LinkedIn Recommendations and Endorsements: The Trust Signal Most Service Businesses Ignore

08 September 2026·6 min read

Quick answer: LinkedIn recommendations are written, detailed testimonials from people who've actually worked with you — and they're seen by exactly the people vetting you before a B2B decision, who almost always check your profile before they buy or sign. Endorsements (the one-click skill taps) are far weaker and mostly decorative. If you're chasing Google reviews but have three or fewer LinkedIn recommendations, you're neglecting the trust signal your actual buyers are most likely to read. 📈

Ask most service business owners about their review strategy and they'll talk about Google. Ask about their LinkedIn recommendations and you'll usually get a blank look — or a sheepish "I think I have two, from years ago." Meanwhile, when a prospect is deciding whether to trust a consultant, an agency, a broker, or any B2B service provider, LinkedIn is very often the first place they click before Google ever comes into it.

We've watched businesses spend real money on review-generation software for their Google profile while a genuinely persuasive trust asset — real, written accounts from real clients — sits completely dormant on their LinkedIn page 💖. It's not that Google reviews don't matter. It's that LinkedIn recommendations do too, and almost nobody is actively building them.

What most businesses get wrong

The gap isn't apathy — it's that nobody has a system for it. A few specific things go wrong:

They confuse endorsements with recommendations. Endorsements are a one-click "skill tap" with zero written context — they carry almost no persuasive weight because anyone can tap "yes" without ever having worked with you.
They wait to be asked instead of asking. Recommendations rarely happen organically. Someone has to prompt the request, at the right moment, with the right framing.
They ask too late or too vaguely. A generic "would you recommend me?" months after a project wraps gets ignored or produces a thin, generic two-liner that doesn't actually persuade anyone.

The recommendation request script

When to ask: Within 1-2 weeks of a clear win — a project wrap-up, a strong result, a "thank you so much" message from the client. That emotional high point is when people are most willing to write something detailed.

The message (send via LinkedIn DM, not email):

"Hi [Name], really glad [specific outcome — e.g. the campaign hit its numbers / the project landed on time]. Would you be up for writing me a quick LinkedIn recommendation about working together? Totally understand if you're flat out, but even a couple of sentences on [specific thing they valued — e.g. how we handled the tight deadline, or the results] would mean a lot. Happy to write one for you too if that's useful."

Why this works:

It's specific — naming the outcome/detail gives them somewhere to start instead of staring at a blank box.
It's low-pressure — "even a couple of sentences" lowers the bar so busy people actually follow through.
The reciprocity offer (writing one back) genuinely increases response rates, and it's a nice thing to do regardless.

The follow-up (if no response after 10 days): "No worries if you're swamped — just a gentle nudge in case this slipped through! Even a one-liner works if you're short on time."

The B2B firm with a long sales cycle: A firm with an average 6-month sales cycle started sending the recommendation request to every closed client within two weeks of project completion. After a year, their profile went from 2 recommendations to 19. A prospect in active vendor comparison mentioned during the pitch that reading through the recommendations — specifically two from companies in a similar industry — was what moved them from "considering" to "shortlisting."

The referral-dependent business: A consultant who gets most work through referrals asked past clients for LinkedIn recommendations as part of their standard project-close routine. Referral partners started forwarding the LinkedIn profile directly to prospects instead of making a verbal introduction — the recommendations were doing part of the vetting work the referrer used to have to do personally.

The booking-based business: A small team offering B2B services on a booking model asked three recent clients using the script above. Two responded within a week with detailed, specific recommendations; the third took a gentle follow-up nudge two weeks later. All three went live within the month — a stronger profile with no ad spend involved.

How to build this into a repeatable system

Set a trigger, not a memory. Tie the ask to a specific moment — project close, a positive client email, a renewal — rather than relying on remembering to do it.
Ask 3-5 clients before you expect one reply. Not everyone will respond, and that's normal — treat this as a numbers exercise, not a personal test.
Diversify who you ask. A spread of recommendations across different client types/industries is more persuasive to a broad prospect pool than five recommendations that all say the same thing from similar clients.
Write recommendations for others first. It's genuinely generous, it's good practice, and it often prompts a "let me return the favour" from the recipient.
Pin or reference the strongest ones in your featured section or in proposals — don't assume prospects will scroll down to find them.

💡 Endorsements are decoration, recommendations are evidence. A profile with 200 skill endorsements and zero written recommendations tells a prospect nothing about what it's actually like to work with you. Five specific, detailed recommendations will outperform it every time.

Mistakes to avoid

Writing the recommendation for them and asking them to "just approve it." It reads as inauthentic when it's clearly not in the client's own voice, and some people will notice and feel odd about it.
Asking too broadly ("would you recommend me?") with no framing. Vague asks get vague, generic answers that don't actually persuade anyone reading them later.
Only asking your happiest, most enthusiastic clients. A mix of measured, specific recommendations reads as more credible than five that all sound like they were written in one excited afternoon.
Never asking at all because it feels awkward. Most clients are genuinely happy to help if you make it easy and specific — the awkwardness is almost always bigger in your head than in the actual ask.
Letting your profile go stale after the initial push. A LinkedIn profile with recommendations that all date from two years ago can read as a business that's coasted since. Keep the ask as an ongoing habit, not a one-off project.

Frequently asked questions

Should I ask a client for a recommendation before or after the invoice is paid?

After payment is generally cleaner — it avoids any perception that the recommendation was tied to settling the bill. If the relationship is ongoing and warm, timing right after a clear positive outcome usually matters more than the payment status itself.

What if a client gives a lukewarm or overly brief recommendation?

You can politely ask if they'd be willing to add a specific detail or two — "would you mind adding a line about [specific result]? It'd really help other people picture what we worked on." Don't push hard if they decline; a short, genuine recommendation is still better than none, and pressuring people tends to backfire.

Do LinkedIn recommendations actually influence B2B buying decisions, or is this overstated?

They're one input among several, not a silver bullet — most B2B buyers are also checking your website, case studies, and possibly Google or industry-specific reviews. Recommendations tend to matter most in close, comparative decisions where a prospect is choosing between a shortlist of similar providers, less so when you're the obvious or only choice already.

How many recommendations is "enough"?

There's no fixed number, but somewhere around 8-15 recent, specific recommendations gives a prospect enough to feel the pattern is real rather than cherry-picked, without them needing to read all of them. Quality and specificity matter more than raw count.