Install Customer Retention Rate % Explained:
Turn One-Time Jobs into Recurring Revenue

How to Build Install Customer Retention Rate % in Excel

1. Filter the dataset to keep only install customers

• Start with the core columns needed for the analysis:
  • Service date
  • Customer ID
  • Install vs. service type
  • Revenue
• Use the FILTER function to pull only the rows where the job type is Install.
• Keep the install date and customer ID together so each install event can be tracked later.
• Paste the results as values so you can build the rest of the analysis on a static table.

2. Create the install event table

• Build a new table that contains:
  • Install date
  • Customer ID
  • Future service flag
  • Future service revenue
  • Month cohort
• This becomes the foundation for the KPI because it isolates every install customer and the date they were installed.

3. Check whether each install customer came back later

• Use COUNTIFS to test whether the same customer ID appears again on a later date.
• The criteria should check:
  • Same customer ID
  • Event date greater than the install date
• A result greater than zero means the customer returned after the install.
• A result of zero means there was no future service activity in the dataset.

4. Calculate the post-install revenue for each customer

• Use SUMIFS to total the revenue generated after the install date for each customer.
• Use the same logic as the prior step:
  • Same customer ID
  • Event date after the install date
• This gives you the total service revenue created after the original install.
• Customers with no future visits will show zero.

5. Assign each install to a monthly cohort

• Use EOMONTH to convert each install date into the end of its month.
• This groups install customers into monthly cohorts such as January 2024, February 2024, and so on.
• Cohorts make it much easier to compare retention patterns over time.

6. Build the monthly cohort summary table

• Create a unique list of the cohort months using UNIQUE.
• If needed, wrap it with SORT so the months appear in chronological order.
• Add summary columns for:
  • New installs
  • Repeat customers
  • Service revenue
  • Retention rate
• This table will power the chart and the final KPI view.

7. Count new installs by cohort

• Use COUNTIFS to count how many install customers belong to each monthly cohort.
• Because the install-only table was already filtered earlier, this count represents the number of new installs in that month.
• This gives you the denominator for the retention calculation.

8. Count how many install customers came back

• Use COUNTIFS again to count how many rows in each cohort have a future service value greater than zero.
• These are the customers who returned for maintenance, repair, or another service after the install.
• This gives you the numerator for the retention calculation.

9. Sum the service revenue generated after the install

• Use SUMIFS to add up the post-install revenue for all customers in each monthly cohort.
• This shows how much service revenue each install cohort produced after the original install.
• It adds an important financial layer to the KPI, not just a customer count.

10. Calculate the retention rate

• Divide repeat customers by new installs for each cohort.
• Format the result as a percentage.
• This creates the monthly Install Customer Retention Rate % view.
• In newer cohorts, retention will often look lower simply because those customers have had less time to come back.

11. Create the cohort chart

• Highlight the cohort month column along with:
  • Service revenue
  • Retention rate
• Insert a Combo Chart.
• Put revenue on the primary axis and retention rate on the secondary axis.
• This makes it easy to compare how customer retention and post-install revenue move together over time.

12. Limit the chart to mature cohorts

• Exclude the newest months if customers have not had enough time to return yet.
• This prevents the chart from showing an artificial drop in retention simply because the cohort is too recent.
• Focus on the months that have had enough time to produce follow-up service activity.

13. Roll the analysis up to the yearly level

• Use the TEXT function to extract the year from each monthly cohort.
• Create a unique list of years using UNIQUE.
• Then use SUMIFS to roll up:
  • New installs
  • Repeat customers
  • Service revenue
• Recalculate retention at the yearly level to create a higher-level summary.

14. Interpret the final output

• Older cohorts should generally show higher retention because customers have had more time to return.
• A drop in newer cohorts does not always mean poor performance. It may just mean the cohort is still too fresh.
• Comparing retention rate and service revenue together helps show whether installs are turning into long-term customer relationships.

15. Use the KPI with the rest of your customer success analysis

• Once this KPI is built, it can be compared with acquisition cost, service revenue, and gross margin.
• That helps answer a more strategic question:
  • Are installs creating loyal, profitable customers over time?
• This makes the KPI useful not only for customer success tracking, but also for marketing and growth decisions.