How Calendly enhances customer support with Conversation Intelligence

This customer story was originally published on Loris.ai and reflects the customer’s experience with its product offering. Following Loris.ai integration under the Contentsquare legal group, this customer story has been migrated to this site [Contentsquare.com] as part of our unified content experience.

Introduction to Calendly

Calendly is the leading online scheduling automation platform, used by millions of customers worldwide. The company offers a range of solutions from free individual accounts to enterprise-level services for sales and recruiting teams. Calendly works with Google, Office 365 and Outlook calendars and apps like Salesforce, Stripe, PayPal, Google Analytics, GoToMeeting and Zapier for a seamless user experience.

Calendly's customer support team, consisting of about 100 people, operates 100% remotely and provides 24/7 support to a global customer base. This makes it important to have a way to collect data to better understand customer issues and how satisfied they are with the product.

Jon Helin, VP of Customer Support at Calendly, emphasizes the importance of customer experience:

We take our customer experience very seriously. In particular, we are always focused on our trial customers. We want to make sure that they are having successful interactions, that we're helping get them enabled and seeing value in the product, so we can drive their success and help them move into paid plans.

Looking to improve customer support with AI

AI was a natural next step for Calendly. As the company grew, their Customer Support team started an extensive six-month search and evaluation process for solutions that could satisfy a few of their needs, including quality management, live agent assistance, and overall customer insight. They knew they needed to replace their existing Quality Assurance (QA) tool, MaestroQA, due to its lack of AI capabilities and complex configuration process. In the consideration set, Calendly examined Conversation Intelligence powered by Loris as well as some of the top AI-powered conversation intelligence and Quality Assurance platforms. The latter group was either too expensive, didn’t inspire confidence as a complete product, required too much model maintenance, or lacked a true agent assist feature.

Calendly saw the potential that Conversation Intelligence powered by Loris had to not only satisfy their immediate need for an agent assist tool, but also expand to Quality Assurance and Voice of the Customer (VOC) insights in the future. Jon Helin explains the decision:

I had been searching for AI tools to utilize on the support team, particularly to drive more efficiency with the agents and consistency in the delivery of the service. The CoPilot tool was one of the few where I just had an immediate aha moment. I saw how that would reduce handle time, how it could help the agents get the correct information they needed to solve the issues faster, and how it could help us onboard agents quicker in training.

The implementation process was swift and efficient. Calendly started with a small test group for the first few weeks in order to test workflows and make iterative changes. They then expanded to all support agents within two months.

Initial success with AI in customer service

The first product Calendly started with was CoPilot, a live agent assist tool that guides customer support representatives to provide the best responses to customer issues in less time. This was an effective test case, as it provided a quick win and clear success metrics. Calendly saw significant improvements in efficiency and consistency:

• Reduced average handle time (AHT) by three minutes. Across 35,000 support cases per month, this time savings is substantial.

• Improved onboarding for new hires for faster ramp time

• Enhanced consistency in customer communications

We have actually shaved a little over three minutes of AHT [Average Handle Time] now that we don’t have the agent tag tickets anymore. That has reduced our cost per case by 23%.

Jon Helin

VP of Customer Support

Expanding the role of AI to Quality Assurance

One of the reasons Calendly chose Conversation Intelligence powered by Loris was the fact that it provides one AI platform for all of their customer service quality and intelligence needs. Quality Assurance (QA) enabled Calendly to utilize an AI-powered replacement to their existing QA tool. This gave them both a more efficient process as well as the ability to use data to focus QA analysts on the right conversations instead of randomly selected ones. As a result, QA enabled Calendly to:

• Move from a checklist-based QA to focus on more subjective, value-driven conversations

• Utilize AI-driven conversational quality scores and resolution indicators to understand conversations objectively and at scale

• Find more meaningful conversations for review and coaching using specific criteria instead of random selection

We were pleasantly surprised at how we were able to use QA to find more meaningful conversations to look at and coach the agents... We've moved from kind of a very checkboxy QA form to where now we are having more subjective conversations about how we are talking to customers and whether we're delivering the value in those conversations that we want to drive...

Jon Helin

VP of Customer Support

Getting greater understanding with Voice of Customer insights

As part of the platform, Calendly found significant value in Voice of the Customer (VOC) capabilities. Using the out-of-the-box library of AI models, Conversation Intelligence powered by Loris turns these customer service conversations into usable data revealing everything from the main issue driving the customer outreach and customer sentiment during the exchange to agent quality issues that warrant review, and dozens of other critical data points.

This gave Calendly a number of benefits, including the ability to:

• Accurately categorize and identify the root cause of customer issues

• Eliminate time spent on manual tagging of customer service conversations by agents by automating this process

• Save countless hours of manual data collection and analysis to build previous VOC reports

We very quickly switched all of our VOC reporting to only use Loris data...which has saved us countless hours of work and sort of dumpster diving into data to pull that data out of our CRM to just have it surfaced in the way it is in Loris... It's just more accurate to have the system doing it. The list of tags was long, so I think agents were frequently picking the one that was maybe not necessarily the right one, but the easy one. I think it also just gives everyone better confidence in the data that we're sharing back to the product team and the engineering teams about what's causing customer friction.

Summarizing conversations so agents don’t have to

In most organizations, customer service agents spend up to 20% of their time documenting and tagging conversations that they just had. While this is critical data to have, it also takes the agent away from their primary role: helping customers. Using Conversation Intelligence, Calendly now uses AI to automatically summarize conversations, removing tedious after call work and helping agents help more customers.

• Automatically summarize conversations for why the customer contacted, how the interaction went, and whether the issue was resolved

• Replaces more subjective manual summaries with consistent, AI-automated ones

• Saves every agent minutes on every call, adding up to major efficiencies

Loris continues to be one of the best examples for AI, making our lives easier and allowing us to focus on what really matters! Engaging in curious conversations with our users!

Customer Service Agent

Using data to improve all aspects of the customer journey

In addition to giving Calendly a more modern approach to agent assist, Quality Assurance and Voice of the Customer analytics, Calendly saw additional ways to use the platform. Using the Frequently Asked Questions (FAQs) Feature, they were able to get a single list of customer inquiries to see where and how they can resolve them ahead of contacting customer support. Calendly sees potential in using the FAQs feature to:

• Improve help center content, so customers can easily self-serve

• Enhance chatbot responses, so the bot can resolve more issues without human intervention

• Identify areas for customer support improvement and where agents can prepare for common questions and objections

• Highlight product issues both as a way to uncover unknown issues and gather feedback on how impactful an issue is

I was meeting with the Head of our Help Center this week and was walking her through the FAQs... This is something we need to look at because these are areas that the Help Center could be better at explaining to our customers, that our chatbot could be better at explaining to our customers, or that customer support could do a better job. And in some cases, it points at product problems that we have. Some of them we're aware of, some of them we're not. And it's really powerful in that way to be able to pull that up, look at the actual customer conversations, and share that with the product team. That has always been a big part of our Voice of the Customer program, has been to share direct feedback from the customers and not have to go searching for all of that data and just have it available to us in an easy to consume way has really been huge and really powerful for the team.

Finding emerging and product issues earlier

One of the built-in features within the Voice of the Customer dashboard is Emerging Contact Drivers. This model looks for an increase in new customer issues, which have not appeared previously. This means, even a relatively low number of new issues will be flagged, helping you get ahead of problems before they impact your customers and your customer service team. Helin mentioned one example of where this was impactful:

The first alert we ever got from Loris was that customers were asking about why our chat had disappeared. And we realized that the engineering team had done a release and had moved the chat link and it had confused a bunch of customers. ... That would have probably taken us a week to realize otherwise.

In addition to finding emerging issues, the ability to collect customer insights faster has given Calendly greater confidence in launching new products and features. This is because they know that they can get product feedback quickly, understand how to operationalize that feedback, and make iterative changes to improve a feature, change how agents respond to specific issues, or even rollback a product update if needed.

Just this week, we released some big UI (User Interface) changes in part of our product. And the entirety of Loris has just been a godsend during this whole process. So what we have seen is, first of all, Loris quickly identified which Contact Drivers had increased volume as a result of that change. We've been able to use the QA piece to then go back and look at how the agents are responding to those customers, identify areas where the customers are confused, where the agents are confused, and it's just allowed us to really dial in and identify areas we need to improve through this process.

Calendly has big plans for AI in the future

While Calendly has already seen success, they are looking forward to making even greater use of the tool – specifically, the ability to analyze chatbot conversations. Jon Helin expresses excitement about this development:

Our chatbot team in particular struggles having good qualitative data to look at to make improvements to that part of the, well, to our self-help in that way. And so I think they're very excited about that. And I'm very excited to see how quickly that'll allow us to iterate on that part of the business.

Jon Helin

VP of Customer Support

Conversation Intelligence powered by Loris has significantly enhanced Calendly's customer support operations, improving efficiency, consistency, and data-driven decision-making across multiple aspects of their customer experience.

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