How to export and analyze Folk CRM data for performance reporting

If you’re responsible for reporting on your team’s outreach, pipeline, or relationship-building, you know that CRM data is only as good as what you can get out of it. This guide is for anyone using Folk who wants to actually do something with their data—namely, export it and turn it into performance reports that make sense.

You don’t need to be a data scientist. But you do need to know what’s possible, what’s a pain, and how not to waste time reinventing the wheel (or copy-pasting like a maniac).

Why Export Data from Folk CRM?

Folk is a solid lightweight CRM for teams who don’t want the Salesforce circus. But, like most CRMs, its built-in reporting is limited. Sometimes you just need the raw data—whether it’s to:

  • Build custom dashboards in Excel or Google Sheets
  • Analyze outreach or sales performance
  • Share results with leadership (or just keep yourself honest)
  • Back up your contacts, just in case

If you’re looking for fancy, automated BI tools, Folk isn’t built for that. But for most small teams, a good export and a little spreadsheet elbow grease is all you need.

Step 1: Get Your Data Out—How Exporting from Folk Works

Let’s cut to the chase: Folk’s export options are basic, but they get the job done.

What You Can Export

Folk lets you export:

  • Contact lists (with custom fields)
  • Interactions (emails, notes, etc.—but only associated with contacts)
  • Tags and segments

You can’t export: email attachments, message threads in full, or custom analytics. Don’t bother looking for hidden “power user” export features—they’re not there.

How to Export

  1. Open the list you want (e.g., “Leads Q2 2024”)
  2. In the top right, click the three dots (“More actions”)
  3. Choose Export as CSV
  4. Select the fields you want to include (or just export all)
  5. Download the CSV file to your computer

Pro tip: Folk exports whatever you see on the screen. If you’ve filtered or sorted your list, the export respects that. Double-check your filters so you don’t accidentally export junk or miss important records.

Pitfalls To Watch Out For

  • Field limits: If you have lots of custom fields, check your CSV—some columns might get truncated or appear in a weird order.
  • Date formats: Folk exports dates in ISO (YYYY-MM-DD), but sometimes Excel “helps” by reformatting them. Open in Google Sheets if you hit weird date issues.
  • Export size: For huge lists (10,000+ contacts), exports can time out or miss some data. Split into smaller lists if you hit a wall.

Step 2: Clean Up the Data (You’ll Thank Yourself Later)

Don’t skip this. Even the cleanest CRM exports need a little tidying before analysis.

Common Issues and Quick Fixes

  • Blank rows or columns: Delete these first—don’t let Excel formulas get tripped up.
  • Duplicate contacts: Use the “Remove duplicates” tool in Excel/Sheets.
  • Strange characters: Watch for odd symbols if you use non-English names or notes. Sometimes encoding gets funky; open in Google Sheets for better support.
  • Broken line breaks: Notes fields sometimes have line breaks that mess up rows. Use “Find & Replace” to clean these up.

Normalize Your Data

If you want to group by company, region, or deal type, make sure those fields are consistent. Fix typos or variations (“Acme Inc.” vs. “Acme Incorporated”) now, not after you’ve built a pivot table.

Pro tip: Add a column for “Reporting Period” if you want to compare month-to-month. It’s much easier to filter later.

Step 3: Set Up Your Performance Metrics

Now for the fun part: figuring out what you actually want to measure.

What Metrics Make Sense?

Don’t overcomplicate it. Most teams just need:

  • Number of new contacts added
  • Number of interactions logged (calls, emails, meetings)
  • Conversion rates: e.g., leads to qualified, or outreach to meeting booked
  • Response times: How long does it take to follow up?
  • Pipeline value: If you’re tracking deals or dollars

If you find yourself adding 20 metrics, stop. Pick 3-5 that your team actually uses to make decisions.

How to Calculate These in Excel or Google Sheets

  • Counts: Use COUNTIF or pivot tables to see how many contacts match a filter.
  • Conversion rates: Divide the number of contacts at each stage (=COUNTIF(Stage, "Qualified")/COUNTIF(Stage, "Lead")).
  • Response times: If you have date fields for “Contacted” and “Responded,” use =DATEDIF(A2, B2, "D") to get days between.

Pro tip: Save your formulas and templates. Don’t start from scratch every month.

Step 4: Build Your Reports

You’ve got your cleaned data and you know what you want to measure. Here’s how to actually build a report that doesn’t suck.

The Simple Approach

  • Pivot tables are your friend. They let you slice data by owner, status, date, etc.
  • Charts: If your audience is visual, add basic bar or line charts. Don’t bother with 3D pie charts—nobody cares.
  • Filters: Make it easy to drill down by month, team member, or deal type.

Example: Monthly Outreach Report

  1. Pivot table for “Contacts Added” by month
  2. Pivot table for “Interactions Logged” by team member
  3. Simple chart showing “Leads Qualified” over time
  4. Table with “Top 10 accounts by activity”

Sharing Results

  • Export to PDF for execs who hate spreadsheets
  • Share a Google Sheet with live updates if your team likes to poke around
  • Keep raw data in a hidden tab—don’t share stuff you don’t want people to edit

Step 5: Automate (Just Enough)

Folk doesn’t have native integrations with BI tools or data pipelines. You can use Zapier or Make, but it’s often more trouble than it’s worth for small teams.

When Automation Helps

  • Scheduled exports: Folk doesn’t offer these, but you can set a calendar reminder to export every Friday. Low-tech, but it works.
  • Google Sheets scripting: If you want to automate calculations or clean-up, Apps Script can help—just don’t go down a rabbit hole unless you really need it.

What’s Not Worth It

  • Building a custom data pipeline from Folk to a fancy dashboard. For 99% of small teams, it’s overkill.
  • Paying for a third-party “CRM reporting” tool just for Folk data. You’ll spend more time troubleshooting than analyzing.

What to Ignore

Some advice you’ll see online isn’t worth your time:

  • Don’t bother with “export everything, just in case.” Big, messy exports are harder to use. Export what you need, when you need it.
  • Ignore “AI-powered” reporting add-ons—they rarely work well with Folk and usually want access to all your data.
  • Don’t try to make Folk do something it’s not built for. If you need deep pipeline analytics or advanced forecasting, you might need a bigger CRM.

Wrapping Up: Keep It Simple, Iterate Often

Exporting and analyzing Folk CRM data doesn’t have to be another project on your to-do list that never gets done. Start small, keep your reports focused, and don’t be afraid to tweak things month by month.

The real value is in the conversations your reports spark—not in the charts themselves. Export, clean up, measure what matters, and move on. If you outgrow Folk’s exports, you’ll know it. Until then, keep it scrappy and useful.