Commerce Cloud Einstein Search Recommendations – Revolutionizing Customer oriented commerce journey

Salesforce Commerce Cloud Einstein, the AI intelligence embedded in the product, has many features to facilitate customer specific searches and recommendations. It helps retailers present relevant search outcomes to the users leading to a higher conversion rate. Each customer expects outcomes based on their interests, and behavior. Hence, presenting the same results for a specific search to all of the customers will not be the right approach. Customers have their own choices. One that is interested in blazers will not want to see tuxedos or coats while searching for jackets, though the customer is not typing the correct term but expecting the results based on interest.

Commerce Cloud Einstein search recommendation feature is about generating a google search like experience at the site level as Google provides results based on our interests, locality, search history, etc. Einstein search recommendation also takes into consideration all these variables to generate extremely customized search recommendations.

Einstein Search Recommendations

1. Type Ahead Search

This feature enables customized type-ahead search results for each shopper on the brand site. Shoppers are guided towards suitable searched terms to the product they are looking for. By utilizing site search result data sets, Einstein search provides a customized search journey for each customer which in-turn creates more conversion rates. Einstein Search Recommendations uses site level commerce data and applies it to the algorithms to make decisions in presenting search results most suited for the particular shopper from a specific region and also other variables like gender, previous search history and past orders.

The site without Einstein Search Recommendations features enabled will not generate suggestions based on interests, previous searches, behavior or products available on the site. In the use case displayed below, the customer is looking for a specific kind of skirt at a Women’s apparel and accessories store.The customer types "Ski,” expecting skirts as an option to display in the search results. But the suggested product is “Ski,” here. The shopper, not expecting ski in the search result, might switch to another site for shopping based on his/her interests and better digital experience.

When Einstein Recommendations features are on, even without typing any character, results will show recent searches and other suggestions related to the clothing and user specific interests.

2. Relevant Products

In this use case, the shopper is now searching for shirts. Einstein is displaying relevant searches that include only shirts. T-Shirts are not included.

3. Customized Results

If shopper in our use case above, has searched previously for pencil skirts, then, clicks and options will be suggested with pencil skirt upon typing skirt. This is tailored to this particular shopper. Other shoppers looking for skirts will have different suggestions and recommendations based on previous searches, region, clicks, etc.

Benefits

  • Customized term completion and correction.

  • Shopper recent search terms.

  • Popular searches to inspire shoppers.

  • Auto-suggested search terms

Royal Cyber can help in achieving personalized commerce journey for each customer by Configuring Einstein Search efficiently and effectively.

Commerce Cloud Einstein, is the most discussed AI engine, embedded as part of the product that brands are opting to implement, generating more personalized search results for their customers and more conversion rates. In order to have the desired results from Einstein, there are many variables needed to be appropriately configured and optimally. Like, search dictionaries, sorting rules, SEO, meta tags, keywords, recommendations, etc. Experts from Royal Cyber, have extensive experience and knowledge in configuring and customizing these features, effectively and productively, and help you take full advantage of these customer-oriented features.

Leave a Reply