depends on many factors. We’ve enhanced our Insights reports with advanced recommendation analytics so that you can not only optimize your campaigns by using the best performing recommendations, but also get visibility into which items are being recommended to your users and which ones they are engaging with more.

Creating rich recommendations can be powerfully engaging for customers, but you don’t need an expanded team of data scientists and machine learning engineers for you to see success. The beauty of our AI and Recommendations Studio is that it works out of the box and provides you with all the tools you need to be successful with a small marketing team. We’ve seen many customers with small marketing teams flourish. Sweden-based online marketplace Tradera, for example, increased sales by 131% with personalized recommendations. 

Alexandra Tham, online marketing manager at Tradera, said: “Our small, time-constrained team has been able to deliver personalized, 1:1 product recommendations across our website, mobile app, and email campaigns at scale, which we could not do with our previous solutions.” (Read more about Tradera’s success.)

Similarly, Zumper, which connects property owners and managers with renters scaled leads by 384% using predictive recommendations. Russell Middleton, Zumper co-founder, said, “If we had stuck with our old system, we’d need to add a number of people across the board, data scientists, data engineers, and marketers, to achieve the complexity of what Blueshift does for us today.” (Read more about Zumper’s success.)

The power of AI is within reach for every retailer and digital media company. Give your customers a reason to love your brand even more by putting the most relevant content and products in front of them. Our recommendations experts are here to help you get started. Explore our free interactive demo, or talk to our experts today. 

Learn more about the inner workings of AI recipes in a blog post written by Blueshift senior data scientist, Anmol Suag. In this article, Anmol discusses how to rank recommendations inside recipes. And in this article, we discuss using Auto-encoders to find similar items to recommend.

Manyam Mallela is co-founder and chief AI officer at Blueshift. We announced the expanded Recommendations Studio and the AI recipes at Engage 2022 San Francisco. Go to our Engage 2022 on-demand page to watch the session and to download the slides that gives a tour of the new updates, as well as the other sessions.