What Is Product-Led Growth and Why Is It Critical for AI Dev Tool Companies?
The misconception that product led growth implies a business neglects sales couldn’t be further from the truth. In reality, product led growth (PLG) involves integrating sales later in the customer journey, placing the purchasing power back in the hands of your users. This self-service approach is highly effective, especially when paired with insightful user data to streamline and target growth. For AI-centric companies, this product-led strategy serves as a fundamental pillar for success.
What Is Product Led Growth?
Product-led growth (PLG) is a business model that places a product itself at the center of the growth engine. Instead of relying solely on traditional sales and marketing approaches, product led companies leverages the perceived value of a product to drive customer acquisition, retention, and expansion. PLG is particularly effective for AI companies thanks to the inherent iterative and user-driven nature of AI development. PLG champions a user-centric approach, which enables customers to experiment with the practical applications and advantages of a given product. This approach resonates strongly with AI companies, as it not only enhances user comprehension but also amplifies engagement levels.
A Different Approach to Sales
A PLG company leverages their sales pipeline with the product experience at the forefront of the customer acquisition and retention strategy. In a PLG model, a product itself becomes the catalyst for growth by leveraging the value customers believe it has through sandbox trials and Proof of Concepts. By influencing users to adopt, engage, and eventually convert into paying customers, PLG allows businesses to capture a wider range of potential developer users who typically are unreachable through traditional sales channels. After all, there’s a reason many developers aren’t converted by customer success professionals - they want to test the product under their terms, for their use cases, in their environments. Giving these self-service users the potential to become customers means frictionless onboarding for organizations already used to your product through a trial.
The key elements of a PLG approach to sales include:
- Self-Service Onboarding: Your product should be designed to be intuitive, allowing users to easily onboard themselves without extensive assistance. This self-service onboarding approach empowers users to explore your product independently.
- Free Trials or Freemium Models: Offering free trials or freemium versions of the product enables users to experience the core functionalities before committing to a purchase.
- Growth Loops and Referrals: Satisfied users become word-of-mouth advocates of your product. Through features like referrals, existing users play a role in bringing in new, organic users.
- Data-Driven Iteration: Continuous analysis of user behavior guides relevant improvements to your product, solving user frustrations and reducing churn with a great user experieince.
- Trial-Driven Conversion: Give your users a chance to experience your most marketable features during a trial period. When upselling features to enterprise level users, consider giving them the chance to test those features as well.
A product led growth model can lead to rapid growth through new users, expanding revenue growth and freeing up your internal marketing team and product team professionals to focus on growth. Product led companies sales model is rooted in the philosophy that a great product, if designed to be user-friendly and value-driven, can attract, retain, and convert users independently based on its inherent value to the market. By focusing on delivering an exceptional product experience to improve your user experience, companies adopting a PLG approach can build a user base that sustains itself and contributes to product recognition and growth.
Why Is Product-Led Growth So Important to AI-Based Companies?
Product-Led Growth can be a significant growth strategy for AI-based companies. Product led sales emphasizes a offering a user-centric experience is particularly complimentary to AI solutions, which often involve complex product development and intricate technologies shaped or built on by the end user. The user-friendly approach of PLG allows users to engage with AI products independently, giving them a chance at hands-on exploration of your product for their use case.
Moreover, the complexity of AI solutions poses a significant challenge in user adoption. PLG addresses this by enabling a product qualified lead to trial the capabilities of AI products through trials , demystifying the complexities associated with integrating your generative AI tool into their stack and encourages technology adoption.
PLG integrates with customer success models, as well. Providing users with personalized onboarding, ongoing support, and proactive engagement aligns with the long-term goals of AI-based companies as well as can be done entirely without the direct involvement of internal sales representatives, fostering sales led growth independently.
Selling to Developers
Selling an AI product through a developer-centric product led strategy approach offers distinct advantages over C-suite targeting. By empowering developers with hands-on experiences, companies accelerate adoption timelines and allow for quicker evaluations thanks to support from the bottom up. Reduced sales friction, a developer advocacy model, and alignment with the use cases of intended developers further benefit AI companies employing a PLG strategy. By turning developers into your stakeholders, you turn them into a key decision-making audience. Ultimately, PLG leverages the influential role of developers in product adoption.
Getting Billing Right
Product-led growth requires some thought around your billing model. This is where usage-based billing comes into play. A developer can pay a token amount to get started with your artificial intelligence product or large language model to trial it in their test account while they complete their proof of concept. After that, when the developer takes the product into production, your usage-based billing model needs to scale alongside the customer’s growing usage.
AI based products are a natural fit for this pay-as-you-go style of billing, as there are various ways that product usage can be tracked. Just be sure that you’re using the right metrics! Broadly speaking, value metrics can be focused around:
- Transaction volume – number of API calls, tokens used
- Revenue/cost share – percentage of revenue, transaction fee
- Data volume – gigabytes sent, custom model training, processed data volume
- Users – monthly active unique users
- Compute Resources Utilization – compute units, active hours
These examples hint at the huge variety that can be built into usage-based billing, and the complexity of getting your invoicing right.
Consider an AI-based language processing API that assists developers in analyzing and extracting insights from large volumes of text data. Developers could pay based on the volume of text data their application sends to the API for analysis. This aligns directly with the value they receive from your product – the more documents processed, the more context for analysis gained. A usage-based approach ensures that developers are billed proportionally to the actual utility and value derived from the AI service without causing strain on the side of the API provider.
Data-Driven Sales
Using data to drive your sales is a major success converter. By focusing on the actual usage of your AI product, your customer success teams can more accurately manage and unpack the flow of conversions. As with any SaaS company, real-time data on your active users can be the true lens into their needs.
With tracking in place for account health and usage growth, sales teams can open discussions with customers at just the right moment to ensure that the customer gets more value out of their product without jumping the gun around usage milestones. This is where a product analytics tool like Moesif can help, providing a scalable solution with automated resources that can notice and alert customer success teams around increases in usage, giving your organization a chance to intervene and ensure the customer is still on the best value plan to meet their needs.
Tracking usage data isn’t just about sniffing out new sales opportunities on your end. It’s also for ensuring that your users understand the cost of your product and the scale at which they actually use it. This way, customers don’t get surprise charges on their invoices following an increase in usage. By giving users (and your sales team) the chance to discuss plan limitations and subscription value, you can minimize the chance of them canceling due to the unexpected jump in cost. Lower churn and more confident users can keep your product afloat.
How to Engage Developers with Your Product
For AI-based companies, engaging with your product should be a seamless journey tailored to enhance their AI endeavors. By proactively engaging with a self-service crowd through user-friendly interfaces and intuitive documentation, you can provide a comprehensive understanding of how your AI product fits into a customer’s tech stack. Giving developers an interactive sandbox environment and allowing hands-on experimentation and testing of robust features will allow them to understand the value of your product, increasing the likelihood of conversion. With transparent pricing models and scalable options, integrating your product into any workflow can be done with confidence on both ends.
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