First-Time Fix Rate Dashboard in Excel
(By Month + Technician)

Learn how to measure your team’s first-time fix rate so you can spot hidden inefficiencies like repeat visits, wasted truck time, and unhappy customers. In this lesson, you’ll build a clear month-by-month view, add a performance benchmark, and create a technician heat map that makes it easy to see who is meeting the standard and where coaching is needed.

Download the Excel file used in this tutorial:

Build First Time Fix Rate by Month and Technician

1. Prepare the Source Data as an Excel Table

  • Start with your exported job dataset (example: ServiceTitan export).
  • Convert the dataset into a table so formulas use structured references:
    • Select any cell in the dataset
    • Press Ctrl + T
  • This makes it easier to reference fields like Service Date, Visit Type, Job Status, Return Visit Flag, and Technician.

2. Create a Month Column from Service Date

  • Add a Month column to your dataset.
  • Use the TEXT function to convert Service Date into month name:
    • Use TEXT([@[Service Date]],”mmmm”)
  • This creates values like January, February, etc., used later in COUNTIFS.

3. Create the First Time Eligible Flag

  • Add a column that labels whether a job is eligible to be considered for first-time fix.
  • Build an IF statement using AND logic so it returns:
    • “Y” when the job meets all criteria
    • “N” otherwise
  • Criteria used in the video:
    • Visit Type equals Initial
    • Call Type equals Repair or Emergency
    • Job Status equals Completed
    • Return Visit Flag equals No

Result: you now have a simple Yes or No flag that filters out jobs that should not count in the KPI.

4. Create the Success Flag

  • Add a Success Flag column that returns:
    • 1 for success
    • 0 for not success
  • Logic used in the video:
    • If the Original Job ID indicates a secondary visit, it is not a first-time success
    • If the Return Visit Flag indicates a return visit, it is not a first-time success
    • If neither of those conditions are true, mark it as a success (1)

This converts the outcome into a clean binary field you can count.

  1. Build the Monthly Summary Table
  • On a new area of the sheet, create the month list:
    • Type “Month”
    • Enter January and drag down to December
  • Add these column headers:
    • First Time Fix
    • Required Return Visit
    • First Time Fix Rate
    • Benchmark

6. Count First Time Fix Jobs by Month

  • In the First Time Fix column, use COUNTIFS with three criteria:
    • Eligible Flag equals “Y”
    • Success Flag equals 1
    • Month equals the month in your summary row (January, February, etc.)

This gives the count of successful first-time fixes per month.

7. Count Required Return Visit Jobs by Month

  • Copy the prior COUNTIFS formula and change only one condition:
    • Success Flag equals 0 instead of 1

This gives the count of eligible jobs that required a return visit.

8. Calculate First Time Fix Rate Percentage

  • First Time Fix Rate equals:
    • First Time Fix divided by the sum of First Time Fix plus Required Return Visit
  • Format as a percentage:
    • Press Ctrl + Shift + 5
  • Drag the formula down to calculate all months.

9. Add a Dynamic Benchmark Column

  • Enter a benchmark value (example used: 95%).
  • Make it dynamic by referencing the benchmark cell above so the full benchmark column updates automatically.
  • This allows your benchmark line on the chart to move up or down when you change a single input.

10. Create the First Time Fix Rate by Month Line Chart

  • Highlight the First Time Fix Rate column and insert a line chart:
    • Insert → Recommended Charts or Insert → Line Chart
  • Add the benchmark line using a quick trick:
    • Copy the Benchmark column
    • Click the chart
    • Press Ctrl + V to paste it as a second series
  • Update the chart title to:
    • First Time Fix Rate by Month
  • Optional formatting shown:
    • Remove decimal places from labels
    • Make the benchmark line thinner and dashed
    • Adjust the Y-axis minimum if desired (example: start at 80%)

11. Build the Technician-by-Month Table for the Heat Map

  • Create a unique list of technicians:
    • Use UNIQUE() on the Technician column
    • Copy and paste values (Ctrl + C, then Ctrl + Shift + V)
  • Add month headers across the top (January through December), left to right.

12. Calculate First Time Fix Rate by Technician and Month

  • Use COUNTIFS to build the numerator:
    • Eligible Flag equals “Y”
    • Success Flag equals 1
    • Month equals the column header month
    • Technician equals the row technician name
  • For the denominator, you need both success and not success.
  • The trick used in the video:
    • Use a curly-brace array {0,1} inside COUNTIFS to return counts for both outcomes
    • Wrap that in SUM to total them into one denominator
  • Final structure:
    • Numerator count divided by SUM of denominator counts
  • Apply correct absolute and relative references so:
    • Month changes as you drag left to right
    • Technician changes as you drag top to bottom
    • The data ranges stay fixed

13. Prevent Divide-by-Zero Errors with IFERROR

  • When a technician has no jobs in a month, you will get divide-by-zero errors.
  • Wrap the formula with IFERROR:
    • Return blank instead of errors
  • This keeps the heat map clean and avoids clutter.

14. Add Conditional Formatting with a Threshold

  • Enter a benchmark threshold (example used: 75%).
  • Highlight the technician-month percentage grid.
  • Apply conditional formatting:
    • Home → Conditional Formatting → Highlight Cell Rules → Less Than
    • Select the benchmark cell
  • Result:
    • Any value below the benchmark is highlighted (red in the example).

15. Optional: Show “Benchmark: 75%” as Text Without Breaking the Number

  • If you want the benchmark cell to display text like “Benchmark: 75%” but still behave as a number:
    • Press Ctrl + 1
    • Go to Custom format
    • Add a custom format that includes the word Benchmark while keeping a numeric percent value

First-Time Fix Rate in Excel for Service Teams

Q1. What is first-time fix rate (FTFR)?
First-time fix rate is the percentage of jobs completed successfully on the first visit, without needing a return trip. It’s a key service KPI for improving efficiency, customer satisfaction, and profitability.

Q2. Why does first-time fix rate matter if revenue looks strong?
Strong revenue can hide problems like duplicate labor, repeat truck rolls, and callbacks. Tracking FTFR helps you uncover operational waste and protect your customer experience before bad reviews start showing up.

Q3. What will I be able to track after watching this video?
You’ll be able to track how many jobs were fixed on the first visit, how many required a return visit, and your overall FTFR percentage. You’ll also create a month-by-month chart and a technician view to compare performance across your team.

Q4. What’s the purpose of setting a benchmark?
A benchmark gives your team a clear target (for example 75% or 95%) so you can quickly identify when performance drops below the standard and take action faster.

Q5. How does the technician heat map help?
The heat map makes patterns obvious at a glance. You can quickly spot which technicians consistently meet the benchmark, which months were weaker, and where follow-up training or process changes will have the biggest impact.

Q6. What if there isn’t enough data for a technician in a month?
If a technician only has one or two jobs in a month, the percentage may be misleading. This video also calls out the importance of checking job volume so you’re making decisions based on enough data.

Q7. Can I use this same approach for other breakdowns besides technicians?
Yes. Once you understand the setup, you can analyze first-time fix rate by customer type, problem category, system type, or any other field you track in your service data.

Q8. Do I need ServiceTitan to use this?
No. The example uses a ServiceTitan-style export, but you can recreate the same structure from other software or manual tracking as long as you have the right job-level fields.

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Databoards

Analysis & Development