Listen Here

|
Subscribe

Actionable
Takeaways

Position CS as central architecture, not emergency response:

Mavi sees founders repeatedly make the same mistake—treating CS as something they can "wait on" until running it themselves becomes untenable. By that point, customers are already unhappy and the damage compounds. At both Wiz and Noma, founders positioned CS as central from the start, creating a competitive advantage in retention and expansion. The difference isn't philosophical—it shows up in metrics. Companies that build CS into their organizational architecture early treat it as the connective tissue between product, sales, and customers, driving mutual success rather than firefighting problems.

Build forcing functions for product-CS alignment with combined signals:

When Mavi encounters product teams dismissing customer requests, she doesn't just advocate—she creates a decision framework. She presents the top five priorities from CS and product side-by-side, backed by renewal dates, customer count, user impact, and revenue at risk. This combination of signals forces leadership to explicitly choose between competing priorities with full visibility into trade-offs. One company moved all five CS priorities up the roadmap after seeing the data. The key isn't having data—it's structuring it to make inaction impossible to justify.

Prioritize curiosity and customer obsession in CS hiring, then layer technical requirements:

While cybersecurity domain knowledge is now non-negotiable for Mavi's team at Noma, she structures her hiring criteria differently than most technical leaders. Curiosity, creativity, and genuine eagerness to help come first because attitude cannot be taught. Technical knowledge about risks, vulnerabilities, and security fundamentals can be developed, but the instinct to be customer-obsessed and think outside the box cannot. She needs people who can have credible conversations with CISOs and practitioners, but that credibility starts with caring deeply about solving their problems.

Deploy AI for signal aggregation and time compression, not customer interaction:

Mavi's team uses AI extensively—for call summaries, action item extraction, and real-time customer signal analysis—but never for customer-facing communication. The insight here is specific: AI helps CS teams move from reactive to proactive by surfacing patterns and risks before they escalate. Customers still want to hear solutions from humans, even when AI could technically provide the answer. The ROI comes from CS teams spending their time on strategic guidance and relationship-building instead of administrative work, enabling one CSM to effectively support more customers without degrading the experience.

Evaluate tools on vendor execution and gap-filling, not market position:

When assessing CS platforms, Mavi explicitly prefers newer AI-native tools over established vendors with AI features bolted on, and her reasoning is tactical. New vendors are hungrier—they work harder, respond faster, and deliver better customer experience because they're still earning market position. More importantly, they built their products specifically to address gaps in existing tools rather than retrofitting old architectures. For budget-conscious early-stage companies, these vendors often provide better value and partnership intensity. The evaluation framework is simple: does it deliver the outcome you need, and will the vendor treat you like their success depends on yours?

Engineer cross-functional transparency to enable CS leverage:

The scenario Mavi describes is specific and fixable: a customer complains to CS about an issue, CS investigates and escalates, only to discover product and engineering already knew about it. This makes CS look uninformed and breaks customer trust. Her solution is systematic transparency—CS must have real-time visibility into known issues, upcoming changes, and technical challenges across the organization. This isn't about nice-to-have communication; it's about enabling CS to be proactive, set accurate expectations, and maintain credibility while supporting multiple customers per CSM. Without this transparency infrastructure, scaling CS becomes impossible.

Conversation
Highlights

 

When Customer Success Becomes a Revenue Driver: Lessons from Wiz’s Hypergrowth

Most founders delay hiring customer success leadership, convincing themselves they can handle it until the workload becomes unmanageable. By then, customers are already unhappy and retention metrics are deteriorating.

Mavi Grizer watched this pattern destroy companies throughout her career. In a recent episode of The CX Front Lines, the VP of Customer Success at Noma Security shared how she built the CS organization at Wiz during its hypergrowth phase—and why the companies that position CS as central from day one outperform those that treat it as an afterthought.

Building CS as Infrastructure at Wiz

Mavi spent three years building customer success at Wiz, calling it “the university of life for me” when it comes to CS leadership. But the foundation was already in place before she arrived. She knew the founders from Microsoft, and they had already decided CS would be structural, not supplemental.

“They looked at customer success as a main function in the org, as a central of the organization, and they were all in for, let’s build this as a really, really centric function,” she explains.

This positioning changed everything. “The most important thing is that CS is not a support tool. It’s a business driver,” Mavi says. “It helps drive everything from sales, revenue, customer satisfaction, everything is baked into that CS function.”

The same architectural decision drives Noma Security today. “The founders are also looking at customer success as a center of the organization. They’re very much customer obsessed,” Mavi notes. This isn’t coincidental—it’s the pattern she’s seen determine which companies scale successfully and which ones fight retention fires indefinitely.

The Data Framework That Forces Product Alignment

Getting product teams to prioritize customer feedback remains one of CS leadership’s hardest challenges. Mavi has encountered dismissive responses from leadership: “Oh, he’s our customer for the next five years. That’s fine. They will not go anywhere” or “That specific feature request is not really interesting. I don’t think we should invest it.”

Her counter-strategy combines multiple data signals into forcing functions that make misalignment impossible to justify. She doesn’t just advocate for features—she quantifies business impact across renewal dates, customer count, user impact, and revenue exposure.

“I just put on this huge table and saying, okay, these are our top five features that we see at CS. These are the top five features that you see in product. They don’t align. How do we work this out together?” The framework creates explicit trade-offs. “I backed everything with data and eventually they took all five and moved them up because they understood the value.”

The insight isn’t that data matters—every CS leader knows that. The tactical approach is creating side-by-side comparisons that force product leadership to explicitly choose between their priorities and CS priorities with full visibility into consequences. “I’m not just coming and say oh, today I woke up this morning with a very nice feature request,” she emphasizes.

Scaling CS Through Systematic Transparency

At Noma, Mavi currently has one senior technical account manager and is hiring two more as the customer base expands rapidly. The constraint is real: “You don’t hire a CS for each customer you get or for even for two or four.”

Her scaling approach depends on cross-functional transparency infrastructure. The failure mode she’s obsessed with preventing: “There is nothing worse than a CX person talks to a customer. The customer complains about something. They say, oh, I’ll check it. And then he goes back and they say, oh, yeah, yeah, we’re already aware of that. It makes them feel so stupid.”

This scenario destroys customer trust and makes CS look incompetent. The solution isn’t heroic individual effort—it’s systematic communication. “You need to make sure that every function in the org has an open communication and transparency with the CX team,” Mavi insists. CS needs real-time visibility into known issues, upcoming releases, and technical challenges across engineering, product, and operations.

This transparency infrastructure is what enables one CSM to support multiple customers effectively. When CS has complete context, they can set accurate expectations, surface issues proactively, and maintain the trusted advisor credibility that drives retention and expansion.

Deploying AI for Leverage, Not Replacement

AI plays a significant role in Mavi’s scaling strategy, but not where most people assume. “We’re using a lot of AI, but for automations, for tasks, to help our customer success team save time. But we’re not replacing them with AI tools.”

The distinction matters. Her team uses AI for call recording summaries, action item extraction, and analyzing customer signals in real time. This shifts the team from reactive firefighting to proactive risk management—they can identify potential churn signals before they escalate into actual problems.

But customer-facing communication remains entirely human. “Nothing can replace the personal touch that customer needs,” Mavi says. “Even if the AI gives you the right answers, you’re like, I still want to hear them from a human.”

The ROI comes from time reallocation. When AI handles administrative tasks and signal aggregation, CSMs spend their time on strategic guidance and relationship-building—the high-value work that actually drives retention and expansion.

Evaluating Tools Based on Vendor Hunger

When assessing CS platforms and tooling, Mavi explicitly prefers newer vendors over established players. Her reasoning is tactical: “All the veteran tools, I would say they’re more expensive. The new tools that are trying to put their foot in the door and just at least get some customers.”

New vendors deliver better outcomes for early-stage companies: “They’re hungry, they will do more, they will work more, they will work harder. Their customer experience is a lot better because they already in the process of trying to make it big.”

More importantly, AI-native companies built their products to address specific gaps in legacy platforms rather than retrofitting old architectures. “They built this company, the new company, whichever, based on understanding gaps that other tools have right now.”

Her evaluation framework is simple: “When I look for a tool, a specific tool that will help me and my team, I don’t care about the company, I care about the outcomes.” The question isn’t pedigree—it’s whether the tool delivers results and whether the vendor will treat your success as critical to theirs.

Hiring for Curiosity Over Domain Knowledge

While cybersecurity expertise is non-negotiable at Noma—”understanding about cybersecurity is a must. You cannot really come to customers and not knowing anything about cybersecurity and then talk to decision makers or CISOs or even practitioners”—Mavi structures her hiring criteria differently than most technical leaders.

“The big qualities I look for is curiosity, creativity and a genuine eagerness to help. Because at the end of the day, tools is something that you can teach, but attitude you cannot,” she explains.

She needs CSMs who can have credible conversations with security practitioners, but that credibility starts with being obsessed with solving their problems. “I’m looking for these people that will be eager to help customer. They will be customer obsessed, they will have the right curiosity, they will have the right creativity to help customers to think out of the box.”

The technical bar is real—candidates need existing experience working with customers in cybersecurity. But technical knowledge without the right attitude doesn’t drive outcomes. “I can teach how much AI is dangerous and how much AI and security goes hand in hand. This I can teach, but not the understanding of risks and vulnerabilities and everything else.”

The Inflection Point Founders Miss

Throughout her career, Mavi has watched founders delay CS hiring until retention problems force their hand. “I’ve seen firsthand from a lot of founders that say, no, we don’t need that function right now, we can wait. And eventually they hired at the very last minute when customers were not happy.”

Her assessment is direct: “If a founder runs CX themselves, that’s a problem.” The function drives too much business value to remain a founder side project. “Make CX the heartbeat of your business. It’s not a side function, it’s not support, it’s a business driver, it’s a growth driver, it’s an engine.”

When founders finally hire, they should look for specific traits: “Someone who’s passionate, someone who is obsessed with customers, is not afraid to go big and have these crazy ideas.” The best CS leaders are empathetic, understand how to scale without proportional headcount, and have strong communication skills. “And I would say fire in their eyes. If you don’t see that fire? I don’t know.”

At both Wiz and Noma, founders positioned customer success as central from the founding stage. That architectural decision created compounding advantages in retention, expansion, product development, and market perception—advantages that become nearly impossible to replicate if you wait until customers are already churning.