Many B2B software companies build outbound campaigns by identifying job titles, selecting target industries, and sending emails to hundreds or thousands of prospects.
For example, a startup selling infrastructure monitoring software might target CTOs, Heads of Engineering, and IT Directors at companies with 50–1,000 employees.
The problem is that a suitable job title doesn't mean the company currently needs your product.
From my experience in enterprise software marketing and sales, outbound targeting becomes more useful when company characteristics are combined with signals indicating a potential technology purchase.
I would prioritise companies that have a reason to evaluate your product now, rather than everyone who could theoretically buy it.
Here's how I would approach this for a B2B developer tool or infrastructure software company.
1. Start with the technology companies already use
For technical products, understanding a prospect's technology stack can be more valuable than knowing their industry.
Imagine you're selling backup software supporting VMware, Proxmox, or Kubernetes. A company using one of these technologies is potentially more relevant than another company of the same size whose infrastructure is unknown.
In my work in the data protection industry, I've used technology enrichment data from Apollo & Clay to identify companies associated with relevant infrastructure technologies.
My approach would be:
- Identify relevant technologies. List the platforms your product supports, integrates with, or replaces.
- Find potential accounts. Use Clay, Apollo or another B2B intelligence platform to identify companies associated with those technologies.
- Apply qualification criteria. Filter by geography, company size, industry, and potential deal value.
- Identify decision-makers. Depending on the product, these might be infrastructure managers, engineering leaders, or CTOs.
- Verify the information. Check company websites, job advertisements, technical publications, and other available sources before contacting prospects.
There is an important limitation here.
Technology enrichment data isn't always accurate, especially for internal infrastructure that isn't publicly visible. A company associated with Kubernetes, for example, may use it only for a limited part of its operations.
I would therefore treat enrichment data as a targeting signal, not verified evidence of a company's infrastructure.
2. Look for events that might create a buying opportunity
Technology fit alone doesn't establish purchasing intent.
A company may have used the same backup or monitoring software for years without any plans to replace it.
However, organisational and technical changes can create reasons to review existing solutions.
Here are several signals worth monitoring:
| Buying signal | Why it might matter |
|---|---|
| New CTO or Head of Infrastructure | New leadership may review existing technologies and suppliers |
| Infrastructure migration | A move between platforms may require new integrations, monitoring, or backup capabilities |
| Engineering team expansion | Additional workloads and users can increase infrastructure requirements |
| New compliance obligations | Regulatory or contractual changes may require additional technical controls |
| Technology replacement initiative | A company may already be evaluating alternatives |
I've found new technical appointments useful as a reason to initiate conversations in the data protection industry.
For example, a newly appointed infrastructure manager may be reviewing backup reliability, recovery procedures, or existing vendor contracts.
That doesn't mean every new hire represents a sales opportunity. However, it provides a more relevant reason to investigate than simply finding another person with the right job title.
3. Combine several signals to prioritise accounts
I wouldn't rely on a single buying signal.
Instead, I would combine technology fit, organisational changes, and evidence of an actual technical requirement.
Consider two hypothetical prospects for backup software:
| Criteria | Company A | Company B |
|---|---|---|
| Employees | 500 | 500 |
| Relevant infrastructure | Unknown | Proxmox identified |
| Technical leadership | No recent changes | New infrastructure manager |
| Infrastructure changes | None identified | Migration project announced |
| Outbound priority | Lower | Higher |
Company B deserves more attention because several signals indicate potential relevance.
This doesn't guarantee that the company needs a new backup solution. But it gives the sales team a specific hypothesis to validate.
For an early-stage startup with limited sales resources, I would rather investigate 30 carefully selected accounts than contact 1,000 companies without understanding their technical requirements.
That being said, targeting quality should ultimately be evaluated through sales results, not assumptions.
4. Write outbound messages around the problem, not your product
Once you've identified a relevant company, the next challenge is making the outreach useful.
One common mistake is opening with a long product description, followed by a list of technical features and a request for a demonstration.
I would approach this differently.
Suppose you sell backup software supporting Proxmox, and you've found a company publicly discussing a VMware-to-Proxmox migration.
Your message could look like this:
Hi,
I noticed your team is planning a VMware-to-Proxmox migration.
One area worth reviewing during this transition is backup and recovery compatibility, particularly whether existing recovery procedures will work across the new infrastructure.
We work with organisations managing these environments. Would it be useful to compare approaches?
This is a hypothetical example, but it illustrates the principle.
The message addresses a specific problem associated with an identified technical change. It doesn't assume the prospect is dissatisfied with the existing vendor or immediately interested in purchasing.
I would only reference a migration, new appointment, or other company event when the information is verified.
5. Add LinkedIn Message Ads to the Account-Based Marketing Mix
Email outreach doesn't have to be the only channel used to reach a prospective customer.
For high-value B2B software accounts, I would also consider LinkedIn Message Ads as part of an account-based marketing (ABM) campaign.
The approach I usually recommend is to build a campaign around one specific target account, or a small group of two or three accounts using similar technologies.
For example, if you've identified three companies running Proxmox infrastructure, you could develop a campaign specifically for relevant technical decision-makers at those organisations.
The process would be:
Select target accounts. Identify one to three companies using the same relevant technology, such as Proxmox.
Create the LinkedIn audience. Use company targeting together with relevant job functions, seniority, or job titles.
Choose a technical whitepaper. Offer a useful resource addressing the technology and the problems your prospective customers may be facing.
Write a short message. Focus on the technical problem and the value of the resource, rather than introducing the company with a long sales pitch.
Track engagement and lead quality. Measure downloads, qualified enquiries, and opportunities generated by the campaign.
For a Proxmox-focused campaign, the message could be:
Subject: Proxmox Backup & Recovery Guide
Hi,
Managing Proxmox infrastructure? We've prepared a technical whitepaper covering backup strategies, recovery considerations, and data protection for Proxmox environments.
Download the whitepaper here: [Link]
I would keep the message short and avoid adding several calls to action. The objective is to offer relevant technical content, not immediately push for a product demonstration.
A whitepaper can also help identify prospects interested in a specific technology before the sales team initiates a more direct conversation.
There is an important practical limitation: LinkedIn campaigns targeting only one to three companies may have insufficient audience size to deliver, particularly when additional job-title filters are applied. Therefore, I would test whether the intended audience is large enough before launching. If necessary, I would expand the campaign to more companies using the same technology while retaining the technology-specific message.
6. Test signal-based outbound against conventional targeting
A common challenge with outbound marketing is proving that a more sophisticated targeting approach actually delivers better results.
I would run a small comparative experiment instead of immediately investing in more data subscriptions and automation.
Step 1: Create two prospect groups
- Group A: Companies selected by conventional criteria, such as industry, employee count, and job title.
- Group B: Companies meeting the same criteria but also showing relevant technology or organisational signals.
Step 2: Prepare comparable outreach
Use similar messaging structures and offers for both groups, adapting the content only where a verified buying signal makes the message more relevant.
Step 3: Record the actual acquisition costs
Include data subscriptions, research time, contact verification, outreach tools, and the cost of sales involvement.
Step 4: Compare outcomes
Evaluate positive replies, qualified conversations, sales opportunities, and cost per opportunity.
For an initial test, I would consider approximately 50–100 prospects per group, depending on the available market and resources.
This sample will not provide statistically conclusive results in most cases. It can, however, help identify whether signal-based targeting is worth investigating further.
I would also expect a longer evaluation period for enterprise software, where sales cycles often extend well beyond the first few weeks of outreach.
7. When signal-based outbound may not be worth the investment
Not every developer-focused company needs this approach.
For a self-service SaaS product costing $20 per month, manually researching technical buying signals might make customer acquisition too expensive.
In that case, organic search, technical documentation, developer communities, and product-led acquisition may be more efficient.
On the other hand, for enterprise software with larger annual contract values, spending additional time researching relevant accounts can be commercially reasonable.
I would evaluate the economics before scaling.
There are also operational limitations:
- Data accuracy: Technology and employment information can be outdated or incomplete.
- Signal relevance: A new technical appointment doesn't necessarily indicate budget or purchasing authority.
- Market size: Very narrow technology segments may not provide enough accounts for scalable outbound.
- Compliance: Contact collection and outreach must comply with applicable privacy, electronic marketing, and anti-spam requirements.
More sophisticated targeting isn't automatically better if the additional research costs exceed the commercial benefits.
What I Would Recommend for an Early-Stage Developer Tool
If I were responsible for acquisition at a B2B developer-tool startup, I would start with three questions:
- Which companies are technically suitable for our product? Identify relevant technologies, company characteristics, and operational requirements.
- Which of these companies have a potential reason to evaluate a solution now? Look for technical migrations, new infrastructure leadership, expansion, or other verifiable events.
- Can we convert these signals into qualified opportunities at an acceptable cost? Compare signal-based outreach with conventional targeting and measure results using CRM data.
I wouldn't abandon company size, industry, or job titles. They're still useful for identifying potential customers. However, I would use buying signals to prioritise accounts and improve the relevance of outreach.
Have questions about B2B outbound or account-based marketing for developer tools? Leave them in the comments. I'm happy to share practical recommendations based on my experience with technology companies, including targeting, buying signals, LinkedIn campaigns, and lead generation.
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