Category: Excel

How to track lead pipeline velocity by Google Ads campaign in HubSpot dashboard

HubSpot can show you how long deals stay in each stage, but it struggles with calculating average velocity across multiple campaigns and creating custom velocity formulas that compare performance over time.

Here’s how to build dynamic pipeline velocity tracking that updates automatically and gives you the campaign-specific insights HubSpot’s native dashboards can’t deliver.

Build real-time velocity tracking using Coefficient

HubSpotCoefficientThe problem withvelocity reports is they can’t handle percentage-based calculations or compare velocity trends across different Google Ads campaigns.solves this by pulling your pipeline data into spreadsheets where you can create custom velocity formulas and automated dashboards.

You’ll get real-time velocity tracking with calculations like deals moved per day by campaign, velocity score comparisons, and dynamic benchmarks that update as deals progress through your pipeline.

How to make it work

Step 1. Import your HubSpot pipeline data.

HubSpotOpen Coefficient’s sidebar and connect to. Import your Deals with these fields: Deal Name, Pipeline Stage, Create Date, Close Date, and your Google Ads Campaign property. Apply filters for your specific date ranges and deal stages, then schedule hourly refreshes to keep your velocity tracking current.

Step 2. Create velocity calculations.

Use spreadsheet formulas to calculate days between stages for each deal. Build AVERAGEIF formulas grouped by campaign: =AVERAGEIF(Campaign_Column,”Campaign Name”,Days_in_Stage). Create a velocity score using =(Deals_Closed/Days_to_Close)*Campaign_Value to weight velocity by deal value.

Step 3. Build your dynamic dashboard.

Create pivot tables showing velocity by campaign and stage. Use conditional formatting to highlight fast and slow-moving campaigns with color coding. Build velocity trend charts using Coefficient’s snapshot feature to capture historical performance data weekly.

Step 4. Set up automated alerts.

Configure Slack alerts when velocity drops below your thresholds. Use Coefficient’s formula auto-fill to automatically calculate velocity for new deals as they enter your pipeline. Set up scheduled snapshots to track velocity metrics over time.

Get campaign-specific velocity insights

Start buildingThis approach gives you stage-to-stage conversion rates by campaign, weighted pipeline velocity, and velocity decay analysis that updates automatically.your velocity dashboard today.

How to troubleshoot blank lookup fields in Activities custom report type in Salesforce

SalesforceBlank lookup fields inActivities custom report types typically occur due to complex object relationships, field-level security restrictions, or data integrity problems. But here’s the thing – these troubleshooting efforts often reveal that the issue isn’t fixable within Salesforce’s reporting framework due to inherent platform limitations.

Instead of spending time troubleshooting unreliable lookup fields, here’s how to get guaranteed data population for your activity reports.

Bypass lookup field issues entirely using Coefficient

CoefficientSalesforce’seliminates the troubleshooting cycle by providing direct access to source data. Rather than fightingunreliable Activities custom report type, you get 100% reliable data population every time.

How to make it work

Step 1. Import Activities directly from source objects.

Use Coefficient’s “From Objects & Fields” method to pull Task and Event data directly, including all activity details and the WhatId/WhoId relationship fields. This bypasses the Activities report type entirely, eliminating lookup field population issues.

Step 2. Import related object data separately.

Pull Opportunity, Account, and Contact data in separate imports with all the fields you need. Include Opportunity Name, Amount, Stage, Account Name, Contact Name, and any custom fields that weren’t showing up in your Activities report.

Step 3. Create reliable relationships using spreadsheet functions.

Use VLOOKUP, XLOOKUP, or INDEX/MATCH to join data using the relationship IDs. For example:to pull opportunity fields, orfor account information.

Step 4. Verify complete data population.

Check that all your lookup relationships are working by using formulas liketo count any remaining blank cells. With direct data access, you should have complete field population without the gaps that plague Activities reports.

Step 5. Set up automated refresh for ongoing reliability.

Schedule regular data updates so your reports stay current without manual intervention. Unlike the Activities report type that may randomly show blank fields, your data will be consistent every time it refreshes.

Get reliable activity data without the troubleshooting headaches

Build reportsThis approach provides consistent results without the unpredictability of Activities report types. You get complete field access, guaranteed data population, and reliable performance that eliminates the need for ongoing troubleshooting.that work reliably every time instead of fighting platform limitations.

How to pull HubSpot contact data into Excel spreadsheets in real-time

HubSpot’s contact export functionality limits you to manual CSV downloads with a maximum of 1,000 contacts per export, requiring multiple manual exports and complex merging for large databases.

Here’s how to import all your contacts into Excel with real-time updates and no row limitations.

Import unlimited HubSpot contacts with real-time sync

Coefficienteliminates HubSpot’s export limitations by importing all contacts directly into Excel with real-time connectivity and advanced filtering options.

How to make it work

Step 1. Connect to HubSpot contacts through Coefficient’s sidebar.

Select “Contacts” object and choose specific contact properties including custom fields. Import all contacts regardless of database size (supports 50,000+ records minimum).

Step 2. Apply advanced filtering to segment contacts.

Use up to 25 filters across 5 filter groups to focus on specific contact segments. Filter by lifecycle stage, lead source, date ranges, or any custom properties.

Step 3. Include association data for complete contact records.

Pull related deals, companies, and tickets with contact records using Row Expanded display. This creates comprehensive contact profiles in your spreadsheet.

Step 4. Schedule automatic refreshes for real-time updates.

Set hourly or daily refreshes to capture new contacts and property changes. Configure Slack or email alerts when new contacts are added to your database.

Step 5. Use dynamic filtering for changeable criteria.

Point filter values to specific spreadsheet cells so you can modify criteria (date ranges, lifecycle stages) without recreating the entire import.

Step 6. Set up append mode for new contacts only.

Add only new contacts without overwriting existing data. Calculated columns like lead scoring and aging formulas automatically apply to new contact records.

Transform static exports into live contact management

StartReal-time contact imports enable dynamic contact analysis and management directly in Excel. Your contact data stays current while you maintain the flexibility to perform complex analysis and calculations that HubSpot can’t handle natively.importing your HubSpot contacts in real-time.

How to pull HubSpot deal pipeline data into Excel without manual export

CoefficientHubSpotlets you pulldeal pipeline data directly into Excel with live connections that update automatically, eliminating the need for manual exports and CSV downloads.

You’ll be able to perform advanced pipeline analysis like stage conversion rates and velocity metrics that aren’t available in HubSpot’s standard reports.

Import live deal pipeline data using Coefficient

HubSpot’s native reporting limits your ability to calculate stage conversion rates, pipeline velocity metrics, or create custom visualizations. Coefficient solves this by providing direct access to live deal data with advanced filtering capabilities.

How to make it work

Step 1. Set up deal object imports with custom field selection.

Import deal objects including deal stage, amount, close date, and any custom deal properties specific to your sales process. Choose exactly which fields you need rather than getting everything or nothing.

Step 2. Apply dynamic filters to segment your pipeline data.

Filter for deals created this quarter, specific deal stages, or deals above certain thresholds. You can reference spreadsheet cells in your filters, making your reports flexible and dynamic. Apply up to 25 filters with AND/OR logic.

Step 3. Pull associated contact and company data.

Use Coefficient’s association management features to bring in related contact and company information alongside your deals. This provides complete context for pipeline analysis without multiple separate imports.

Step 4. Schedule automatic refreshes.

Set up hourly, daily, or weekly imports so your pipeline reports always reflect current deal status. The data updates in the background while preserving your Excel formulas and calculations.

Advanced pipeline analysis capabilities

HubSpot’sWith live deal data in Excel, you can perform weighted pipeline calculations, stage duration analysis, and conversion rate metrics that HubSpot can’t handle. Use Coefficient’s Snapshots feature to capture pipeline data at specific intervals for trend analysis and forecasting, and import your entire pipeline withoutreporting row limitations.

Get startedStop wrestling with HubSpot’s reporting limitations.with Coefficient and build the pipeline analysis you actually need.

How to pull HubSpot engagement analytics into Excel automatically

CoefficientHubSpotprovides access toengagement data that can be analyzed with Excel’s advanced analytical capabilities, going beyond HubSpot’s native reporting limitations for email performance, meeting analytics, and call tracking metrics.

You’ll be able to import calls, emails, meetings, notes, and tasks with automated refreshes and sophisticated analysis capabilities.

Import HubSpot engagement data automatically using Coefficient

HubSpot’s native engagement reporting has significant limitations for detailed analysis. Coefficient provides access to engagement data with full property access and the ability to perform complex calculations that HubSpot can’t handle natively.

How to make it work

Step 1. Set up engagement object imports with proper permissions.

Import calls, emails, meetings, notes, and tasks with full property access. Note that E-commerce permissions are required for engagement objects, so check your HubSpot permissions before starting.

Step 2. Apply engagement-specific filtering.

Filter by engagement type, date ranges, associated contacts/deals, and custom engagement properties. Use up to 25 filters with AND/OR logic to focus on specific engagement activities relevant to your analysis.

Step 3. Pull associated data for complete context.

Import engagement data alongside related contact, deal, and company information for comprehensive analysis. This provides the context needed to understand engagement effectiveness across your sales process.

Step 4. Set up automated engagement reporting.

Schedule daily or weekly engagement data refreshes for current performance tracking. Set up alerts to notify managers when engagement metrics hit specific thresholds, and use Snapshots to capture engagement data over time for trend identification.

Advanced engagement analytics capabilities

HubSpot’sWith engagement data in Excel, you can calculate email open rates, click-through rates, and response rates by rep, campaign, or time period. Analyze meeting frequency, duration, and outcomes across sales stages, track call volume and conversion rates by sales rep or territory, and examine engagement patterns leading to deal progression. This provides much deeper analysis thanstandard reporting allows.

Get startedReady to unlock deeper engagement insights?with Coefficient and build the engagement analysis HubSpot can’t provide.

How to migrate products to Salesforce line items with pricing

Product migration gets complex because different CRMs structure products and pricing differently. Your source system’s product catalog doesn’t map directly to your destination platform’s product and line item requirements, especially when pricing tiers and discounts are involved.

Here’s how to handle product structure differences and preserve pricing data, even when your source and destination systems handle product catalogs in fundamentally different ways.

Transform product structures with two-step migration using Coefficient

CoefficientWhile product migration has specific complexities,can assist through data transformation and export capabilities. The key is understanding that product and line item structures differ significantly between systems, requiring a systematic two-step approach to preserve both product data and pricing relationships.

How to make it work

Step 1. Export and transform product data structure.

SalesforceSalesforcePull your product catalog data into Google Sheets or Excel where you can transform it to matchorproduct requirements. Use spreadsheet formulas to handle price formatting, currency conversions, and product category mapping to align with your destination system’s structure.

Step 2. Create products first, then handle line item associations.

Use Coefficient’s UPSERT capabilities to create products in your destination system first, capturing the system-generated product IDs. This two-step process is necessary because most CRMs require products to exist before they can be associated with deals as line items.

Step 3. Map product categories and properties.

Transform your source system’s product categorization to match your destination platform’s product property structure. Create mapping tables that convert product types, categories, and custom attributes to align with your new system’s requirements.

Step 4. Handle pricing data with validation formulas.

Use batch processing for large product catalogs while building validation formulas to check pricing data accuracy. Different systems handle pricing tiers and discounts differently, so manual validation ensures pricing relationships transfer correctly.

Step 5. Preview pricing mappings before export.

Use Coefficient’s preview feature to identify pricing mapping issues before export. This is especially important because pricing data errors can impact deal calculations and quote generation in your destination system.

Preserve your product catalog integrity

Start planningProduct and pricing data forms the foundation of your sales process. While some manual validation may be needed for complex pricing structures, systematic transformation and two-step migration can preserve most of your product catalog data.your product migration today.

How to migrate smart contact data to Salesforce lists

Smart contact data migration gets complex because different CRMs handle dynamic segmentation differently. Your source system’s smart lists and automated contact segments don’t translate directly to your destination platform’s list structure and segmentation logic.

Here’s how to preserve contact segmentation intent by transforming dynamic criteria into compatible list structures, even when the underlying segmentation technology differs between systems.

Transform dynamic segments with criteria mapping using Coefficient

Coefficientcan effectively handle smart contact data migration by transforming dynamic contact segments into compatible list structures. While you can’t directly replicate smart functionality, you can preserve the segmentation intent that drives your business processes.

How to make it work

Step 1. Export contacts with smart list criteria tags.

Pull contacts from each smart list in your source CRM into Google Sheets or Excel with identifying tags that show which segments they belong to. Include the underlying criteria data that drove the smart list membership for reference during transformation.

Step 2. Transform criteria logic into compatible property values.

SalesforceSalesforceUse filtering and conditional logic with spreadsheet formulas to recreate smart list logic in a format that works withor. Map complex source criteria to destination contact properties that can drive your new system’s smart list features.

Step 3. Create static lists through export functionality.

Use Coefficient to export contacts with appropriate segmentation properties to recreate your contact groupings. While these start as static lists, they preserve the contact groupings that matter to your business processes and campaigns.

Step 4. Map segmentation criteria to destination system properties.

Transform your source system’s segmentation logic into contact property values that align with your destination platform’s smart list capabilities. This creates the foundation for rebuilding dynamic segmentation using your new system’s features.

Step 5. Rebuild dynamic functionality in destination system.

After migration, set up smart lists in your destination system based on the contact properties Coefficient helped migrate. This recreates the dynamic segmentation functionality using your new platform’s native capabilities.

Preserve your segmentation strategy

Start transformingContact segmentation drives your marketing campaigns and sales processes. While the technology may change, you can preserve the segmentation logic that matters to your business by systematically transforming criteria and rebuilding dynamic functionality.your contact segments today.

How to preserve deal stage history when migrating CRM data to Salesforce

Deal stage history gets lost during CRM migrations because most platforms handle stage tracking differently. You need a way to preserve those critical timeline transitions that show how deals progressed through your sales pipeline over time.

Here’s how to capture and migrate historical stage data using snapshots and timestamp tracking, even when your source and destination systems structure stage history differently.

Capture stage transitions with snapshot functionality using Coefficient

CoefficientWhile you can’t directly replicate every CRM’s native stage tracking,can help preserve deal stage history through its snapshot and append capabilities. The key is exporting your historical stage data first, then using spreadsheet functionality to build comprehensive stage history tables that can be migrated to your destination system.

How to make it work

Step 1. Export your historical stage data to spreadsheets.

SalesforceSalesforcePull deal records with stage transition data from your source CRM into Google Sheets or Excel. Include all available timestamp information and stage change details. This gives you the raw material to reconstruct stage history in a format that works withor.

Step 2. Create timestamped snapshots of stage transitions.

Use Coefficient’s Snapshots feature to create timestamped records of each stage change. This preserves historical data with “Written by Coefficient At” timestamp columns that maintain the chronological order of stage progressions. Each snapshot becomes a historical record you can reference later.

Step 3. Build comprehensive stage history tables.

Use the append new data feature to build complete stage transition histories. Transform your source system’s stage names to match your destination CRM’s stage structure using spreadsheet formulas. This creates a clean historical dataset ready for migration.

Step 4. Map stage history to destination system fields.

Use Coefficient’s field mapping to transform your historical stage data into the format your destination CRM expects. Since different platforms handle stage history differently, you may need to create custom fields to fully preserve the timeline rather than relying on native stage tracking.

Step 5. Export with proper timestamp preservation.

Use Coefficient’s UPSERT functionality to create historical records in your destination system with proper timestamps. The detailed tracking shows exactly which historical records succeeded, making it easy to verify that your stage history migrated correctly.

Keep your sales history intact

Start preservingDeal stage history provides crucial insights into your sales process and pipeline performance. Don’t let migration wipe out this valuable data when you can preserve it systematically with the right approach.your stage history today.

How to preserve existing checkbox values when importing new selections via CSV in HubSpot

Preserving existing checkbox values during CSV imports is impossible with HubSpot’s native functionality. Each import completely overwrites the field rather than merging values, creating a critical data loss risk when updating records with additional checkbox selections.

Here’s how to preserve and augment checkbox values through a controlled workflow that eliminates data loss risk.

Extract, merge, and sync for complete preservation using Coefficient

CoefficientHubSpotHubSpotprovides a complete solution for preserving and augmenting checkbox values. You can extract the current state, merge new selections while preserving existing ones, then sync the complete dataset back toand.

How to make it work

Step 1. Import HubSpot records with existing checkbox values.

Extract current contact data showing existing selections. For example, you might see “john@example.com | Webinars, Whitepapers” in your spreadsheet with current interests clearly displayed.

Step 2. Add new selections while preserving existing ones.

Use formula concatenation like =CONCATENATE(B2,”, “,C2) to combine existing values with new ones. This results in “Webinars, Whitepapers, Case Studies” with all selections preserved. Alternatively, use conditional logic: =IF(ISNUMBER(SEARCH(“Webinars”,B2)),B2,CONCATENATE(B2,”, Webinars”)).

Step 3. Sync merged data using Coefficient’s UPDATE action.

Export the complete checkbox selections back to HubSpot. Coefficient’s UPDATE action replaces the entire field with your merged values, ensuring no data loss since you’re explicitly including all values.

Step 4. Use advanced preservation features for audit and control.

Capture checkbox states before updates with Snapshots, track when and why selections were added with audit formulas, maintain rollback capability to restore previous values if needed, and schedule regular syncs to merge new selections from multiple sources.

Transform data loss risk into controlled preservation

Start preservingThis approach transforms a HubSpot limitation into a controlled, auditable process where existing data is always preserved and new selections are thoughtfully integrated. Ready to eliminate checkbox data loss?your data today.

How to pull API usage metrics from Event Monitoring instead of Administrative Reports in Salesforce

When Administrative Reports are unavailable, you can pull API usage metrics from Salesforce Event Monitoring objects with enhanced data processing and visualization capabilities.

This approach transforms Event Monitoring’s raw log data into actionable API usage insights while providing better data retention and analysis capabilities than Salesforce’s native interface offers.

Transform Event Monitoring data using Coefficient

CoefficientSalesforcecan effectively import API usage metrics fromEvent Monitoring objects with enhanced data processing and visualization capabilities compared to accessing Event Monitoring through Salesforce’s native interface.

SalesforceYou can connect directly to EventLogFile and other Event Monitoring objects if available in your org, choose specific API-related fields without being limited to pre-built views, and apply complex AND/OR filter logic to focus on specific API usage patterns or time periods. Automated processing lets you schedule regular imports to maintain current Event Monitoring data from.

How to make it work

Step 1. Connect to Event Monitoring objects.

Use “From Objects & Fields” to access EventLogFile object with API-related event types. Apply filters for specific date ranges and API consumption events to focus on relevant data.

Step 2. Set up automated data processing.

Schedule daily imports to maintain current Event Monitoring visibility and use formula auto-fill to calculate rolling averages and trend analysis. This transforms raw log data into business-ready metrics.

Step 3. Create custom calculations and analysis.

Transform raw Event Monitoring logs into actionable API usage metrics using spreadsheet formulas. Create derived metrics like API calls per hour, peak usage identification, and consumption forecasting.

Step 4. Build historical aggregation.

Combine multiple Event Monitoring files to build comprehensive API usage trends over time. Use snapshots to preserve processed Event Monitoring data for long-term analysis beyond Salesforce’s retention limits.

Step 5. Integrate with operational datasets.

Merge Event Monitoring data with other operational datasets for comprehensive analysis. Create dashboards that combine API usage patterns with user activity and system performance metrics.

Turn raw logs into business intelligence

Start transformingThis approach transforms Event Monitoring’s raw log data into business-ready API usage insights while providing better data retention and analysis capabilities than Salesforce’s native Event Monitoring interface offers. You’ll have actionable intelligence instead of raw log files.your Event Monitoring data today.