Recommendation Engine Making Advertising Relevant

Thinking About Personalizing Your Digital Marketing? Think Machine Learning.

Machine learning will boost digital marketing personalization and give your brand the competitive edge. Learn about the four key benefits of this technology and its impact on ad performance when machine learning-based recommendation engine is used in real-time to deliver highly relevant ads to micro-segments of consumers.

Accelerate Digital Marketing Personalization At Scale With Jivox IQ Ad Content Recommendation Engine, Powered By Neuron™

The Jivox IQ Ad Content Recommendation Engine is the first of a series of apps powered by Neuron Machine Learning technology. This machine learning based app is designed to deliver precise, relevant and high-impact digital marketing campaigns in real-time, allowing brands to build a one-to-one experience with their customers.

Jivox IQ Recommendation Engine Is The Difference Between A Sale And A Missed Opportunity

Traditional recommendation engines operate in batch mode. And often by the time product recommendations have been processed, the user is no longer in the market for that product or service. In today’s fast-paced retail environment driven by mobile users, it is critical to deliver messaging and creative about a relevant product or service in real-time.

With Jivox IQ Recommendation Engine, customers are able to predict and serve messages with increased precision, moving beyond simple retargeting to more sophisticated personalization.

Machine Learning Based Elimination of the guesswork of product recommendations
Real-time Recommendations Relevant recommendations since the consumer will still be in the market for the product or service
Continuous Machine Learning A smarter system that updates and improves recommendations increasingly over time
Scalability to millions of products and consumers Increased efficiency of marketing campaigns and cost savings

Collaborative Filtering Recommendations

The Jivox IQ Ad Content Recommendation Engine uses a hybrid of collaborative filtering and content-based methods to increase relevance, through a combination of personalization strategies, such as predictive product recommendations with environmental and date/time-based messages.

  • Automatically predicts and serves products based on preferences of other consumers within a large consumer base.
  • Uses Behavioral Clustering to identify clusters based on a person’s past behavior, as well as similar historical activities by other people.
  • Example: A male consumer that is a sports enthusiast from Indiana and viewed products on the Sony PlayStation site is put into a cluster, a microsegmentation of shoppers.

Behavior Clustering: Micro-Segmentation of Shoppers

Content-Based Recommendations

  • Automatically predicts and serves products based on a consumer’s interest and similar products available from the brand.
  • Uses Product Clustering, which are tags, categories, pricing, and similar attributes to identify and recommend additional items with similar properties.
  • Example: A female consumer is looking for climbing gear on the REI website. Her shopping behavior feeds into the Personalization Hub and the Recommendation Engine predicts and serves ads showing categories of products she has searched or clicked on.

Product Clustering