Introduction
In the fast-paced world of B2B SaaS, delivering exceptional customer experiences while rapidly iterating on product can be a delicate balancing act. We had the pleasure of speaking with Andy Magnusson, a seasoned customer engineering leader with experience at companies like StrongDM and Gitpod. Our conversation explored how customer-facing technical teams can serve as a vital bridge between users and product development, the importance of maintaining tight feedback loops as organizations scale, and the potential role of AI and machine learning in enhancing customer support. Here are the key takeaways from our discussion:
Q: Can you give us an overview of your background and the role of customer engineering in a B2B SaaS context?
Andy: My background is in information security, but I've spent the last decade in customer-facing technical roles. At StrongDM, I joined as one of the first ten employees to build and lead their support function. As the company grew, my team’s role expanded to encompass a broader customer experience remit, including what you might think of as customer success work - building relationships with customers, deeply understanding their use cases, and figuring out how we could best support them.
The core mission of customer engineering, as I see it, is to be the voice of the customer within the organization while also being a technical expert who can help customers get the most value from the product. We're often the first line of defense in identifying and triaging issues, but we also play a crucial role in shaping the product roadmap by surfacing user needs and pain points.
Q: How do you approach gathering and actioning customer feedback, especially as an organization scales?
Andy: In the early days at StrongDM, our feedback loop was incredibly tight. I was handling most support inquiries myself, and our CEO was still reading every support email until we had a team of about eight people. That direct line of communication allowed us to act on feedback almost immediately.
As we grew, we had to build more formal processes to maintain that responsiveness. We implemented a system where any feature request submitted to the product team had to include two key pieces of information:
1. The specific technical details of what the customer was requesting
2. The business use case behind the request - why they needed it, what they'd tried already, any workarounds we'd suggested, etc.
Without both of those elements, Product wouldn't even consider the request. This ensured that we weren't just throwing random ideas over the wall, but providing context that allowed for proper prioritization.
We also maintained regular check-ins between customer-facing teams and Product to discuss priorities. While Product ultimately owned the roadmap, we had opportunities to advocate for specific customer needs, especially in cases where a feature could make or break an important renewal or expansion opportunity.
Q: It sounds like you had a very customer-centric culture at StrongDM. How did you maintain that as you scaled?
Andy: One of the key things was that leadership consistently reinforced the idea that everyone in the company was empowered to escalate customer issues or product concerns. It wasn't just lip service - they actively encouraged people to keep pushing until they were satisfied with the response or until the CEO herself said no.
For customer-facing teams, it was drilled into us that our primary responsibility was to make customers successful, and we should do whatever it took to accomplish that. That included advocating strongly for customer needs, even if it meant challenging decisions made by other teams.
This open communication was crucial because it kept everyone aligned on our priorities and ensured that customer voices were always being heard, even as we grew and added more layers to the organization.
Q: How do you see the role of AI and machine learning evolving in customer support and success?
Andy: I think we need to be cautious about viewing AI as a silver bullet, especially when it comes to customer-facing roles. There's a lot of hype around using large language models to replace support teams, but I don't see that as viable anytime soon. The stakes are too high in terms of potentially damaging customer relationships if the AI provides inaccurate information or fails to fully understand the nuances of a customer's situation.
That said, I do see potential for machine learning in areas like ticket classification and routing, identifying trends in customer feedback, and surfacing relevant knowledge base articles or past ticket resolutions. The key is to use these tools to augment and empower human support agents, not replace them outright.
One area where I think ML could be particularly valuable is in analyzing large volumes of customer feedback and support interactions to surface patterns and insights that might not be obvious to human reviewers. This could help product and support teams prioritize issues more effectively and identify emerging problems before they become widespread. Of course, this is only useful if you have a large enough feedback corpus—smaller or low-volume support teams will just have to do things the hard way!
Q: Looking ahead, what do you see as the biggest opportunities or challenges for customer-facing technical teams?
Andy: I think one of the ongoing challenges, especially for startups and fast-growing companies, is maintaining that tight feedback loop between customers, support, and product as the organization scales. It's easy to fall into silos where customer-facing teams feel disconnected from product decisions, or where product teams lose touch with the day-to-day realities of user experiences.
The companies that will excel are those that can build robust processes for capturing, analyzing, and acting on customer feedback without losing the sense of urgency and customer-centricity that often comes naturally in the early stages.
I also think there's a huge opportunity for customer engineering teams to play a more strategic role in product development. As products become more complex and customizable, having technical experts who deeply understand both the product capabilities and customer needs will be invaluable. These teams can help bridge the gap between what customers say they want and what they actually need, translating business requirements into technical specifications that product and engineering teams can act on.
Conclusion
Our conversation with Andy underscored the critical role that customer-facing technical teams play in driving product innovation and customer satisfaction. By maintaining tight feedback loops, empowering employees to advocate for customer needs, and leveraging data and technology judiciously, organizations can scale their customer experience efforts without losing the personal touch that builds lasting relationships.
Some key principles emerged that can benefit any B2B SaaS company looking to enhance their customer experience:
- Ensure customer feedback includes both specific technical requests and broader business context to aid in prioritization
- Foster a culture where everyone feels empowered to escalate customer concerns and challenge decisions
- Use AI and ML to augment human support capabilities, not replace them entirely
- Invest in processes and tools that allow you to analyze customer feedback at scale while maintaining a sense of urgency
- Position customer engineering teams as strategic partners in product development, not just tactical problem-solvers
As the B2B SaaS landscape continues to evolve, the companies that can most effectively translate customer needs into product innovations will be best positioned for long-term success. By bridging the gap between technical capabilities and business outcomes, customer engineering teams like those Andy has led are poised to play an increasingly vital role in driving that success.
P.S. Check out Andy's newsletter, Andy's Support Notes, here!
