Machine learning, deep learning, predictive analytics, natural language processing and smart data discovery.
- Official:Einstein Analytics for Partners
- Einstein Analytics & Discovery Cert FP
- Einstein PLATFORM for Partners
- FY20 AMER Einstein AI PARTNER SUMMIT - Seat Request Form
- https://metamind.readme.io/v1/docs
- https://github.com/MetaMind/apex-utils
- https://github.com/salesforceidentity/jwt
- Learn Einstein Lead Scoring on Trailhead
- Einstein Platform How-to Webinar: Get Started with Einstein Discovery - Replay
- Einstein Platform How-to Webinar: Get Started with Einstein Discovery - Slides
- Workbook_Deep_Learning_and_Natural_Language_Processing
- Salesforce Event Log File Browser
- Apex Trigger Event Type
- Einstein AI Learning Path
- Einstein Analytics Learning Path
- Salesforce Certified Einstein Analytics and Discovery Consultant
- Learn Einstein Analytics Plus Trailmix
DB_TOTAL_TIMEis the time in nanoseconds for a database round-trip.CPU_TIMEis the CPU time in milliseconds used to complete the request. This measure indicates the amount of activity taking place in the app server layer.- Compare the
DB_TOTAL_TIMEtoCPU_TIMEto determine whether performance issues are occurring in the database layer or in your own code.
/services/data/v46.0/query?q=SELECT+Id+,+EventType+,+LogDate+,+LogFileLength+,+LogFile+FROM+EventLogFile++WHERE+EventType+=+'ReportExport'
https://${instance}.salesforce.com/services/data/v34.0/query?q=Select+Id+,+EventType+,+LogDate+From+EventLogFile+Where+LogDate+=+${day}
A model is a machine learning construct used to solve a classification problem. The model learns from data instead of from explicit rules.
- Einstein Activity Capture - Sales reps just connect their email and calendar to Salesforce. Then, their activities are automatically added to related Salesforce records. Plus, emails sent from Salesforce go through their regular email account.
- Einstein Lead Scoring - gives each lead a score based on how well it matches your company’s particular lead conversion patterns. Einstein Lead Scoring shows you exactly which details about each lead have the greatest effect on its score. Predictive Lead Scoring constantly adjusts its analysis in order to discover any new patterns that emerge.
- Opportunity Insights - uses machine learning and sentiment analysis to help sales reps close more deals. The same data collection that’s used to log customer data and identify the best leads can also move the sales needle. Insights are tailored to the patterns and data specific to your organization.
Three types of Insights
-
Deal Predictions—Your reps see predictions based on recent activity and existing opportunity data. For example, whether a deal is more or less likely to close, or if a deal seems unlikely to close in time.
-
Follow-Up Reminders—Reps get reminders to follow up when a contact hasn’t responded in a while. They also get reminders if there hasn’t been any communication related to an important opportunity for a significant period of time.
-
Key Moments—Reps are notified at key moments related to a deal, such as when a contact mentions a competitor or is leaving their company.
-
Einstein Account Insights - helps your sales team maintain their relationships with customers by keeping the team informed about key business developments that affect customers.
Language contains two NLP services: Einstein Intent and Einstein Sentiment.
The Einstein Language APIs support data in these file formats: .csv (comma-separated values) .tsv (tab-separated values) .json
The Einstein Intent API categorizes unstructured text into user-defined labels to better understand what users are trying to accomplish. Use this API to analyze text from emails, chats, or web forms to:
- Determine which products prospects are interested in, and send customer inquiries to the appropriate sales person.
- Route service cases to the correct agents or departments, or provide self-service options.
- Understand customer posts to provide personalized self-service in your communities.
The Einstein Sentiment API classifies text into positive, negative, and neutral classes to understand what the words people use can tell us about how they’re feeling. Use this API to analyze emails, social media, and text from chat to:
- Identify the sentiment or emotion in a prospect’s emails to trend a lead or opportunity up or down.
- Provide proactive service by helping dissatisfied customers first or extending promotional offers to satisfied customers.
- Monitor how people perceive your brand across social media channels, identify brand evangelists, and note customer satisfaction.