Summary
RateMyAgent, a global digital marketing platform for real estate professionals, chose CloudTalk after struggling with unreliable phone systems and poor support. Using CloudTalk’s AI Conversation Intelligence, the team identified customer confusion after a product launch, updated help content and messaging, and reduced feature-related support calls by 66% in just three weeks — while also cutting chat and email queries by 25%.
About RateMyAgent
RateMyAgent is a leading digital marketing platform for real estate professionals. Based in Melbourne, Australia, the company helps agents grow their reputation and visibility through verified reviews, automated testimonials, and social proof campaigns. The platform supports real estate agents, agencies and mortgage brokers across Australia, New Zealand and the United States, while simultaneously helping consumers find the right agent or mortgage broker to work with.
We spoke with Madeline Moloney, Customer Experience Manager (Global) at RateMyAgent, about how her team uses CloudTalk to improve customer experience and reduce support volume.
With a globally distributed support and sales team of 35–40 people, the company needed a communication platform that could flex across time zones, integrate seamlessly with HubSpot, and provide clear insight into what was actually being said on customer calls.
Problem
Before CloudTalk, RateMyAgent had tested—and abandoned—a number of other tools: Aircall, Kixie, Dialpad, and even physical handsets. Said Madeline,
“It was a combination of dropped calls, long connection times, and support that just wasn’t available when we needed it.”
Even worse, when things went wrong, their providers couldn’t offer answers.
“We’d report an issue and get told everything looked fine on their end. Meanwhile, we had customers and agents frustrated on live calls with no fix.”
Madeline also struggled with clunky setups, devices not connecting, and random mic issues that required daily troubleshooting.
“I had people calling me in tears or saying they wanted to quit because their calls wouldn’t connect and it was stopping them from hitting their calls and sales targets. It was a full time job managing our staff across multiple timezones – just trying to make and receive calls effectively. It was exhausting.”
Evaluation
A Spreadsheet, a Certified Integration, and a Gut Feeling
Determined not to make the same mistake again, Madeline took a rigorous approach to vendor selection.
“I literally made a telephony comparison spreadsheet. If a solution didn’t tick all our boxes, I didn’t even want to talk to them.”
Her non-negotiables included:
- SMS support in AU & US
- Certified HubSpot integration
- Reliable call recording
- Mobile + desktop apps
- Local server infrastructure
- Flexible call routing (IVR, custom flows)
- AI capabilities and real-time analytics
- Dedicated support and account management during AU business hours
“I even rated things like AI as low, medium, or high capability. I wanted facts, not fluff.”
For Madeline, the promise of AI wasn’t about buzzwords, it was about real, usable insight.
“As a customer experience manager, I don’t care how many calls someone makes. I care what was said, and how the customer felt.”
From the start, she saw the potential of AI Conversation Intelligence not just for tracking call volume, but for coaching, quality control, and feedback loops.
Each week I filter for neutral or negative sentiment and review those calls with my team. What went wrong? Could we have explained something better? That’s where the value is.
A few months after onboarding, the team launched a new product feature, and almost immediately, the support queues lit up.
“We had a new feature released and we had people calling us and asking questions. We also had people chatting and emailing us asking for calls to go through specific issues or questions they were having.”
The support team had already published help articles and trained their AI chatbot to guide users, but something was missing.
“We had all of that done, but we were still finding different bits of questions. And we wanted to know how we could update that without going through all of the conversations that we’d had with customers over chat and email and phone calls.”
Solution
From Analytics to Action: Fixing Docs and Clarifying Messaging
To avoid sifting through dozens of transcripts manually, Maddy turned to CloudTalk’s Conversation Intelligence tools. She filtered for calls between the feature’s release date and the following three weeks, and used call summaries, topic trends, and sentiment analysis to pinpoint exactly what customers were confused about—and how often it was happening.
“I didn’t have to go through the whole transcript of each call. I could quickly glance at the summary of the call—but more so even just the questions that were asked. Which was really easy for me to do, and it saved me hours.”
She focused on topics with neutral or negative sentiment to isolate areas of friction and gathered a clean list of high-frequency questions.
With the insights in hand, Madeline took two immediate steps:
- Expanded the help center with new Q&As tailored to the exact language customers were using
- Worked with the marketing team to clarify email messaging that had caused unnecessary confusion
“There were two key outcomes there that we were able to get from AI analytics: improving our help docs and tightening the messaging in our emails. Both came directly from what customers were saying on the phone.”
Results
66% Reduction in Support Calls in Just 3 Weeks
By comparing the three-week period after implementing the changes to the three weeks prior, Madeline was able to clearly measure the impact:
- 66% reduction in support calls about the feature
- 25% drop in chat and email queries on the same topics
- 4 hours of support time saved in just three weeks
- Improved email clarity and fewer follow-ups
- More targeted internal coaching based on real call data
“We were able to find that we reduced the number of calls with those topics in them with our support team by 66%.”
Takeaway
Long-Term Gains, Powered by Customer Insight
Although Madeline plans to continue tracking results over time, the early success has already proven the value of acting on real-time insights from customer calls.
“If customers can self-serve more effectively, we’ll continue to see long-term impact. But even in the short term, this was such an efficient way to surface key issues early and use real call data to quickly improve the experience.”
