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Bulk Actions Documentation

Process multiple rows of data efficiently with Plyable's AI-powered bulk actions

Google Sheets™ Plugin

Bulk Actions

Plyable's Bulk Actions allow you to process multiple rows of data at once, saving you time and effort. Apply AI processing to entire columns of data with just a few clicks.

Custom Actions

Create custom AI prompts that process data from your spreadsheet using variables from your column headers.

How to Use

  1. Enter the row number containing your column headers
  2. Create a custom prompt using the available variables
  3. Click on a variable name to insert it into your prompt as {{variableName}}
  4. Choose where to put the responses (column)
  5. Click "Send Bulk Request" to process all rows

Tips

  • Variables in your prompt will be replaced with the actual cell values
  • For best results, make your prompt clear and specific
  • Processing may take some time depending on the number of rows

Temperature Setting

Use lower temperature (0.0-0.3) for consistent formatting and factual responses. Use higher temperature (0.7-1.0) for creative content generation.

Usage Examples

Customer Feedback Analysis
Scenario: Column headers: CustomerName, FeedbackText, PurchaseAmount
Prompt:
Analyze the following customer feedback and provide a concise summary of the sentiment, key issues mentioned, and any action items: {{FeedbackText}}. Customer: {{CustomerName}}, Purchase Amount: ${{PurchaseAmount}}
Expected Result: The AI will analyze each customer's feedback and return a structured summary with sentiment, issues, and action items for each row.
Product Description Generation
Scenario: Column headers: ProductName, Features, Category, Price
Prompt:
Create a compelling 50-word marketing description for the following product: {{ProductName}}. Key features: {{Features}}. Category: {{Category}}. Target price point: ${{Price}}.
Expected Result: The AI will generate concise marketing copy for each product based on its specific features and category.

Translate Actions

Translate text from one language to another in bulk for an entire column of data.

How to Use

  1. Select the source column containing text to translate
  2. Choose the target language for translation
  3. Select a source language (or leave as "Auto-detect")
  4. Specify the first row to process
  5. Choose the response column where translations will be written
  6. Select the AI model to use
  7. Click "Send Bulk Request" to translate all rows

Tips

  • Auto-detect works well for major languages
  • Be explicit about source language for better results with less common languages
  • Choose more powerful models for nuanced or specialized content

Temperature Setting

Low temperature (0.0-0.3) is recommended for most translations to ensure accuracy. Higher temperatures may be useful for literary or creative text.

Usage Examples

Customer Support Responses
Scenario: Column A contains standard English responses to common customer inquiries
Setup: Source Column: A, Target Language: Spanish, Source Language: English
Expected Result: All support responses will be professionally translated to Spanish while maintaining the original tone and meaning.
Multilingual Product Catalog
Scenario: Column C contains product descriptions in French that need translation to multiple languages
Setup: First translate with Source Column: C, Target Language: English, Source Language: French, then run additional translation operations for other target languages
Expected Result: Creates a multilingual product catalog with consistent descriptions across all language versions.

Classify Actions

Categorize text data into predefined categories across an entire column.

How to Use

  1. Select the source column containing text to classify
  2. Add the categories you want to classify data into
  3. Specify the first row to process
  4. Choose the response column where classifications will be written
  5. Select the AI model to use
  6. Click "Send Bulk Request" to classify all rows

Tips

  • Use clear, distinct categories for best results
  • Include 3-10 categories for optimal performance
  • Add an "Other" category to capture edge cases

Temperature Setting

Very low temperature (0.0-0.1) is recommended for classification tasks to ensure consistent categorization across similar items.

Usage Examples

Customer Support Ticket Categorization
Scenario: Column B contains customer support ticket descriptions
Categories:
Account Access Billing Issue Technical Problem Feature Request Product Question Other
Expected Result: Each support ticket will be classified into the most appropriate category, allowing for better routing and prioritization.
Content Sentiment Analysis
Scenario: Column D contains social media posts or reviews about your product
Categories:
Positive Negative Neutral Question Feature Request
Expected Result: Posts will be categorized by sentiment, helping you identify trends and areas for improvement in customer perception.

Extract Actions

Extract structured data from unstructured text across an entire column.

How to Use

  1. Select the source column containing text to extract from
  2. Define the variables you want to extract (e.g., Name, Age, Profession)
  3. Specify the first row to process
  4. Choose the response column where extracted data will be written
  5. Select the AI model to use
  6. Click "Send Bulk Request" to extract data from all rows

Tips

  • Be specific about the information you want to extract
  • Use variable names that match the concepts in your text
  • Check a few examples manually to verify extraction quality

Temperature Setting

Use very low temperature (0.0-0.2) for extraction tasks to ensure consistent formatting and more reliable data extraction.

Usage Examples

Contact Information Extraction
Scenario: Column A contains unstructured text with personal details
Sample Text:
John Smith is a 42-year-old software developer from Seattle. Contact him at john.smith@example.com or (555) 123-4567.
Variables to Extract:
Name Age Profession Location Email Phone
Format Pattern:
Name: %Name%, Age: %Age%, Job: %Profession%, City: %Location%, Email: %Email%, Phone: %Phone%
Expected Result: The system will extract structured contact information from each text entry, making it easy to create a formatted contact database.
Product Specification Extraction
Scenario: Column C contains product descriptions with various technical details
Sample Text:
The XPS-5000 Monitor features a 27-inch 4K display with HDR support, 144Hz refresh rate, and uses USB-C for connectivity. Retail price is $499.99.
Variables to Extract:
ProductName Size Resolution RefreshRate Connectivity Price
Format Pattern:
Product: %ProductName%, Display: %Size% %Resolution%, Refresh: %RefreshRate%, Ports: %Connectivity%, MSRP: %Price%
Expected Result: Technical specifications will be consistently extracted and formatted across all product descriptions.

Common Bulk Action Controls

Response Column

The column where results will be written. Enter a single letter (e.g., D).

Model Selection

Choose the AI model to use for processing. More powerful models may provide better results but may process more slowly.

Temperature Setting

Controls the randomness of the AI's responses. Lower values (0.0-0.3) produce more consistent and deterministic outputs, while higher values (0.7-1.0) produce more varied and creative responses.

Progress Tracking

A progress bar will display while your request is processing. You can continue working in your spreadsheet during processing.

Apply Results

Results are automatically applied to your sheet when processing completes. If results aren't applied automatically, you can click the "Apply results to sheet" button that appears.

Need Help?

For more information or support with Plyable's Bulk Actions, check our Support Center or contact us at support@plyable.com.