Category: Excel

How to extract NPS raw data for custom segmentation analysis

Extracting raw NPS data from HubSpot typically means manual exports that become outdated quickly. You’re working with static snapshots while new survey responses continue flowing in, making your analysis stale before you finish it.

Here’s how to get direct access to raw survey response data with live connections that enable sophisticated segmentation analysis updating in real-time.

Connect directly to individual survey responses using Coefficient

Coefficientprovides direct access to raw NPS survey response data with live connections that update automatically. Instead of static exports, you get granular, current data that enables dynamic segmentation analysis as your customer base evolves.

How to make it work

Step 1. Import individual responses with complete associated data.

HubSpotHubSpot’sConnect toand import each survey response with timestamp, contact ID, actual score (0-10), and all associated contact and company properties. This gives you the granular dataset thatstandard reports aggregate away, with no volume limitations.

Step 2. Set up real-time updates with scheduled imports.

Schedule imports to refresh hourly, daily, or weekly so your raw dataset stays current as new responses are collected. This eliminates the export-refresh cycle and ensures your segmentation analysis always includes the latest customer feedback.

Step 3. Create sophisticated segmentation analysis.

Use the raw data for advanced analysis like cohort analysis by signup date, segmentation by multiple variables simultaneously (geography + product + customer tier), and custom scoring models using response patterns. The granular data enables statistical analysis that aggregated reports can’t support.

Step 4. Build dynamic segments that evolve with your data.

Create segmentation rules that automatically categorize new responses as they arrive. Set up filters and formulas that adapt to changing customer characteristics, product associations, and business segments without manual intervention.

Analyze customer sentiment with the depth your business deserves

ExtractRaw NPS data extraction enables segmentation analysis that evolves with your customer base and survey responses in real-time. You get the granular insights that drive strategic decisions instead of surface-level summaries.your raw NPS data for custom analysis today.

How to filter Salesforce reports when object has both direct and chained lookup relationships to same parent

Salesforce’s native Report Builder struggles with filtering when objects have both direct and chained lookup relationships to the same parent, creating confusion about which relationship path the filter applies to.

Here’s how to create precise filters that work correctly for each relationship path without the confusion of native report filtering.

Apply precise filtering with advanced AND/OR logic using Coefficient

Coefficient’sSalesforce’sadvanced filtering capabilities provide precise control over complex relationship filtering scenarios. You can create separate imports for each relationship path and apply specific filters to each, something impossible withstandard report filtering.

How to make it work

Step 1. Create separate imports for each relationship path.

Set up one import for the direct D→A relationship and another for the D→C→B→A chain. This allows you to apply different filtering criteria to each path based on your specific business requirements.

Step 2. Apply path-specific filters.

Filter the direct relationship based on Object A’s criteria while simultaneously filtering the chained relationship based on different criteria from the intermediate objects. Use AND/OR logic to create complex filtering conditions for each path.

Step 3. Set up dynamic filters for interactive control.

Use dynamic filters that point to cell values in your spreadsheet. This enables interactive filtering that users can adjust without editing import settings, particularly powerful when filtering the same parent object differently based on relationship path.

Step 4. Write custom SOQL for ultimate filtering flexibility.

Create WHERE clauses that explicitly handle the logic for multiple relationship paths. For example: WHERE (Direct_Parent__c = ‘Value1’) OR (Intermediate__r.Parent__c = ‘Value2’) allows conditional filtering based on which relationship path contains data.

Step 5. Combine filtered data intelligently.

SalesforceUse spreadsheet formulas to merge your separately filtered relationship paths. Apply business logic that determines which relationship path takes precedence when both contain data for the same parent object in.

Get the filtered data you actually need

Start using CoefficientThis approach eliminates the confusion and limitations of standard Salesforce report filtering when dealing with complex relationship structures.to create filters that actually work the way you need them to.

How to filter Salesforce users with null login dates when date picker is mandatory

Salesforce’s native reporting forces you to select date ranges for LastLoginDate filters, which automatically excludes users who have never logged in since they have null login dates.

Here’s how to bypass this limitation and identify active users with no login history using direct data access.

Access user data without date picker constraints using Coefficient

CoefficientSalesforceeliminates the mandatory date picker problem by connecting directly to yourdata through API calls rather than the constrained reporting interface. This gives you complete access to User object data, including records with null login dates that standard reports can’t capture.

How to make it work

Step 1. Import User object data directly.

SalesforceIn, select “From Objects & Fields” and choose the User object. Include fields like Id, Username, IsActive, LastLoginDate, CreatedDate, and Profile.Name. No date picker will appear since you’re accessing raw object data.

Step 2. Apply null login date filters.

Use Coefficient’s advanced filtering with “LastLoginDate is blank” condition combined with “IsActive = TRUE” to identify active users who have never logged in. This filtering happens in your spreadsheet environment, which naturally handles empty cells.

Step 3. Use custom SOQL for complex queries.

For more control, write a custom query:. This bypasses all UI limitations entirely.

Step 4. Set up automated monitoring.

Schedule daily refreshes to track unused active accounts for security compliance. You can also create formulas liketo categorize users automatically.

Start tracking unused accounts today

Get startedThis approach gives you complete visibility into active users with null login dates while eliminating the date picker constraints that block native Salesforce reporting.with Coefficient to access your full user data without limitations.

How to fix date format errors when importing Excel leads to Salesforce

You’ve spent hours preparing your lead list in Excel. Everything looks perfect. You hit import in Salesforce’s Data Import Wizard and… error after error. 

“Invalid date format.” “Date field cannot be processed.” Sound familiar?

Here’s the maddening part: your dates look fine in Excel. They’re all formatted consistently. 

But Salesforce’s Data Import Wizard is notoriously picky about date formats, often requiring exact MM/DD/YYYY formatting while failing with cryptic error messages that don’t tell you what’s actually wrong. Regional date differences (DD/MM vs MM/DD) make this even worse.

Coefficient is an Excel add-in that connects your business systems directly to your spreadsheets, eliminating these formatting headaches by handling date conversions automatically during import.

Why Date Format Errors Happen (And Why They’re So Frustrating)

The root problem? Excel and Salesforce speak different date languages:

  • Excel stores dates as serial numbers with display formatting on top
  • Salesforce expects specific text patterns (usually MM/DD/YYYY)
  • Regional settings can flip your dates without warning
  • Text that looks like dates isn’t always recognized as actual date values

The traditional fix involves manually reformatting every date column, converting to text, checking for edge cases, and crossing your fingers during import. One missed cell? The entire import fails, and you’re back to square one.

The Better Way: Fix Date Formats Directly in Excel with Coefficient

Instead of the manual formatting marathon, Coefficient’s Excel Add-in recognizes and converts date formats automatically. No switching to Google Sheets. No complex formulas. Just reliable date handling that works with your existing Excel workflow.

Step-by-Step: Import Excel Leads to Salesforce Without Date Errors

Step 1: Install Coefficient Excel Add-in and Connect to Salesforce

Open your Excel file with the lead data. Install the Coefficient add-in from the Microsoft AppSource (takes 30 seconds). Connect to your Salesforce instance with one click – Coefficient handles the authentication.

Step 2: Standardize Your Date Formats in Excel

Before importing, use Excel’s TEXT function to ensure consistency:

=TEXT(A2,”MM/DD/YYYY”)

Pro tip: Coefficient will recognize most date formats automatically, but standardizing first gives you full control over the output format.

Step 3: Validate Date Values Using Excel Functions

Identify any text strings masquerading as dates:

=IF(ISNUMBER(A2),”Valid Date”,”Text String”)

This catches those sneaky cells that look like dates but will cause import failures. Convert any text strings to proper date values before proceeding.

Step 4: Map Your Fields in Coefficient

Click “Import Data” in the Coefficient sidebar. Select your Salesforce Lead object. Map your Excel columns to Salesforce fields – Coefficient automatically detects date fields and shows you exactly how each date will be interpreted.

Step 5: Preview and Validate Before Import

Here’s where Coefficient shines: the preview function shows you exactly how Salesforce will receive your dates. No guessing. No surprises. Any formatting issues appear immediately with clear explanations of what needs fixing.

Step 6: Import with Confidence

Hit import. Coefficient handles the date conversion behind the scenes, ensuring Salesforce receives dates in the exact format it expects. Your leads import successfully on the first try.

What This Means for Your Workflow

Before Coefficient:

  • 2-3 hours fixing date formats manually
  • Multiple failed import attempts
  • Cryptic error messages
  • Regional date format confusion
  • Manual validation of hundreds of cells

With Coefficient:

  • 15-minute setup and import
  • Automatic date format recognition
  • Clear preview of how dates will import
  • One-click import with confidence
  • Reusable connection for future imports

Ready to Eliminate Date Format Errors for Good?

Stop wrestling with Salesforce’s Data Import Wizard. Let Coefficient handle the complexity while you focus on what matters – getting your leads into Salesforce and driving revenue.

Get Coefficient for Free  

How to fix “filter logic references an undefined filter” error for specific user in Salesforce dashboard

When one user gets an “undefined filter” error while others can access the same dashboard, the problem usually stems from corrupted filter cache or user-specific data conflicts that standard troubleshooting can’t fix.

Here’s a more reliable approach that eliminates these filter logic issues completely by creating an independent data connection.

Skip the broken dashboard filters with direct data imports using Coefficient

Instead of trying to fix Salesforce’s corrupted filter logic, you can bypass the dashboard infrastructure entirely. Coefficient lets you import the same data directly from your Salesforce reports or objects, giving you access to all the fields without relying on dashboard filters that break for individual users.

How to make it work

Step 1. Connect Coefficient to your Salesforce org.

Install Coefficient from the Google Workspace Marketplace or Microsoft AppSource. Authorize the connection to your Salesforce org using your login credentials.

Step 2. Import data from your existing report.

In your spreadsheet, open the Coefficient sidebar and select “Import from Salesforce.” Choose “From Existing Report” and select the problematic report from your org’s report list.

Step 3. Set up filtering that actually works.

Apply filters using Coefficient’s AND/OR logic system. You can filter by number, text, date, boolean, or picklist fields. For flexible filtering, use dynamic filters that point to cell values.

Step 4. Enable automatic refresh.

Schedule your import to refresh hourly, daily, or weekly. This keeps your data current while maintaining consistent access for all users regardless of their individual Salesforce dashboard issues.

Step 5. Share with your team.

Share the spreadsheet with affected users. They’ll have reliable access to the same live data without dealing with undefined filter errors.

Connect Salesforce Data to Sheets

Get consistent report access for everyone

This approach eliminates user-specific filter corruption while providing more robust filtering capabilities than Salesforce’s native dashboards.to create reliable reports that work for your entire team. Try Coefficient.

How to fix Salesforce report subscription rendering failure after Summer 24 update

Salesforce’s Summer 24 update broke report subscription rendering for many organizations, leaving teams without their automated reports. The platform’s native subscription system is vulnerable to these update disruptions.

Here’s how to create a more reliable alternative that won’t break when Salesforce pushes platform updates.

Replace broken subscriptions with automated spreadsheet reports using Coefficient

CoefficientSalesforceSalesforceInstead of waiting for Salesforce to fix their rendering issues, you can bypass the problem entirely.imports yourreports directly intoor Excel with automated refresh capabilities and independent email alerts. This approach eliminates dependency on Salesforce’s email infrastructure that’s currently experiencing issues.

How to make it work

Step 1. Connect Coefficient to your Salesforce org.

Install Coefficient in Google Sheets or Excel, then authenticate using your existing Salesforce credentials. The connection uses your current permissions, so you’ll have access to the same reports that were failing in your subscriptions.

Step 2. Import your existing Salesforce reports.

Use Coefficient’s “From Existing Report” feature to pull in any report from your org. This includes pipeline reports, lead reports, opportunity forecasts, and campaign performance data. The import captures all fields and formatting from your original reports.

Step 3. Set up automated refresh schedules.

Configure your reports to refresh automatically with options for hourly intervals (1, 2, 4, or 8 hours), daily, or weekly updates. Unlike Salesforce’s rigid scheduling, you can customize timing based on your team’s actual needs.

Step 4. Configure email alerts with custom recipients.

Set up email notifications that trigger on scheduled refreshes, new data, or specific value changes. You can customize email content, include charts and formatting, and route messages to different recipients based on the data. These emails use Google or Microsoft’s email systems instead of Salesforce’s problematic infrastructure.

Get your automated reports working again

Start buildingThis solution provides better reliability than native Salesforce subscriptions while offering enhanced customization options.your replacement reporting system today and avoid future platform update disruptions.

How to fix “required field missing” error when importing Excel leads to Salesforce

SalesforceThe “required field missing” error happens because’s Data Import Wizard doesn’t tell you which specific fields are missing until after the import fails. This forces you into a frustrating trial-and-error cycle where you guess which fields need data.

Here’s how to identify missing required fields before you import and avoid failed uploads entirely.

Preview and validate required fields using Coefficient

Coefficientshows you exactly which records will fail due to missing required fields before you attempt the import. Unlike the Data Import Wizard, you can see validation issues upfront and fix them in your spreadsheet first.

How to make it work

Step 1. Upload your Excel file to Google Sheets.

Open Google Sheets and upload your Excel lead file. This removes the file size restrictions you’d face with the Data Import Wizard and gives you more flexibility to clean your data.

Step 2. Install Coefficient and connect to Salesforce.

Salesforce

Add Coefficient to your Google Sheets and authorize your Salesforce connection. This gives you access to all your Salesforce objects and fields for mapping.

Step 3. Set up your export mapping to the Lead object.

Click “Export to Salesforce” in the Coefficient sidebar and select the Lead object. Map each column from your Excel data to the corresponding Salesforce field, including all required fields like Company, Last Name, and any custom required fields.

Step 4. Run the preview to identify missing required fields.

Before executing the import, use Coefficient’s preview function. This shows you exactly which records have missing required fields and which specific fields need data. You’ll see validation errors before any import attempt.

Step 5. Fix the missing data and save your mapping.

Go back to your Google Sheets and fill in the missing required field data. Once your preview shows no validation errors, save your field mapping as a template for future lead imports.

Stop guessing and start importing with confidence

Try CoefficientThe preview functionality eliminates the guesswork that makes Salesforce’s native import tools so frustrating. You’ll know exactly what will work before you commit to the import.to see your validation errors upfront instead of after the fact.

How to get all 900 rows from Salesforce report when copy paste limits to visible screen

Extracting all 900 rows when copy-paste only captures visible screen data requires bypassing browser viewport limitations. Salesforce’s pagination means only 30-50 rows are typically rendered and available for copying at any time.

Here’s how to capture your complete 900-row dataset in a single operation instead of 30+ separate copy attempts.

Import complete large datasets using Coefficient

CoefficientSalesforce’sSalesforcedirectly solves this challenge by importing the complete 900-row dataset throughAPI rather than copying from the limited visible interface. This eliminates the need for repetitive copy-paste operations across multiple pages withintegration.

How to make it work

Step 1. Install Coefficient and establish your Salesforce connection.

Add Coefficient to your Google Sheets or Excel environment from the respective app stores. Connect to Salesforce using your existing credentials and API access permissions.

Step 2. Navigate to “Import from Existing Report” in the Coefficient sidebar.

Select this option to view all available reports in your Salesforce org. You’ll see reports with hundreds or thousands of rows, including your 900-row target report.

Step 3. Select your 900-row report and import the complete dataset.

Choose your target report from the list and click import. Coefficient will pull all 900 rows in a single operation while maintaining original data formatting and field relationships from Salesforce.

Step 4. Apply additional analysis or filtering as needed.

With the complete dataset now in your spreadsheet, you can filter, sort, and analyze all 900 rows without losing access to any data. The full dataset enables comprehensive analysis that wasn’t possible with 30-row chunks.

Step 5. Set up automatic refreshes for ongoing access.

Configure scheduled updates so your 900-row dataset stays current as the underlying Salesforce data changes. This eliminates the need for repeated manual copying as your data grows.

Transform tedious manual copying into automated complete imports

Try CoefficientThis approach changes a tedious manual process requiring 30+ separate copy operations into a single automated import with reliable results. You get your complete 900-row dataset with preserved formatting and ongoing updates.for complete dataset access.

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 handle duplicate leads when importing from Excel to Salesforce

Salesforce‘s Data Import Wizard only offers basic duplicate detection that often misses existing records, creating unwanted duplicates even when matching leads already exist in your system. The wizard lacks sophisticated matching logic beyond simple field comparisons.

Here’s how to prevent duplicate creation and update existing leads when importing Excel data.

Use upsert operations to prevent duplicates with Coefficient

Coefficientprovides upsert functionality that updates existing records or creates new ones based on External ID field matching. This prevents duplicate creation while allowing you to update existing lead information from your Excel file.

How to make it work

Step 1. Ensure your Excel data includes a reliable matching field.

Your Excel file should include email addresses, company names, or custom ID fields that can identify existing leads. Email is the most common and reliable matching field for lead records.

Step 2. Set up External ID fields in Salesforce.

Salesforce

In Salesforce Setup, mark your matching field (like Email) as an External ID if it isn’t already. This allows Coefficient to use it for duplicate detection and record matching during the upsert process.

Step 3. Import your Excel data to Google Sheets and connect Coefficient.

Upload your Excel file to Google Sheets and install Coefficient. Connect to your Salesforce org to access the upsert functionality.

Step 4. Configure the upsert action instead of insert.

In Coefficient’s export settings, select “Upsert” as your action type instead of “Insert.” Map your Excel matching column (email) to the External ID field in Salesforce.

Step 5. Preview to see update vs. create actions.

Run a preview to see which records will update existing leads versus create new ones. This shows you exactly how duplicate prevention will work before executing the import.

Maintain clean data with smart duplicate handling

Use CoefficientUpsert operations ensure data integrity while allowing you to update existing lead information without creating unwanted duplicates. This approach is far more sophisticated than basic duplicate detection.to handle duplicates intelligently during your Excel imports.