What is Product-Led Growth?
Product-Led Growth (PLG) is a go-to-market strategy where the product itself is the primary driver of user acquisition, conversion and expansion. Instead of first selling and then delivering – the traditional sales-led model – PLG allows the user to experience value before a purchase decision. Slack, Notion, Figma, Spotify and Dropbox are classic PLG examples.
Why is PLG so effective?
The cost to acquire a customer (CAC) is drastically lower when the product sells itself. Organic spread via free users recommending the product to their networks – viral loop – creates growth without proportional advertising budget. Additionally, a free user provides data on how the product is used, which informs product development in a way that traditional marketing does not.
Freemium vs Free Trial – Which Should You Choose?
Freemium means that the user gets access to a limited version of the product without a time limit. It provides long-term opportunity for conversion but risks users never changing if the free level is sufficient.
Free trial means access to full product for a limited period – 14 or 30 days are most common. It creates urgency but gives less time to build up dependence.
When is PLG the wrong strategy?
PLG does not work for all products. If a purchase decision requires the approval of multiple stakeholders, if the product requires complex implementation, or if the value is not obvious within minutes - then traditional sales-led movement is more effective. Enterprise software with complex compliance requirements and long hiring cycles rarely suits PLG.
PLG metrics you must measure
Time to Value (TTV) is how long it takes from registration to the user understanding and experiencing the core value of the product. That's the key. A long TTV is the most common reason why PLG strategies fail. Activation rate measures the percentage of users who reach a given milestone that proves they understood the value. Product Qualified Leads (PQL) are leads identified by product usage rather than demographic data.
