I've worked with B2B technology companies, proprietary / open source software vendors, and startups for almost 20 years. One problem I've observed repeatedly is that technically strong products often struggle to generate enough qualified leads and paying customers.
The product may solve a real problem, have good documentation, and receive positive feedback from early users. However, organic traffic remains limited, paid advertising is expensive, and the number of new customers grows too slowly.
In my experience, this often happens because founders concentrate on product development while postponing customer acquisition until the product is ready.
Here are four areas I would prioritise when marketing a developer-focused product.
1. Analyse search demand before investing in marketing
One mistake is building marketing campaigns around product features rather than problems potential customers are trying to solve.
For example, developers may not search for a particular database monitoring platform. Instead, they search for:
- How to monitor PostgreSQL performance
- PostgreSQL slow query troubleshooting
- Open-source database monitoring tools
- Datadog alternatives for PostgreSQL monitoring
These queries represent different stages of the buyer's journey. Some users need technical information, while others are already comparing products.
I would start by identifying 20–30 relevant search queries, analysing the existing Google results, and grouping them by search intent. A good idea is to export the first 50K rows of your Google Search Console API data, if you already have a website, and find tons of prompts to promote there.
Tools such as Google Search Console, Mangools, and Google Keyword Planner can help identify opportunities. However, keyword search volume alone is not enough. You also need to understand whether the searcher is a potential customer.
Your objective is not simply to attract more visitors, especially in the age of AI discovery, when the classic SEO is dead because of AI Overviews. It is to attract developers and decision-makers who might eventually adopt the product.
2. Use technical content to generate qualified leads
Many developer-focused companies invest heavily in product documentation but relatively little in content that attracts new users.
Documentation is essential for existing customers and developers evaluating the product. However, it may not generate enough visibility among people who have not yet discovered your solution.
My recommendation would be to create content around specific technical problems, including:
- Troubleshooting guides with reproducible examples
- Comparisons between alternative technologies
- Integration tutorials and walkthroughs
- Performance optimisation recommendations
- Explanations of architectural trade-offs
For example, an article about reducing PostgreSQL query latency could demonstrate actual SQL queries, explain the diagnostic process, and show where a monitoring tool becomes useful.
This approach provides practical value while introducing the product in a relevant context. It is highly citable in AI discovery pathways.
The content should be technically accurate. If marketing specialists cannot produce it independently, I would involve developers in reviewing examples and recommendations.
3. Measure customer acquisition, not just organic traffic
I've seen marketing reports where organic traffic increased substantially, but the number of qualified leads remained almost unchanged.
This usually indicates that the company is attracting the wrong audience, that visitors aren't converting, or that the measurement setup doesn't capture their subsequent actions.
For a developer product, I would monitor:
- Organic traffic: How many relevant visitors reach product pages, technical articles, and documentation?
- Signup conversion rate: What percentage of these visitors create an account or start a trial?
- Activation rate: How many registered users complete a meaningful product action, such as connecting a database or executing their first backup?
- Paid conversion rate: How many activated users become paying customers?
- Customer acquisition cost (CAC): How much does the company spend to acquire one paying customer?
For B2B products with sales-assisted acquisition, I would also track qualified opportunities and the pipeline generated by each marketing channel.
Google Analytics 4 can help analyse acquisition and conversion behaviour. Product analytics and CRM data are needed to connect initial visits with activation, sales opportunities, and revenue.
Without this connection, it is difficult to understand whether content marketing is commercially effective.
4. Test various distribution channels before increasing the budget
SEO is important, but it can take time to produce results, especially for a new domain competing against established technology vendors.
I would therefore test several acquisition channels in parallel, rather than relying exclusively on organic search.
For a developer-focused product, these might include:
- GitHub: Open-source examples, integrations, and useful technical resources.
- DEV Community: Practical tutorials and technical discussions.
- Stack Overflow and Stack Exchange: Genuine answers to relevant technical questions, without unnecessary product promotion.
- Google Ads: Targeted campaigns for high-intent queries where potential customers are actively evaluating solutions.
- LinkedIn: More relevant when targeting engineering managers, CTOs, and enterprise decision-makers.
- ChatGPT Ads: An emerging acquisition channel that allows companies to reach users researching technical problems, comparing software solutions, and evaluating alternatives. In my recent experience with B2B technology campaigns, this channel has generated high-quality leads, including relevant business enquiries. I would recommend testing it early, particularly for products addressing complex technical problems.
Not every channel will work equally well. For example, LinkedIn Ads may be too expensive for a low-priced self-service developer tool. On the other hand, they can be worth testing for enterprise software with larger contract values.
I would allocate a limited initial budget, compare acquisition costs and lead quality, and increase investment only in channels demonstrating commercial potential.
What I Would Do During the First 90 Days
For a startup with limited marketing resources, my approach would be relatively simple.
Month 1: Analyse and prepare
- Identify the target audience and the problems the product solves.
- Analyse competitors, search demand, and existing traffic.
- Configure conversion tracking and define activation events.
- Select 10–15 priority keywords/prompts and content opportunities.
Month 2: Launch initial acquisition activities
- Publish 3–5 technically useful articles.
- Improve relevant product landing pages.
- Distribute content through appropriate developer communities.
- Test a small paid search campaign if commercial search demand exists.
Month 3: Evaluate and optimise
- Analyse which content attracts relevant traffic.
- Compare signup and activation rates by acquisition channel.
- Identify technical articles contributing to conversions.
- Reduce spending on ineffective activities and prioritise channels producing qualified users.
Ninety days may not be enough to establish substantial organic visibility. However, it should provide initial data about acquisition costs, conversion behaviour, and which marketing activities deserve further investment.
Final Recommendations
In my opinion, early-stage dev marketing should not begin with a large content calendar or an expensive advertising campaign.
I would first validate three things: whether potential customers are actively looking for the problems your product solves, whether you can reach them efficiently, and whether the resulting users convert into paying customers.
Only after that would I increase the marketing budget. For developer-focused startups, technical expertise is already a competitive advantage. The challenge is converting that expertise into search visibility, relevant traffic, product adoption, and eventually revenue. And this should be measured from the beginning.
I'm a tech marketing consultant based in Australia, with 19+ years of experience in B2B and B2C technology, particularly data protection, virtualization, database performance tuning, coding addons, SharePoint and much more. I work with software companies and startups on improving acquisition efficiency and generating qualified sales pipeline.
Top comments (6)
Point 3 is where most early teams lose the thread. Before 100 customers, I'd track one number ahead of signups: how many people paid for a specific outcome, even a small one. At Moltgate the rule we work by is learn from what sells, build upon what repeats. A fixed-price offer in the $49 to $299 range is a cheap channel test too. If a channel can't bring one paying buyer in a few weeks, more content there usually won't change it. In the B2B cases you've seen, which channel brought the first 10 paying customers?
Bonjour Florian, it depends on the niche, but I'd say it's a mix of search and paid social, incl. remarketing, or affiliate.
I'd push back a little on 'classic SEO is dead'. For dev tools, the searches that convert are still mostly the 'alternative to' and 'how to fix' pages, since those get clicked by people already close to buying. I'd guess AI Overviews hit the broad 'what is' queries much harder than those. Have you split your Search Console data by query type, or is that more of a general read?
I did, for me alternative queries fall into AI Overviews dominated category, as it'll instantly show all alternatives. With detailed walkthroughs or how to fix stuff, I tend to agree.
Point 1 has an outbound twin that developer-tool founders skip for the same reason they postpone marketing: analyse the demand before building the list. Most first-100 outbound for a dev tool goes to a title ("VP Engineering at a Series A company") and gets ignored, because a title is not a reason to look at a new tool this month. The lists that got replies for me were built from a change instead: engineering leaders in their first ninety days, where a stack review is part of the job, and founders who left a large employer this year, who buy their tooling in the first weeks, before procurement exists.
Both are countable before you write a word, the same way you count search queries. The people databases expose an aggregate mode now, so "how many engineering leaders started a new role in the last twelve months at 50 to 5,000-person companies in my three countries" is a number you read before deciding whether the campaign is worth running. The month I did this, the founder list across five countries was about six thousand people, and the first email that worked looked like your "how to fix" page: the problem they are about to have, not the product.
What also worked for me in the data protection industry is to touch base when the new tech hire happens (as you also suggest) or also the data on what tech is used inside (from Apollo enrichment).