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            Einstein Decisions

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            Einstein Decisions

            When a user views a promotion on your website, Marketing Cloud Personalization captures the user's context—including whether they’re a returning visitor, the device they’re using, and other information that provides insight into that user. Einstein Decisions, Personalization’s machine learning approach for next-best-offer decisioning, uses that context to predict the value of showing a specific offer to a particular user.

            Required Editions

            Available in: Premium edition
            • About Einstein Decisions
              Einstein Decisions automates the process of deciding who sees what content, so you don’t have to manually create complex rules. For example, you define an area on the homepage for five different promotions—credit cards, mortgages, auto loans, checking accounts, and retirement plans—and you're not sure which promotion to display when. Einstein Decisions takes away the guesswork by continuously learning from the data Personalization collects about users, and presents the promotion with the highest likelihood of generating the most lift.
            • Feature Engineering in Einstein Decisions
              Feature engineering refers to the process of selecting features to use in a machine learning model. A feature is a data type that the model can observe and include in its training. To determine which data Einstein Decisions references, you define the machine learning features on the Feature Engineering page.
            • Configure Einstein Decisions
              Einstein Decisions analyzes many unique data points when determining the right experience to present to a particular user. This data includes user behaviors as well as contextual data, such as device type, time of day, and demographic data. Einstein Decisions can also reference segment membership and user attribute data, along with catalog objects you create in your catalog. To determine which data Einstein Decisions references, you define the machine learning features on the Feature Engineering page. Typically, a data science or analytics team member configures the feature engineering options for Einstein Decisions.
            • Promotion Management for Einstein Decisions
              After you configure Einstein Decisions, add an initial set of promotions and assets to a dataset, and publish an Einstein Decisions campaign, you can begin planning your ongoing promotion management.
            • Einstein Reports
              Einstein Decisions provides reports as an outcome of Marketing Cloud Personalization’s continuous model training. Each time the machine learning model trains, which typically occurs multiple times per week, Personalization creates a report. These reports provide your analytics and data science teams with additional insight into how and why the Einstein Decisions algorithms are making their decisions.
            • Einstein Decisions Limits
              Marketing Cloud Personalization has limits for some Einstein Decisions features.

            See Also

             
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