Introducing OQLs: A New Way for Businesses to Quantify Open Source Adoption
In the open source ecosystem, user behaviors are diverse and conversion tracking poses unique challenges frequently leaving traditional marketing strategies insufficient. Amidst these obstacles, we've observed that our customers consistently face difficulties when attempting to prioritize the growing list of companies adopting their open source project. With thousands of users and companies engaging in a variety of ways, determining where to direct attention becomes a daunting task.
Recognizing this gap, we are excited to introduce a brand new way for businesses to make sense of this opaque and noisy signal – Open Source Qualified Leads (OQLs). This innovative approach enables open source companies and projects to identify, understand, quantify and prioritize potential leads based on their interaction and activity within the open-source community.
Unlike traditional software conversion funnels, tracking open source adoption at scale presents a complex and difficult task. Quantifying user interest and conversion is challenging as adoption data, if you have any at all, tends to be noisy and not necessarily tied to commercial intent. However, by strategically leveraging a variety of signals with respect to open source usage and engagement, OQLs and heuristics to score them offer a new approach to deal with this problem.
OQLs focus on tracking software usage behaviors to decipher open-source adoption and reliance, filling the void left by conventional Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs). The challenge with MQL/SQL lies in their failure to incorporate the most crucial aspects of open-source software (OSS) usage.
Unlike MQLs, which primarily center around commercial interest and engagement in a very broad sense, OQLs emerge as a fundamentally different category of lead. OQLs delve deeper into the open source journey, combining signals like package downloads, telemetry and product usage, documentation usage, and more. By combining these OSS-specific signals, a comprehensive 360-degree view of a user's path emerges, which are more amenable to more classic lead-scoring approaches.
In this recent blog post, we delve into the significance of OQLs and their immense potential for open-source businesses.
Baseline Building and Growth Tracking:
Establish a foundation by tracking the growth of your open source user base. OQLs provide a holistic view of user interactions, allowing you to build a baseline for future strategic planning.
Accelerating Open Source Adoption:
Understanding how users move through your adoption funnel is the first step towards optimizing it. By observing trends in where users get stuck, you can accelerate your project’s growth.
Sales Pipeline Enrichment:
OQLs enrich and expand the sales pipeline by providing insights into user behavior in an important segment that has historically been largely opaque. Understanding the nuances of user interest, ongoing usage, and potential commercial intent, can thereby enhance the effectiveness of sales activities towards this group.
Risk Determination and Addressing Issues Proactively:
Maintain visibility into users who are engaging frequently and those who are not. Any sudden drops in usage or engagement become signals to investigate and address potential issues proactively, mitigating the risk of users leaving the ecosystem.
Effective Prioritization and Categorization:
Given the vast number of organizations within Scarf data, prioritization is paramount. OQLs facilitate a detailed categorization of each interaction by assigning a status level such as Interest, Investigation, Experimentation, Ongoing Usage, or Inactive. This categorization is achieved through a point system that evaluates company activities, including actions like viewing documentation sites and downloading packages. With this comprehensive information, companies can employ a point-based scoring system to filter and prioritize their list of leads. As a result, the data derived from Scarf becomes more actionable, enabling companies to focus their efforts on the most valuable and prospective opportunities.
To make OQLs actionable and a valuable part of business operations, we’ve introduced a novel lead scoring system specific to this unique pipeline channel. Actions like viewing a blog post signal different levels of intent when compared to viewing your pricing page. Downloading the `latest` version of a container several times in one day versus downloading a stable release consistently over time imply different stages of adoption. The result is a robust model of the adoption funnel of an open source user that can be operationalized by sales and marketing teams to prioritize the stages of the funnel they care about most.
To delve deeper into the OQL scoring system or learn more about how to set one up for the specific goals of your open source business, visit our Scarf Documentation.
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By pioneering this approach and integrating it into our product, it is our hope that we can continue empowering open source companies and projects with the tools they need to quantify their efforts effectively. As the first to introduce a concept tailored specifically to open source leads, we aim to propel a shift towards more insightful, targeted, and quantifiable open source strategies. Embrace OQLs, and redefine the way you understand and engage with your open source community.
Published
December 6, 2023
This article was originally posted on
Hackernoon
In the open source ecosystem, user behaviors are diverse and conversion tracking poses unique challenges frequently leaving traditional marketing strategies insufficient. Amidst these obstacles, we've observed that our customers consistently face difficulties when attempting to prioritize the growing list of companies adopting their open source project. With thousands of users and companies engaging in a variety of ways, determining where to direct attention becomes a daunting task.
Recognizing this gap, we are excited to introduce a brand new way for businesses to make sense of this opaque and noisy signal – Open Source Qualified Leads (OQLs). This innovative approach enables open source companies and projects to identify, understand, quantify and prioritize potential leads based on their interaction and activity within the open-source community.
Unlike traditional software conversion funnels, tracking open source adoption at scale presents a complex and difficult task. Quantifying user interest and conversion is challenging as adoption data, if you have any at all, tends to be noisy and not necessarily tied to commercial intent. However, by strategically leveraging a variety of signals with respect to open source usage and engagement, OQLs and heuristics to score them offer a new approach to deal with this problem.
OQLs focus on tracking software usage behaviors to decipher open-source adoption and reliance, filling the void left by conventional Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs). The challenge with MQL/SQL lies in their failure to incorporate the most crucial aspects of open-source software (OSS) usage.
Unlike MQLs, which primarily center around commercial interest and engagement in a very broad sense, OQLs emerge as a fundamentally different category of lead. OQLs delve deeper into the open source journey, combining signals like package downloads, telemetry and product usage, documentation usage, and more. By combining these OSS-specific signals, a comprehensive 360-degree view of a user's path emerges, which are more amenable to more classic lead-scoring approaches.
In this recent blog post, we delve into the significance of OQLs and their immense potential for open-source businesses.
Baseline Building and Growth Tracking:
Establish a foundation by tracking the growth of your open source user base. OQLs provide a holistic view of user interactions, allowing you to build a baseline for future strategic planning.
Accelerating Open Source Adoption:
Understanding how users move through your adoption funnel is the first step towards optimizing it. By observing trends in where users get stuck, you can accelerate your project’s growth.
Sales Pipeline Enrichment:
OQLs enrich and expand the sales pipeline by providing insights into user behavior in an important segment that has historically been largely opaque. Understanding the nuances of user interest, ongoing usage, and potential commercial intent, can thereby enhance the effectiveness of sales activities towards this group.
Risk Determination and Addressing Issues Proactively:
Maintain visibility into users who are engaging frequently and those who are not. Any sudden drops in usage or engagement become signals to investigate and address potential issues proactively, mitigating the risk of users leaving the ecosystem.
Effective Prioritization and Categorization:
Given the vast number of organizations within Scarf data, prioritization is paramount. OQLs facilitate a detailed categorization of each interaction by assigning a status level such as Interest, Investigation, Experimentation, Ongoing Usage, or Inactive. This categorization is achieved through a point system that evaluates company activities, including actions like viewing documentation sites and downloading packages. With this comprehensive information, companies can employ a point-based scoring system to filter and prioritize their list of leads. As a result, the data derived from Scarf becomes more actionable, enabling companies to focus their efforts on the most valuable and prospective opportunities.
To make OQLs actionable and a valuable part of business operations, we’ve introduced a novel lead scoring system specific to this unique pipeline channel. Actions like viewing a blog post signal different levels of intent when compared to viewing your pricing page. Downloading the `latest` version of a container several times in one day versus downloading a stable release consistently over time imply different stages of adoption. The result is a robust model of the adoption funnel of an open source user that can be operationalized by sales and marketing teams to prioritize the stages of the funnel they care about most.
To delve deeper into the OQL scoring system or learn more about how to set one up for the specific goals of your open source business, visit our Scarf Documentation.
By pioneering this approach and integrating it into our product, it is our hope that we can continue empowering open source companies and projects with the tools they need to quantify their efforts effectively. As the first to introduce a concept tailored specifically to open source leads, we aim to propel a shift towards more insightful, targeted, and quantifiable open source strategies. Embrace OQLs, and redefine the way you understand and engage with your open source community.
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In the open source ecosystem, user behaviors are diverse and conversion tracking poses unique challenges frequently leaving traditional marketing strategies insufficient. Amidst these obstacles, we've observed that our customers consistently face difficulties when attempting to prioritize the growing list of companies adopting their open source project. With thousands of users and companies engaging in a variety of ways, determining where to direct attention becomes a daunting task.
Recognizing this gap, we are excited to introduce a brand new way for businesses to make sense of this opaque and noisy signal – Open Source Qualified Leads (OQLs). This innovative approach enables open source companies and projects to identify, understand, quantify and prioritize potential leads based on their interaction and activity within the open-source community.
Unlike traditional software conversion funnels, tracking open source adoption at scale presents a complex and difficult task. Quantifying user interest and conversion is challenging as adoption data, if you have any at all, tends to be noisy and not necessarily tied to commercial intent. However, by strategically leveraging a variety of signals with respect to open source usage and engagement, OQLs and heuristics to score them offer a new approach to deal with this problem.
OQLs focus on tracking software usage behaviors to decipher open-source adoption and reliance, filling the void left by conventional Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs). The challenge with MQL/SQL lies in their failure to incorporate the most crucial aspects of open-source software (OSS) usage.
Unlike MQLs, which primarily center around commercial interest and engagement in a very broad sense, OQLs emerge as a fundamentally different category of lead. OQLs delve deeper into the open source journey, combining signals like package downloads, telemetry and product usage, documentation usage, and more. By combining these OSS-specific signals, a comprehensive 360-degree view of a user's path emerges, which are more amenable to more classic lead-scoring approaches.
In this recent blog post, we delve into the significance of OQLs and their immense potential for open-source businesses.
Baseline Building and Growth Tracking:
Establish a foundation by tracking the growth of your open source user base. OQLs provide a holistic view of user interactions, allowing you to build a baseline for future strategic planning.
Accelerating Open Source Adoption:
Understanding how users move through your adoption funnel is the first step towards optimizing it. By observing trends in where users get stuck, you can accelerate your project’s growth.
Sales Pipeline Enrichment:
OQLs enrich and expand the sales pipeline by providing insights into user behavior in an important segment that has historically been largely opaque. Understanding the nuances of user interest, ongoing usage, and potential commercial intent, can thereby enhance the effectiveness of sales activities towards this group.
Risk Determination and Addressing Issues Proactively:
Maintain visibility into users who are engaging frequently and those who are not. Any sudden drops in usage or engagement become signals to investigate and address potential issues proactively, mitigating the risk of users leaving the ecosystem.
Effective Prioritization and Categorization:
Given the vast number of organizations within Scarf data, prioritization is paramount. OQLs facilitate a detailed categorization of each interaction by assigning a status level such as Interest, Investigation, Experimentation, Ongoing Usage, or Inactive. This categorization is achieved through a point system that evaluates company activities, including actions like viewing documentation sites and downloading packages. With this comprehensive information, companies can employ a point-based scoring system to filter and prioritize their list of leads. As a result, the data derived from Scarf becomes more actionable, enabling companies to focus their efforts on the most valuable and prospective opportunities.
To make OQLs actionable and a valuable part of business operations, we’ve introduced a novel lead scoring system specific to this unique pipeline channel. Actions like viewing a blog post signal different levels of intent when compared to viewing your pricing page. Downloading the `latest` version of a container several times in one day versus downloading a stable release consistently over time imply different stages of adoption. The result is a robust model of the adoption funnel of an open source user that can be operationalized by sales and marketing teams to prioritize the stages of the funnel they care about most.
To delve deeper into the OQL scoring system or learn more about how to set one up for the specific goals of your open source business, visit our Scarf Documentation.
By pioneering this approach and integrating it into our product, it is our hope that we can continue empowering open source companies and projects with the tools they need to quantify their efforts effectively. As the first to introduce a concept tailored specifically to open source leads, we aim to propel a shift towards more insightful, targeted, and quantifiable open source strategies. Embrace OQLs, and redefine the way you understand and engage with your open source community.
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