RealPlus LLC
How a support team replaced guesswork with precision-targeted CSAT surveys in Zendesk, turning sporadic feedback into reliable service quality data.
Industry: Financial and Real Estate Technology
Client Profile: RealPlus LLC provides technology solutions and managed services for clients in the financial and real estate sectors. Their support team handles a steady volume of tickets across email, chat, and phone.
Key Results
- Automated CSAT surveys with precision targeting across all qualifying interactions
- Response rates above industry median (vs. typical 5–15%)
- Zero-to-baseline — first structured feedback program ever implemented
About the Client
RealPlus LLC provides technology solutions and managed services for clients in the financial and real estate sectors. Their support team handles a steady volume of tickets across email, chat, and phone, covering everything from platform configuration to time-sensitive operational issues. In their space, client retention depends heavily on how support interactions feel, but leadership had no structured way to measure whether customers were actually satisfied with the help they received.
Client Voice
Automated CSAT surveys with smart targeting replace guesswork with real numbers. No survey fatigue, no wasted sends, just usable feedback from every qualifying interaction.
Making support decisions without satisfaction data?
The Challenge
Feedback came in randomly, if it came in at all. A customer might reply to a closed ticket with "thanks, that helped" or escalate to a manager when something went wrong. But there was no consistent mechanism to ask every qualifying customer the same question at the same time in the same way.
Decision-Making Without Data
Without structured data, leadership made decisions based on gut feel. Which agents were performing well? Which issue types generated the most frustration? Were response times actually correlating with satisfaction? Nobody knew. The data simply didn't exist.
Failed Manual Attempts
Agents occasionally sent follow-up emails asking for ratings, but compliance was low and the results weren't comparable across the team.
The Targeting Problem
The company needed automated surveys that triggered at the right moment, asked the right question, and only went to the right people. Sending a satisfaction survey on a ticket with only internal notes would confuse the recipient. Sending one on an already-closed ticket would feel stale. Surveying a customer who'd already been surveyed on the same ticket would be annoying. Each of these scenarios had happened during the manual process.
The Solution
The team configured automated CSAT surveys in Zendesk with a four-condition qualifying filter. A survey only fires when all four conditions are true:
- The ticket is solved but not yet closed, so the interaction is still fresh
- At least 24 hours have passed since the ticket was solved, giving the customer time to confirm the fix actually worked
- No satisfaction survey has already been offered on the same ticket, preventing duplicate sends
- The ticket contains at least one public comment, meaning there was an actual customer-facing exchange to rate
Brand-Matched Experience
The survey experience was customized to match RealPlus's brand. Colors, logo placement, and messaging all reflect the company's visual identity rather than Zendesk's default look.
Self-Service Documentation
A documentation package delivered alongside the configuration details every trigger condition, automation rule, and management procedure. RealPlus can adjust survey timing, modify conditions, or troubleshoot delivery issues without external help.
The Results
By the Numbers
- Zero-to-baseline — first structured CSAT measurement program ever implemented
- Response rates above industry median vs. typical 5–15% benchmarks, driven by precision targeting
- 4-condition qualifying filter ensures only genuine support interactions are surveyed
Operational Impact
Before the automation, RealPlus had no baseline CSAT data. None. Now they have a consistent measurement running across every qualifying support interaction. By filtering out tickets with no public interaction, already-surveyed tickets, and tickets closed before the customer could respond, the surveys reach only customers who had a genuine support experience worth rating.
With surveys running consistently month over month, leadership can now track which ticket categories correlate with lower satisfaction, whether specific shifts produce different outcomes, and how individual agents compare on satisfaction scores. The 24-hour delay ensures ratings reflect the actual outcome, not just the speed of response — a distinction that matters when using the data for coaching and process improvement.
Automated CSAT surveys with smart targeting replace guesswork with real numbers. No survey fatigue, no wasted sends, just usable feedback from every qualifying interaction.
Making support decisions without satisfaction data?