Einstein Recommendation Builder is a tool that predicts what a user may or may not like among a list of given items. Admin can use a point and click interface to build AI-driven recommendations for custom and standard matching problems. It is used to fulfill the customer expectations by collecting relevant data for all customers and predict their preference of the individual customer, Like Best solution for resolving the cases, Top product to purchase, and more.

Why is it useful:

1. Improve Business Outcomes: Deploy real-time, personalized recommendations to boost revenue, customer satisfaction, and more.   

Process:

Best practices:

Before deploying recommendations, admins can configure the settings of their recommendation to improve the quality and performance. Here are some best practices to keep in mind.

1. Exclude irrelevant fields: By default, Einstein considers all the fields in the Recipient and Recommended Items objects. You can exclude fields that aren’t relevant to your recommendation. Doing so can improve performance and mitigate some kinds of bias.

2. Define positive and negative interactions: The way you define positive and negative interactions can affect your recommendation’s performance. You can get better results if you define a positive interaction as the desired outcome. An example of a positive interaction is when a contact purchases a product.

Real-time personalization is when the users are provided with highly targeted content with relevant alternatives based on their (customers’) history. Using this technique, marketers can access customer interactions on online platforms. Along with information about their behaviors, marketers can also access customer demographics.

Using this loads of information, the marketers can show users directly, targeted, and relevant information related to their interests and needs. This information is dynamic and takes milliseconds to reflect on different online channels. Thus, the content is more relevant to the customers, instantly.

Real-time personalization is not limited to websites, but the information is also seen on their email platforms, and social media. Thus, it increases the convenience with which users can access information. It further helps companies nurture customers, so they become loyal customers, and bring more business.

Best practices of real-time personalization:

1. Listen to your customers

Personalization starts when you really understand your customers. For this, marketers need to focus on customer responses and reactions towards products, campaigns, their reviews apart from their browsing activities.

2. Make sure that the personalization is happening at all touchpoints

Several companies have already adopted personalization at some level. However, it is important to personalize at all touchpoints of customers’ interaction to reap maximum benefits from real-time personalization. For this, it is helpful to map their online behaviors into your systems. Having updated customer information is also very helpful; otherwise you will be misled.

3. Send out location-specific push messages

This works like a gem. Send location-specific content, including promotions and offers to individual customers. This is also immensely helpful in evaluating content performance.

4. Use deep learning tools to keep your recommendations relevant

Real-time personalization involves sending targeted communication, including recommendations to customers. This is done based on their behaviors and activity on digital platforms. To do it better, it is recommended to use predictive learning analytics tools to be more precise with the recommendations.

Predictive Leaning:

Example: Healthcare providers also use predictive analytics in a variety of ways. For example, Texas Children’s Hospital has developed a predictive model that uses information about social and psychological factors that affect patients to predict their risk of developing diabetic ketoacidosis, a dangerous complication of diabetes. This allows caregivers to identify high-risk patients and monitor them more closely. Using this model has resulted in a 30.9% reduction in repeat admissions for the complication annually.