Category: HubSpot

HubSpot dashboard blocks can only display simple metrics and cannot perform the calculations needed to generate KPI scores. The platform’s dashboard functionality is limited to showing raw data without custom formulas or weighted calculations.

Here’s how to transform HubSpot’s limited dashboard blocks into powerful KPI visualization tools by providing the calculated data they need.

Display calculated KPI scores using Coefficient

Coefficient enables calculated KPI score display in HubSpot dashboards through a two-step process that performs complex calculations externally and displays results in native HubSpot dashboard blocks.

How to make it work

Step 1. Process KPI calculations externally.

Import HubSpot data and perform complex KPI calculations in spreadsheets using custom formulas and weight tables. Build calculations that multiply activity counts by point values, apply conversion rates, or create composite scores.

Step 2. Export calculated scores to HubSpot properties.

Push calculated KPI scores back to HubSpot as custom number properties on relevant objects like contacts, companies, or deals. These properties integrate seamlessly with HubSpot’s native functionality.

Step 3. Configure HubSpot dashboard blocks.

Create HubSpot dashboard blocks that display the calculated KPI properties as single value displays, gauge charts, or trend lines. Use the custom properties just like any native HubSpot metric.

Step 4. Set up automated refresh cycles.

Schedule regular data imports and exports to keep KPI scores current in dashboard displays. Your dashboard blocks automatically show updated calculated scores without manual intervention.

Step 5. Build comprehensive multi-metric dashboards.

Combine multiple calculated KPI scores in comprehensive dashboard views. Create executive dashboards that show various weighted metrics, conversion rates, and performance indicators in one place.

Step 6. Configure threshold alerts and notifications.

Set up notifications when KPI scores reach specific thresholds using HubSpot workflows triggered by your calculated properties. Create alerts for both high-performing and underperforming metrics.

Transform your HubSpot dashboards today

This approach transforms HubSpot’s limited dashboard blocks into powerful KPI visualization tools by providing the calculated data they need to display meaningful metrics. Start displaying calculated KPI scores in your HubSpot dashboards.

HubSpot’s reporting tools can’t determine if a line item’s flight is “active today” because they lack dynamic date comparison functions that reference the current date in real-time.

Here’s how to create a system that automatically identifies which flights are currently running and updates daily without manual intervention.

Track active flights with dynamic date formulas using Coefficient

Coefficient solves this limitation by connecting your HubSpot line items to spreadsheets where you can build dynamic formulas that automatically identify active flights. This creates real-time visibility that HubSpot alone simply can’t provide.

How to make it work

Step 1. Import flight data from HubSpot.

Connect your HubSpot line items including flight start dates, end dates, and associated deal information to your spreadsheet via Coefficient. This gives you the raw data needed for active flight identification.

Step 2. Create the active flight formula.

Use this formula: =IF(AND(TODAY()>=Flight_Start, TODAY()<=Flight_End), "Active", "Inactive"). This automatically compares today's date against each flight's start and end dates to determine current status.

Step 3. Set up filtering and alerts.

Apply filtering to show only active flights, or use your spreadsheet’s filtering on the “Active” column. Configure Coefficient alerts to notify you when flights become active or end, so you never miss important campaign transitions.

Step 4. Schedule automated daily updates.

Set Coefficient to refresh this data daily. Your active flight status will update automatically as campaigns start and end, ensuring your dashboard always reflects current campaign activity.

Never miss an active campaign again

This creates a dynamic dashboard that updates automatically, giving you real-time visibility into revenue-generating campaigns. Build your active flight tracker today.

Cross-object duplicate detection requires analyzing shared custom identifiers across contacts, companies, and deals simultaneously. This capability is completely unavailable in HubSpot’s native duplicate detection, leaving data integrity issues hidden within individual object silos.

Here’s how to set up comprehensive cross-object duplicate detection that reveals relationship problems and ensures proper data connections across your entire HubSpot ecosystem.

Set up multi-object duplicate analysis using Coefficient

Coefficient enables comprehensive cross-object duplicate detection through multi-object imports and advanced formula capabilities, revealing data integrity issues that impact customer experience and business operations.

How to make it work

Step 1. Import comprehensive multi-object data.

Import contacts, companies, and deals from HubSpot with shared custom identifier fields. Include object-specific metadata like creation date, source, and owner for context analysis. Apply consistent filtering across all objects for relevant record subsets to focus your analysis.

Step 2. Create cross-reference analysis systems.

Compile all custom identifiers across objects using =UNIQUE() functions to create a master identifier list. Set up object mapping to track which objects contain each shared identifier. Add relationship validation to verify proper HubSpot associations exist between objects sharing identifiers.

Step 3. Build advanced cross-object formulas.

Use multi-object counting: =COUNTIF(Contacts_CustomID,A2)+COUNTIF(Companies_CustomID,A2)+COUNTIF(Deals_CustomID,A2) to see identifier distribution. Create object distribution analysis to identify identifiers appearing in unexpected object combinations. Add orphaned record detection to find objects with shared identifiers lacking proper associations.

Step 4. Identify complex duplicate scenarios.

Set up customer lifecycle tracking where the same customer ID appears as contact, company, and multiple deals. Detect account management issues where multiple contacts with the same company identifier aren’t properly associated. Find sales process gaps where deals have customer IDs not linked to corresponding contacts or companies.

Step 5. Implement cross-object validation rules.

Enforce business logic where customer IDs should appear in contacts AND companies, not deals alone. Add hierarchy validation to ensure parent-child relationships are properly reflected across object types. Include timeline consistency checks where creation dates are logical across related objects.

Step 6. Set up automated monitoring and reporting.

Configure comprehensive alerts when new cross-object duplicates are detected. Set up workflow integration to trigger HubSpot workflows based on cross-object duplicate status. Create escalation protocols with different alert levels for various cross-object scenarios.

Step 7. Create data integrity reporting and resolution.

Generate cross-object health scores showing the percentage of shared identifiers with proper object relationships. Perform gap analysis to identify missing objects in customer lifecycle representation. Use Coefficient’s association management to link related objects and consolidate data while preserving relationships.

Reveal hidden data integrity issues across your entire ecosystem

This cross-object duplicate detection provides unprecedented visibility into data relationships across your entire HubSpot ecosystem, revealing and resolving integrity issues that impact customer experience. Get started with Coefficient to uncover the hidden duplicate problems in your data.

When HubSpot exports separate deal and customer data without common identifiers, you’re left with valuable datasets that can’t be meaningfully connected for analysis or reporting.

Here’s how to merge these separate exports using advanced matching techniques and data enrichment to create unified datasets.

Prevent merging issues with association-preserved imports using Coefficient

Coefficient eliminates the need for post-export merging by pulling unified data from HubSpot that includes proper identifiers from the start, but also provides powerful tools for working with existing separated exports.

How to make it work

Step 1. Import deals with associated contact IDs.

Use Coefficient to pull deals with associated contact IDs automatically included, company associations for additional matching opportunities, and contact emails in deal imports for alternative matching keys. This prevents the identifier problem entirely.

Step 2. Apply fuzzy matching for existing separate exports.

When working with already-separated data, bring both datasets into a single spreadsheet and create matching algorithms using company name variations with SEARCH or FIND functions, phone number matching, and email domain matching for B2B scenarios.

Step 3. Build multi-criteria matching formulas.

Create formulas that attempt matching on multiple fields: primary matching on company name + contact last name, secondary matching on email domain + deal source, and tertiary matching on phone area code + industry for comprehensive coverage.

Step 4. Enrich data with additional HubSpot fields.

Use Coefficient to pull additional HubSpot data that might provide missing linking fields, such as recent activity associations, form submission data, and campaign attribution data that can serve as alternative matching criteria.

Achieve higher match rates with comprehensive data

This approach significantly improves match rates compared to manual spreadsheet manipulation alone, giving you unified datasets that preserve valuable relationships between deals and customers. Start creating better data connections today.

You can migrate individual Zoho accounts to HubSpot without bulk import by creating a controlled staging environment that lets you select specific accounts based on custom criteria.

This approach gives you complete control over which accounts transfer and when, eliminating the risk of importing unwanted data or overwhelming your HubSpot system.

Create a selective migration process using Coefficient

Coefficient provides the perfect solution for selective Zoho to HubSpot migration by connecting both systems through a spreadsheet staging environment. You can filter specific accounts, validate data before migration, and control exactly when each account transfers.

How to make it work

Step 1. Set up your Zoho data connection and apply dynamic filters.

Connect to Zoho CRM through Coefficient’s sidebar and import specific accounts using dynamic filtering. You can apply up to 25 filters with AND/OR logic to target individual accounts by criteria like account value, status, or custom fields. Reference spreadsheet cells to specify which accounts to pull, making your filters completely flexible.

Step 2. Create your migration staging area for validation.

Use Coefficient’s field selection to import only the necessary Zoho account data into your spreadsheet. Review and validate account information in the familiar spreadsheet environment, then add columns for migration status tracking and HubSpot field mapping to maintain complete control over the process.

Step 3. Execute selective migration with conditional exports.

Set up Coefficient’s scheduled exports to push individual accounts to HubSpot using INSERT actions to create new HubSpot companies. Use conditional exports to migrate only when a “Ready to Migrate” column equals “TRUE”, giving you precise control over timing and selection.

Step 4. Monitor and validate your migrations.

Leverage Coefficient’s automatic data refresh to ensure you’re working with current Zoho data throughout the process. Set up automated alerts to notify you when migrations complete, and use the bi-directional connectivity to validate that accounts transferred correctly to HubSpot.

Start your selective account migration today

This approach eliminates the complexity of API coding while providing granular control over your selective account migration process. Visual validation, easy field mapping, and built-in scheduling make individual account migration both simple and reliable. Try Coefficient to start migrating your Zoho accounts selectively.

HubSpot’s daily-only reporting granularity completely obscures peak hour identification because it aggregates all 24 hours into single data points, making it impossible to identify when staffing should be concentrated.

Here’s how to bypass HubSpot’s daily reporting limitations by accessing raw timestamp data to identify statistically significant peak hours for optimal staffing decisions.

Identify statistical peaks with Coefficient

HubSpot can’t break down daily totals to show which specific hours drive high-volume days. By importing raw timestamp data, you can circumvent daily aggregation limitations and perform sophisticated peak analysis using HubSpot ticket data.

How to make it work

Step 1. Import raw timestamp data.

Import all HubSpot tickets with complete “Create Date” timestamps, circumventing the daily aggregation limitation entirely. This gives you access to the granular data that HubSpot’s reports hide.

Step 2. Create hourly frequency distributions.

Extract hours using =HOUR(timestamp) and create frequency distributions showing ticket counts for each hour (0-23). Use pivot tables or COUNTIFS formulas to count tickets by hour.

Step 3. Calculate statistical peak thresholds.

Calculate averages and standard deviations by hour to identify statistically significant peaks. Use formulas to identify hours with volumes >1.5 standard deviations above the mean as true peaks.

Step 4. Analyze day-specific peak patterns.

Use =WEEKDAY(timestamp) to analyze peaks separately for different days of the week. Monday peaks might occur at different hours than Friday peaks, requiring different staffing strategies.

Step 5. Create peak intensity scoring.

Build formulas ranking hours by intensity using =(hourly_volume – daily_average) / daily_average * 100 to quantify peak severity. This helps prioritize which peaks need the most attention.

Step 6. Set up automated peak detection alerts.

Configure alerts that trigger when current hour volume exceeds historical peak thresholds, enabling real-time staffing adjustments. This provides proactive notification of unusual volume spikes.

Step 7. Track peak trends over time.

Compare monthly peak hour patterns to identify seasonal or business-driven changes. Use conditional formatting to highlight how peak hours shift over time.

Transform daily data into peak hour intelligence

This transforms HubSpot’s limited daily data into actionable peak hour intelligence that directly supports optimal staffing decisions and resource allocation. Start identifying your peak hours today.