A major challenge for developer-focused sales and marketing teams is distinguishing between accounts that are merely exploring and those seriously evaluating their product. This timing is crucial for selling DevTools.
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Misjudging when to engage can lead to missed opportunities. Engaging too early can result in longer sales cycles as CTOs may ignore outreach. Engaging too late may mean the account has already chosen a competitor.
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While your open source projects may have many GitHub stars, Docker pulls, and package manager installations, not every company engaging with them is ready to buy. Many struggle to interpret these signals to understand the buying stage.
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Reo.Dev’s AI-based Dev Funnel feature visualizes where each account is in their buying journey based on developer activities. It identifies which accounts are ripe for sales opportunities by collecting developer activity signals and feeding them into our proprietary AI model, which predicts the buying stage.
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In this article, we discuss the Dev Funnel feature, the methodology behind it, and provide actionable insights for sales and marketing teams to leverage this intelligence.
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‍Traditional SaaS Buying Funnel vs Developer Buying Funnel
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Most ABM and intent tools are designed to map the traditional customer buying journey, which follows the stages of the ToFu, MoFu, BoFu. These tools gather buyer signals from sources like blogs, websites, and other typical touch points corresponding to each stage.

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On the other hand, the developer buying journey is more nuanced because developers thoroughly evaluate a product before advocating for it within their organization. This journey is determined purely by developer activities and they are usually not active on traditional platforms.
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Hence, a lot of ABM and intent tools tools often fail to capture the true picture of a developer focused buying journey.

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Reo.Dev’s Dev Funnel
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To effectively guide sales and marketing strategies, we have categorized the developer account journey into distinct Dev Funnels based on developer engagement:
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1. Exploring: At this initial stage, developers have recognized a problem and began their search for solutions. They discover your product and start to assess its relevance and potential utility. This phase is crucial for making a strong first impression and positioning your product as a viable solution.
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2. Evaluating: As interest grows, developers enter a more advanced stage where they investigate your product thoroughly. This involves a deep dive into features, performance, and compatibility to ensure it aligns with their specific requirements.
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3. Building POC: Once preliminary assessments are positive, developers move to experiment with the product by building proof of concepts (POCs). This stage is pivotal as they test various use cases to validate the product's effectiveness and adaptability to their environments.
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4. Deployed: This stage marks the transition from testing to adoption, where developers have committed to integrating the product into their production systems. They continue to evaluate its performance, particularly focusing on scalability and reliability in a live environment.

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How our AI models are trained to identify the Funnel Stage
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We've developed our sophisticated AI engine based on two streams of data.

1. Analyzing CRM Data: An essential component of training our AI model involved analyzing CRM data across hundreds of our customer tenant. By studying the pipeline progression and observing billions of data points, we identify which activities are more frequent at each stage of the buying journey. This analysis helps us recognize patterns and understand the transition triggers from one stage to another.
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2. Gathering Intent data: Combined with the CRM analysis, our AI-driven process begins with meticulous data collection, where we track a variety of developer activities across platforms like GitHub, product documentation sites, and your corporate website to assess engagement levels. This data is then transformed into intent signals, providing actionable insights and quantifiable measures of interaction intensity. These signals feed into our proprietary AI model, which is enriched with additional account-related parameters and insights from over 1 Million+ organizations where we track and maintain activities on a daily basis. Employing advanced algorithms, the model dynamically predicts an account's buying stage, continuously learning from both historical and real-time activities to ensure accurate and timely predictions.
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‍Note: Our AI model does not take your competitors' GitHub repositories as input. We are focused solely on mapping the developer buying journey for your product

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Unboxing the AI
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For each customer, we analyze specific developer activities and assign a buying stage based on how these activities align with predefined threshold ranges for each stage.
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Activities we track:
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We track a variety of activities to gauge developer engagement and interest, categorized under Documentation and GitHub Activities. For Documentation, we monitor the number of package manager installations, CLI commands executed, documentation page visits, and the count of developers involved. On GitHub, we track the cumulative number of issues created and comments made, as well as the count of GitHub stars and forks. These metrics help us understand how developers interact with our product and their level of investment in its capabilities.
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Let’s take an example to understand how we determine the stages better.
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In this image, Busbud is categorized in the 'Evaluating' stage. This classification is based on activities including the execution of CLI commands, documentation reviews, and the involvement of multiple developers. The green dots adjacent to each activity visually represent the proximity of these activities to the predefined threshold range of the Evaluation stage.
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Compared to this the image below show Yahoo is categorized in the 'Exploring' stage, primarily due to the fewer green dots next to their activities. These green dots quantitatively represent the level of engagement with the product, such as the number of documents reviewed and repositories forked on GitHub. The lower intensity of these activities, compared to accounts like Busbud, suggests that Yahoo is still in the early phases of engagement and evaluation.

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How to action on the Buying Stage intel
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Reo.Dev's Dev Funnel feature enhances 1. Account Prioritisation and 2. Triangulate funnel data with other sources.
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1. Account Prioritisation

Reo.Dev's Dev Funnel feature enables precise account prioritization by providing each team — Marketing, DevRel, and Sales — with critical insights into where customers are in the buying journey. This allows for targeted engagement strategies that efficiently allocate resources and efforts to the accounts most likely to move the account from one buying stage to another.
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DevRel:

• Building developer community: DevRels can identify and engage with developers in the Exploring and Evaluation phases, fostering a community that supports detailed product exploration.
• Cultivating champions: By understanding which developers are deeply engaged, DevRel can nurture these individuals into champions through educational content, social proofs within their organizations, facilitating smoother transitions through the buying stages.
• Warm introductions to buyers: As developers move deeper into the Evaluation and Trial stages, DevRel can make warm introductions to sales teams, ensuring a seamless handover.
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Marketing:

• Product discovery and awareness: The marketing team can use insights from the Dev Funnel to enhance strategies around product discovery and initial customer engagement to build a robust top-of-funnel pipeline. By understanding where a customer is in their journey, they can tailor content and campaigns to raise awareness effectively.
• Funnel progression: They can track how accounts move from initial interest (Exploring) to actively evaluating the product (Evaluation), employing targeted strategies to nurture leads through these phases.
• Revenue opportunities: Identify accounts that are transitioning from evaluating to decision stages helps focus efforts on MQLs that are more likely to convert, optimizing resource allocation.
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Sales

• Buyer outreach and relationship uuilding: Sales teams use the insights from the Dev Funnel to initiate outreach at the right time, ensuring that their efforts are met with interest rather than resistance, especially as accounts move into the POC and Deployment stages.
• Customizing sales pitches: Understanding the specific stage of the buyer’s journey allows sales representatives to personalize pitches and demonstrations to address the unique needs and concerns at each stage.
• Reduce sales cycles: By knowing when an account is in the Deployment phase, sales can apply closing strategies tailored to the final concerns of decision-makers, leveraging all preceding interactions and engagements.

Here’s a teardown of how each team can effectively impact every stage of the Dev Funnel.
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Pro tip: You can create Segments based on Dev Funnel in Reo.Dev.

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‍2. Triangulate funnel data with other sources
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• Segments in Reo.Dev: By uploading a LIST of accounts to Reo.Dev, you can combine this with Dev Funnel insights to refine your focus on preferred accounts, enhancing targeted strategies.
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• CRM: Merge Dev Funnel insights with CRM data and deal pipeline information. This allows for a comprehensive view of each account's engagement and potential readiness to buy, facilitating more informed decision-making and strategic allocation of resources.

Pro tip: You can edit the Dev Funnel stage if you believe the Dev Funnel stage should be different.
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If you’d like to learn more about the Dev Funnel and how to leverage it to monetize your open-source project, we’re happy to assist!