Isaac Asimov and Philip K Dick painted a future of Androids dreaming of electric sheep and robots doing just about everything. While we might not be there just yet, artificial intelligence (AI)—sci-fi’s mainstay—has very much become a reality, providing intelligent solutions to complex everyday problems. A great use case has been in retail. Take, for example, Commerce Cloud Einstein (CCE). With the power to predict customer’s futures by guessing what they need before they need it, CCE AI has become a reliable way for e-commerce platforms to attract customers.
Once businesses decide to set up shop by integrating CCE, they will need to create an implementation plan. Roles need to be assigned to the execution team, including those of an administrator, the merchant, and a developer (or development team). Ask your technology partners for a Salesforce Commerce Cloud Implementation that includes the integration of tools like CCE to automate the storefront.
Once the implementation plan has been set in motion, it’s time to install and set up the many features that CCE offers. Before starting, an Einstein Data Privacy Agreement has to be signed for the security and transmission of data related to the smart search dictionary. For product recommendations and the predictive sort feature, a validator needs to be installed to run the features.
A Harvard Business Review report states that business cloud computing solutions have been on the rise, and doubling every year, since 2010. AI-driven tech like CCE is powered by collecting data to offer customers predictive searches and product recommendations.
There is no better time for brands to adopt CCE. Here are three simple steps to get CCE up and running on e-commerce platforms:
Harness the power of Einstein by enabling data
CCE collects and uses data from one-time historical order feeds, daily order feeds, and daily product feeds. Data is collected from various sources on the e-commerce platform. SFCC’s catalog offers a plethora of information that can be accessed by running a catalog feed. Next, orders offer an invaluable amount of data relating to customer orders, which can be generated by an order feed. Finally, clickstream data is live information that is continuously streamed and collected. This requires no feeds to access the data. By using the customer behavior data that CCE stores, predictive suggestions can be made. If the database hasn’t been set up yet, CCE uses the current data set, no matter how small.
Testing, testing 1-2-3
Salesforce Commerce Cloud development templates provide the ability to quickly create an implementation plan that works for a brand’s storefront. Merchants can create and modify the storefront page templates and the product recommendations feature will be ready to test. Finally, they need to utilize A/B testing to test out templates to see what works best. By continuously evolving, they can learn how to keep improving the CCE-enabled storefront.
And it’s (a)live!
Once the platform is ready for customers, it is all set to be taken live. Before taking the Einstein-enabled page live, best practices should be reviewed to ensure the efficiency of the system. After taking the page to live, A/B testing should be run often to measure and ensure success and to recognize which methods work best.
The CCE features one chooses will impact the overall implementation, so the business needs to select what the storefront needs. For Commerce Insights, all one needs are data feeds and a Configurator login. All the other features require a structured plan and effort to implement.
Go ahead, finish people’s sentences
Using the preview and validator tool helps to ensure that there are no issues with recommenders. Next up is choosing where the recommenders go on the storefront. This will become available in the Business Manager content slot configuration after CCE deployment.
The Configurator tool
The Configurator offers powerful commerce insights, helps discover which products are most often purchased together, and enables the user to take advantage of the Einstein product recommendations to predict the most relevant products. The brand is then able to promote these products to shoppers based on recommendation specifications. Reviewing a product, configuring a report, and planning all become easy with insights received from CCE.
By using the Configurator to create recommenders, the parameters that CCE requires can be fine-tuned. Recommenders are a set of rules and strategies used by the product recommendation tool. Some of the recommendations include popular search, recent searches, and a unique personalized search that recommends products as the customer types a product name into the search bar.
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