Building a product-led growth engine isn't about adding features or running isolated experiments--it's about fundamentally changing how your product drives business outcomes. Lauren Schuman, who built Mailchimp's growth practice from scratch and later scaled it at MURAL, reveals hard-won lessons about experimentation, team building, and the organizational changes required to make product growth sustainable.
Mailchimp Growth Engine Results
183%
Increase in landing page usage with targeted homepage
95%
Adoption increase for new landing pages feature
4
Growth teams specializing in customer journey stages
From Instinct to System: Building Growth at Mailchimp
When Lauren Schuman joined Mailchimp as director of marketing optimization, product-led growth wasn't a formal discipline--it was more of an instinct. The company had growth happening organically, but no systematic approach to expanding it. Her role was ambiguous: a blurry set of problems with no clear team to solve them.
The first challenge was identifying where to focus. Mailchimp's leadership cared about feature adoption--the company had invested significant resources in building new capabilities, but users weren't discovering or adopting them at expected rates. Traditional marketing tactics were being used to drive feature engagement, with mixed results at best.
Lauren saw an opportunity to apply experimentation where it mattered most. Her approach started scrappy: with access to mailchimp.com and email marketing channels, she ran hypothesis-driven tests targeting existing users with specific feature announcements. The results spoke loudly--one experiment targeting the homepage alone drove a 95% increase in adoption of a brand new landing pages feature according to LogRocket's interview with Lauren Schuman.
This early win did more than improve metrics. It demonstrated the value of a systematic growth approach and helped executive leadership buy into the concept of a dedicated growth team. From one team focused on activation, Mailchimp evolved to multiple teams specializing in different stages of the customer journey--paid conversion, monetization, and retention.
The Key Insight: Work Backwards from Business Problems
Lauren's framework for building growth practices begins with working backwards. Rather than starting with what growth teams typically do, she identified the specific business problem Mailchimp needed to solve and built the capability around that. This meant starting with feature adoption because it directly tied to retention and revenue--the metrics leadership cared about. For organizations implementing AI-powered automation, this principle translates to starting with the business outcome you need to move and building your AI capabilities around that specific objective. Implementing systematic experimentation helps teams test hypotheses quickly and scale what works.
The 183% Experiment: Targeted Experiences for Existing Customers
One of Lauren's most impactful experiments at Mailchimp challenged a fundamental assumption: that the homepage experience should be the same for everyone. The original approach showed all visitors--new and existing users--the same "sign up for free" messaging. For customers already using Mailchimp, this created friction and missed opportunity.
The growth team's solution was elegantly simple: segment the homepage experience by user status. Existing customers saw targeted messaging about new features they hadn't tried, complete with deep links directly into the product. New visitors continued seeing signup messaging. The results were dramatic--a 183% increase in landing page usage among existing customers per LogRocket's coverage of Lauren's growth methodology.
This experiment illustrates several principles that apply directly to AI-powered automation:
First, targeting matters more than creative quality. The same feature announcement delivered to the right audience at the right moment dramatically outperformed generic messaging to everyone. For AI systems, this means personalization and segmentation capabilities are often more valuable than improvements to the underlying model. Strategic targeting through AI enables businesses to deliver personalized experiences at scale.
Second, deep linking reduces friction in conversion. By sending users directly to the relevant feature rather than making them navigate, the team eliminated drop-off points. In automation contexts, this translates to minimizing steps between trigger and action.
Third, measuring impact requires proper attribution. The team could only demonstrate the 183% improvement because they had tracking in place to compare behavior between segmented and non-segmented experiences. For any growth or automation initiative, instrumentation must be built before the experiment runs.
The broader lesson is that growth opportunities often exist in the gap between what users need and what you're currently delivering. Existing customers represent an underserved segment in many products--focusing on their needs can yield outsized returns compared to continuously optimizing for new user acquisition.
The Email Editor Rebuild: Infrastructure as Growth Enabler
When Lauren noticed engagement with Mailchimp's core email editor was stalling, the team tried incremental improvements. But after a decade of layering features onto an aging foundation, the editor had accumulated what she described as technical debt that limited further optimization.
The breakthrough came from a bold decision: rebuild the editor from scratch. But Lauren knew this wasn't just a technical upgrade--it was a strategic investment that would enable future growth experiments. To build organizational support, she took an unconventional approach. She brought executives into a room and had them watch real users struggle with the old editor. As she put it, "It was like nails on a chalkboard. They were all like, this is bad, this is really bad. And I was like, I know, I've been telling you."
This moment broke through organizational inertia. The new editor wasn't just cleaner--it unlocked capabilities that made growth experiments easier to run and more effective. Use-case-based flows, contextual templates, and more personalized onboarding experiences all became possible because the foundation supported them.
For AI and automation implementations, this story offers a crucial lesson: infrastructure investments enable downstream growth opportunities. Before you can run sophisticated personalization experiments, you need a flexible foundation that supports rapid iteration. The 183% homepage improvement depended on having the segmentation and targeting infrastructure in place. Partnering with experienced AI development teams helps organizations build the technical foundation needed for scalable growth initiatives.
The ROI question is important here. Rebuilding a core product component is expensive and risky. Lauren's approach--making the cost of inaction visible through direct observation--helped stakeholders understand why the investment was necessary. The 95% adoption improvements and 183% engagement spikes that followed validated the investment. This pattern holds for AI implementations where building robust data pipelines and integration infrastructure often precedes measurable business impact.
When PLG Fails: The Sales-Led Organization Trap
After building a successful growth engine at Mailchimp, Lauren joined another company to replicate the model. The motion didn't stick--not because her approach was wrong, but because the organization fundamentally conflicted with PLG principles.
"It was a sales-led company that wanted PLG but didn't really understand what it would take to support it," she explained. "The hardest part was getting alignment on definitions. What is a product-qualified lead? What are the stages of the journey? Nobody agreed" as reported in LogRocket's interview.
She built detailed documentation, onboarding flows, and tracking plans. But without support from go-to-market partners, none of it moved the needle. "We didn't have alignment from marketing, sales, or customer success. It was all uphill."
The failure wasn't in the growth methodology--it was in the organizational prerequisites. PLG isn't a feature or a funnel. It's a company-wide motion that requires coordinated changes across functions. If marketing, sales, and customer success aren't aligned on what PLG means and how it supports their objectives, the effort will stall regardless of how well-designed the growth experiments are.
This has profound implications for AI and automation initiatives. When implementing AI-powered features, you need cross-functional alignment on what success looks like, how leads and opportunities are defined, and how automated touchpoints integrate with human-led ones. Without this alignment, even technically excellent implementations will fail to deliver expected business outcomes. Successful AI transformation requires buy-in across marketing, sales, and operations teams.
The lesson for practitioners is to assess organizational readiness before launching sophisticated PLG or AI initiatives. If sales and customer success teams aren't bought into how automated engagement affects their workflows, they may actively or passively resist. Building that alignment requires explicit work--it doesn't happen automatically.
Systematic Experimentation
Move from isolated tests to a structured experimentation framework that builds organizational learning over time.
Targeted User Segmentation
Deliver different experiences based on user status and behavior to maximize relevance and conversion.
Infrastructure Investment
Build flexible foundations that enable rapid iteration rather than incremental improvements on limiting systems.
Cross-Functional Alignment
Coordinate marketing, sales, and product around shared definitions and objectives for sustainable growth.
The Growth Team Framework: Hiring for Impact
Lauren's experience building growth teams from scratch has yielded a clear hiring framework. Growth teams work best scrappy--there's such a thing as over-resourcing, and becoming too large can sacrifice the ability to move fast and pivot as she detailed for OpenView Partners.
Her four-step hiring framework provides a blueprint for building effective growth capabilities:
Step One: Find an Analytics Superstar
The first roles Lauren fills when building a new team are analysts who can create an analytics powerhouse. Business-minded curiosity matters more than technical skill depth. The ideal candidate combines data capabilities with a genuine interest in understanding why things work the way they do and how they might work differently.
This person translates raw data into actionable hypotheses that product managers can test. For AI implementations, this translates to needing team members who can bridge technical capabilities and business application--people who understand what the model does and can articulate what experiments it enables.
Step Two: Get the Right Product Manager
Growth PMs need to be data-driven decision makers. This doesn't mean writing SQL themselves, but it does mean asking good questions, interpreting data correctly, and generating testable hypotheses. Lauren looks for quantifiable results on resumes--if she doesn't see data on the outcomes of their work, she moves to the next candidate.
The ability to field deep questions about results matters because growth is inherently experimental. PMs must defend their hypotheses, interpret mixed results honestly, and iterate based on what they learn. For AI-powered products, this means PMs need to understand model limitations and edge cases, not just ideal performance.
Step Three: Round Out the Team
After securing strong analytical and product foundations, Lauren recommends hiring designers, growth engineers, and cross-functional marketers. Generalist capabilities matter--people who can work outside their immediate role and contribute across functions.
Some of her best hires were less experienced than other candidates but demonstrated hunger and curiosity. These traits matter because growth requires constant learning and adaptation. The specific skills can be taught; the disposition to continuously question and improve is harder to develop.
Step Four: Rethink Problem Approaches
New growth teams shouldn't work like traditional product or marketing teams. The fundamental shift is working off specific key results and business problems rather than feature roadmaps or campaign calendars. This requires teaching teams to design sprints focused on generating experiments for specific problems rather than optimizing existing workflows.
The cultural change takes time. Expect initial resistance as team members adjust to a different way of working. Success depends on early wins--shipping experiments with statistically significant business impacts that demonstrate the approach works. Eventually, the whole organization can get engaged and excited about the growth capability.
ROI and Cost Optimization in Growth Engines
The Mailchimp growth engine generated substantial returns, but the ROI wasn't about flashy technologies--it was about systematic experimentation applied to high-impact opportunities. The 95% adoption improvements and 183% engagement increases all came from relatively simple changes: better targeting, clearer messaging, reduced friction.
This has direct implications for AI and automation cost optimization:
First, start with process understanding before applying AI. The growth team succeeded because they understood where users were getting stuck and why. AI implementation without this understanding often optimizes the wrong things.
Second, measure everything and iterate based on data. Each experiment built understanding of user behavior that informed future experiments. The accumulation of learning generated compounding returns over time.
Third, invest in infrastructure that enables many experiments. The email editor rebuild was expensive, but it unlocked faster iteration across all growth initiatives. Front-loading infrastructure investment often yields higher long-term returns than continuous incremental improvements on limiting foundations.
Fourth, focus on high-impact opportunities rather than evenly distributing effort. The team identified activation as a major business opportunity and concentrated resources there. This prioritization meant accepting lower investment in other areas that had less potential for movement. Organizations looking to optimize their AI automation investments should apply the same discipline--identify the highest-impact opportunities and concentrate resources there rather than spreading effort thin across too many initiatives.
Practical Integration Patterns for AI-Powered Growth
Applying Lauren's lessons to AI and automation implementations requires translating growth principles into technical patterns:
For targeting and personalization, build segmentation infrastructure before implementing sophisticated models. The ability to show different experiences to different user segments enables the kind of targeted messaging that drove Mailchimp's 183% improvement. AI models are only as effective as the targeting infrastructure that delivers their outputs to the right users.
For experimentation, implement proper instrumentation before launching features. The growth team's ability to measure the 183% improvement depended on having tracking in place. AI systems require similar instrumentation to understand when they're working and when they're not.
For team structure, hire for business-minded curiosity before technical specialization. The analytics superstars Lauren prioritized could translate between data and business implications. For AI teams, this means valuing people who understand both model capabilities and business application.
For organizational alignment, invest in cross-functional agreement before implementing complex automations. The sales-led company failed not because PLG was wrong but because functions weren't aligned. AI implementations face similar risks--automated touchpoints that conflict with human workflows will struggle to deliver expected returns.
By applying these patterns, organizations can build AI-powered growth capabilities that deliver sustainable business impact rather than isolated technical wins.
The Foundation for Sustainable Growth
Lauren Schuman's Mailchimp journey reveals that sustainable growth engines aren't built through isolated experiments or flashy technologies. They're built through systematic application of experimentation to high-impact opportunities, supported by infrastructure that enables rapid iteration and cross-functional alignment on objectives.
The 183% homepage improvement and 95% feature adoption gains came from understanding users deeply, targeting them effectively, and measuring rigorously. The email editor rebuild enabled these wins by providing a flexible foundation for future experiments. The team expansion happened because early successes proved the approach's value.
For AI and automation practitioners, the path to ROI looks similar: understand the business problem deeply, build infrastructure that enables experimentation, measure rigorously, and maintain cross-functional alignment. The technology matters less than the discipline with which it's applied.
The most valuable insight may be that growth fails when organizations want the outcomes without doing the work. PLG isn't a feature you bolt onto an existing sales-led motion. It's a fundamental reorientation of how the product drives business outcomes, requiring coordinated changes across functions and sustained investment in experimentation capability. Organizations that understand this reality--and commit to building the foundations that enable it--are positioned to generate the kinds of returns Mailchimp achieved. Building these foundations requires expertise in both growth strategy and AI implementation.
Key Questions About Building Growth Engines
How long does it take to build a PLG engine?
Lauren built Mailchimp's growth practice from a single team to multiple specialized teams over nearly four years. The timeline depends on organizational readiness, but expect 12-24 months to demonstrate sustainable impact and 3-4 years to fully scale across the customer journey.
What metrics should a growth team focus on first?
Start with activation metrics that directly tie to business outcomes--features that, when adopted, correlate with retention and revenue. Avoid vanity metrics. The key is choosing focused, simple, actionable metrics that you can actually measure, monitor, and act upon.
How do you get leadership buy-in for growth initiatives?
Demonstrate value through early wins. Lauren found that running targeted experiments with measurable results was more persuasive than presentations. Make the cost of inaction visible--showing stakeholders what happens when users struggle often builds more support than explaining potential gains.
What team size is optimal for a growth team?
Growth teams work best scrappy. There's such a thing as over-resourcing--becoming too large can sacrifice the ability to move fast and pivot. Start with an analytics superstar and a strong PM, then add designers, engineers, and cross-functional partners as specific opportunities emerge.
Can PLG work in sales-led organizations?
It requires fundamental alignment between sales, marketing, and product on definitions and objectives. Without this alignment, PLG initiatives will struggle. The sales-led company Lauren joined wanted PLG but wasn't willing to make the organizational changes required--it failed despite strong methodology.