Einstein Discovery by Salesforce is a powerful tool that integrates statistical modeling and supervised machine learning into your business intelligence processes. This platform offers a user-friendly, no-code-required environment for rapid iteration and analysis. This blog presents an overview of Einstein Discovery. Here’s how it works:
Einstein Discovery, part of Salesforce, empowers businesses by:
- Identifying and Visualizing Insights: It helps in identifying, surfacing, and visually representing key insights within your business data.
- Predicting Future Outcomes and Offering Improvements: By leveraging statistical modeling and machine learning, it predicts future outcomes and provides actionable suggestions to enhance those predicted outcomes within your workflows.
- Target Business Outcomes to Improve
When implementing Einstein Discovery solutions, the initial step involves identifying a business problem to address. Begin by examining key performance indicators (KPIs) that could benefit from leveraging Einstein Discovery. These solutions cater to various use cases:
- Numerical Use Cases: These focus on numeric outcomes represented by quantitative data, such as currency, counts, or percentages. For instance, predicting the closing date of a loan falls under this category.
- Binary Classification Use Case: Here, the goal is to classify business outcomes into two distinct categories, typically represented as text data. For example, predicting the probability of a lead converting.
- Multiclass Classification Use Cases: These solutions aim to predict outcomes that can fall into multiple categories, typically between 3 to 10 possible results, represented as text data. An example could be predicting the most likely next phase for an opportunity.
By categorizing your business problem into one of these use cases, you can effectively tailor your approach and leverage Einstein Discovery to derive actionable insights and predictive solutions.
2. Prepare Data
With your business objectives in focus, the next step involves preparing the data required for analysis and model training by Einstein Discovery. This platform is capable of analyzing vast amounts of data, even millions of rows and numerous columns. Moreover, Einstein Discovery assists in identifying the most relevant columns correlated to the desired outcome for improvement.
To facilitate this data preparation, leverage the robust data integration capabilities of CRM Analytics. This enables seamless loading and transformation of data from multiple sources into a CRM Analytics dataset. You can pull data from Salesforce as well as external sources, ensuring a comprehensive dataset for analysis.
In addition, consider supplementing data cleansing and wrangling tasks with third-party tools and utilities to ensure data quality and consistency. Effective data preparation plays a crucial role in driving successful outcomes with Einstein Discovery, setting the foundation for accurate insights and predictive models.
3. Create Model
An Einstein Discovery model comprises a collection of essential components including performance metrics, settings, predictions, and data insights. The process of creating a model is guided by Einstein Discovery, tailored to the desired outcome for improvement (model’s goal), and the dataset assembled for analysis, typically housed within the CRM Analytics dataset. Importantly, this process requires no coding or prior machine learning expertise.
Starting with the dataset prepared earlier, the creation of an Einstein predictive model involves specifying the goal, which defines the outcome to be analyzed and predicted. Additionally, it’s necessary to indicate whether the goal is to maximize or minimize the outcome result.
During model creation, there are two approaches available:
- Automated Model Selection: In this method, Einstein automatically selects the columns that are most relevant to the specified goal. This streamlined approach simplifies the process, leveraging Einstein’s intelligence to identify key predictors.
- Manual Model Configuration: Alternatively, users can opt for a manual model setup, allowing them to manually select and configure the columns to be included in the model. This hands-on approach provides greater flexibility and control over the modeling process, catering to specific business requirements and preferences.
The further steps will be covered in an upcoming article.
To learn more, click Introduction to Einstein Discovery (salesforce.com).
Take Five Consulting is a technology company, based in Virginia U.S., that specializes in the Mortgage Banking vertical especially LOS implementation and application development. Take Five Consulting creates and implement mortgage technology and software specifically for Mortgage Industry.
