Happy Halloween! I’ll just leave this here. If anyone can make sense of what LinkedIn Job Search does in the two screenshots below, please get in touch. (As with people search, I have no idea. LinkedIn Developers have started to write code in mysterious ways.)
(I doubt there is anything underlying here like in the Google case with quotes. It is a coincidence that quotes add results in both observed cases.)
The conclusion for now is – do not trust the job search function. If you want to search for a job on LinkedIn, do so via Google, like
Note that even if they fix the LinkedIn Job Search Dialog algorithm, it does not let you to search by job title, while Google does – put the desired role keywords under the intitle: operator.
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Using a premium account on LinkedIn? Unfortunately, the search is broken. Consider upgrading or X-Raying. 😉
Googling? I have run into something interesting – The Behavior of the Quotes (Google Search Report). Luckily, we got unique insights from Google’s Danny Sullivan (we are told that Google’s team “had a lively discussion” over the findings, lol). It turns out that when you Google, the search engine assesses your string and gives you different results based on whether it “senses” an “open-ended” or “restrictive” search. There is still a lot of mystery left – let us explore it! This discussion affects those who want to see as many results as possible for a search.
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The post reflects my current understanding of how Google works. I hope to share extra detail with you as we do more testing or if we hear more from Google Search people.
SUMMARY
When you write a search string, Google assesses whether it is:
Open-ended (assuming you, the user, require assistance) or
Specific, or “restrictive.”
Google decides on “restrictive” in some cases with quotes or when you use advanced operators (maybe something else). For restrictive searches, Google does a fair job of searching with less interpretation. That sometimes leads to more results.
Looking closer, it’s not one or the other; there seems to be a measure of restrictiveness (from open-ended to more and more specific, depending on input) to which Google reacts; see some examples below.
Conclusion. When you Want to Get More Google Search Results:
Be aware of “open-ended,” “restrictive,” and “in-between” (more and more restrictive) searches
Putting keywords in quotes or using operators sometimes makes a search restrictive, showing extra results
When running “open-ended” searches, for different results, consider making the search more specific by adding keywords or operators.
Google’s Danny Sullivan‘s comments on my Quotes on Google post shed some light on the observed phenomena: words in quotes produced more results than without in my examples. I had expected words without quotes to be interpreted, therefore leading to more results, not less.
Thanks to Danny for taking the time to comment. The word therefore above was false logic. I still have many related questions and wonder whether there is a channel to ask them and interact with the team. (@Google, we would be glad to hear your thoughts.)
Evolving semantic input interpretation while allowing to keep control over search via special syntax – when everyone uses the same search box – is not easy.
NOTES for Hands-On Sourcers:
If you think that as a practical result of the “quotes” insight, you need to start putting all your keywords in quotes – perhaps you should try that. But quotes help to see more results only sometimes.
I.e., quotes around single words sometimes help find more results (perhaps working as a “restrictive” indicator). A researcher needs to know that might happen and search with and without quotes around single words.
From my tests, an advanced operator like site: makes a query restrictive.
In addition to your strings appearing to Google as “restrictive” (sophisticated!) searches, you can increase the number of combined results by repeating keywords, moving them around (as Nicolas Darcis demonstrated at #sosueu), and searching in Images.
any couple of words in quotes – around 200 results (in-between).
What? I expect the numbers of results to be the other way around, i.e., for Google to bring in synonyms if I do not use quotes – and show more results.
Quotes even affect OR statements, e.g., “developer” OR “engineer” <keywords> finds more than developer OR engineer <keywords> (I expect to see the same results if OR is used):
I noticed the unexpected behavior while at #sosueu in Amsterdam last week. None of us could explain it. It is as if Google works harder if you use quotation marks. And most Google users do not use them around single words.
As far as I can tell, it is a bug (unusual for Google search). Let’s see if they fix it or give us an explanation. In the meantime, keep in mind the phenomena.
If you drag an image into Google’s reverse image search, you now arrive at Google Lens. It (often):
identifies an area on your photo that looks like goods for sale and
produces “visual matches” (payment links to such goods) on the right.
People and faces are ignored.
Do you want to dress like your candidate? Use their photo (Someone joked like this on my LinkedIn post).
At times, Google appears to be “creative” (and clueless) in finding goods for sale:
I am all for this way of sourcing things to buy. I like a certain style of clothing and am interested in finding more. It is just please call it a Visual Shopping Engine vs. “visual matches,” to make things transparent.
To be fair, Lens is not always about shopping. Lens will identify architecture, landscape, plants, animals, and a few more things. It is not new but is perhaps better. It can also OCR and translate.
Not that the existing Google’s reverse image is brilliant, but you can return to it either by clicking “find image source” or one of the tools like Chrome extension Search by Image.
Yandex’s Reverse Image search is superior, especially from a Russian IP address. But Yandex has not indexed LinkedIn profiles.
Invitations from recruiters have become a standard practice on LinkedIn. So have invitations related to potentially doing any business together. If an invitation is accepted, a conversation starts.
However, LinkedIn notifications have poor deliverability, and “passive” candidates often miss our messages and invitations. It helps to invite a larger volume of potential matches and follow-up by email.
The following method meets the goal: it does not have the limit of 100 invitations per week and provides you with emails. It has somewhat changed since the UX lost the uploading a file button.
Start with a list of promising email addresses. They may come from your ATS, other social sites, X-Raying for lists, enriching LinkedIn X-Ray with SalesQL, and other sources.
STEP 1. Upload the list of emails to a Gmail account (make sure to clear out the existing list before uploading).
As a bonus, you will see non-generic pictures by the valid emails:
You can start connecting here, but only with a limited number of members.
STEP 4. Connect to any number of members (one by one; it helps to review the profiles) from Contacts. You cannot customize the initial message, but this way is scalable.
STEP 5. You have their emails as well for follow-ups, and can personalize your outreach. (You can InMail them also.)
This technique will work for lists of thousands if desired.
In a recent post, How we’re improving search results when you use quotes, Google informed us that it would force the terms in quotation marks into snippets – as many as possible. It is a significant improvement for research; take note of it.
I am finding that in daily sourcing, as well as training, I have been increasing the number of search strings with quoted and repeated search terms. Here is an example.
While asterisks (*) and Boolean operators within quotation marks do not always help to alter snippets, a few words in quotes (“me at”) do.
In the second search, the snippets look reaffirming, displaying the desired info and the wording around it. But more importantly, you can scrape the emails into a list without visiting any result URLs; I recommend Julia’s Email Extractor.
While snippets are described as “the first piece of information that influences people’s decisions on what results to click or read,” we are here for not clicking results and being productive.
We so much depend on the platform. But LinkedIn.com people search remains broken for everyone with a premium, job seeker, or a basic account. LinkedIn does not find members by keywords in the “About” section and job description. It has been over eight months. 🙁
No matter how long and complex Boolean you write, it will miss a significant percentage of qualified Software Developer candidates. Search on – or X-Ray – LinkedIn, Github, Gitlab, dev.to, Stackoverflow, HackerRank, Reddit, Slack, Discord, or Twitter – and you will still miss them.
Consider this. You can search by (“preferred”) programming language and location on Github. But many Github members do not write code for a living – they are students, professors, retirees, managers who miss coding, and so on. Staying on Github, you won’t know that; user profiles do not even have a field for the job title. Meanwhile, you can search by the job title on LinkedIn, but a significant percentage of professionals with the requested programming language skills (who are popular on Github) have barely mentioned the language name on LinkedIn.
Combining knowledge and data from several sources uncovers “invisible” talent.
My followers know I have been obsessed with cross-referencing profiles, in particular, in technical recruiting, Github and LinkedIn (only because it works!). It finds prospects who did not fully reveal their professional background on any one platform. But that is not the only way to tap into that hidden talent pool.
Here is a complementary approach. Do your research and find which companies or teams use the required technology. There is a good chance all Developers on the team use it, whether they have been vocal about it or not.
As a simple example, running a recent sourcing project, I saw many North-American Developers at Shopify write in Ruby. It looked like it was the language of choice for the team. So, if I search on LinkedIn and find Developers at Shopify with no summaries and no recent job descriptions (a turn-off for Recruiters!) but Ruby in the skills or past job experience, I can safely assume that they currently use Ruby. I would also have a good guess at the years of experience with the language if they used it at past jobs. It is like reading between the lines. 😀
If you think you would benefit from sourcing ideas and practical advice on how to uncover untapped technical talent, join Master Sourcer David Galley for a unique free webinar
Incidentally, the webinar’s sponsor is AmazingHiring, a tool that helps to uncover technical talent based on multiple sources. If you are hiring IT talent, give it a try.
