How to Find Companies Using a Specific Software (Technographic Prospecting)

The companies most likely to buy your software are already paying for software exactly like it. They have the budget allocated, the internal use case validated, and a decision-maker who owns that category. Technographic prospecting — finding companies based on the specific tools they use — is the fastest way to build a prospect list that converts, because you're not guessing at fit. You're reading it directly from the tools already running in their stack.

What is technographic prospecting and why does it work?

Technographic prospecting is the practice of identifying and targeting companies based on the software products they use. Instead of filtering prospects purely by industry, headcount, or revenue, you add a technology layer: this company uses Salesforce, that one runs on HubSpot, this other one is actively using your direct competitor.

It works because software adoption is a proxy for buying intent. A company running a legacy CRM has a clearly scoped problem. A company using your competitor's product has already committed budget to solving the exact problem you solve. Both are far warmer prospects than a company that fits your ICP on paper but shows no signal of active category engagement.

The underlying logic is the same as account-based selling — fish where the fish are — but technographic data makes that concrete. Instead of guessing which accounts are in-market, you can read it from their stack.

"When we shifted from firmographic to technographic targeting, our booked-meeting rate from cold outreach doubled in the first quarter. The reps weren't working harder — they were just talking to companies that already cared about the problem."

— Head of Sales, 60-person SaaS company (Stealery customer)

According to Gartner's B2B buying research, buyers spend only 17% of their total purchase journey talking to potential vendors — the rest happens through independent research, peer conversations, and internal evaluation. Technographic data helps you reach companies during that independent research phase, before they've formally entered a procurement cycle, when your outreach still reads as timely rather than reactive.

How do you find companies using a specific software?

There are four main methods, each with different accuracy profiles, coverage, and cost. Most serious prospecting workflows use at least two.

1. Browser-based technology detection

Tools like BuiltWith, Wappalyzer, and SimilarTech scan the publicly visible code of a company's website and identify what's running on it — analytics platforms, CMS systems, marketing automation tools, live chat providers, A/B testing software, and more. This is highly accurate for front-end and marketing technologies and covers millions of domains globally.

The limitation: it only sees client-side tools. If a company uses Salesforce as their CRM or Slack internally, a website scan won't surface that. These tools excel for MarTech and ecommerce stack research, less so for back-office software.

2. Job posting intelligence

Job postings are the most underused free signal in B2B sales. When a company posts a role requiring "experience with Gong, Outreach, and Salesforce," they've just told you their entire revenue tech stack. This data is public, constantly refreshed, and covers tools that website scanners never see. We'll cover how to extract it systematically in the next section.

3. Dedicated technographic data providers

Platforms like Bombora, Demandbase, and 6sense aggregate technographic signals from multiple sources and layer them with intent data. These are typically sold as part of a broader enterprise ABM platform. The coverage is excellent; the cost reflects that.

4. Competitor-specific tools

If your goal is specifically to find companies using a competitor — rather than any arbitrary tool — dedicated tools do this more efficiently than general technographic databases. This is where Stealery fits: you type in a competitor name, and it returns a filtered list of companies known to be using that product, enriched with company size, location, and hiring signals. What would take hours of cross-referencing job boards and LinkedIn takes about 30 seconds. It's purpose-built for SDR workflows where the use case is "find me everyone paying for X so I can offer them Y."

5. LinkedIn and social signals

LinkedIn job postings, employee endorsements for specific tools, and company page updates sometimes surface software usage. This is manual and doesn't scale, but it's useful for high-value account research where you need to confirm a signal before investing outreach effort.

How do you read job postings for tech stack signals?

Job postings are a real-time, high-accuracy source of technographic data that most SDRs ignore. The reason they work: when companies write job descriptions, they name the tools employees need to use. That list is essentially a technology audit published for free.

The signal is strongest in these role types:

• Revenue Operations / Sales Ops: Almost always lists the full CRM, sales engagement, forecasting, and BI stack.
• Marketing Ops / Demand Gen: Names the MAP, analytics, SEO, and paid platforms in use.
• SDR / AE roles: Lists sales engagement tools (Outreach, Salesloft, Apollo), dialers, and CRMs explicitly.
• IT / DevOps / Engineering: Surfaces infrastructure tools, cloud providers, data platforms, and security software.
• Finance / Accounting: Names ERP, billing, and financial reporting tools.

To do this at scale, you can search LinkedIn Jobs or Indeed with a boolean query combining the software name and role type. For example: "Salesforce" AND ("Sales Operations" OR "RevOps"). Filter to the last 30 days to ensure the companies are actively hiring and the stack signal is current.

The key discipline here is recency. A job posting from 18 months ago that mentioned a tool doesn't confirm current usage — companies switch tools, consolidate stacks, or post evergreen roles they update infrequently. Prioritize postings from the last 90 days.

How do you prioritize and filter a technographic prospect list?

A raw technographic list — every company using software X — is a starting point, not a prospect list. Volume without prioritization produces noise. The best-performing SDR workflows apply three filter layers before a single email goes out.

Layer 1: Firmographic fit

Start with your ICP parameters: company size (headcount or revenue), geography, and industry vertical. A list of 10,000 companies using a competitor tool is only useful if you then cut it to the 400 that are in your serviceable market. Apply these filters first — they're the cheapest to run and eliminate the most irrelevant volume.

Layer 2: Timing signals

Prioritize companies showing signals of current activity: active hiring in roles that touch the category you sell into, recent funding rounds (fresh budget, often accompanied by a tech stack review), or expansion into new markets. A company that raised a Series B three months ago and is hiring a Head of RevOps is in active evaluation mode. That's a very different conversation than a stable, slow-growth business with no recent activity.

Layer 3: Contract timing estimates

Software contracts typically run 12 or 24 months. If you can estimate when a company adopted a competitor's tool — based on the age of relevant job postings, a LinkedIn post from the time of implementation, or integration guides they published — you can time your outreach to land roughly 60–90 days before their likely renewal window. This is when they're evaluating whether to renew, not six months after they've already signed again.

McKinsey research on B2B sales consistently shows that relevance and timing are the two highest-leverage variables in converting cold outreach. Technographic targeting solves relevance; contract timing estimation solves timing. When you have both, you're no longer doing cold outreach in the traditional sense — you're doing timely outreach to companies with an active reason to listen.

How do you write cold outreach using software usage data?

The signal you've gathered only converts if your outreach message uses it in a way that feels specific, not surveillance-y. The goal is to make the prospect feel like you did your homework on their specific situation — not like you scraped a database.

Lead with the shared context, not the feature

Don't open with "I noticed you use [Competitor]." That reads like a data pull. Instead, reference what that tool usage implies about their current workflow or problem:

"Most RevOps teams running on [Competitor] hit the same wall around pipeline visibility — the reporting is flexible, but rolling up forecasts across multiple regions requires a lot of manual work."

This shows you understand the tool deeply enough to know its pain points. That's the credibility signal that earns a response.

Name the trigger, not just the tool

If you found the prospect through a job posting, the job posting itself is the trigger:

"Saw you're hiring a Sales Ops Manager — the job description mentioned [Competitor] and [Tool]. We work with a few teams in that setup who were spending 8+ hours a week on [specific manual process]."

This is a legitimate, professional use of the signal. It's the same reasoning a recruiter or a consultant would use. It doesn't feel like surveillance because the information was published publicly for exactly this purpose.

Keep the ask narrow

The outreach is not the close. You're not selling the product — you're selling 20 minutes. End with one question or one specific ask, not a pitch for all your features. The technographic insight earns you the right to ask for a conversation; it doesn't close the deal on its own.

What are the limits of technographic data — and how do you work around them?

Technographic data is powerful, but treating it as ground truth leads to bad outreach. Three limitations matter most for SDRs.

Data staleness

Software stacks change. A company that was on [Competitor] 18 months ago may have already switched, consolidated, or churned out of the category entirely. Stale technographic data produces outreach that lands wrong — referencing a tool the prospect stopped using is worse than not mentioning it at all. Apply a recency filter to every list. If you can't confirm a signal is less than 90 days old, treat it as unverified and adjust your messaging accordingly.

Coverage gaps for internal tools

Browser-based detection is blind to anything that doesn't touch the company's public website. CRMs, ERPs, HR systems, finance software, and internal communications platforms will not appear in a BuiltWith scan. For these categories, job postings and LinkedIn are your primary signals, supplemented by tool-specific community data (Salesforce AppExchange reviews, G2 profiles, Capterra listings where companies self-identify).

Usage ≠ satisfaction

A company using your competitor isn't necessarily unhappy. They might be deeply embedded, mid-contract, and actively defending the tool internally. Technographic data tells you they're in the category; it doesn't tell you where they are in the satisfaction curve. Your outreach should be structured to surface dissatisfaction, not assume it. Ask questions that let the prospect surface their own frustrations rather than telling them they should be frustrated.

The workaround for all three: layer technographic data with at least one additional signal before prioritizing an account. A company that shows technographic fit and recent relevant hiring and a recent funding event is a far stronger bet than technographic fit alone. Signals compound.