Tag: RevOps

How to export Salesforce field API names by record type without data values

Standard Salesforce reporting can’t extract field API names without showing actual record data, making it impossible to create clean field inventories for documentation or schema analysis.

Here’s how to pull pure field metadata by record type using Custom SOQL queries that bypass standard reporting limitations.

Extract field API names using Coefficient

Salesforce’s native tools fall short because they’re built for data reporting, not metadata extraction. Field visibility varies by record type and page layout, making comprehensive field inventories nearly impossible through standard interfaces.

Coefficient’s Custom SOQL Query functionality lets you query metadata objects directly, extracting field API names without touching actual record data.

How to make it work

Step 1. Connect to Salesforce and set up Custom SOQL Query.

Open your spreadsheet and launch Coefficient. Select Salesforce as your data source, then choose “Custom SOQL Query” from the import options. This gives you direct access to metadata objects that standard reporting can’t reach.

Step 2. Query the FieldDefinition object for field API names.

Use this SOQL query to extract field API names for your target object:

Replace ‘Case’ with your target object name. This returns all field API names, labels, and data types without any record data.

Step 3. Add record type information if needed.

For record type-specific field visibility, run a separate query against the RecordType object:

This shows which record types exist for your object, helping you understand field associations.

Step 4. Export and schedule automatic updates.

Export your field inventory directly to your spreadsheet format of choice. Set up automated refreshes to keep your field documentation current as your Salesforce schema evolves, ensuring your field inventory stays accurate without manual updates.

Build comprehensive field documentation

This approach gives you complete field metadata without exposing sensitive record information. You can create shareable field inventories that update automatically and provide the schema documentation your team needs. Start building your field inventory today.

How to export SFCC customer group data for external analysis in Salesforce

Salesforce Commerce Cloud doesn’t provide native reporting for customer group performance metrics, forcing teams to export raw data and analyze it externally. While SFCC offers several export methods, the real challenge lies in transforming that exported data into actionable insights.

Here’s how to extract SFCC customer group data and set up powerful analysis workflows that deliver the customer segmentation insights your native platform can’t provide.

Export SFCC data then analyze with Coefficient

The most effective approach combines SFCC’s export capabilities with Coefficient’s advanced analysis features. First, you’ll extract the raw customer group data from SFCC, then import it into spreadsheets where you can build the sophisticated customer group analytics that Salesforce Commerce Cloud simply can’t deliver natively.

How to make it work

Step 1. Extract customer group data from SFCC using your preferred method.

Use SFCC’s Data Export API with custom scripts to pull customer records including group assignments, or leverage Business Manager’s bulk export functionality. You can also use OCAPI Customer endpoints to programmatically retrieve customer group relationships. The key is getting both customer data and their associated group memberships in a format you can work with.

Step 2. Import your exported SFCC data into Google Sheets or Excel using Coefficient.

Once you have your CSV exports, use Coefficient’s import capabilities to bring the data into your spreadsheet. Set up automated refresh schedules so your analysis stays current as you generate new SFCC exports. This creates a reliable pipeline from your SFCC data to your analysis environment.

Step 3. Create dynamic customer group segments using advanced filtering.

Apply Coefficient’s AND/OR logic filtering to segment customers by group membership without rebuilding reports. You can filter by group type, assignment dates, or any custom attributes included in your SFCC export. Dynamic filters let you point to cell values, making it easy to change your analysis focus without editing import settings.

Step 4. Build calculated fields for customer group performance metrics.

Create formulas that automatically compute group-specific conversion rates, average order value per customer group, and customer lifetime value by segment. Use Formula Auto Fill Down to apply these calculations across all customer group segments automatically as new data comes in.

Step 5. Set up automated snapshots to track customer group trends over time.

Configure scheduled snapshots to capture customer group performance at regular intervals. This creates historical trend analysis that’s completely unavailable in SFCC’s native reporting, letting you see how different customer segments perform over weeks, months, or quarters.

Transform raw SFCC exports into actionable customer insights

This approach fills the critical gap in SFCC’s analytics capabilities by providing customer group visibility that would otherwise require complex custom development. Start building your customer group analysis workflow today.

How to extract all record IDs from a Salesforce report without exporting to Excel

You can extract all record IDs from Salesforce reports without the tedious export-to-Excel process by using automated data integration that pulls IDs directly into Salesforce spreadsheets with live connectivity.

This approach eliminates manual downloads and gives you real-time access to record IDs that automatically update when your report data changes.

Skip the export process with automated ID extraction using Coefficient

Coefficient connects your Salesforce reports directly to Google Sheets or Excel, automatically importing all record IDs along with other report data. Instead of manually exporting files, you get live data that refreshes on your schedule.

How to make it work

Step 1. Connect your Salesforce org to Coefficient and import your report.

After installing Coefficient, select “Import from Existing Report” and choose your target report. The import automatically includes all fields, with record IDs typically appearing in the first column of your spreadsheet.

Step 2. Set up automated refreshes to keep your ID list current.

Configure scheduled refreshes (hourly, daily, or weekly) so your extracted IDs stay up-to-date without any manual intervention. This ensures you’re always working with the latest Salesforce data.

Step 3. Use spreadsheet formulas to isolate and clean your ID list.

Create a dedicated column containing only the record IDs using formulas like =UNIQUE() to eliminate duplicates. You can also use filtering functions to extract IDs that meet specific criteria.

Get live ID extraction that beats manual exports

This automated approach maintains live connectivity to your Salesforce data and eliminates the manual export routine entirely. Start extracting record IDs automatically and save hours of repetitive work.

How to extract and preserve all custom field values before Salesforce account merge

Extracting and preserving all custom field values before Salesforce account merges is critical for preventing permanent data loss. Native merge operations delete all custom field values from the losing account without any recovery options.

Here’s a comprehensive guide to ensure complete custom field preservation with automated extraction workflows and validation systems.

Extract and preserve every custom field value with automated workflows using Coefficient

Coefficient handles comprehensive custom field extraction exceptionally well, providing automated workflows that capture all field types, validate data completeness, and create permanent preservation records.

How to make it work

Step 1. Set up comprehensive field discovery and extraction.

Create a Salesforce import using “From Objects & Fields” for the Account object. Click “Select All” for custom fields to include all fields ending in “__c”, plus formula fields and lookup relationship fields. Sort by field type to organize Text, Number, Date, and Picklist fields systematically.

Step 2. Configure multi-account extraction with merge targeting.

Set up filters with ID equals [Master_Account_ID] OR ID equals [Loser_Account_ID] to pull both accounts in the same import. This creates side-by-side comparison views and enables immediate extraction before planned merges with scheduled backups and on-demand pulls.

Step 3. Build organized preservation templates with validation.

Create tabs for Raw Data Import (all accounts with all custom fields), Merge Comparison (Field API Name, Field Label, Master Value, Loser Value, Values Match formula, Action Required), and Preservation Archive (historical record with append-only structure).

Step 4. Handle complex field types with specialized preservation.

Extract HTML content completely from Rich Text fields preserving formatting and embedded images. Capture multi-select picklist values in “Value1;Value2;Value3” format and document available options. Store lookup relationships as both ID and Name in “Related_Account__c: ID (Name)” format.

Step 5. Implement validation checks and snapshot version control.

Use validation formulas like =COUNTIF(C2:C100,”<>“)/COUNTA(A2:A100) to check data completeness and =IF(AND(D2<>“”,C2<>D2),”UNIQUE DATA – PRESERVE”,””) to identify unique loser values. Configure snapshots with “Pre_Merge_[Date]_[AccountNames]” naming and permanent retention.

Never lose custom field data again

This comprehensive extraction and preservation approach ensures no custom field data is lost during Salesforce account merges while providing complete audit trails and recovery capabilities. Ready to protect your custom field data? Start building your extraction system now.

How to extract customer health score data with timestamps from HubSpot CS space

HubSpot’s CS space blocks timestamp access for customer health score data, making it impossible to track score changes over time through native reporting channels.

Here’s how to extract timestamped health score data and build the historical tracking capabilities that HubSpot’s CS space can’t provide.

Extract timestamped health score data using Coefficient

Coefficient connects directly to HubSpot’s API to pull customer health score data with full timestamp preservation. This bypasses the CS space reporting limitations that block timestamp access in native HubSpot dashboards.

How to make it work

Step 1. Set up your HubSpot connection and import health score data.

Connect to HubSpot through Coefficient’s sidebar and select your customer health score data from the CS space. Use custom field selection to pull the specific health score metrics you need along with customer identifiers and any associated properties.

Step 2. Configure scheduled snapshots for timestamp preservation.

Set up daily or weekly snapshots using Coefficient’s snapshot feature. This captures point-in-time copies of your health score data with preserved timestamps, creating the historical dataset that HubSpot’s native reporting blocks.

Step 3. Enable automated data refresh with append functionality.

Configure your import to append new health score readings without overwriting previous data. This builds a comprehensive time-series dataset where each row includes automatic timestamp tracking for when the data was captured.

Step 4. Build time-series analysis with auto-fill formulas.

Use Formula Auto Fill Down to automatically calculate health score changes, trend velocity, and period-over-period comparisons as new timestamped data arrives. Create formulas like =B2-B1 for score changes and =(B2-B1)/B1 for percentage movements.

Start tracking health score trends today

This approach transforms HubSpot’s timestamp-blocked health score data into a robust tracking system that enables proactive customer success management. Get started with Coefficient to build the historical health score analysis that HubSpot’s CS space limitations prevent.

How to extract filtered closed won total from Salesforce report using REST API

Salesforce’s REST API can technically access report data, but it doesn’t return pre-calculated totals and requires complex SOQL queries plus custom aggregation code.

Here’s a simpler approach that gets you the same filtered closed won totals without wrestling with API limitations or authentication headaches.

Get filtered closed won totals directly in spreadsheets using Coefficient

Coefficient eliminates the need for complex REST API calls by importing your Salesforce Opportunity data directly into Google Sheets or Excel. You can apply up to 25 filters to match your report criteria exactly, then use simple SUM functions for instant totals.

How to make it work

Step 1. Connect Salesforce to your spreadsheet.

Install Coefficient and authenticate your Salesforce connection through the sidebar. This handles all the API authentication automatically so you never have to manage tokens or refresh cycles.

Step 2. Import Opportunity data with filters.

Select Opportunities as your object and choose the Amount field along with any other fields you need. Apply filters like Stage = “Closed Won” and set your date ranges using AND/OR logic to match your report exactly.

Step 3. Set up automatic aggregation.

Use a simple SUM formula on the imported Amount column to get your closed won total. The formula automatically handles null values and currency conversions that would require custom code in a REST API solution.

Step 4. Schedule automatic refreshes.

Set up hourly, daily, or weekly refreshes to keep your totals current. You can even point filter values to specific cells, so changing a date range updates your data without rebuilding queries.

Start pulling Salesforce data without API complexity

This approach gives you the same filtered closed won totals as complex REST API solutions while eliminating authentication management and custom aggregation code. Try Coefficient to streamline your Salesforce reporting.

How to extract HubSpot payment link analytics data to spreadsheet for reporting

HubSpot’s native reporting provides limited customization options for payment link analytics and struggles with cross-object analysis connecting payment links to broader sales metrics. You need flexible reporting that combines payment link performance with deal conversion and revenue data.

Here’s how to extract comprehensive payment link analytics for advanced reporting and dashboard creation.

Extract comprehensive analytics using Coefficient

Coefficient provides superior payment link analytics extraction compared to HubSpot’s native reporting limitations. You can create custom metrics, maintain historical data, and build sophisticated dashboards with real-time updates.

How to make it work

Step 1. Configure multi-object data import for comprehensive analytics.

Set up imports to pull payment link performance metrics, associated deal conversion data, contact engagement statistics, and revenue transaction details. This creates a complete view of payment link effectiveness.

Step 2. Use snapshot features for historical data preservation.

Capture monthly payment link performance baselines, track conversion rate trends over time, and preserve historical analytics for year-over-year comparisons that HubSpot reports can’t maintain.

Step 3. Build advanced reporting calculations with spreadsheet formulas.

Create payment link ROI calculations, conversion funnel analysis, product performance comparisons, and sales rep attribution metrics using Excel or Google Sheets’ calculation capabilities.

Step 4. Set up automated report generation with scheduled updates.

Schedule daily performance summaries, weekly trend analysis, and monthly comprehensive reports that update automatically as new payment link data flows from HubSpot.

Step 5. Create dynamic dashboards with real-time performance charts.

Build dashboards with real-time payment link performance charts, conversion rate tracking, and revenue attribution analysis using your spreadsheet’s visualization tools.

Unlock payment link insights

Advanced analytics extraction transforms basic payment link data into actionable business intelligence with historical context and custom metrics. Start building comprehensive payment link analytics reports today.

How to extract pipeline coverage data from HubSpot forecasting module

HubSpot’s forecasting module calculates pipeline coverage as a proprietary metric that isn’t directly accessible through standard reporting or exports. The coverage calculations happen behind the scenes, making it impossible to extract this data for custom analysis.

Here’s how to recreate and enhance these metrics using live HubSpot data in your spreadsheets.

Pull pipeline coverage data using Coefficient

Coefficient provides a powerful workaround to extract and recreate pipeline coverage metrics by importing the underlying deal data from HubSpot into HubSpot . You can then build custom coverage formulas that give you complete control over the calculations.

How to make it work

Step 1. Import essential pipeline data from HubSpot.

Connect to HubSpot and pull all deals with their amounts, close dates, stages, and probability percentages. Include any custom fields that affect your coverage calculations, like deal source or product type.

Step 2. Sync your revenue goals and quotas.

Import your sales goals from HubSpot properties or connect to a separate data source where you store quota information. This gives you the denominator for your coverage ratio calculations.

Step 3. Build dynamic coverage calculations.

Create custom pipeline coverage formulas in your spreadsheet: – Weighted Pipeline = SUM(Deal Amount × Stage Probability) – Pipeline Coverage = Weighted Pipeline ÷ Revenue Goal

Step 4. Schedule automated updates.

Set hourly or daily refresh schedules to keep your coverage metrics current without manual intervention. Use Coefficient’s snapshot feature to capture pipeline coverage data at regular intervals for trend analysis.

Step 5. Create historical snapshots for trend analysis.

Unlike HubSpot’s forecasting module, you can build historical coverage data that shows how your pipeline coverage changes over time. This helps identify patterns and improve forecasting accuracy.

Start building better pipeline coverage reports

This approach gives you complete control over coverage calculations and the ability to combine data from multiple pipelines or date ranges. Try Coefficient to build pipeline coverage reports that go beyond HubSpot’s limitations.

How to extract unique IDs from Salesforce reports without manual copy-paste

You can extract unique IDs from Salesforce reports without any manual copy-paste by importing your report data directly into spreadsheets and using automated formulas to identify and isolate unique records.

This approach completely eliminates manual operations while providing superior unique ID extraction that automatically updates when your source Salesforce data changes.

Automate unique ID extraction with zero manual effort using Coefficient

Coefficient completely eliminates manual copy-paste operations while providing superior unique ID extraction capabilities that maintain live connections to your Salesforce data.

How to make it work

Step 1. Connect your Salesforce report directly to your spreadsheet.

Use Coefficient to import your Salesforce report data directly into Google Sheets or Excel. This eliminates the need for any manual exports or file downloads while providing access to all your record IDs.

Step 2. Apply unique formulas to automatically extract distinct IDs.

Use =UNIQUE(A2:A1000) in Google Sheets or Excel to automatically extract unique IDs from your imported data. This formula dynamically updates whenever your source data refreshes, ensuring your unique list stays current.

Step 3. Create advanced unique filtering with conditional criteria.

Build more sophisticated unique extraction using formulas like =UNIQUE(FILTER(A2:A1000, B2:B1000=”Active”)) to extract IDs that are unique based on multiple criteria, or =UNIQUE(IF(C2:C1000>TODAY()-30, A2:A1000)) for unique IDs from recent records only.

Step 4. Set up automated refreshes for live unique ID lists.

Configure scheduled refreshes (hourly, daily, or weekly) so your unique ID extraction happens automatically in the background. This transforms a manual, error-prone daily task into a reliable, automated process.

Step 5. Enable cross-report deduplication for comprehensive uniqueness.

Import multiple Salesforce reports and use formulas to identify IDs that are unique across all reports, providing organization-wide deduplication that’s impossible with manual methods.

Transform manual tasks into automated processes

This automated approach eliminates human error from copy-paste operations and provides scalable unique ID extraction that works with any volume of data. Set up your automated unique ID extraction and eliminate manual copy-paste operations forever.

How to filter custom object history tracking data by quarterly time periods for status changes

Salesforce’s native filtering for custom object history tracking is extremely limited for quarterly analysis. You can only use basic date ranges without dynamic quarterly calculations and can’t create reusable quarterly filters.

Here’s how to set up dynamic quarterly filtering that automatically adjusts time periods and captures exactly the status changes you need for each quarter.

Create dynamic quarterly filters using Coefficient

Coefficient revolutionizes quarterly filtering with cell-based dynamic filters and advanced configuration options. You can create quarterly date ranges that update automatically and filter for specific status transitions without manually adjusting settings each quarter.

How to make it work

Step 1. Set up cell-based date filtering.

Create cells for “Quarter Start” and “Quarter End” dates in your spreadsheet. For example, set A1 = “2024-01-01” and B1 = “2024-03-31” for Q1 2024. Point your Salesforce import filters to these cells dynamically, so you can change quarters instantly without editing import settings.

Step 2. Configure advanced filter logic.

Set up filters using Field History Date >= {QuarterStartCell} AND Field History Date <= {QuarterEndCell} AND Status.OldValue != Status.NewValue. This captures only actual status changes within your specified quarterly timeframe and excludes non-changes.

Step 3. Create a quarterly control panel.

Build a “Control Panel” sheet with all quarterly date ranges using EOMONTH() formulas to automatically calculate quarter boundaries. Use formulas like =DATE(YEAR(TODAY()),1,1) for Q1 start and =EOMONTH(DATE(YEAR(TODAY()),3,1),0) for Q1 end to create dynamic quarterly ranges.

Step 4. Set up multi-quarter comparison filtering.

Create multiple imports for different quarters that all reference your control panel dates. Use Refresh All to update all quarterly data simultaneously and build comparison views showing Q1 vs Q2 vs Q3 vs Q4 performance.

Step 5. Apply specialized quarterly filters.

Filter for specific status transitions (Active to Inactive), quarterly cohort analysis (objects by creation quarter), or rolling quarter windows using TODAY()-90 formulas for dynamic 3-month periods. Adjust date ranges to match your fiscal calendar if needed.

Eliminate manual quarterly adjustments

This dynamic filtering approach eliminates the need to manually adjust date ranges for each quarterly report and provides flexibility that Salesforce’s native reports cannot match. Get started with automated quarterly filtering that adapts to your reporting needs.