Reviews Are a Data Problem
Most local service businesses know they should ask for reviews. Few do it systematically. The ones who do it successfully have one thing in common: their customer data is good enough to support timely, personalized outreach.
"Timely" means within 24–48 hours of a completed job. "Personalized" means addressing the customer by name and referencing the specific service. Both require accurate data.
The Review Request Data Requirements
To send an effective review request, you need:
- Customer's first name (for personalization)
- Email address and/or mobile phone number (for delivery)
- Completed job date (for timing)
- Service type (for personalizing the message: "Thanks for letting us handle your spring landscaping…")
If any of these are missing or wrong, your review request either doesn't send, sends impersonally, or sends too late to be effective.
Auditing Your Review Request Data Quality
Export the last 90 days of completed jobs. For each job, check:
See exactly what's wrong with your data — try Sohovi free — try Sohovi free.
- Does the customer record have a valid email or mobile number?
- Is the first name populated (not blank, not "Customer," not "Unknown")?
- Is the job date recorded (not blank or approximate)?
- Is the service type specified?
Calculate a completeness rate for each field. Anything below 85% is a meaningful gap in your review request pipeline.
Connecting Data Quality to Review Volume
The math is simple. If you complete 100 jobs per month and:
- 20 customers have no email or phone → 20 review requests can't be sent
- 15 have no first name → 15 requests go out with "Hi there," reducing conversion
- 10 are sent 2 weeks after the job because the data wasn't entered in time → 10 requests miss the optimal window
That's potentially 45 lost review opportunities per month. Over a year: 540 fewer reviews than you could have.
Building the Data Discipline
Make data completeness part of job completion. Before a technician marks a job complete:
- Email or phone is confirmed (or updated) with the customer on-site
- First name is confirmed correct
- Service type is selected from a dropdown (not free text)
Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.
Many field service apps (Jobber, Housecall Pro) support a job completion checklist. Add data confirmation to it.
For review request timing, use an automated workflow that triggers when a job status changes to "Complete." Don't rely on someone remembering to send the request manually.
