Use cases

In each of these examples,

• Data transformation, mapping, and filtering are crucial.

• The Boolean and Branch connectors are often used for decision-making and data filtering.

• The Script connector or JSON Transformer can be used for more complex data transformations.

• When dealing with large datasets, consider using the bulk operations (bulk_update_records, bulk_upsert_records) for better performance.

Lead Scoring and Prioritization 

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Objective: Automatically score and prioritize leads based on their attributes and activities. 

Steps:

1. Trigger: Use the record_create_update trigger for the Lead object.

2. Fetch Lead Data: Use the find_records operation to get full lead details.

3. AI-Powered Scoring: Use the Merlin functions connector with the classify text operation to analyze lead description and assign a category.

4. Data Enrichment: Use an external data enrichment service to get additional company information.

5. Calculate Score: Use a Script connector to calculate the final lead score based on AI classification and enriched data.

6. Update Lead: Use the update_record operation to update the lead's score and priority in Salesforce.

This example leverages AI for lead classification while combining it with traditional data processing for a comprehensive lead scoring system.

Customer Support Ticket Triage 

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Objective: Automatically categorize and route customer support tickets. 

Steps:

1. Trigger: Use the record_create trigger for the Case object.

2. Fetch Case Details: Use the find_records operation to get full case details.

3. AI Sentiment Analysis: Use the Merlin functions connector with the sentiment analysis operation to analyze the case description.

4. AI Categorization: Use the Merlin functions connector with the classify text operation to categorize the case.

5. Determine Priority: Use a Boolean connector to set priority based on sentiment and category.

6. Update Case: Use the update_record operation to update the case with the category, sentiment, and priority.

7. Route Case: Use the update_record operation to assign the case to the appropriate team based on the category.

This example uses AI for both sentiment analysis and categorization to improve the efficiency of ticket routing.

Automated Sales Pipeline Management 

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Objective: Keep the sales pipeline up-to-date by automatically moving opportunities through stages. 

Steps:

1. Trigger: Use the record_fields_change trigger for the Opportunity object, watching for changes in specific fields like "Last Activity Date".

2. Fetch Opportunity Data: Use the find_records operation to get full opportunity details.

3. Check Conditions: Use Boolean connectors to check if the opportunity meets criteria for stage advancement.

4. Update Stage: If conditions are met, use the update_record operation to move the opportunity to the next stage.

5. Create Tasks: Use the create_record operation to create follow-up tasks for the sales team.

This example doesn't use AI but showcases how to automate sales processes based on data changes in Salesforce.

AI-Assisted Content Personalization 

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Objective: Personalize email content for marketing campaigns based on customer data. 

Steps:

1. Trigger: Use a Scheduled trigger to run the workflow periodically.

2. Fetch Contacts: Use the find_records operation to get a list of contacts for the campaign.

3. Segment Contacts: Use Boolean and Branch connectors to segment contacts based on their attributes.

4. Generate Personalized Content: For each segment, use the Merlin functions connector with the generate text operation to create personalized email content.

5. Update Campaign: Use the update_record operation to update the campaign with the generated content.

6. Create Campaign Members: Use the batch_create_records operation to add contacts as campaign members.

This example uses AI to generate personalized content while leveraging Salesforce data for segmentation and campaign management.

Data Quality Management 

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Objective: Maintain high-quality data in Salesforce by identifying and correcting inconsistencies. 

Steps:

1. Trigger: Use a Scheduled trigger to run the workflow daily.

2. Fetch Records: Use the find_records operation to get a batch of records (e.g., Accounts, Contacts).

3. Data Validation: Use Boolean connectors and regular expressions to check for data quality issues (e.g., formatting, missing fields).

4. AI-Powered Correction: For certain fields (e.g., company descriptions), use the Merlin functions connector with the generate text operation to suggest corrections or completions.

5. Update Records: Use the batch_update_records operation to apply corrections to the records in Salesforce.

6. Report Generation: Use the create_record operation to log a summary of changes made in a custom object for auditing purposes.

This example combines traditional data validation techniques with AI-powered text generation for more complex data corrections.