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Why is my Product Led Growth (PLG) strategy not working?

July 5, 2024
Reading time: 
8 mins

Product-led growth (PLG) strategies fail when teams work in isolation. When product development, marketing, sales, and customer support operate in silos, they miss opportunities, stifle innovation, and make fragmented decisions, hurting the overall business strategy.

That does not mean PLG is ineffective! In fact, it can be quite powerful when implemented properly and aligned with your Marketing-Led Growth (MLG) efforts.

By breaking down siloed work environments, improving communication between product and marketing teams, and using integrated testing tools, companies can enhance their PLG strategy and their wider business strategy.

What is Product-Led Growth (PLG)?

Product-led growth, or PLG, is a business strategy that emphasizes creating a product with exceptional features, minimal friction, clear value, stickiness, and virality to drive customer acquisition and retention.

The goal of a PLG strategy is to develop products which sell themselves. Conversion pathways are self-serve and growth is acquired via reviews or reputation as opposed to traditional marketing channels.

This goal is often achieved by allowing users to engage with the product via freemium models or free trials to enhance user experience and satisfaction. By providing an intuitive and valuable product, companies can naturally attract and retain users, resulting in higher retention rates and organic growth through word-of-mouth referrals.

What is the goal of a Product-Led Growth strategy?

PLG strategies aim to create a scalable and efficient growth engine, leveraging the product to drive expansion without a corresponding increase in sales and marketing efforts. A well-designed product with built-in virality can reach a wider audience at minimal cost.

PLG emphasizes using data-driven insights to continuously refine the product based on user behavior and preferences, ensuring it evolves to meet user needs and stays competitive in the market.

What does a PLG strategy look like?

PLG strategies can be as variable as the products they support. But all effective product-led growth strategies center around a few core principles:

Developing feature-driven demand

Product features drive customer satisfaction and adoption. Essential features boost engagement and referrals, growing the user base. But great features don’t just appear out of thin air.

Design features with a deep understanding of your customers, their expectations, and your target market. Using data insights from experiments and direct feedback, you can create a product that becomes essential to their lives.

Feature experimentation is a proven, effective way to generate real-world insights. A strong experimentation program helps you develop and refine a product that's market-ready and relevant to target customer segments.

Products have minimal friction and minimal barriers to access

Easy access to your product is key to a successful PLG strategy. Many SaaS platforms offer time-limited free trials, letting prospective customers quickly and easily start using the product.

This practice also entails avoiding asking for too much information upfront, like payment details or lengthy questions about intent or company characteristics.

Immediate value to users boosts engagement, giving your team invaluable user data. More users mean more insights to enhance your product development.

Designing products that are easy to use and have built-in virality

Every customer interaction is a chance to help them achieve their goals. In a PLG strategy, we aim to design intuitive, easy-to-use products. Easy-to-use products are easy to share, and virality happens when users naturally want to share them. This creates a self-perpetuating PLG flywheel, turning new customers into product advocates.

Common challenges in applying a PLG strategy

Implementing a PLG strategy can generate newfound growth, but certain common roadblocks and product issues can hinder success. Understanding these potential issues can help you fine-tune your PLG process.

Common challenges in applying a PLG strategy

  • Product lacks market fit. Your product will struggle to attract and retain users if it doesn’t address a real need or solve an immediate, real-world problem for your target audience. Conducting market research and validation through experimentation are easy ways to avoid this issue.

Common product issues affecting PLG

  • Poor user experience (UX). If your product is difficult to use, buggy, or slow, users can become frustrated and leave. Remember: PLG strategy suggests minimal friction. Ensuring a smooth, enjoyable, and efficient user experience is crucial for maintaining user engagement.
  • Unoptimized features. If product features are irrelevant, too complex, or don’t work as expected, users won’t see the value in continuing to use the product. Regularly gathering user feedback and iterating on feature design through feature experimentation programs can help ensure that your product effectively meets user needs.
  • Lack of necessary integrations. Users expect software products to work flawlessly with the other tools they use. If your product doesn’t integrate with other major players in the same niche, it may be perceived as less valuable and drive users to seek alternatives that offer better compatibility.

The case for feature experimentation in PLG

Feature experimentation is a common method used in Product-Led Growth (PLG) strategies to enhance the product and better meet user needs continuously. Companies can make data-driven decisions that drive user engagement, satisfaction, and retention by systematically testing new features before they are adopted in full. How feature experimentation is used for PLG

Feature experimentation involves rolling out different versions of a product or feature to segments of users and measuring their responses. This can include A/B testing, where two versions (A and B) of a feature are compared to see which performs better or more complex multivariate testing.

By testing features with real users, companies can gather direct feedback on what works and what doesn’t, ensuring that only the most effective enhancements are implemented.

Feature experimentation allows companies to base decisions on user data rather than assumptions. This method provides insights into how users interact with different aspects of the product, revealing preferences, pain points, and behaviors.

The data collected from feature experimentation helps product teams understand user behavior. Good testing reveals how users engage with the product, what features they find valuable, and where they encounter difficulties. Analytics from these experiments can highlight trends and patterns that inform future product development.

Where is the friction between feature experimentation and marketing?

The friction often arises from differing objectives and timelines. Marketing teams typically focus on immediate user acquisition and campaign performance, while feature experimentation is a longer-term process to optimize the product.

Marketing may push for rapid feature rollouts to support their latest campaigns, whereas product teams may prefer a more measured approach to test and refine features thoroughly. This might lead to misalignment or overlap in resource or budget allocation within some organizations.

But it doesn’t need to be this way.

Fostering cross-functional collaboration and aligning goals between product and marketing teams is important to creating win-win scenarios where all teams work towards overall strategic goals.

By integrating marketing insights into the feature experimentation process and ensuring transparent communication, companies can balance short-term acquisition goals with long-term product optimization, which will drive overall growth more effectively.

How all-team experimentation achieves the best of both worlds

All-team experimentation combines the strengths of marketing-led web experimentation with feature experimentation. This approach retains the benefits of product-led growth without sacrificing the benefits of its market-led counterpart. Engaging all departments—product, marketing, sales, and support—ensures that every aspect of both growth strategies is aligned and optimized with your KPIs.

For example, a marketing experiment on a banking website might reveal that a blue CTA reading "Submit Your Mortgage Application Here" performs better than a green CTA reading "Get Your Mortgage Application." This insight can then be applied to the app, ensuring consistency and improving performance.

The role of AI in facilitating all-team experimentation

Artificial intelligence (AI) is profoundly impacting the way companies implement all-team experimentation. AI acts as a dedicated copilot, working alongside your team to enhance coordination and efficiency.

Rather than just a purely product-led growth tool, AI functions as a team member, playing a crucial role in synchronizing the various elements of your PLG strategy.

AI can automate routine tasks, manage workflows, and ensure critical information is shared across departments. For example, AI can track user behavior data and summarize those actions into meaningful insights for product and marketing teams. By training them to monitor your KPIs, AI can help teams identify areas for improvement and optimize their strategies.

Whether refining product features based on user feedback or adjusting marketing campaigns to meet the target audience's needs better, AI provides the insights needed to drive continuous improvement. This iterative approach ensures that the product evolves in response to user behavior and market trends, maintaining its relevance and competitive edge.

Kameleoon’s commitment to all-team experimentation

We’re committed to pushing the boundaries of what is possible with all-team experimentation. Kameleoon is the only optimization solution that allows you to leverage experimentation, AI-powered personalization, and feature management capabilities in a single unified A/B testing platform. Teams can build hybrid experiments using Kameleoon, build no/low-code server-side tests without a developer, and generate web-based data for targeting and analytics.

By making it easy to implement PLG principles within individual teams and throughout your organization, we ensure that your experimentation programs are efficient, data-driven, innovative, and adaptive to market needs. With Kameleoon, you have a partner committed to transforming your experimentation efforts into strategic growth.

Learn how Kameleoon enables all-team experimentation

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All-Team Experimentation