Account Engagement/Pardot Einstein, main features
Posted: Sun Dec 22, 2024 9:56 am
Salesforce's B2B automation tool , known as Pardot and soon to be called Account Engagement , is packed with AI-powered features. In this article, we'll walk you through some of Einstein's key smart features and how you can use them to power your business.
Einstein Lead Scoring
This feature detects different patterns by analyzing recorded demographic data, identifies similar characteristics between converted customers and potential leads , and thus establishes a score for each of them (it can be negative or positive). In this other article we show you tips to build a good lead scoring model for your business , its advantages and some of the actions to take into account to increase the score in your leads.
Lead Scoring Model
Einstein Behaviour Scoring
It is very similar to Einstein Lead Scoring, however, it is taiwan telegram powered by machine learning, which allows for more practical and personalized interaction models. This feature analyzes prospects' actions and contacts' activities and, based on their behavior, establishes a score between 0 and 100. The different factors taken into account are found in a dashboard in B2B Marketing Analytics. At least 6 months of activity data is recommended for more accurate analysis and scoring.
Einstein Send Time Optimization
Einstein develops a model based on the analysis of historical engagement data, i.e. openings, clicks, unsubscriptions, spam flags, among others. After evaluating all this data, it makes a weighting based on the frequency of the campaigns sent and recommends an optimal time to send campaigns for each prospect (within a set time range). This feature only applies to prospects with at least one engagement data in the last 90 days.
Einstein Send Time Optimization
Einstein Engagement Frequency
This feature also uses AI to find patterns of prospect interaction and is based on the analysis of data on openings, clicks, unsubscriptions, etc. With these patterns, it is able to predict the appropriate frequency of sending email campaigns . In addition, the collected data is stored in a new field, which allows you to subsequently create automations or segmentations from them.
Einstein Lead Scoring
This feature detects different patterns by analyzing recorded demographic data, identifies similar characteristics between converted customers and potential leads , and thus establishes a score for each of them (it can be negative or positive). In this other article we show you tips to build a good lead scoring model for your business , its advantages and some of the actions to take into account to increase the score in your leads.
Lead Scoring Model
Einstein Behaviour Scoring
It is very similar to Einstein Lead Scoring, however, it is taiwan telegram powered by machine learning, which allows for more practical and personalized interaction models. This feature analyzes prospects' actions and contacts' activities and, based on their behavior, establishes a score between 0 and 100. The different factors taken into account are found in a dashboard in B2B Marketing Analytics. At least 6 months of activity data is recommended for more accurate analysis and scoring.
Einstein Send Time Optimization
Einstein develops a model based on the analysis of historical engagement data, i.e. openings, clicks, unsubscriptions, spam flags, among others. After evaluating all this data, it makes a weighting based on the frequency of the campaigns sent and recommends an optimal time to send campaigns for each prospect (within a set time range). This feature only applies to prospects with at least one engagement data in the last 90 days.
Einstein Send Time Optimization
Einstein Engagement Frequency
This feature also uses AI to find patterns of prospect interaction and is based on the analysis of data on openings, clicks, unsubscriptions, etc. With these patterns, it is able to predict the appropriate frequency of sending email campaigns . In addition, the collected data is stored in a new field, which allows you to subsequently create automations or segmentations from them.