How to Find People Looking for Alternatives to Your Competitor

There are two ways to find people who want to switch from a competitor to your product.

The first is research-based: identify who the competitor's customers are (through review sites, case studies, LinkedIn, job postings, BuiltWith data), build a target list, and run cold outreach. You're finding people who *might* be switchable based on their profile and the competitor's known weaknesses.

The second is signal-based: monitor the communities where your competitor's customers are active, and surface the specific posts where someone publicly states they want an alternative — "thinking about switching from [competitor]", "[competitor] alternatives?", "[competitor] just changed their pricing and I'm looking for something new". You're finding people who *are* actively switching, right now, in public.

The difference in conversion rate is significant. A cold email to a verified competitor customer based on a list hit rate of 1-2%. A timely, helpful response to someone who just asked for alternatives to a competitor in a Reddit thread or LinkedIn post converts at 10-15%+. One is interruption; the other is relevant timing.

This guide covers both approaches — and why most teams should be doing both simultaneously.

Research-based methods: build your competitor customer list

Review sites (G2, Capterra, TrustRadius)

Review sites are the most direct source of competitor customer data. Every reviewer on G2 lists their company, industry, company size, and job title — along with what they think of the product. This is your competitor's customer demographics, self-reported by actual buyers.

More valuable than the star ratings: the review text. People write things like "we switched from [your product] because...", "we evaluated four tools and chose this one because...", and "at our 200-person company, we use this for...". These are buying signals and switching triggers, volunteered publicly.

Competitor case studies and logo bars

Your competitor published their case studies to impress buyers. Those same case studies tell you exactly who to target. Company name, industry, size, what problem they were solving, how they use the product — all public.

The logo bar on their homepage is the fast version: these are the accounts they think will impress prospects. Enterprise logos mean they sell enterprise. Startup and mid-market logos reveal their sweet spot.

LinkedIn: three search approaches

Skills search: Search for the competitor's product name in LinkedIn profile Skills sections. People list tools they use as skills. Every profile that appears is a current or recent user — their employer is a customer.

Experience search: Search the competitor name in Experience descriptions. "Implemented [competitor] across the revenue team" or "managed our [competitor] instance" — each match is a confirmed customer.

Engagement analysis: Look at who consistently likes and comments on the competitor's LinkedIn posts. Repeat VP-level engagers from different companies are almost always customers or very warm prospects.

Job postings

When companies hire, they list required tool experience. Search "[competitor name]" on LinkedIn Jobs or Indeed. "3+ years experience with [competitor product] required" means the posting company is a customer. This produces a rough customer list generated from public job data, and the volume tells you market penetration.

BuiltWith and tech stack data

For SaaS and software competitors, BuiltWith identifies companies using specific technologies on their websites. If your competitor's product is deployed client-side, you can find a list of companies using it. This works well for marketing tech, analytics tools, e-commerce platforms, and developer tools.

Signal-based approach: find people actively looking to switch

The research-based methods above produce a list of competitor customers you can cold-reach. The signal-based approach finds the subset of those customers who are actively looking to switch — right now — in public.

What "actively looking to switch" looks like

People who are mid-evaluation don't typically announce it by emailing vendors. They post in the communities they trust:

This is the highest-intent prospect category you'll ever encounter. They're not guessing they might need something different. They're actively seeking it, publicly, right now.

Monitoring for switching signals

Manual approach: Identify the 10-15 subreddits, LinkedIn Groups, and communities where your competitor's customers are active. Do daily searches for the competitor name sorted by new. Check for the specific intent language — "alternative", "switch", "replace", "moving off", "frustrated". Respond within the thread's active window (typically 2-4 hours on Reddit, 24-48 hours on LinkedIn).

The limitation: manual monitoring across 15+ communities is unsustainable. You'll miss posts. You'll respond too late. And you're not checking Hacker News, Facebook Groups, and industry forums alongside the major platforms.

Tool-based approach: Dedicated monitoring tools surface competitor switching signals in near-real time.

Handshake monitors Reddit, LinkedIn, X, Facebook Groups, Hacker News, Instagram, TikTok, and industry forums simultaneously for competitor mentions in switching and evaluation contexts. The intent classification layer distinguishes "someone is actively evaluating alternatives" from "someone mentioned the competitor in passing" — surfacing only the high-value posts worth responding to.

When Handshake identifies a post where someone is actively seeking alternatives to a competitor, it surfaces the post with context, drafts a contextually appropriate reply calibrated to that specific community's culture, and either routes it for review or posts automatically via Chrome extension.

The timing advantage is significant: competitor switching posts on Reddit have most of their community engagement concentrated in the first two to four hours. Monitoring tools that send daily digests miss the response window. Near-real-time detection is necessary to capture these opportunities while the poster is still active and engaged.

Platforms monitored: Reddit, LinkedIn, X (Twitter), Facebook Groups, Instagram, TikTok, Hacker News, industry forums

Best for: B2B SaaS, professional services, agencies, and consumer brands competing in categories where buyers discuss tools and switching decisions in online communities.

Combining both approaches

The research-based and signal-based methods are complementary:

Research-based gives you a broad list of competitor customers for cold outreach campaigns. Higher volume, lower intent, requires compelling messaging to overcome the interruption.

Signal-based gives you a smaller, highly concentrated list of people actively evaluating alternatives. Lower volume, extremely high intent, requires showing up quickly and helpfully.

The optimal stack for competitive displacement:

How to respond when you find someone looking for alternatives

Finding the post is the first step. Responding effectively is what converts it.

Don't pitch immediately. Someone asking "any alternatives to [competitor]?" in a Reddit thread wants a helpful answer, not a sales pitch. The first response should be a genuinely useful comparison — your product mentioned as one option, with honest context about when it's a good fit and when it isn't. This looks like community participation, not sales.

Reference the specific post. Generic replies ("check out our product!") don't land. Specific replies that address the exact situation described in the post ("if the pricing change is the main issue, here's how we compare on that...") signal that you actually read what they wrote.

Match the platform's culture. Reddit expects more casual, candid tone. LinkedIn is more professional. Hacker News rewards technical depth. A reply calibrated to the platform's norms performs dramatically better than a copy-pasted template.

Move to DM after establishing value. After engaging in the thread, reach out directly. Reference the specific conversation. Not a pitch — a specific, relevant offer to help with their evaluation.

For implementation context, review Google Trends. For implementation context, review Relevant subreddit discussions.