Category: Excel

Export HubSpot deal forecast data with probability percentages to Excel

HubSpot’s forecast reports can’t be easily exported with all probability data and deal details needed for comprehensive Excel analysis. The native forecast exports lack field selection options and become outdated immediately after download.

Here’s how to export complete deal forecast data with probability percentages for advanced Excel-based forecast analysis that HubSpot’s native tools can’t match.

Access comprehensive forecast data with probability analysis using Coefficient

CoefficientHubSpotprovides superiordeal forecast access compared to HubSpot’s limited export options. You get live access to probability percentages, weighted values, and all custom forecast-related properties in Excel.

This enables sophisticated forecast analysis including weighted pipeline calculations and historical accuracy tracking that HubSpot simply cannot provide.

How to make it work

Step 1. Import deals filtered by close date and probability ranges.

Set up imports for deals within specific forecast periods using close date filters. Include probability ranges that matter for your forecasting – typically deals above 20% probability for pipeline analysis and above 70% for commit forecasts.

Step 2. Include probability percentages and weighted values.

Select deal probability, deal amount, and any custom forecast-related properties during field selection. Also include deal stage, close date, and deal owner to enable comprehensive forecast segmentation and analysis.

Step 3. Create Excel formulas for weighted pipeline calculations.

Build formulas to calculate probability-weighted pipeline values using =Deal_Amount*Probability_Percentage. Create SUMIFS formulas to calculate weighted totals by time period, sales rep, or deal source for detailed forecast analysis.

Step 4. Set up daily refreshes for current forecast data.

Configure daily scheduled refreshes to keep forecast data current as deal probabilities and amounts change. This ensures your Excel forecast analysis always reflects the latest pipeline updates without manual re-exports.

Step 5. Use Snapshots for forecast accuracy tracking.

Enable monthly snapshots to capture forecast states for accuracy analysis over time. Compare historical forecast snapshots to actual outcomes to calculate forecast accuracy metrics and improve future forecasting precision.

Build forecasts that actually help predict revenue

Start buildingThis approach provides live access to comprehensive deal forecast data with probability percentages, enabling sophisticated Excel-based analysis that HubSpot’s native forecast tools simply cannot deliver.more accurate forecasts today.

Export HubSpot pipeline data including custom calculated fields to Excel

HubSpot’s calculated properties are restricted to basic math operations and can’t handle cross-object calculations or complex conditional logic. You’re limited by HubSpot’s calculation capabilities when you need sophisticated pipeline analysis.

Here’s how to export HubSpot custom calculated fields and extend them with advanced Excel calculations that exceed HubSpot’s limitations.

Extend calculated field capabilities using Coefficient

CoefficientHubSpotexcels at importingcustom calculated fields and enables additional Excel-based calculations that extend beyond HubSpot’s calculation limitations. You get proper formatting preservation plus the ability to create advanced formulas using associated object data.

This combines HubSpot’s custom calculated fields with Excel’s superior calculation capabilities for comprehensive pipeline analysis.

How to make it work

Step 1. Import all HubSpot custom calculated properties.

Select all custom calculated properties during field selection to ensure proper formatting preservation. Include the underlying data fields used in calculations so you can extend or modify the calculations in Excel if needed.

Step 2. Include raw data fields for additional calculations.

Import underlying data like deal amounts, dates, percentages, and associated object properties. This enables cross-object calculations and time-based metrics that HubSpot’s calculated properties cannot handle.

Step 3. Create advanced Excel calculation columns.

Build Excel formulas adjacent to imported data that use complex conditional logic, statistical functions, and cross-reference analysis. Create calculations like weighted probability scores using multiple deal and contact properties simultaneously.

Step 4. Set up Formula Auto Fill Down for new deals.

Configure Formula Auto Fill Down to automatically apply your Excel calculations to new deals added during refreshes. This ensures consistent calculation application across your entire dataset without manual formula copying.

Step 5. Use conditional formatting based on calculated results.

Apply conditional formatting, data bars, and color scales based on your calculated values. Create visual indicators for deal scoring, risk assessment, or priority ranking that update automatically with your calculations.

Break free from HubSpot’s calculation limitations

Expand your calculationsThis approach combines HubSpot’s custom calculated fields with Excel’s superior calculation capabilities, creating a comprehensive pipeline analysis system that far exceeds HubSpot’s native calculation limitations.beyond what HubSpot allows.

Export HubSpot pipeline data with associated contact and company information to Excel

HubSpot’s native exports break the connections between deals, contacts, and companies. You end up with separate files that require manual matching in Excel, losing valuable relationship context for your pipeline analysis.

Here’s how to export pipeline data with all associated contact and company information preserved in a single Excel file for comprehensive relationship analysis.

Preserve data relationships during pipeline export using Coefficient

CoefficientHubSpotexcels at handlingdata relationships through its Association Handling capabilities, which preserve complex data connections during Excel import. You get deals with their associated contacts and companies in the same rows, eliminating manual data matching.

The key advantage: you can analyze pipeline performance by contact source, company characteristics, and stakeholder engagement without complex VLOOKUP operations.

How to make it work

Step 1. Import deals with association display configured.

Select deals as your primary object and configure association display options. Choose “Primary Association” to show the main contact and company for each deal in the same row, or “Comma Separated” if you need multiple associated contacts visible.

Step 2. Select relevant contact fields for analysis.

Include specific contact properties like name, email, title, lead source, and engagement scores. These fields will appear alongside deal data, enabling analysis of how contact characteristics affect deal progression and outcomes.

Step 3. Include company fields for comprehensive context.

Add company properties such as name, industry, company size, annual revenue, and custom company scoring fields. This creates a complete picture of each deal with full stakeholder and account context in single Excel rows.

Step 4. Set up scheduled refreshes to maintain current associations.

Configure automatic refreshes to keep association data current as relationships change in HubSpot. This ensures your Excel analysis always reflects the latest contact and company connections without manual updates.

Step 5. Create relationship-based analysis formulas.

Build Excel formulas to analyze pipeline performance by contact source, company industry, or engagement level. Use SUMIFS and COUNTIFS to calculate conversion rates by company size or deal velocity by contact title.

Stop losing valuable relationship context in your exports

Preserve your data relationshipsThis unified approach provides complete HubSpot pipeline data with maintained relationships, enabling Excel analysis that would require complex manual data combining with standard exports.and unlock deeper pipeline insights.

Creating unified Salesforce reports for opportunities with specific related products and product-less opportunities

SalesforceCreating a unifiedreport that includes both opportunities with specific related products and product-less opportunities is impossible using native reporting due to cross filter logic limitations.

Here’s how to create these unified reports by extracting and consolidating Salesforce data outside of platform restrictions.

Build unified reports with comprehensive data consolidation

CoefficientSalesforceexcels at creating unified reports by extracting and consolidatingdata outside of platform restrictions. You get single comprehensive views that eliminate the need to switch between multiple reports with real-time unified analysis impossible with native reporting.

How to make it work

Step 1. Import comprehensive data with relationship mapping.

Import opportunities with all relevant fields including Name, Amount, Stage, Close Date, Account, and Owner. Import OpportunityLineItem data including Product2.Name, Product2.Family, and Quantity. Use Coefficient’s relationship mapping to maintain data connections between opportunities and products.

Step 2. Apply unified filtering logic for both scenarios.

Apply complex criteria that Salesforce cannot handle:. This unified filtering combines both opportunity types in a single query that cross filters fundamentally cannot process.

Step 3. Consolidate data with advanced spreadsheet functions.

Use spreadsheet functions to merge both datasets:. Add categorization columns:. Calculate unified metrics across both opportunity types for comprehensive analysis.

Step 4. Enable advanced unified report features.

Create segmented analysis to compare conversion rates between product-based and service-only opportunities. Build pipeline forecasting that includes all opportunity types and track revenue attribution from both specific products and services. Monitor trend analysis for changes in product vs. service opportunity mix.

Step 5. Implement automated unified reporting.

Schedule automatic data refresh to maintain unified view accuracy and set up alerts for new opportunities in either category. Create snapshots for historical unified analysis and export consolidated results back to Salesforce for team access.

Get comprehensive insights impossible with separate reports

Start buildingThis unified approach transforms what requires multiple disconnected Salesforce reports into a single, comprehensive analysis tool. You’ll have cross-category metrics and comparisons not available in separate reports with streamlined reporting that reduces manual data management.your unified opportunity analysis today.

Dynamic cross-object filtering for Salesforce dashboards without creating duplicate dashboards

Salesforce dashboard filters only apply to components from the same object or related objects through lookup relationships. When objects like Opportunities, Leads, and custom Forecast objects aren’t directly related, you’re forced to maintain separate dashboards for each filter value.

You can eliminate dashboard duplication by creating a single dynamic view that filters across multiple objects instantly, without the limitations of Salesforce’s native filtering system.

Build dynamic dashboard filtering with Coefficient

CoefficientHubSpotHubSpotprovides a superior alternative for dynamic dashboard filtering across multiple objects by importing all relevant data into a single spreadsheet environment. This eliminates the need for multiple dashboards while enabling global filtering across non-related objects that share common field values inor.

How to make it work

Step 1. Import all relevant objects into one workbook.

Use Coefficient’s Salesforce connector to import Opportunities, Leads, and custom objects into separate tabs or sections of the same spreadsheet. Ensure each import includes your common filtering field like “Business Line.”

Step 2. Establish a master filter cell.

Create a dedicated cell that controls data display across all imported datasets. This becomes your central command for filtering all objects simultaneously, regardless of their Salesforce relationships.

Step 3. Apply dynamic filters to all imports.

Use Coefficient’s dynamic filters feature to reference your master filter cell from each import. This enables instant filtering without editing import settings or refreshing individual components.

Step 4. Set up dashboard-like visualizations.

Create conditional formatting and pivot tables to build visual representations of your data. These update automatically when you change your master filter selection.

Step 5. Configure automatic refresh schedules.

Set up hourly, daily, or weekly refresh cycles to maintain data currency across all objects. This ensures your unified dashboard always reflects current Salesforce data.

Step 6. Enable complex filter combinations.

Implement AND/OR filter logic to support advanced filtering scenarios. You can combine business line filtering with date ranges, ownership, or status filters across all object types.

Replace multiple dashboards with one dynamic solution

Start buildingThis approach delivers forecast dashboard consolidation while maintaining the flexibility to view all business lines or focus on specific segments. You get immediate filter updates without page refreshes or dashboard navigation, all within a single automatically updating interface.your unified dashboard solution today.

Export all Salesforce report folder sharing rules and profile assignments to CSV

Salesforce provides no direct export functionality for report folder sharing rules and profile assignments, requiring manual compilation from multiple Setup areas with no consolidated view available.

Here’s how to automate this entire export process through comprehensive SOQL queries and built-in CSV export capabilities.

Export comprehensive sharing rules automatically using Coefficient

Coefficientautomates this entire export process with comprehensive SOQL queries targeting all permission-related objects. You get single-click CSV export, automated scheduling for regular permission backups, and cross-referencing between sharing rules and profile assignments.

How to make it work

Step 1. Set up automated data extraction for sharing rules.

SalesforceConnect toand create a consolidated folder sharing rules export:

Step 2. Import profile assignment mappings.

Get user-profile relationships:. This creates the cross-reference between sharing rules and profile assignments in your unified export.

Step 3. Use Coefficient’s join capabilities for unified data.

Link sharing rules with user/profile data using VLOOKUP formulas (auto-filled by Coefficient). This creates a comprehensive dataset showing sharing rules alongside user and profile information.

Step 4. Apply filtering for specific export requirements.

Use dynamic filtering for specific folders, profiles, or date ranges. Apply conditional formatting to highlight different sharing rule types or access levels before export.

Step 5. Schedule automated exports and CSV downloads.

Set up scheduled refreshes for current data and use your spreadsheet’s native CSV export functionality for immediate download. Configure automated snapshots for historical permission tracking.

Get complete sharing rule export automation

SalesforceStart exportingThis provides complete sharing rule and profile assignment export automation thatcannot achieve natively, with scheduled backups and compliance documentation.your sharing rules automatically today.

Creating automatic weekly exports that combine deals from multiple pipelines

You can create automatic weekly exports that combine deals from multiple pipelines by setting up a single import that includes all pipelines or separate imports combined in one spreadsheet.

This approach lets you perform advanced cross-pipeline analysis like stage conversion rates by pipeline and rep performance across different sales processes, all with automated weekly updates.

Combine multiple pipelines in automated exports using Coefficient

CoefficientHubSpot’shandles multi-pipeline deal combining more effectively thannative reporting, which requires complex custom reports or dashboard combinations to display deals across different pipelines in a single view.

This solves HubSpot’s limitation where cross-pipeline reporting requires custom dashboard creation or report combinations that don’t provide the analytical flexibility of spreadsheets. With Coefficient, you can perform advanced cross-pipeline analysis while maintaining automated weekly updates of the underlying data.

How to make it work

Step 1. Create a single deal import that includes all pipelines.

Set up one comprehensive deal import that pulls from all your pipelines, or create separate imports for each pipeline that feed into the same spreadsheet. This gives you flexibility in how you organize and analyze your multi-pipeline data.

Step 2. Use pipeline filtering to identify and label deals by source.

Add pipeline-specific filters and labels so you can easily identify which pipeline each deal comes from. Include the pipeline name as a column in your export to enable pipeline-based analysis and reporting.

Step 3. Add pipeline-specific columns for stage mapping.

Create additional columns that map different stage names across pipelines. For example, if Pipeline A uses “Proposal” and Pipeline B uses “Quote,” create a standardized stage column that normalizes these for cross-pipeline comparison.

Step 4. Schedule weekly refreshes to maintain current data across all pipelines.

Set up weekly scheduled refreshes that update all pipeline data simultaneously. This ensures your cross-pipeline analysis always reflects the current state of deals across your entire sales organization.

Step 5. Create summary calculations that aggregate metrics across pipeline types.

Build formulas that calculate metrics like average deal velocity by pipeline, conversion rates across different sales processes, and rep performance comparisons. Use Excel’s pivot table functionality to create dynamic cross-pipeline reports.

Start analyzing across all your pipelines

Begin combiningAutomated multi-pipeline deal exports give you comprehensive visibility into your entire sales organization with the analytical flexibility that native CRM reporting simply can’t match.your pipeline data for deeper insights into your sales performance across all processes.

Creating scheduled deal reports that only export changed records since last week

You can create scheduled deal reports that only export changed records since last week using append new data functionality that tracks incremental changes without overwriting existing data.

This approach solves the common problem where standard CRM reports show current state data rather than change tracking, giving you clear audit trails of what actually changed week over week.

Implement change tracking for weekly deal reports using Coefficient

Coefficient’sHubSpot’sappend new data functionality addresses the challenge of tracking incremental changes, which native CRM reports cannot accomplish without complex custom properties or workflow automation. This approach solveslimitation where standard reports show current state data rather than change tracking.

Native HubSpot reporting requires custom date properties and complex filtering to identify changed records, while Coefficient automatically handles incremental updates and provides clear audit trails of when data was modified or added to your weekly reports.

How to make it work

Step 1. Set up a base deal import with “Last Modified Date” filtering.

Create a deal import that filters for records modified within the past week using the “Last Modified Date” field. This ensures you only capture deals that have actually changed since your last report.

Step 2. Enable “Append New Data” to avoid overwriting existing records.

Turn on the “Append New Data” feature in your import settings. This adds only new or modified records to your existing dataset without overwriting previous data, creating a cumulative change log over time.

Step 3. Add timestamp tracking for audit trails.

Enable timestamp tracking to show exactly when each record was added to your report. This creates a clear audit trail showing not just what changed, but when it was captured in your weekly tracking.

Step 4. Create weekly snapshots to preserve historical states.

Set up weekly snapshots that capture the complete state of your deal data at specific points in time. This gives you both incremental change tracking and historical point-in-time views for comparison.

Step 5. Schedule weekly refreshes for automatic change capture.

Configure weekly scheduled refreshes that automatically capture incremental changes. Each refresh will identify and append only the deals that have been modified since the last run, building a comprehensive change history.

Start tracking deal changes automatically

Begin trackingAutomated change tracking for deal reports eliminates the guesswork about what actually changed week over week, giving you precise audit trails and incremental data capture.deal changes with automated weekly reports that show exactly what’s different.

Creating single source of truth dashboard with business line filter across different Salesforce objects

Creating a single source of truth for cross-object business line reporting is impossible with native Salesforce dashboards due to filtering limitations across unrelated objects. Each object exists in its own reporting context, preventing unified dashboard consolidation.

Here’s how to break down these object-based barriers and create a true single source of truth that consolidates all your business line reporting in one dynamic view.

Enable true single source of truth reporting using Coefficient

CoefficientHubSpotHubSpotenables genuine single source of truth dashboard creation by operating above Salesforce’s object limitations. You can pull data from all relevant objects into a unified environment where business line filtering works seamlessly across everything inor.

How to make it work

Step 1. Import comprehensive data from all relevant objects.

Pull data from Opportunities, Leads, Campaign Members, and custom Quota/Forecast objects using Coefficient’s full Salesforce access. Include all business line fields and related metrics to create your comprehensive dataset.

Step 2. Create a unified business line filter control.

Set up a single dropdown or input cell that controls data visibility across all imported objects. This becomes your central command for filtering everything by business line simultaneously.

Step 3. Align data structure for seamless filtering.

Ensure consistent field names and data types across objects to enable seamless filtering. Map business line values consistently so your unified filter works across all data sources.

Step 4. Import each object type into organized sections.

Structure your imports in separate sections of the same workbook while maintaining the ability to filter everything through your central business line control. This keeps data organized while enabling unified filtering.

Step 5. Apply uniform filtering using dynamic capabilities.

Use Coefficient’s dynamic filter functionality to point all imports to the same filter cell. This ensures that changing your business line selection updates every piece of data instantly.

Step 6. Create summary views for consolidated analysis.

Build summary sections that aggregate data across all objects, showing metrics like total pipeline value, lead volume, and quota attainment in a single view. These update automatically with your business line selections.

Step 7. Set up automated refresh for data currency.

Configure hourly, daily, or weekly refresh schedules to keep your single source current with Salesforce data. This maintains accuracy while preserving your unified filtering capabilities.

Establish unified reporting that grows with your business

Create your single sourceThis approach eliminates data inconsistencies from maintaining separate dashboards while enabling cross-object analysis and trend identification impossible in native Salesforce. You get instant business line switching without navigation and reduced maintenance overhead from managing one dynamic view instead of multiple static dashboards.of truth dashboard today.

Create Salesforce report showing accounts with pipeline but no closed revenue since date

SalesforceThis revenue analysis scenario exposes key limitations innative reporting. Combining pipeline (open opportunities) with historical closed revenue filtering is difficult, especially when you need negative revenue criteria over specific date ranges.

Here’s how to build this pipeline vs. closed revenue analysis using spreadsheet formulas that can handle the complex date and revenue logic.

Build comprehensive revenue analysis using Coefficient

CoefficientSalesforceexcels at this type of pipeline vs. closed revenue analysis because you can import all opportunity data and create sophisticated calculations thatstandard reporting can’t handle.

How to make it work

Step 1. Import comprehensive opportunity data.

Import opportunities with Amount, Stage, Close Date, and Account ID. Use Coefficient’s filtering to include both open opportunities (pipeline) and closed opportunities since your target date.

Step 2. Calculate current pipeline per account.

Create formulas to calculate current pipeline:

Step 3. Analyze closed revenue since specific date.

Calculate closed won revenue since your target date:

Step 4. Identify net new accounts with pipeline.

Combine criteria to find accounts with pipeline but no closed revenue:

Step 5. Track historical opportunity data trends.

Use Coefficient’s Append New Data feature to maintain historical snapshots of pipeline vs. revenue trends over time.

Step 6. Set up automated reporting.

Schedule daily refreshes and set up alerts when accounts meet your net new criteria, enabling immediate sales follow-up.

Get more accurate revenue insights

Start buildingThis approach provides more accurate revenue analysis than Salesforce’s standard reporting capabilities and eliminates the complexity of joined reports or custom report types.better revenue reports today.