Category: HubSpot

How to create filtered NPS reports when standard reporting lacks custom fields

HubSpot’s standard NPS reporting ignores custom contact properties, severely limiting segmentation for businesses with complex customer categorization. You can’t filter by industry, company size, subscription tier, or any custom fields that matter to your analysis.

Here’s how to create NPS reports that use all your custom contact properties for sophisticated segmentation that HubSpot’s native reporting simply can’t handle.

Import survey data with complete custom field access using Coefficient

CoefficientHubSpot’ssolves this by importing NPS survey data alongside all custom contact properties thatstandard reports ignore. You get unlimited filtering combinations using any custom fields you’ve created.

How to make it work

Step 1. Import NPS responses with all custom contact properties.

HubSpotConnect toand import survey responses along with custom fields like industry, company size, subscription tier, or any business-specific properties you’ve created. This gives you the complete dataset that standard reporting leaves out.

Step 2. Apply advanced filter logic using custom fields.

Use up to 25 filters with AND/OR combinations across your custom fields. Create segments like “Custom_Industry = Healthcare AND Custom_Tier = Enterprise” or “Product_Line = Software AND Customer_Stage = Onboarding” for precise analysis that matches your business structure.

Step 3. Combine survey data with deal and company custom fields.

Filter using custom fields from multiple objects – contact custom fields, deal properties, and company custom fields. This creates sophisticated segmentation like “Enterprise customers using Product Line A with recent support tickets” that’s impossible with standard reporting.

Step 4. Set up dynamic custom field filtering.

Point filter values to spreadsheet cells containing custom field values, allowing instant report reconfiguration. Change a cell from “Healthcare” to “Manufacturing” and your entire NPS analysis updates to show the new industry segment automatically.

Segment NPS data the way your business actually works

Start filteringCustom field filtering reveals NPS patterns that align with your specific business categories and customer segments. Your filtered data refreshes automatically, keeping custom field-based analysis current without manual work.your NPS data by custom fields today.

How to create email automation rules based on contact type and seniority in sheets

Creating email automation rules based on contact type and seniority in sheets lets you deliver highly targeted email campaigns that adapt messaging and timing based on both the contact’s role and their level within the organization.

This approach ensures your email automation respects organizational hierarchies while delivering appropriate content to prospects, customers, and partners based on their decision-making authority.

Build type and seniority-based rules using Coefficient

CoefficientHubSpotenables sophisticated email automation rules based on contact type and seniority through its advanced data management and conditional logic capabilities, particularly when working withcontact properties and hierarchical data.

How to make it work

Step 1. Set up contact type and seniority data management.

Import contact data with custom field selection to pull contact type, job title, seniority level, and related properties from HubSpot. Use association handling to gather additional context like company size, industry, and deal involvement for enhanced rule creation, and schedule regular data refreshes to ensure rules operate on current information.

Step 2. Create your rule framework.

Apply up to 25 filters with AND/OR logic to create sophisticated rules combining contact type and seniority criteria. Use dynamic filtering that references spreadsheet cells containing rule definitions, allowing non-technical users to modify automation criteria, and implement hierarchical filtering where contact type determines the seniority evaluation logic.

Step 3. Build advanced seniority logic.

Create rule matrices that cross-reference contact types (prospect, customer, partner) with seniority levels (C-level, VP, Manager, Individual Contributor). Handle complex seniority determinations using job title analysis, reporting structure data, and custom seniority scoring, and use Formula Auto Fill Down to automatically categorize new contacts.

Step 4. Execute conditional rules and monitor performance.

Set up conditional exports that trigger different email automation sequences based on type-seniority combinations. Export rule-based contact segments to specific HubSpot Contact Lists designed for each automation track, and configure automated alerts when contacts change type or seniority, triggering rule re-evaluation.

Deliver precision email targeting

Start buildingThis approach transforms basic contact categorization into sophisticated, automated email marketing that adapts to contact evolution and organizational changes. Your email campaigns become more relevant and effective across all contact types and seniority levels.your type and seniority-based automation rules today.

How to deduplicate leads when HubSpot has multiple entries for the same company

HubSpot’snative duplicate management can’t effectively identify all company duplicates, especially when comparing against external Excel lead lists. When your CRM contains multiple entries for the same company with slight name variations, lead deduplication becomes complex and error-prone.

Here’s how to handle HubSpot’s internal company duplicates during lead deduplication with comprehensive data analysis and consolidation workflows.

Manage company duplicates for accurate lead deduplication using Coefficient

Coefficientprovides superior capabilities for handling HubSpot’s internal company duplicates by importing comprehensive company and contact data with association handling. You can reveal hidden duplicate patterns and create consolidated matching workflows.

How to make it work

Step 1. Import companies with association data.

Pull both HubSpot companies and their associated contacts simultaneously using Coefficient’s association handling features. Choose “Row Expanded” display to see all contact-company relationships, which reveals duplicate company patterns that aren’t obvious from company names alone.

Step 2. Create domain-based duplicate detection.

Import company domains and use Excel COUNTIF functions to identify multiple companies sharing the same domain: `=COUNTIF(domain_column, A2)>1`. This catches duplicates like “ABC Corp” and “ABC Corporation” that share “abccorp.com” but have different names in HubSpot.

Step 3. Set up phone and address clustering.

Group companies by main phone numbers and addresses to identify duplicates with different names but same contact information: `=COUNTIFS(phone_column, B2, address_column, C2)>1`. This reveals companies at the same location with slight name variations.

Step 4. Identify master company records.

Import companies with associated contact counts and creation dates, then use Excel formulas to identify the “master” company record: `=IF(AND(contact_count=MAX(contact_count), creation_date=MIN(creation_date)), “Master”, “Duplicate”)`. Typically choose the oldest record with the highest contact count as the master.

Step 5. Build consolidated company lookup tables.

Create Excel lookup tables that map all company name variations to master company records: Use VLOOKUP or XLOOKUP to ensure your lead deduplication catches matches against any company duplicate variation. This prevents leads from appearing as “new” when they match a duplicate company instead of the master.

Step 6. Set up advanced filtering for duplicate groups.

Use Coefficient’s filtering capabilities (up to 25 filters with AND/OR logic) to focus on specific company duplicate scenarios. Filter by domain to see all companies sharing websites, filter by phone area codes to identify regional duplicate patterns, or filter by recent creation dates to catch newly created duplicates.

Step 7. Monitor for new duplicates with alerts.

Set up scheduled imports with Slack/email alerts to notify when new potential company duplicates are created in HubSpot. Use variables in alerts to show specific duplicate details like shared domains or phone numbers, enabling proactive duplicate prevention.

Eliminate duplicate company confusion in lead matching

Start managingComprehensive company duplicate handling ensures your lead deduplication works accurately even when HubSpot contains multiple entries for the same company. You’ll catch all potential matches regardless of which company variation your leads match against.company duplicates effectively today.

How to export and manipulate NPS survey data for custom group analysis

Traditional NPS data exports from HubSpot become outdated quickly and require manual manipulation for custom group analysis. You’re stuck in a cycle of export, manipulate, analyze, then repeat when new data arrives.

Here’s how to transform that static process into dynamic, automated custom group analysis with live data connections and advanced manipulation capabilities that update automatically.

Replace static exports with live data connections using Coefficient

Coefficienttransforms static NPS data exports into dynamic, automated custom group analysis. Instead of working with outdated files, you get live connections with advanced manipulation capabilities that update automatically as new survey responses arrive.

How to make it work

Step 1. Establish live connections instead of static exports.

HubSpotConnect directly toNPS survey data with automatic refreshes – hourly, daily, or on-demand. This eliminates the export-manipulate-repeat cycle by maintaining current data connections that update without manual intervention.

Step 2. Create sophisticated custom groups with advanced filtering.

Use Coefficient’s filtering and contact property imports to create any custom groups: geographic segments (Region, Country, State), behavioral groups (Purchase frequency, Product usage, Support tickets), or demographic segments (Company size, Industry, Role). Apply up to 25 filters for precise group definitions.

Step 3. Perform advanced analysis impossible with basic exports.

Execute complex manipulation like cross-tabulation of NPS by multiple group variables, cohort analysis showing NPS changes over customer lifecycle, and statistical analysis of group differences. Work with live data that supports sophisticated analysis techniques.

Step 4. Set up automated group updates for dynamic membership.

HubSpotAs contacts move between groups – tier changes, new purchases, geographic moves – their NPS data automatically reflects in the correct group analysis from. Your custom group analysis stays accurate without manual data management.

Analyze customer groups with current data and advanced techniques

TransformDynamic custom group analysis eliminates the export-manipulate-repeat cycle while providing manipulation capabilities far beyond static files. Your analysis stays current with live data while supporting sophisticated techniques that drive strategic insights.your NPS group analysis today.

How to export HubSpot activities from free version without reports access

HubSpot’s free version blocks access to reporting features that normally allow activity exports, but you can still extract your valuable customer interaction history through API connections.

Here’s how to bypass the free tier’s export limitations and pull complete activity data into spreadsheets for analysis or migration.

Extract complete activity data using Coefficient

CoefficientHubSpot’sconnects directly toAPI to pull activity data into spreadsheets, completely bypassing the free tier’s export restrictions. This works because the API access is more permissive than the UI export capabilities.

How to make it work

Step 1. Connect your HubSpot free account through Coefficient’s sidebar.

HubSpotInstall Coefficient and authenticate youraccount. The connection uses official API endpoints, so there’s no risk of violating terms of service or triggering anti-bot detection.

Step 2. Import engagement objects including notes, calls, emails, and meetings.

Select the specific engagement types you need and apply date range filters to focus on relevant time periods. Coefficient handles API pagination automatically to capture complete datasets.

Step 3. Use filtering to focus on specific contact segments or time ranges.

Apply up to 25 filters to narrow down your activity data by contact properties, deal associations, or activity types. This helps you extract exactly what you need for your analysis or migration.

Step 4. Export the resulting data as CSV for migration or analysis.

Once your activity data is in the spreadsheet, you can format it for your target system, perform calculations, or export as CSV for use in other tools.

Get your HubSpot activity data today

Start extractingStop letting HubSpot’s free tier limitations block access to your valuable activity data. Coefficient provides a reliable way to extract complete customer interaction history with proper timestamps and contact associations.your HubSpot activities today.

How to export HubSpot CRM data to Google Sheets for lead enrichment workflows

HubSpot’snative export options require manual CSV downloads, lack real-time updates, have limited filtering options, and can’t maintain live connections for ongoing enrichment workflows.

Here’s how to create a fully automated lead enrichment pipeline that maintains live connections between HubSpot and your enrichment tools.

Advanced HubSpot export using Coefficient

Coefficientis specifically designed for this use case and provides superior capabilities compared to HubSpot’s native export options. This creates a comprehensive enrichment pipeline that’s impossible with native HubSpot export functionality.

How to make it work

Step 1. Configure live data connection.

Connect directly to your HubSpot CRM through Coefficient’s sidebar “Connected Sources” menu. Select specific objects like contacts, deals, or companies and choose exactly which fields you need for enrichment to optimize data transfer.

Step 2. Apply intelligent filtering for enrichment targets.

Use Coefficient’s advanced filtering with up to 25 filters across 5 filter groups to export only the contacts that need enrichment. Apply Dynamic Filtering that references spreadsheet cells for flexible criteria, such as contacts without company data or leads from specific sources.

Step 3. Schedule automatic refreshes.

Set up Import Refreshes to update your lead data automatically on hourly, daily, or weekly schedules. This ensures your enrichment workflows always work with current CRM data without any manual intervention required.

Step 4. Handle associations properly for complete data.

Configure how associated records appear in your export. Choose Primary Association for single values, Comma Separated for multiple values, or Row Expanded to create separate rows for each association, depending on your enrichment tool requirements.

Step 5. Build automated enrichment workflows.

Use the exported data with third-party enrichment tools, then apply Coefficient’s Formula Auto Fill Down feature to automatically copy enrichment formulas to new rows as they’re added during refreshes. This maintains consistency across your enrichment process.

Step 6. Push enriched data back to HubSpot.

Use Coefficient’s UPDATE export actions to push enriched data back to HubSpot contact properties. Set up Scheduled Exports to automate this reverse sync process, creating a complete bi-directional enrichment pipeline.

Step 7. Monitor enrichment progress with alerts.

Configure Snapshots to preserve historical enrichment data and set up Alerts to notify you when new leads require enrichment or when enrichment workflows complete successfully.

Build your automated enrichment pipeline

Start buildingThis creates a fully automated lead enrichment pipeline that maintains live connections between HubSpot and your enrichment tools.your comprehensive enrichment workflow today.

How to export HubSpot parent-child company data for cleanup in external tools

HubSpotnative export functionality has significant limitations for parent-child company data extraction, lacking comprehensive field selection and proper association formatting for external analysis.

Here’s how to extract complex hierarchy data with associations intact and prepare it for advanced cleanup in external tools.

Export comprehensive hierarchy data using advanced capabilities

CoefficientHubSpotHubSpot’s basic CSV exports can’t handle complex association data or provide the filtering sophistication needed for hierarchy-specific data extraction.is specifically designed to address these limitations and provides superior capabilities for parent-child company data extraction that work seamlessly with.

How to make it work

Step 1. Set up comprehensive association exports.

Use Coefficient’s advanced import functionality to extract companies with their parent-child relationships intact. Choose from Primary Association, Comma Separated, or Row Expanded display formats to match your external tool requirements, unlike HubSpot’s flat CSV exports.

Step 2. Select hierarchy-specific fields.

Export targeted fields including Company Name, Domain, Parent Company, Number of Child Companies, custom hierarchy properties, and associated contact/deal counts. Coefficient’s field selection capabilities far exceed HubSpot’s limited export options for complex data scenarios.

Step 3. Apply advanced filtering for targeted extraction.

Use up to 25 filters across 5 filter groups to focus on specific parent-child scenarios, such as companies with broken associations, mismatched domains, or circular relationships. Create filters like “Parent Company is empty AND Company Type equals Child” to target specific cleanup needs.

Step 4. Structure data for external tool compatibility.

Export data in formats optimized for your external cleanup tools, with proper relationship mapping and hierarchical structure preservation. Include validation columns and reference IDs that HubSpot’s flat exports can’t maintain effectively.

Step 5. Set up automated export scheduling.

Create scheduled exports to maintain current data for ongoing cleanup projects, ensuring your external tools always work with fresh HubSpot data. Set up alerts when export data changes significantly or new hierarchy issues appear.

Step 6. Prepare for seamless re-import.

Structure your export to include HubSpot Object IDs and proper field mapping for easy re-import after external cleanup. Use Coefficient’s import functionality to push corrected data back to HubSpot with proper association management.

Get your data ready for advanced cleanup

Start exportingThis approach provides comprehensive data extraction and structured export capabilities that HubSpot’s native tools simply can’t deliver for complex hierarchy cleanup projects.your company hierarchy data today.

How to extract contact information and route emails based on spreadsheet criteria

Extracting contact information and routing emails based on spreadsheet criteria lets you automatically segment and direct contacts to appropriate email campaigns using complex filtering rules that update dynamically.

You’ll learn how to pull contact data from multiple sources, apply sophisticated routing criteria, and automatically assign contacts to email workflows based on your spreadsheet logic.

Extract and route contacts efficiently with Coefficient

CoefficientHubSpotexcels at contact information extraction and spreadsheet-based routing through its robust data import capabilities and conditional logic features, particularly when working withand other CRM systems.

How to make it work

Step 1. Set up comprehensive contact extraction.

Import contact data from HubSpot with full custom field selection, pulling exactly the properties needed for routing decisions. Extract data from multiple sources (Salesforce, databases, APIs) into a unified spreadsheet for routing analysis, supporting all contact object types with no maximum row limits.

Step 2. Create advanced routing criteria processing.

Apply up to 25 filters with AND/OR logic to segment contacts based on complex spreadsheet criteria. Use dynamic filtering that references specific cells containing routing rules, and handle association data to include related information like deals, companies, and activities in routing decisions.

Step 3. Implement intelligent data management.

Schedule automatic data refreshes to ensure routing decisions are based on current contact information. Use Snapshots to maintain historical routing data while continuing live extractions, and implement Formula Auto Fill Down to automatically apply routing logic to newly extracted contacts.

Step 4. Execute conditional routing and monitoring.

Set up conditional exports that route contacts only when specific spreadsheet criteria are met. Export routed contact segments directly to HubSpot Contact Lists for email campaign execution, and configure automated alerts triggered when new contacts meet routing criteria with real-time performance monitoring.

Streamline your contact routing workflow

Start extractingThis comprehensive approach transforms static spreadsheet criteria into dynamic, automated contact routing systems that scale with your database size and complexity requirements. Your email targeting becomes more precise and efficient.and routing contacts automatically today.

How to handle case sensitivity when matching company names between Excel and HubSpot

HubSpot’snative search has inconsistent case sensitivity handling and can’t compare against external Excel data effectively. Lead lists often contain company names with different capitalization like “ABC Corporation” vs “abc corporation” vs “Abc Corporation” that prevent accurate matching.

Here’s how to create reliable case-insensitive company name matching with text normalization formulas and live CRM data.

Create case-insensitive company matching using Coefficient

Coefficientenhances case-insensitive matching by providing live HubSpot company data that you can process with Excel’s text normalization functions. You’ll work with current, complete company name data rather than potentially outdated manual exports.

How to make it work

Step 1. Import live HubSpot company data.

Pull HubSpot company names directly into Excel using Coefficient’s custom field selection. This ensures you’re working with current, complete company name data rather than static exports that may have inconsistent capitalization or missing records.

Step 2. Apply case normalization formulas.

Create standardized versions of both Excel lead company names and imported HubSpot company names: Use UPPER function for all-caps comparison: `=UPPER(A2)` and `=UPPER(B2)`. Apply LOWER function for lowercase comparison, or use PROPER function to handle mixed-case scenarios consistently. Combine with TRIM to remove extra spaces: `=TRIM(UPPER(A2))`.

Step 3. Build case-insensitive lookup formulas.

Replace basic VLOOKUP with case-insensitive alternatives: Use XLOOKUP with normalized text: `=XLOOKUP(UPPER(company_name), UPPER(hubspot_companies), hubspot_data, “No Match”)`. Apply INDEX/MATCH combinations: `=INDEX(company_data, MATCH(UPPER(lookup_value), UPPER(company_range), 0))`. Use SEARCH instead of FIND for case-insensitive partial matching.

Step 4. Set up dynamic case-insensitive filtering.

Use Coefficient’s dynamic filtering feature to create case-insensitive company name filters that automatically adjust based on your Excel lead list. Point filter values to cells containing normalized company names, importing only relevant HubSpot companies regardless of case variations.

Step 5. Extend case consistency to related fields.

Apply case-insensitive matching beyond company names to associated fields like domains, contact names, and addresses using Coefficient’s association handling. This creates comprehensive case-insensitive matching across multiple data points.

Step 6. Add visual indicators for case variations.

Set up Excel conditional formatting that highlights potential matches with different case patterns. This helps identify companies that might be the same entity with different capitalization conventions: `=AND(UPPER(A2)=UPPER(B2), A2<>B2)` highlights exact matches with different cases.

Match companies regardless of capitalization differences

Build reliableCase-insensitive matching eliminates frustrating mismatches caused by capitalization variations in lead lists from different sources. Your matching logic works consistently regardless of how company names are formatted.case-insensitive matching workflows today.

How to highlight duplicate leads in Excel based on partial address matches from HubSpot

HubSpot’snative duplicate detection can’t perform partial address matching against external Excel data. B2B lead lists often contain address variations like “123 Main St” vs “123 Main Street” or “Suite 100” vs “Ste 100” that prevent exact matches.

Here’s how to create sophisticated address-based duplicate highlighting with conditional formatting that catches variations and abbreviations.

Set up partial address matching with conditional formatting using Coefficient

Coefficientenables sophisticated address-based duplicate detection by importing comprehensive HubSpot address data that you can analyze with advanced Excel conditional formatting workflows. You’ll work with complete address datasets rather than limited export options.

How to make it work

Step 1. Import comprehensive HubSpot address data.

Pull all address fields (street, city, state, zip, country) from both contacts and companies using Coefficient’s custom field selection. This provides complete address datasets for partial matching analysis across multiple HubSpot objects.

Step 2. Create partial address matching formulas.

Build formulas that identify partial matches: Use SEARCH and FIND functions to identify partial street address matches. Try `=IF(AND(ISNUMBER(SEARCH(UPPER(city_excel),UPPER(city_hubspot))), LEN(address_excel)>0), “City Match”, “”)` to find city matches. Handle abbreviations with SUBSTITUTE functions that convert “St.” to “Street”, “Ave.” to “Avenue”, etc.

Step 3. Set up conditional formatting rules.

Create Excel conditional formatting that highlights cells based on your partial address matching formulas. Set up multiple highlighting levels: Yellow for partial street + exact city/state matches, Red for high-confidence duplicates where multiple address components match, Orange for potential matches requiring manual review.

Step 4. Use dynamic filtering for geographic targeting.

Use Coefficient’s dynamic filtering feature to automatically import HubSpot records from specific geographic areas matching your Excel lead list. Filter by state, city, or zip code ranges to reduce dataset size and focus on relevant potential matches.

Step 5. Combine contact and company address validation.

Leverage Coefficient’s association handling to compare both contact and company addresses simultaneously. This catches duplicates where leads might have home addresses in contact records but business addresses in company records: `=IF(OR(contact_address_match, company_address_match), “Address Match Found”, “”)`.

Step 6. Set up automated address duplicate detection.

Configure scheduled imports (daily/weekly) so your address-based duplicate highlighting automatically updates as new addresses are added to HubSpot. Use Coefficient’s Formula Auto Fill Down feature to extend your partial matching formulas to new rows automatically.

Catch address duplicates that exact matching misses

Start buildingPartial address matching with conditional formatting provides far more nuanced duplicate detection than basic address field comparison. You’ll identify potential duplicates even when addresses have common variations and abbreviations.smarter address-based duplicate detection today.