Product-Led Growth & Signal Substitution Syndrome

The Evolution of Understanding Buyer Intent

Several years ago, while at SiriusDecisions, my colleagues and I initially developed a model known as the Intent Data Framework (IDF).

Subsequently, this model was refined approximately a year later to incorporate signals beyond behavioral data, resulting in the Buyer Signals Framework (BSF).

Recognizing a Missing Component

It has become apparent that both the IDF and the subsequent BSF were incomplete.

A crucial element was not adequately addressed within these frameworks: the impact of product-led growth strategies.

This omission highlights the dynamic nature of understanding how buyers demonstrate their needs and preferences.

Continuous evaluation and adaptation of these frameworks are essential to accurately reflect the evolving landscape of buyer behavior.

Signal Substitution Syndrome Explained

Two distinct frameworks have been developed to tackle a prevalent misapprehension within the B2B sector, one I’ve termed signal substitution syndrome. This syndrome’s core principle is straightforward: in B2B, both marketing and sales professionals frequently perceive each new data source regarding prospective customers – each signal – as a replacement for previous signals that proved ineffective.

A comprehensive history of B2B could be chronicled through the repeated disappointments of these signals failing to meet expectations. This includes instances like attendance at trade show exhibits, responses to magazine inserts, form submissions from both individuals and automated bots, webinar sign-ups, leads generated through content syndication, third-party intent data, and user activity on review platforms.

The fundamental error driving signal substitution syndrome is the belief that any single one of these signals should be regarded as a reliable indicator of buyer intent. While occasional leads have fortuitously resulted in successful business outcomes, this is often a matter of chance.

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My experience as an analyst has demonstrated that leads typically exhibit a remarkably high failure rate, ranging from 95% to 99%. Standalone intent data performs even worse. Nevertheless, both represent an improvement over prior methods. Crucially, none of these signals inherently represent genuine intent; they are merely expressions of interest.

The Integration of Product-Led Growth

Product-led growth (PLG) introduces a model where a complimentary or low-cost version of a solution is offered, leveraging its adoption as a key indicator for generating larger enterprise deals. However, this approach isn't universally applicable; certain products aren't suited for a PLG strategy.

For instance, envisioning Oracle implementing PLG for its manufacturing cloud proves challenging.

The majority of B2B solutions necessitate a fully functional scope to deliver value. This concept isn’t novel, as free trials, freemium models, and “lite” versions have existed since the advent of distributable software.

Websites like Download.com previously functioned as distribution hubs embodying early PLG principles.

Nevertheless, PLG remains a viable strategy for solutions designed for individual users or small teams within larger organizations. Having users actively utilizing your software within a target company presents a significant advantage when pursuing enterprise-level sales.

In some instances, the product’s inherent value will be so substantial that minimal direct sales intervention will be needed.

If a viral adoption model aligns with your solution, pursuing it is advisable. However, for many, PLG will primarily serve as a demand creation strategy, generating signals of varying reliability.

The temptation to categorize individual users as traditional “leads” will be strong, and they certainly can be. More accurately, they represent “intent.”

A more effective approach is to consider these users as signals – akin to form submissions, website visitor data, or event attendees.

Like other data points, these signals should be integrated into an algorithmic analysis alongside existing information.

This combined analysis will reveal patterns that can effectively prioritize sales initiatives.

Product Usage as a Signal: Powerful, Yet Limited

Most indicators of potential customers, including leads and various intent signals, demonstrate an expressed interest. Conversely, active product users are directly communicating a requirement. When individuals are utilizing a product, the need isn't potential or hypothetical; it's currently being experienced.

Confirmed, articulated need constitutes a valuable signal. However, the demonstration of need by only a small group – for example, three individuals within a company of 10,000 employees – doesn't necessarily reflect a widespread organizational need.

In fact, limited expression of need might suggest that the broader enterprise, or even a specific department, doesn't perceive a significant requirement. This highlights the importance of considering the scale of user engagement.

Understanding this nuance is crucial for accurately interpreting user signals and gauging overall market demand. Focusing solely on active users can provide a strong indication of current need, but it's essential to avoid overgeneralization.

The Limitations of PLG Signals

While Product-Led Growth (PLG) relies heavily on user activity, it's vital to recognize that usage data alone isn't always representative. A small, highly engaged user base doesn't automatically translate to a large, unmet need across an entire organization.

Consider these points:

• Sample Size: A limited number of users may not accurately reflect the needs of the wider company.
• Departmental Needs: The need might be isolated to a specific team or function, not the entire organization.
• Alternative Solutions: The organization may already have existing solutions addressing the same need.

Therefore, while PLG user signals are valuable, they should be interpreted cautiously and supplemented with other data points to gain a comprehensive understanding of market demand.

Synthesizing Data for Actionable Insights

The culmination of various signals is crucial for effective analysis. As a consultant, I consistently recommended that companies view leads and intent signals as corroborating evidence when identifying potential customers actively seeking their offerings.

A single marketing qualified lead (MQL) rarely translates into a successful sale. However, receiving two MQLs from the same company, concerning the same product or service, simultaneously significantly increases the likelihood of conversion – although it remains imperfect.

The addition of de-anonymized website traffic data, coupled with third-party intent signals, further strengthens this assessment. Each supplementary signal serves to validate the preceding information, or at least, it should.

Evaluating Product-Led Growth Signals

Similarly, within product-led growth (PLG) strategies, individual user activity should be evaluated in conjunction with other indicators of interest and necessity. Consider a scenario where three users within a large enterprise are utilizing your product.

If the potential for an enterprise-level license encompasses 300 users, it’s essential to determine: Is there any indication that other individuals within the organization are also demonstrating a need or interest? Are there traditional leads being generated?

Is there a noticeable increase in anonymous website traffic? Furthermore, is there a surge in third-party intent data related to your solutions? If the answer to these questions is negative, pursuing those three users, or even their manager, mirrors the futility of chasing MQLs.

The Importance of Signal Integration

Successfully integrating these diverse signals allows for the creation of a robust and unified signal that empowers sales teams to prioritize their activities and refine their engagement strategies.

Conversely, falling prey to signal substitution syndrome – simply passing usernames to sales without context – will inevitably lead to a renewed search for alternative signal sources.