Developer GTM is fundamentally different from traditional SaaS GTM. This comes from the reality of how developers discover and engage with products, and how developer teams move through a buying journey.
Yet most Developer GTM teams still run legacy SaaS GTM models and funnels that ignore this reality — forcing round pegs into square holes and operating with poor visibility into developer adoption and buyer interest.
It’s like playing baseball with a cricket bat. You might get a flashy hit for a 30-second TikTok clip, but you can’t win a 9-inning game with the wrong equipment. A few deals might land, but systemically the traditional SaaS funnel is too blunt an instrument to support Developer GTM.
This results in GTM with a poor MQL to SQL ratio, “hot” accounts late in the funnel suddenly going dormant, and a ultimately weak correlation between end-funnel pipeline value and actual Win Rate and Revenue.
Unique developer adoption and buying behaviour
Bottom Line; Up Front: Developer interest doesn't guarantee revenue opportunity, but revenue opportunity without developer interest will never turn into revenue realisation.
Developer interest doesn’t automatically translate to buying intent. Many developers explore tools out of curiosity or for skill-building.
Conversely, without developer buy-in, organizational purchase intent rarely converts. Even with a strong top-down business case, frontline developers must validate features, compatibility, performance, and other technical requirements before moving forward.
It’s like a jewellery store assuming every window shopper is ready to buy. Most people are just looking until the product works its magic and one day, lures them into the store for a real purchase.
Developers always engage deeply: Whether driven by personal curiosity or organizational purchase intent, developers dive deep into evaluating developer tools. They signup or deploy the product, read documentation thoroughly, test APIs, fork repositories, and experiment with real implementations. Activities that traditionally signalled high intent in traditional SaaS - such as e-book downloads, demo video views, pricing page visits, even conference attendance - are too shallow to carry any value in Developer GTM.
Interest and intent are decoupled. A developer's personal interest in your technology doesn't automatically translate to organizational buying intent. Developers will “experiment” with a tool or technology out of their own curiosity or desire to learn, in anticipation of applying this knowledge to a future business use-case.
Conversely, organizational purchase intent without developer buy-in rarely converts to actual revenue. Purchase intent will always follow on from a defined need or problem to be solved, with buy-in both from frontline developers who will implement the solution and developer team leadership who will pay for the solution. At this time, even shortlisting a set of tools to evaluate from the myriad options available in the market will bias towards tools which frontline developers are familiar with, vs. “unheard of” options.
Purchase decisions stall without bottoms-up validation. Regardless of who holds the budget or makes the final call on paper, front-line developers wield enormous influence over which solutions get evaluated and ultimately adopted.
A product might look attractive on executive parameters such as ROI and Cost, but it still needs frontline developer validation. Frontline developers who will implement the solution need to validate feature coverage along with a host of other factors (eg: version compatibility, security and encryption, support levels and SLAs, latency, etc) before “green lighting” a solution. Without this frontline developer validation, it is very difficult to push the adoption of a developer tool with purely top-down intent.
Funnels built on Shallow signals leads to Pipeline Fog
Bottom Line; Up Front: The traditional SaaS identifies the ICP based on weakly-correlated but readily available firmographic and demographic filters instead of using more strongly-correlated technographic filters. Prospects are then force-fitted into progressing through a linear funnel, when the prospect’s buying journey is inherently non-linear. This progression is done basis shallow signals - vague, internet-activity based signals which don’t strongly correlate with buying intent.
The end result is Pipeline Fog - a state of a full pipeline, but with no clear objective indication of Prospect-Solution fit, and willingness to buy in the immediate term.
All these issues have been inherited into Developer GTM, where the situation is exacerbated due to the “deep signal” nature of Developer-Product evaluation.
Most developer tools companies inherit the traditional SaaS funnel model:
Contacts → Leads → MQLs → SQLs → SQOs → Closed-Won/Lost
This linear progression worked poorly even in traditional SaaS (where a 15% MQL-to-SQL conversion rate is considered "good") due to the reliance on inaccurate ICP definitions, shallow signals and forcing a non-linear buying journey into a linear model.
In developer GTM, these problems are exacerbated and the funnel is completely broken. Here's why:
1. Vague ICP Definitions
First, the idea of an ICP is interchangeably used between “person ICP” and “Company ICP” → meaning both or either. That’s before we even get into other vague ideas like “buying committees” that muddy the water.
Second, the definition of an ICP (company or person) has traditionally been built respectively on firmographic and demographic data, vs other filters that actually signal buying interest.
A developer tool should qualify companies by employee count and revenue instead of their infrastructure reality (Kubernetes clusters, database choices, deployment patterns).
A Developer tool should define "front-end developers" by job titles and years of experience rather than their actual technology stack (React, TypeScript, Vite, Tailwind).
Firmographic and demographic data has been relied upon over the years due to this data being more readily available, not because it correlates with buying intent.
However, technographic signals have been improving over the years, both in availablity and accuracy. Its time for developer tools to rely on accurate ICP-defining parameters.
2. Shallow Signals Drive Progression
The SaaS industry has mastered mapping these signals onto the traditional cusotmer journey from Awareness through Consideration to purchase, and assigning meaning to these activities.
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But the reality is over time, as content on the internet has exploded - these “content consumption” signals are largely meaningless. Ebook downloads, blog views or product demo video views hardly mean that someone is looking to buy a solution.
In Developer GTM, these Shallow Signals completely bypass the actual signals of interest from developers and fail to capture how developers actually engage with products or why accounts are evaluating solutions.
The result? GTM teams operate blind to real buying intent, making every interaction interruptive rather than helpful.
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3. Buying Journey is non-linear, but forced into a linear funnel
The traditional funnel assumes time-bound, linear progression—a massive oversimplification of complex B2B buying journeys, especially in Developer GTM.
Developer evaluation can often happen much before actual purchase decisions, yet our funnels mark these accounts as "gone cold." The contact-centric (rather than account-centric) approach compounds the problem, losing track of account-level interest aggregated over multiple developers evaluating different aspects of a producy.
Outcome: Pipeline Fog
The outcome is Pipeline Fog.
Developer GTM teams may have a “healthy” pipeline from outward appearances - huge traffic on the website, lots of signups, even a health number converting into MQLs, and even a sufficient number talking to Sales and being marked as an SQL.
But, dig deeper, and the number of guesses and subjective opinions increases, as a prospect progresses down the shallow signal funnel:
- Will they buy?
- Will they buy this quarter?
- Have they evaluated the product?
- What do they even need it for? Really?
- What concerns on product capability do they have?
- Who else in the org needs to be convinced?
All these questions, and more, rely on qualitative feedback from Sales Reps and extrapolations from intentional obfuscated statements from buyers, vs. relying on objective measures of evaluation, testing and usage by developers.
Pipeline fog is like holding your hand to a person’s forehead and deciding if they’re sick or not. It often seems to work, but there’s a lot more being missed under the surface.
The Solution: Signals-Everything Developer GTM
Bottom-Line; Up Front: Replace the Company ICP with the “Tech Qualified Organization. Replace the Person ICP with the Tech Qualified Developer. Both are built fundamentally on Technographic filters, with firmographic / demographic filters layered on after.
Remove the shallow-signals funnel, remove the concept of MQLs and SQLs (subjective gates) and replace with them with two funnels:Developer Adoption Journey → Awareness, Curiosity, Experimentation, Advocacy Ready
Developer Team Buying Journey → Problem Identification, Solution Discovery, Validation & Testing, Building POC, Adoption, Purchase Decision.
While they may run independently in time, the Dev-Team Buying journey cannot progress without the adoption journey. Conversely, the more developers deep into the adoption journey, the higher the likelihood of not only progressing deeper into the buying journey, but also accelerating and skipping ahead directly to PoC and purchase.
We need to rebuild developer GTM from the ground up, starting with a "signals are everything" approach where real developer activity informs every aspect of the go-to-market motion.
Tech-Qualified Teams (TQT) and Tech-Qualified Developers (TQD) become the new ICPs, while Developer Qualified Accounts (DQA) and Developer Qualified Opportunities (DQO) replace subjective funnel gates - MQLs and SQLs.
It’s like swapping a street map for a topographic one. You now see the real terrain, with all the elevations, obstacles, and fastest paths forward.
Tech-Qualified Team (TQT)
The “Tech Qualified Team” replaces the traditional company ICP. We propose defining the target at a team level, as this helps identify potential teams using the right tools with a large organization.
Organizations are qualified based on technographic fit as the primary filter - their actual infrastructure, tooling, and technical requirements. Additional firmographic filters can refine the definition, but the foundation is always technographic.
For example, instead of targeting "Series B SaaS companies with 50-200 employees," a data streaming platform might define TQT as:
- High fit: Teams running Kafka in production, streaming over 500GB of data a month, and managing large-scale, high-frequency event pipelines.
- Medium fit: Teams running Kafka with moderate volumes or limited real-time processing needs.
- Low fit: Teams working on data streaming but using entirely different tooling such as Kinesis or Pulsar.
Tech-Qualified Developer (TQD)
Tech-Qualified Developer(TQD) replaces demographic-based personas and Person-ICPs with technographic profiles.
Instead of "Senior Frontend Developer II," we describe "developers working daily with React, TypeScript, and modern tooling like Vite, Tailwind, and Storybook, with key concerns around UI performance, rapid iteration, and seamless CI/CD integration."
The New Funnel: Developer Qualified Accounts
Lets build the new funnel by first removing MQLs and SQLs.
Accounts should not be “qualified” by Marketing or Sales, either on seemingly objective or overtly subjective criteria. Accounts should be qualified based on developer usage and evaluation of the product.
Developer Qualified Account (DQA) replaces both MQL and SQL with objective intent signals showing genuine purchase intent. These are the Accounts from your TQO Universe which have manifested real Account-level activity and thus, showing some level of objectively-measurable buying interest.
Since individual developer signals without account-level aggregation are useless, DQAs aggregate contact-level developer intent signals to create account-level qualification. Individual developer signals may be leveraged for other activities (eg: DevRel) but the funnel is built consistently from beginning to end on Accounts.
The New Funnel - Developer Qualfied Opportunities
Developer Qualified Opportunity (DQO) represents a DQA that has progressed to become an Opportunity. While every developer GTM team will have (and should have) their own threshold for graduating a DQA into a DQO, A simple model using BANT criteria would look like this:
- Budget in place
- Decision-maker Authority defined
- Clear Need/use-case identified
- Purchase Timeline established.
In addition, Developer GTM teams over time should look to incorporate signal-based criteria as an additional mandatory filter for graduating a DQA to a DQO.
The New Funnel: Distinct “Buying” and “Adoption” Journeys
The new model recognizes two distinct but interconnected journeys:
The Developer Adoption Journey (Individual)
This journey is individual, non-linear, and independent of organizational buying intent. A developer might experiment with a new framework even if their company is locked into a different technology stack. All stages are based on increasing usage and more complex use-cases being experimented, based on actual usage signals.
The stages and some example signals are below:
- Awareness: Every developer who shows up even once
- Curiosity: Developers who spend time reading documentation
- Experimentation: Signed up, installed packages, forked repositories
- Advocacy Ready: Using the product, potentially not paying yet
The Developer-Team Buying Journey (Organizational)
This journey is account-level, initiated by organizational problems, and still non-linear with inevitable back-and-forth movement. Progression is based on measurable intent signals, with signals indicating an intent to adopt the product in the near future broadly corresponding with a Developer-Qualified Opportunity.
- Developer Qualified Accounts:
- Problem Identification: Organization recognizes a technical need
- Solution Discovery: Evaluation of available solutions begins
- Validation & Testing: Deep technical evaluation and testing
- Developer Qualfied Opportunities:
- Building POC: Proof-of-concept development and validation
- Adoption: Implementation in production environments
- Purchase Decision: Commercial agreement and contract execution
Where the Journeys Intersect
The magic happens when these journeys intersect. When a TQT enters the buying journey AND individual developers within that organization have progressed along the adoption journey, two things happen:
- Higher likelihood of the solution advancing through the buying journey
- Higher velocity through each buying stage
The further developers have progressed on their individual adoption journeys, the greater the acceleration through the organizational buying process.
Real-world examples:
- A Confluent power-user joins a Kafka-using company that hasn't adopted Confluent yet—the likelihood and speed of Confluent adoption increases dramatically
- When evaluating feature flag tools, having a developer who's already an advocate for Unleash internally accelerates both likelihood and speed of Unleash selection over competitors
Practical Implementation: The Day in the Life
Here's how this translates to daily GTM operations:
For SDRs:
- Flag accounts newly progressed to POC stage for AE pipeline consideration
- Filter accounts in Evaluation stage for targeted outreach
- Within those accounts, prioritize outreach based on individual developer adoption journey progression—engage with a junior developer who's become an advocate over a senior developer who's merely aware
For AEs:
- Focus deal energy on DQOs with clear BANT qualification
- Leverage internal champions identified through adoption journey tracking
- Customize demos and technical discussions based on specific buying journey stage
For Marketing:
- Create content targeted at different adoption journey stages
- Nurture individual developer interest even when organizational buying intent isn't clear
- Measure success by progression through both journeys, not just traditional funnel metrics
The Path Forward
Developer GTM requires fundamentally different thinking. Traditional demographics and firmographics become secondary to technographics. Shallow engagement signals give way to deep technical intent signals. Linear funnels evolve into interconnected journey mapping.
The companies that recognize this shift—and rebuild their GTM accordingly—will create sustainable competitive advantages. Those that continue forcing developer buyers through traditional SaaS funnels will keep wondering why their pipeline never converts.
The question isn't whether developer GTM is different. It's whether you're ready to embrace that difference and build your go-to-market motion accordingly.
Ready to implement signals-based developer GTM? Reo.Dev provides the technographic intelligence and journey tracking capabilities that make this new approach possible. Learn more about how leading developer tools companies are rebuilding their GTM motions with real developer intent signals.
