Over the past eight years, Daria Minakova has worked on both B2C and B2B technology products: consumer apps and SaaS platforms for corporate clients. Most recently she led the growth of a B2B super app used by more than 100,000 companies and over one million users, owning the full funnel end-to-end: acquisition, activation, retention, monetization, and unit economics. In her previous role at Yandex Go for Business, she worked on the corporate ecosystem that became a new revenue stream with over $xxM GMV annually and doubled revenue year over year for two consecutive years.
In this work, Daria relied heavily on Product-Led Growth (PLG): making the product itself the main engine of acquisition, activation and monetisation rather than depending only on sales and marketing. This PLG shift is also part of a broader industry trend: according to OpenView’s Product Benchmarks, companies with a strong product-led motion tend to grow faster on average while spending a smaller share of revenue on sales and marketing than their purely sales-led peers.
Drawing on this practical PLG experience, Daria also shares her expertise with the wider community: she speaks at industry conferences, writes about her experience, and mentors startups and product managers in international communities.
In this interview, she explains how and why she shifted a mature B2B business from a traditional sales-led model to PLG, what worked in practice, and where the limits of PLG really are.
What was broken in the traditional sales-led motion, and why did growth plateau?
The short answer: growth became too expensive, too slow, and too dependent on people.
On the acquisition side, traffic costs kept rising, competition intensified, and CAC was gradually approaching LTV. Any attempt to scale by “just adding more budget” in marketing or more headcount in sales eroded unit economics, it is a pattern many SaaS benchmarks also highlight as CAC grows faster than revenue in mature markets.
The classic sales funnel also meant long cycles and a slipping time-to-value, especially in SMB and micro-business segments. Decisions were driven by decks and promises rather than real product usage, which naturally hurt retention and increased churn once clients saw the actual experience. PLG literature often points to this gap between promise and realized value as a root cause of poor net revenue retention.
Operationally, scaling was almost linear with headcount, more revenue required more salespeople and more support. Experimentation was slow: every change needed new scripts, approvals, training. Organic growth channels were weak: word of mouth existed, but most new clients still came through paid marketing and a classic sales motion, so the product didn’t really “sell itself” inside client organizations..
At some point you realize: you can still grow, but every additional unit of growth is getting disproportionately more expensive and fragile. That’s the typical signal that a sales-led model has hit its ceiling.
What was the objective of moving to PLG, and how did you start?
The goal was very pragmatic: make the product the main growth engine and improve core business metrics at the lowest possible cost.
More specifically, we wanted to:
- decouple growth from linear increases in sales and marketing spend;
- shorten time-to-value for new customers;
- improve activation, retention, and LTV by letting users experience value early and often.
I started with a full assessment of the current state. First, I mapped the “as-is” customer journey (CJM), from first visit to repeat usage.. I aligned business and product metrics and looked at funnels and KPIs across marketing, sales, and support to understand where value was leaking, not just “where traffic drops,” but where customers lose motivation or don’t see enough value to move forward.
That diagnostic phase was critical. It gave me a shared, data-driven picture and created buy-in across teams that PLG wasn’t just a trendy acronym, but a necessary next step.
How did you make the transformation data-driven?
Without deep analytics, PLG is just a slogan, so we started there.
We built out product analytics to track behavior across the full journey: activation events, feature usage, cohort retention, conversion between key milestones, and the link to revenue. Metrics from product, marketing, and sales were brought into a single system so everyone could see the same reality instead of arguing from their own dashboards. This approach is consistent with how PLG leaders track CAC, LTV, time-to-value, and product-qualified leads as core decision metrics.
Then we invested heavily in a culture of experimentation. A/B tests became the default way to ship changes, whether it was onboarding copy, a new pricing experiment, or changes to activation triggers. The important part was not just running tests, but agreeing upfront on success metrics and time frames.
Because of that, we could ship continuously, learn quickly, and make decisions based on evidence rather than opinion or hierarchy. This is also a topic I often cover when mentoring: PLG isn’t just about UX patterns, it’s about building an organization that’s comfortable being proven wrong by data.
What did “self-service” look like, and how did you fix activation?
For me, self-service meant that a company could discover the product, sign up, configure the basics, and place their first order with minimal friction and without needing to interact with sales or support teams that is very close to how PLG playbooks describe an ideal onboarding path.
We started by identifying every drop-off point in the journey, from registration to first successful order. Using product analytics and support insights, we mapped where users got stuck, what they asked in chat, and what made them abandon the process entirely.
Then we redesigned the Customer Journey Map end-to-end. That included:
- simplifying registration and company data setup;
- introducing a clear, guided onboarding flow inside the product;
- adding contextual hints and default configurations for common scenarios;
- aligning communication across channels so that emails, in-product messages, and support told the same story.
We reinforced all of this with analytics to see, step by step, what actually improved behavior.
As a result of the decisions I made and the work we did as a cross-functional team, we increased conversion from registration to first order by 44% and significantly shortened time-to-value. More clients in the SMB segment became “self-propelled”: they activated and started using the product without ever needing a sales or support touchpoint.
How did you unlock organic growth at (near) zero CAC?
We approached this from two angles: building a product-led acquisition channel and increasing the “pull” from existing users.
First, we launched a new product channel that attracted organic traffic with conditionally zero CAC. In practice, this meant a mobile-led entry point and integrations that allowed customers to discover and start using our services from within other high-traffic products. Over time, this channel became our primary acquisition path, generating 64% of all organic leads and 50% of all new inbound leads.
Second, we introduced loyalty mechanics directly in the product: bonuses, discounts, and tailored offers based on behavior. These mechanics increased the volume of new clients and lifted the conversion from registration to order by 11%.
Finally, we shipped product features that pulled users from partner channels, turning partnerships into a scalable, low-CAC acquisition engine rather than just a marketing campaign.
What moved monetization and LTV, and how did teams operate?
On monetization, we focused on two things: capturing more value from existing clients and making pricing better reflect real usage.
Inside the product, I launched cross-sell and up-sell mechanics, for example, surfacing adjacent services and packages at the right moments in the journey, not just via outbound sales. These mechanics increased LTV by roughly 25%. Separately, I adjusted monetization and tariffs to be more value-based and aligned with the way customers actually used the ecosystem.
Organizationally, PLG forced us to work as one system rather than as separate departments. I led the core product development team (engineering, design, analytics) and a cross-functional growth “V-team” that included colleagues from marketing, sales, support, and CRM.
We:
- aligned business growth targets with product strategy;
- set up omnichannel communication so the customer received a consistent experience;
- used analytics from product, support, marketing, and sales as a feedback loop into product decisions.
I was directly accountable for GMV, YoY revenue growth, and unit economics, which meant product, marketing, and sales goals had to be coherent, not competing.
What are the key lessons and the limits of PLG?
The main lesson is that PLG works: the product became the primary driver of GMV, revenue, and LTV. A strong PLG motion gave us faster hypothesis testing around onboarding, pricing, value propositions, and activation triggers; lower acquisition costs; shorter decision cycles for customers; and more sustainable growth. This mirrors what many external PLG case studies report, from shorter sales cycles to higher expansion and retention.
But there are important caveats.
First, PLG is impossible without deep analytics and a genuinely valuable, intuitive product. The product has to explain itself, deliver value quickly, and handle most of the “sales conversation” on its own.
Second, there is almost always cultural friction. Sales, marketing, and product teams often operate with different mental models and incentives. Moving to PLG means redefining processes, goals, and accountability, not just adding a freemium tier. This is something I see repeatedly when mentoring other teams: the hardest part is rarely the UX; it’s the mindset shift.
Finally, PLG is a hybrid, not a replacement for sales. In mid-market and enterprise segments, a sales motion is still essential, but it can be supported by a strong product-led engine that warms up accounts, proves value early, and makes sales conversations much more efficient. That aligns with what B2B PLG research now consistently shows: the best-performing companies combine product-led usage data with targeted human sales, rather than choosing one over the other.
The upside is scalability without linear cost growth: once the product and analytics foundation are in place, each additional unit of growth depends more on code and customer insight than on adding more people.
