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Square Staff Performance: Team Metrics & Reports

Updated October 2026

Category: Square Analytics | Reading Time: 12 minutes

Introduction to Staff Performance Analysis

Understanding which employees are your top performers and who needs additional training is crucial for maximizing revenue and improving customer satisfaction. When each sale is taken under an individual team member login, Square lets you filter transactions by team member, but knowing how to extract, summarize and read that information is a separate job.

This comprehensive tutorial will guide you through the complete process of analyzing staff performance data from Square. You'll learn how to identify sales leaders, spot training opportunities, and make data-driven decisions about team development. By the end of this guide, you'll have a per-employee summary of sales, average transaction and refund rate, and a clear idea of what those numbers can and cannot tell you about coaching.

Whether you manage a retail store, restaurant, or service business, staff performance analytics help you answer critical questions: Who are your most efficient salespeople? Which employees have the highest average transaction values? Are there performance gaps that training could address? Let's dive into the step-by-step process of unlocking these insights.

Step 1: Prerequisites and Data Requirements

Before beginning your staff performance analysis, ensure you have the following components in place:

Required Access and Permissions

Check That Sales Are Attributed to People

Before exporting, confirm that recent sales carry a team member:

  1. Log into your Square Dashboard
  2. Go to Orders & payments (on some accounts Payments), then Transactions
  3. Open the filters and choose Team member, the filter Square's help article on viewing and searching transactions lists next to date, payment method, location and source
  4. Check that each active employee appears on recent sales

Important: Sales rung up under a shared login cannot be split by person. If employees shared a login for part of your date range, leave that part out of the comparison.

Expected Data Elements

Your Square staff performance export should include these key fields:

Step 2: Export Staff Performance Data from Square

Square's Transactions page exports payment-level detail as a CSV file. You then total it per employee, which gives the summary this tutorial analyzes.

Export the Transactions

  1. Log into your Square Dashboard and go to Orders & payments, then Transactions
  2. Set the date range (90 days is used here)
  3. Click Export to download the transaction details as a CSV file (export is available in the Dashboard, not in the point of sale app)
  4. Open the CSV in Excel, Google Sheets or a text editor and check that a column names the team member who took each sale

Square's help article does not list the columns of the transactions CSV, so check the headers of your own file. If you cannot find a team member column, check that the sales were taken under individual logins (see Step 1).

Total It Per Employee

Build a pivot table with the team member as rows and, as values, the sum of gross sales, the count of transactions, the sum of refunds and the sum of tips. Net sales is gross sales minus refunds. The exact column names in your export may differ from the ones used here, so rename them to match this layout.

Summary Layout Used in This Tutorial

The per-employee summary looks like this. The names and figures are invented for the tutorial (they are not from a real business), and every amount that contains a comma is in quotes so the file parses into seven columns:

Employee Name,Gross Sales,Transactions,Avg Transaction,Refunds,Net Sales,Tips
"John Smith","$15,420.50",247,"$62.43","$340.00","$15,080.50","$1,245.00"
"Sarah Johnson","$18,950.75",312,"$60.74","$220.50","$18,730.25","$1,890.00"
"Mike Davis","$12,305.00",198,"$62.15","$450.00","$11,855.00","$980.50"

Tip: If you operate multiple locations, build a separate summary for each location to see location-specific patterns.

Step 3: Prepare Your Data for Analysis

Raw data from Square often requires cleaning before meaningful analysis. This preparation step ensures accurate results and prevents common analytical errors.

Data Cleaning Checklist

Review your exported CSV file and perform these essential cleaning tasks:

1. Remove Incomplete Records

2. Standardize Employee Names

Ensure consistency in how employee names appear:

Before Cleaning:
John Smith
john smith
J. Smith
Smith, John

After Cleaning:
John Smith
John Smith
John Smith
John Smith

3. Check Numeric Fields

4. Calculate Additional Metrics

Add calculated columns that will enhance your analysis:

Net Sales per Transaction = Net Sales / Number of Transactions
Refund Rate = (Refunds / Gross Sales) × 100
Tips per Transaction = Tips / Number of Transactions
Sales per Hour = Net Sales / Hours Worked (if you also have hours worked)

Save Your Prepared Dataset

Once cleaning is complete:

  1. Save the file as square_staff_performance_cleaned.csv
  2. Verify the file contains no formatting errors
  3. Keep the original export as a backup

Step 4: Compute the Team Metrics

With the summary prepared, load it and check that it adds up. The code below parses the amounts, then confirms that net sales equals gross sales minus refunds for every row.

import io
import pandas as pd

# Per-employee summary, with every amount that contains a comma in quotes.
# To use your own file, replace io.StringIO(raw) with 'square_staff_performance_cleaned.csv'.
raw = """Employee Name,Gross Sales,Transactions,Avg Transaction,Refunds,Net Sales,Tips
"John Smith","$15,420.50",247,"$62.43","$340.00","$15,080.50","$1,245.00"
"Sarah Johnson","$18,950.75",312,"$60.74","$220.50","$18,730.25","$1,890.00"
"Mike Davis","$12,305.00",198,"$62.15","$450.00","$11,855.00","$980.50"
"""
df = pd.read_csv(io.StringIO(raw))

money = ["Gross Sales", "Avg Transaction", "Refunds", "Net Sales", "Tips"]
for col in money:
    df[col] = df[col].str.replace(r"[$,]", "", regex=True).astype(float)

# Net Sales should equal Gross Sales minus Refunds
print((df["Gross Sales"] - df["Refunds"] - df["Net Sales"]).abs().max() < 0.005)
print(df[["Employee Name", "Gross Sales", "Transactions", "Net Sales"]].to_string(index=False))

Expected Output

True
Employee Name  Gross Sales  Transactions  Net Sales
   John Smith     15420.50           247   15080.50
Sarah Johnson     18950.75           312   18730.25
   Mike Davis     12305.00           198   11855.00

The first line prints True when net sales equals gross sales minus refunds for every row. The remaining blocks in Step 5 continue in the same session, so run them in order.

Step 5: Interpret Your Results

Understanding the analysis output is critical for making informed decisions about staff development and resource allocation.

Key Performance Metrics Explained

1. Sales Volume Rankings

This metric shows net sales for each employee during the period, compared with the average of the employees' net sales.

What it tells you: Who brought in the most sales in this period. It does not account for hours worked, shifts, locations or transaction complexity.

team_avg_net = df["Net Sales"].mean()
print(f"Net sales by employee (team average {team_avg_net:,.2f}):")
ranked = df.sort_values("Net Sales", ascending=False).reset_index(drop=True)
for i, row in ranked.iterrows():
    gap = (row["Net Sales"] / team_avg_net - 1) * 100
    side = "above" if gap >= 0 else "below"
    print(f"{i + 1}. {row['Employee Name']} - ${row['Net Sales']:,.2f} ({abs(gap):.1f}% {side} the team average)")

Example output:

Net sales by employee (team average 15,221.92):
1. Sarah Johnson - $18,730.25 (23.0% above the team average)
2. John Smith - $15,080.50 (0.9% below the team average)
3. Mike Davis - $11,855.00 (22.1% below the team average)

2. Average Transaction Value (ATV)

Measures the typical sale amount for each employee: gross sales divided by transactions, compared with the team figure (all gross sales divided by all transactions).

What it tells you: How large each person's typical sale is. Differences can come from what each person sells, which shifts and counters they work and which customers they serve, as well as from selling technique.

team_atv = df["Gross Sales"].sum() / df["Transactions"].sum()
print(f"Team average transaction (all gross sales / all transactions): ${team_atv:.2f}")
for _, row in df.iterrows():
    atv = row["Gross Sales"] / row["Transactions"]
    gap = (atv / team_atv - 1) * 100
    side = "above" if gap >= 0 else "below"
    print(f"{row['Employee Name']}: ${atv:.2f} ({abs(gap):.1f}% {side} the team average)")

Example output:

Team average transaction (all gross sales / all transactions): $61.66
John Smith: $62.43 (1.3% above the team average)
Sarah Johnson: $60.74 (1.5% below the team average)
Mike Davis: $62.15 (0.8% above the team average)

Here all three averages sit within about 1.5% of the team figure, so this example gives no reason to single anyone out on average transaction value.

3. Refund Rate

Refunds as a percentage of gross sales, compared with the team rate (all refunds divided by all gross sales).

What it tells you: Which employees' sales are refunded more often than the team's. It does not say why. A higher rate is a prompt to read the refunds themselves (reasons, products, dates) before drawing a conclusion about the person.

team_refund_rate = df["Refunds"].sum() / df["Gross Sales"].sum() * 100
print(f"Team refund rate (refunds / gross sales): {team_refund_rate:.1f}%")
for _, row in df.iterrows():
    rate = row["Refunds"] / row["Gross Sales"] * 100
    ratio = rate / team_refund_rate
    print(f"{row['Employee Name']}: {rate:.1f}% ({ratio:.2f}x the team rate)")

Example output:

Team refund rate (refunds / gross sales): 2.2%
John Smith: 2.2% (1.02x the team rate)
Sarah Johnson: 1.2% (0.54x the team rate)
Mike Davis: 3.7% (1.69x the team rate)

Mike Davis's rate is about 1.7 times the team rate. The summary holds only the refund total ($450), not how many refunds there were, so one or two large refunds could produce the gap. Check the individual refunds before acting.

4. Performance Consistency

How much each person's sales vary from day to day or week to week. This needs a dated export (one row per day or week per employee); the summary above has no dates, so it cannot show this. To get it, pivot the transactions by date and team member instead of by team member alone.

What it tells you: Whether a total is steady or driven by a few large days, and how much scheduling and day of week matter.

Identifying Top Performers

Look for employees who excel across multiple metrics:

Identifying Training Opportunities

These patterns are worth a closer look. Check the cause (shifts, product mix, refund reasons) before deciding on training:

Differences between people can come from random variation in who served which customers and when. With three employees and one summary row each, this tutorial cannot tell whether any gap is larger than that variation. That needs transaction-level data (each person's individual sale amounts) and an interval or a test, and even then it shows a difference between people, not what causes it. For how significance tests are read, see A/B testing and statistical significance.

Step 6: Take Action on Insights

Analysis is only valuable when it drives action. Here's how to translate your findings into business improvements.

For Top Performers

  1. Recognition: Acknowledge exceptional performance through rewards, bonuses, or public recognition
  2. Best Practice Documentation: Interview top performers to identify techniques that can be taught to others
  3. Mentorship Roles: Pair high performers with employees needing improvement
  4. Career Development: Identify leadership potential and create advancement opportunities

For Employees Needing Development

  1. Specific Training Plans: Create targeted development based on metric weaknesses
    • Low ATV → Product knowledge and upselling techniques training
    • High refunds → Customer needs assessment and expectation setting
    • Low volume → Time management and customer engagement skills
  2. Regular Coaching: Schedule weekly or bi-weekly one-on-one sessions to track improvement
  3. Performance Goals: Set SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound)
  4. Progress Monitoring: Re-run analysis monthly to track improvement trends

Team-Wide Improvements

Sample Action Plan Template

Employee: Mike Davis
Current Performance: 22% below the team average net sales, 3.7% refund rate (team 2.2%)
Hypotheses to Check (the figures do not show causes): limited product knowledge? rushing customer interactions? first read the refund reasons, shifts and product mix
Action Plan (if the hypotheses hold):
  Week 1-2: Product training (2 hours) + shadow top performer
  Week 3-4: Role-playing customer scenarios, refund prevention techniques
  Week 5-8: Weekly coaching sessions, track refund rate improvement
Target: Reduce refund rate to <2%, increase sales to team average
Review Date: [8 weeks from start]

Run It on Your Own Square Export

Export your Square transactions as a CSV and upload it at MCP Analytics. The analysis runs a staff performance comparison on your file and shows the result with the method used. You choose the columns; nothing connects to your Square account.

Try it on your data

Next Steps with Square Analytics

Once you've mastered staff performance analysis, expand your Square analytics capabilities:

Additional Analysis Opportunities

Integrate with Other Data Sources

Combine Square data with complementary information for deeper insights:

Continuous Improvement Cycle

  1. Monthly Analysis: Run staff performance reports every 30 days
  2. Quarterly Reviews: Deep-dive sessions with each employee to discuss trends
  3. Annual Benchmarking: Compare year-over-year improvements and set new targets
  4. Ongoing Training: Adjust development programs based on emerging data patterns

Troubleshooting Common Issues

Encountered a problem during analysis? Here are solutions to frequent challenges:

Issue 1: "No Staff Data Available" in Square Export

Cause: Sales were not taken under individual team member logins, so they carry no team member.

Solution:

  1. In Orders & payments → Transactions, filter by Team member and check that your employees appear on recent sales
  2. Ensure employees log in to Square POS with individual accounts (not shared)
  3. Check that each employee has a team member profile in Square
  4. If employees shared a login, leave those dates out of the comparison

Issue 2: Extremely High or Low Numbers in Export

Cause: Data formatting issues, test transactions, or refunds not properly categorized.

Solution:

  1. Filter out transactions marked as "test" or "training"
  2. Verify currency formatting (ensure decimal points are correct)
  3. Check that refunds are shown as negative values or in a separate column
  4. Remove any obvious outliers that represent data errors (e.g., $1,000,000 transaction)

Issue 3: Too Few Transactions per Employee

Cause: Not enough transactions per employee for a reliable comparison; a small count makes averages and rates swing widely.

Solution:

  1. Extend your date range to include more data
  2. Exclude very new employees who haven't completed enough transactions
  3. Combine data from multiple periods if analyzing seasonal workers

Issue 4: Inconsistent Employee Names

Cause: Name variations in Square system or data entry errors.

Solution:

  1. Use find/replace in Excel or Google Sheets to standardize names
  2. Update employee names in Square to ensure consistency going forward
  3. Create a mapping table for name variations before uploading

Issue 5: Results Don't Match Square Dashboard

Cause: Different date ranges, filters, or calculation methods.

Solution:

  1. Verify exact date ranges match between Square export and analysis parameters
  2. Check that filters (location, transaction type) are identical
  3. Confirm whether Square includes or excludes tips, taxes, refunds in calculations
  4. Review how averages are calculated (per transaction vs. per day vs. per hour)

Issue 6: Can't Export Team Performance Report

Cause: Permission restrictions or report not available in your Square plan.

Solution:

  1. Verify your team member permissions include viewing and exporting reports
  2. Export from Orders & payments → Transactions and filter by team member instead
  3. Contact Square support to confirm what your account can export

Still Having Issues?

If you continue experiencing problems:

Conclusion

Staff performance analysis transforms raw Square transaction data into actionable insights about your team's effectiveness. By following this step-by-step tutorial, you now have the tools to:

Remember that effective staff performance analysis is an ongoing process, not a one-time exercise. Schedule regular monthly analyses to spot trends early, celebrate improvements, and address emerging issues before they impact your bottom line.

Treat the numbers as questions to ask, not verdicts on people: a gap in net sales, average transaction or refunds says where to look, and the shifts, product mix and refund reasons say whether it matters.

Explore more: Square Analytics: all tools, tutorials, and guides →

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