How to Find People Asking for Software Recommendations on Reddit, X & LinkedIn

Most founders don’t need more traffic - they need to catch the moment someone publicly says, “What tool should I use for this?" The problem is those posts disappear fast: they get buried by memes, hot takes, and self-promo, or they’re answered within hours and then the thread dies.

If you can consistently show up in those moments (without being weird or spammy), you stop doing “lead gen" and start doing helpful problem-solving in public.

The real problem: recommendation requests are fleeting - and noisy to track

If you’ve tried doing this manually, you already know the failure modes:

• You search a few subreddits, see the same “best tool for X?" question, and it’s either 3 days old or already has 60 replies.
• On X, keyword searches fill up with people pitching themselves, not people buying.
• On LinkedIn, posts that look like recommendation requests are often bait for engagement.

And if you switch to a traditional “social listening" tool, you often trade time for irrelevance - getting pinged for every keyword mention instead of intent.

That’s why the target isn’t “mentions". The target is active buying intent: people explicitly asking for software recommendations, alternatives, or “what should I use?"

What “high-intent recommendation" language looks like (and why it matters)

Across Reddit, X, and LinkedIn, recommendation requests cluster into a few repeatable patterns:

1) Direct recommendation requests (highest intent)

Look for phrasing like:
- “What tool do you recommend for…?"
- “Any alternatives to [tool]?"
- “What are you using for…?"
- “Best software for…"

2) Comparison shopping (strong intent)

These posts tell you what they’re already considering:
- “Trying to decide between [A] and [B]".
- “Is [competitor] worth it?"
- “We outgrew [tool] - what’s next?"

3) Constraint-based requests (the easiest to win)

These are gold because they contain selection criteria:
- “Need something cheap/free".
- “Must support SOC2 / SSO / API".
- “Works for a small team / agency / enterprise".

When you respond, mirror their constraints. Don’t start with features.

Reddit: how to find recommendation threads before they’re answered to death

Reddit is the best place to find unfiltered “what should I use?" requests - because people ask when they’re stuck, not when they’re performing.

Step 1: Use intent-first search operators

In Reddit search (or Google with site:reddit.com), combine:

• "recommend" OR "recommendation" OR "suggest"
• "tool" OR "software" OR "platform"
• "alternatives" OR "vs" OR "worth it"
• Your category keyword (e.g., “CRM", “screen recorder", “LLM", “expense", “product analytics")

Examples:
- site:reddit.com "alternatives" "Notion" "team wiki"- site:reddit.com "what tool" "user onboarding"

Step 2: Prioritize subreddits where buying decisions happen

Not all communities are equal. Look for:

• Communities tied to a job-to-be-done (e.g., analytics, marketing ops, recruiting, accounting)
• Communities tied to a role (e.g., founders, indie hackers, growth)
• Communities tied to platform ecosystems (Shopify, Webflow, HubSpot, AWS)

The best threads often come from “implementation" subreddits where people need working solutions.

Step 3: Filter for freshness + specificity

A simple triage rule:

• Freshness: posted in the last 24–72 hours
• Specificity: includes constraints (budget, team size, integration, data, compliance)
• Engagement: not yet “solved" by a top comment with 200 upvotes

If it’s older but still unresolved, it can still be worth answering - just be extra helpful and avoid pitching.

Step 4: Reply like a peer, not a vendor

A good Reddit response usually has:

1) A quick restatement of their constraint (“If you need X + Y and want to avoid Z…")
2) 1–2 options, including a non-you option if relevant
3) A short explanation of why, and what trade-offs to expect
4) A gentle offer to share specifics if they want (no DM push)

The goal is to be the person they trust, even if they don’t pick you.

X (Twitter): how to catch “what should we use?" posts in real time

X is tricky because the search results are polluted by:

• People farming engagement (“Drop your favorite tool below")
• Builders doing “soft launches"
• SEO-like threads that reuse the same tool lists

But high-intent posts exist - you just need to narrow to question formats and first-person need.

Step 1: Search for question syntax + problem words

Use search queries like:

• "anyone recommend" + your category
• "what tool" + your category
• "alternatives to" + competitor
• "looking for" + "tool" + your category

And add negative filters where possible (mentally, if not via advanced search): avoid posts that are clearly listicles or “comment bait".

Step 2: Follow “operators", not only prospects

The fastest way to be early is to build a small list of:

• People in your ICP role (ops leads, marketers, engineers)
• People who frequently ask for tooling recommendations
• Community connectors who answer those questions

When they ask, they usually do it publicly first.

Step 3: Respond with a mini-diagnosis

On X, long replies are fine, but the best responses feel like:

• “What’s your team size + must-have integrations?"
• “Do you need this for internal use or customer-facing?"
• “What’s the thing you’re trying to stop happening?"

That’s how you turn a random question into a qualified conversation.

LinkedIn: how to separate real requests from engagement traps

LinkedIn has two parallel universes:

1) Real operators asking peers for recommendations
2) Posts engineered to attract comments (and therefore reach)

You can still win here - especially in B2B - because recommendation requests often come with business context.

Step 1: Look for “peer ask" phrasing

High-intent LinkedIn posts often include:

• “We’re evaluating…"
• “We’re switching from…"
• “Has anyone implemented…?"
• “What are you using for…?"

The strongest signal is when the author references a real constraint: security review, procurement, budget, integrations, reporting needs, or workflow fit.

Step 2: Pay attention to comments (the real buying signals)

Often the main post is broad, but the author reveals specifics in replies:

• “We’re a 10-person team"
• “We need SSO"
• “We’re on HubSpot"

If you can answer that context, you stand out.

Step 3: Don’t pitch in the first comment

On LinkedIn, you earn permission by being useful first:

• Ask 1 clarifying question
• Offer a short framework for choosing
• If you mention your product, do it lightly and alongside alternatives

If they want more, they’ll ask.

The hard part isn’t searching - it’s doing it every day without losing hours

Most advice stops at “use search operators". That helps once.

The problem is consistency:

• Recommendation requests show up across thousands of communities and timelines.
• They spike at random times.
• Manual monitoring turns into three tabs you never close.

This is where a lightweight system matters more than a perfect query.

A practical daily workflow (15–20 minutes)

1) Scan new high-intent posts (don’t deep-read everything)
2) Sort by fit (persona, constraint match, urgency)
3) Reply to 3–5 with high-quality answers
4) Track which ones convert into follow-up questions or demos

If you can’t do step 1 reliably, the whole loop collapses.

How Achiv.com helps you catch recommendation requests without living in feeds

If you’re doing this manually, you’re effectively working a part-time job: monitoring Reddit, X, and LinkedIn for “what tool should I use?" moments.

Achiv.com is designed specifically for that workflow:

• You paste your website URL, and it infers positioning to build Ideal Customer Profiles.
• It scans Reddit, X, and LinkedIn daily.
• It filters out spam, bots, and promotional noise.
• Every morning, you get a curated kanban board of conversations where real people are describing problems your product solves.

The useful part isn’t just finding the post - it’s the context you need to reply well. Each surfaced lead includes:

• Extracted pain points
• Detected objections
• Competitor context (what they’re already considering)

That’s what turns “I saw someone ask for a tool" into “I know how to respond in a way that fits their situation".

Common objection: “I don’t want to connect my accounts or risk my brand"

That concern is valid. Many tools want OAuth access or automate outreach, which can create risk.

Achiv.com doesn’t require connecting your social accounts or sharing credentials, and it doesn’t send automated DMs. It uses its own crawlers to index publicly available content, then you decide if and how to engage.

How to respond when you find a recommendation request (templates that don’t feel gross)

Use these as patterns, not scripts.

Template A: direct request (“What tool should I use?")

• “If you need [constraint 1] and [constraint 2], I’d shortlist [option A] and [option B]. A is better for [reason]; B is better for [reason]. If you share [one missing detail], I can narrow it down".

Template B: competitor alternative request

• “If the issue is [pricing/support/complexity], you’ll want something that [criterion]. A few alternatives people pick are [alt 1], [alt 2]. If it helps, here’s how they differ on [key trade-off]".

Template C: constraint-based (“must have SSO / budget / integration")

• “SSO narrows it quickly. Are you on Okta/Azure AD/Google? If yes, prioritize tools that support SAML/SCIM; otherwise you’ll hit a wall in procurement".

Notice what’s missing: a hard pitch.

Make it measurable: what to track so you know this is working

If you want this channel to compound, track:

• Time-to-first-reply (speed matters in recommendation threads)
• Reply-to-follow-up rate (do they ask you questions back?)
• Qualified conversations per week (not “impressions")
• Competitor mentions (what keeps showing up in your market)

Tools like Achiv.com help by turning daily monitoring into a repeatable pipeline: same time every morning, same format, fewer distractions.

The takeaway: don’t “build audience" - catch the question

The highest-leverage moment isn’t when someone discovers your landing page. It’s when they publicly admit a need and ask peers what to buy.

Build a system that finds those questions daily, shows you the context (pain points, objections, competitors), and lets you respond like a helpful peer. If you can’t justify spending hours hunting across Reddit, X, and LinkedIn, that’s exactly the kind of work Achiv.com is meant to handle in the background - so you can spend your time replying, not scrolling.