Pioneer innovator spotlight: How Synthesia managed a 690% spike in customer contact without increasing headcount using AI and automation
We spoke with Constantina Samara, Head of Support at Synthesia, about the impact of AI and automation on scaling support in a cost-effective way, changing customer attitudes towards these technologies in customer service, and lessons learned from rolling out AI.
In just four months, the number of Synthesia’s customers seeking support on a monthly basis rose from 40,000 to 316,000 – a 690% increase. To meet this level of demand without AI and automation, the team would have needed to grow to 150 people, but with Intercom, they were able to swiftly tackle the spike without increasing headcount, all while reducing resolution time by 96% and maintaining high customer satisfaction.
Let’s take a closer look at how they did it.
Can you tell us a bit about Synthesia, your role, and how you came to be a customer service leader?
Synthesia is an AI video generation platform that enables our customers to create studio-quality videos with AI avatars and voiceovers in over 120 languages. My role is Head of Support, and my path to becoming a leader in this space actually happened sort of unexpectedly. I have a background in psychology and an interest in human behavior, so when I found myself in a customer service role, I wanted to apply my background to analyzing interactions with customers and teammates and understanding what makes people respond in certain ways to different situations.
“I’m really passionate about service being the best service”
I’m really passionate about service being the best service, and by understanding the behaviors of your customers and your team, you can use those insights to create the best possible experiences for them.
What motivated you to implement AI with Intercom?
We’re a fast-growing business, and naturally, as our customer base scales, our support volume increases alongside it. We were seeing our customer contact rate rise by anywhere from 20-30% month over month, which was becoming challenging to manage with the number of people we had on the team.
We knew we needed to leverage AI and automation to help us, and that Intercom had the tools to help us do that.
Did you face any challenges during the implementation? How did you go about solving them?
We encountered two main obstacles that we needed to overcome:
1. Preparing our knowledge base
We started testing Fin AI Agent as soon as it was in beta, and that’s when we had the hard realization that our knowledge base was really not fit for purpose. The responses we were getting back were kind of all over the place – Fin was contradicting itself because our knowledge base was clearly contradicting itself. So I’d say the biggest challenge for us was having to almost redo our knowledge base. Of course, we had a bit of a starting point, but it was a big piece of work.
In the early stages of optimizing our help content, we joined Fin in every second customer conversation to make sure we were getting it right. That was challenging at the time, but really beneficial and has helped hugely in the long run. We can now be sure that Fin has what it needs, and that our knowledge base is up to date and accurate. So even for customers who navigate to the help center and don’t open a conversation with Fin, they now have much better information available to them too.
“The return on investment you see when you’re successful with [Fin] far outweighs the cost of having to introduce a role or two to make it a success in the first place”
To help us revamp our knowledge base and get it ready for Fin, we needed to hire people to manage that work. That’s obviously an extra cost, and something I’d imagine many businesses are having to weigh up right now, but the return on investment you see when you’re successful with it far outweighs the cost of having to introduce a role or two to make it a success in the first place.
2. Getting buy-in from the support team
The second challenge we faced was implementing it in a way that the support team was on board with. And that ultimately came down to how we positioned the rollout.
Something I hear a lot in the customer service space – and that we encountered ourselves – is that there’s a fear around AI taking jobs and meaning support teams won’t be required anymore. So we wanted to offer our team reassurance that we were bringing in AI to alleviate pressure and enable them to be more satisfied and fulfilled in their roles, rather than answering refund questions over and over, every single day.
It’s a really fine balance between introducing automation and maintaining team engagement. Because automation can be great, but if you’ve lost engagement with your team and they don’t have the same passion and energy to provide the same level of service that they did before automation, you’ve lost human support.
Once the team actually started using AI, they were able to truly realize its impact and the opportunities it created for their roles. They suddenly had more time to do investigative work and actually learn and grow, whereas before they were just doing those repetitive tasks. Without us even going out to the team and trying to gather that feedback, they were coming to us and saying, “We haven’t seen questions about X for a very long time,” and we were like “Yeah, because Fin resolved 1,000 of them.” That was a big milestone moment where the benefits came to the forefront for the team – that they were able to deal with meaningful conversations on a daily basis.
Fast forward to now and we’ve never seen so much engagement in support. If anything, Fin has now increased appetite on the team to introduce as much AI and automation as possible.
What level of impact have AI and automation had on your support operations? Any highlights or metrics you could share?
The ability to manage our rising customer contact rate was definitely a highlight for us. Like I mentioned, we were seeing a 20-30% increase in customers seeking support month over month, but between April and August of 2024 alone, we saw an increase of 690%. Instead of 40,000 customers seeking support each month, we were suddenly seeing 316,000.
“Even if we continue to see a big increase each month, I don’t anticipate us having to increase our team headcount for a significant amount of time”
Without the level of automation we have with Fin and Intercom, I’d have needed a team of 150 people to manage that. But with their tools, we were able to handle the spike without having to grow our team to meet the demand. In fact, of the 316,000 customers seeking support in August, 98.3% were able to resolve their query through self-serve support, which meant only 1.7% needed help from our agents. And even if we continue to see a big increase each month, I don’t anticipate us having to increase our team headcount for a significant amount of time. I think that showcases the gravity of the benefits that we’re getting from Fin AI Agent and Intercom’s other automation features.
Outside of that, we’re also seeing results in other areas, like:
- CSAT: Our human CSAT is consistently high, currently sitting at 93%. And since implementing Fin, our Fin CSAT has actually doubled. One thing that I keep finding across different industries and people I speak to is that there’s a fear of frustrating customers and decreasing customer satisfaction by introducing AI and automation. I personally think it’s really important to bust that myth and let people know that’s actually not the case. We’ve got some really good stats that can evidence that. You just need to invest the time in setting it properly.
- Fin AI Agent answer rate: Our Fin answer rate is anywhere up to 98%, which in my opinion is really good. That means that in nearly all of the conversations it’s involved in, it’s able to understand and provide an answer to a customer’s question.
- Fin AI Agent resolution rate: Right now our resolution rate with Fin is 55%, which frees up a lot of time for our team. Our goal is to get that number up to 80% in a controlled manner, so that’s a big area of focus for us in the coming months.
- Resolution time: Since launching Fin, our resolution time has gone from five days and five hours to four hours and 37 minutes – a 96% decrease.
Do you think AI has changed customer behavior at all? If so, how?
I’ve noticed a remarkable change in customers when it comes to Fin, specifically. Automation is not new to support, but more often than not, you’d find that customers would greet any level of automation with dissatisfaction straight away and seek human support. And I think that was down to lack of intelligence behind those automations in the past, where it was always a tick box activity of sending something out to the customer that wasn’t really relevant or didn’t cover what they were trying to achieve. Whereas with Fin, the change in customer behavior I’m seeing is that they’re a lot more receptive.
“That level of intelligence that sits behind [Fin] has really changed the dynamics with customers and automation:
I’ve got so many examples of conversations where customers are thanking Fin for giving them the right response. So that level of intelligence that sits behind it has really changed the dynamics with customers and automation.
What does the next chapter of AI-first customer service look like at Synthesia?
AI isn’t a “turn it on and let it work its magic” kind of technology. It requires maintenance and optimization to make sure it’s successful. So we’ll continue to enhance our knowledge base and train Fin to give the best possible answers to our customers, and part of that will be identifying our outlier questions to further expand Fin’s coverage rate across our support volume.
And now that we have more time freed up on the team, we’re exploring what “premium support” could look like and how we can offer support as a service. There’s no way we would be able to do that if we didn’t have the level of automation we have with Intercom and Fin and if it wasn’t successful.
I’m also really excited for the next wave of Intercom’s AI features. We’ve gotten a preview of what’s in the works and I think those new features will help us achieve our goal of reaching 80% resolution rate and continue to scale our support in a way that protects our team and is cost-effective. For example, Fin being able to take actions and read data in the background, or being able to customize its tone of voice. These will completely change how we interact and support customers, and being able to customize the tone of voice
What advice would you give to other customer service leaders embarking on this journey based on your own experience? Any lessons learned?
One lesson I’ve learned is to involve your teams in the process from day one. Let them know what it is that you’re trying to achieve and make them part of the objective. Tell them what the problem is and have them be part of how you scope this. Because nine times out of 10 that enables them to get bought into what you’re trying to do. And not only that, but it also gives them the chance to highlight challenges and issues your customers and support functions are facing that you’re not necessarily aware of.
So even though we’re seeing record levels of engagement in support now, had I brought the team in prior to going live and made them part of the implementation process, I think we would have seen more engagement from the outset. That was a big lesson for me.
