Identity/biometrics/technology marketing and writing services
Biometric product marketing expert.
Modalities: Finger, face, iris, voice, DNA.
Plus other factors: IDs, data.
John E. Bredehoft has worked for Incode, IDEMIA, MorphoTrak, Motorola, Printrak, and a host of Bredemarket clients.
(Some images AI-generated by Google Gemini.)
(NOT part of the biometric product marketing expert series)
Friction is bad.
I know some people think that friction is good, because if you tolerate the friction to get to the thing, then you must really want it.
But more often than not, friction is bad.
Which is why when I create a reel, I try to post the native reel in all places where appropriate.
Take my most recent 8 second “biometric product marketing expert” reel.
But you don’t have to leave this blog post to see the original reel.
See how easy a frictionless experience can be?
More here.
(Part of the biometric product marketing expert series)
You may remember the May hoopla regarding amendments to Illinois’ Biometric Information Privacy Act (BIPA). These amendments do not eliminate the long-standing law, but lessen its damage to offending companies.
Back on May 29, Fox Rothschild explained the timeline:
The General Assembly is expected to send the bill to Illinois Governor JB Pritzker within 30 days. Gov. Pritzker will then have 60 days to sign it into law. It will be immediately effective.
According to the Illinois General Assembly website, the Senate sent the bill to the Governor on June 14.
While the BIPA amendment has passed the Illinois House and Senate and was sent to the Governor, there is no indication that he has signed the bill into law within the 60-day timeframe.
So BIPA 1.0 is still in effect.
A proposed class action claims Photomyne, the developer of several photo-editing apps, has violated an Illinois privacy law by collecting, storing and using residents’ facial scans without authorization….
The lawsuit contends that the app developer has breached the BIPA’s clear requirements by failing to notify Illinois users of its biometric data collection practices and inform them how long and for what purpose the information will be stored and used.
In addition, the suit claims the company has unlawfully failed to establish public guidelines that detail its data retention and destruction policies.
(Part of the biometric product marketing expert series)
Any endeavor, scientific or non-scientific, tends to generate a host of acronyms that the practitioners love to use.
For people interested in fingerprint identification, I’ve written this post to delve into some of the acronyms associated with NIST MINEX testing, including ANSI, INCITS, FIPS, and PIV.
And, of course, NIST and MINEX.
After defining what the acronyms stand for, I’ll talk about the MINEX III test. Because fingerprints are still relevant.
We have to start with NIST, of course. NIST is the National Institute of Standards and Technology, part of the U.S. Department of Commerce.
NIST was involved with fingerprints before NIST even existed. Back when NIST was still the NBS (National Bureau of Standards), it issued its first fingerprint interchange standard back in 1986. I’ve previously talked about the 1993 version of the standard in this post, “When 250ppi Binary Fingerprint Images Were Acceptable.”
But let’s move on to another type of interchange.
It’s even more important that we define MINEX, which stands for Minutiae (M) Interoperability (IN) Exchange (EX).
You’ll recall that the 1993 (and previous, and subsequent) versions of the ANSI/NIST standard included a “Type 9” to record the minutiae generated by the vendor for each fingerprint. However, each vendor generated minutiae according to its own standard. Back in 1993 Cogent had its standard, NEC its standard, Morpho its standard, and Printrak its standard.
So how do you submit Cogent minutiae to a Printrak system? There are two methods:
First, you don’t submit them at all. Just ignore the Cogent minutiae, look at the Printrak image, and use an algorithm regenerate the minutiae to the Printrak standard. While this works with high quality tenprints, it won’t work with low quality latent (crime scene) prints that require human expertise.
The second method is to either convert the Cogent minutiae to the Printrak minutiae standard, or convert both standards into a common format.
Something like ANSI INCITS 378-2009 (S2019).
So I guess we need to define two more acronyms.
Actually, I should have defined ANSI earlier, since I’ve already referred to it when talking about the ANSI/NIST data interchange formats.
ANSI is the American National Standards Institute. Unlike NIST, which is an agency of the U.S. government, ANSI is a private entity. Here’s how it describes itself:
The American National Standards Institute (ANSI) is a private, non-profit organization that administers and coordinates the U.S. voluntary standards and conformity assessment system. Founded in 1918, the Institute works in close collaboration with stakeholders from industry and government to identify and develop standards- and conformance-based solutions to national and global priorities….
ANSI is not itself a standards developing organization. Rather, the Institute provides a framework for fair standards development and quality conformity assessment systems and continually works to safeguard their integrity.
So ANSI, rather than creating its own standards, works with outside organizations such as NIST…and INCITS.
Now that’s an eye-catching acronym, but INCITS isn’t trying to cause trouble. Really, they’re not. Believe me.
INCITS, or the InterNational Committee for Information Technology Standards, is another private organization. It’s been around since 1961, and like NIST has been known under different names in the past.
Back in 2004, INCITS worked with ANSI (and NIST, who created samples) to develop three standards: one for finger images (ANSI INCITS 381-2004), one for face recognition (ANSI INCITS 385-2004), and one for finger minutiae (ANSI INCITS 378-2004, superseded by ANSI INCITS 378-2009 (S2019)).
When entities used this vendor-agnostic minutiae format, then minutiae from any vendor could in theory be interchanged with those from any other vendor.
This came in handy when the FIPS was developed for PIV. Ah, two more acronyms.
One year after the three ANSI INCITS standards were released, this happened (the acronyms are defined in the text):
Federal Information Processing Standard (FIPS) 201 entitled Personal Identity Verification of Federal Employees and Contractors establishes a standard for a Personal Identity Verification (PIV) system (Standard) that meets the control and security objectives of Homeland Security Presidential Directive-12 (HSPD-12). It is based on secure and reliable forms of identity credentials issued by the Federal Government to its employees and contractors. These credentials are used by mechanisms that authenticate individuals who require access to federally controlled facilities, information systems, and applications. This Standard addresses requirements for initial identity proofing, infrastructure to support interoperability of identity credentials, and accreditation of organizations issuing PIV credentials.
So the PIV, defined by a FIPS, based upon an ANSI INCITS standard, defined a way for multiple entities to create and support fingerprint minutiae that were interoperable.
But how do we KNOW that they are interoperable?
Let’s go back to NIST and MINEX.
So NIST ended up in charge of figuring out whether these interoperable minutiae were truly interoperable, and whether minutiae generated by a Cogent system could be used by a Printrak system. Of course, by the time MINEX testing began Printrak no longer existed, and a few years later Cogent wouldn’t exist either.
You can read the whole history of MINEX testing here, but for now I’m going to skip ahead to MINEX III (which occurred many years after MINEX04, but who’s counting?).
NIST then performs its calculations and comes up with summary values of interoperability, which can be sliced and diced a few different ways for both template generators and template matchers.
And this test, like some others, is an ongoing test, so perhaps in a few months someone will beat Innovatrics for the top pooled 2 fingers spot.
And entities WILL continue to submit to the MINEX III test. While a number of identity/biometric professionals (frankly, including myself) seem to focus on faces rather than fingerprints, fingers still play a vital role in biometric identification, verification, and authentication.
Fingerprints are clearly a 21st century tool.
Even if one vendor continues its obsession with 1970s crime fighters.
And no, I’m NOT going to explain what the acronym FAP means. This post has too many acronyms already (TMAA).
(Part of the biometric product marketing expert series)
Is your firm asking the following questions?
Bredemarket can help you answer these questions.
For those who don’t know, or who missed my previous discussion on the topic, Bredemarket performs analyses that contain one or more of the following:
Bredemarket analyses only use publicly available data.
These analyses can range in size from very small to very large. On the very small side, I briefly analyzed the markets of three prospect firms in advance of calls with them. On the large side, I’ve performed analyses that take between one and six weeks to complete.
Obviously I can’t provide specifics upon the analyses I’ve already performed since those are confidential to my customers, but I always discuss the customers’ needs before launching the analysis to ensure that the final product is what you want. I also provide drafts along the way in case we need to perform a course correction.
Do you need a market, competitor, or self analysis? Contact me. Or book a meeting with me at calendly.com/bredemarket to talk about your needs (and check the “Market/competitor analysis” check box).
(Part of the biometric product marketing expert series)
Normal people look forward to the latest album or movie. A biometric product marketing expert instead looks forward to an inaugural test report from the National Institute of Standards and Technology (NIST) on age estimation and verification using faces.
I’ve been waiting for this report for months now (since I initially mentioned it in July 2023), and in April NIST announced it would be available in the next few weeks.
Yesterday I learned of the report’s public availability via a NIST news release.
A new study from the National Institute of Standards and Technology (NIST) evaluates the performance of software that estimates a person’s age based on the physical characteristics evident in a photo of their face. Such age estimation and verification (AEV) software might be used as a gatekeeper for activities that have an age restriction, such as purchasing alcohol or accessing mature content online….
The new study is NIST’s first foray into AEV evaluation in a decade and kicks off a new, long-term effort by the agency to perform frequent, regular tests of the technology. NIST last evaluated AEV software in 2014….
(The new test) asked the algorithms to specify whether the person in the photo was over the age of 21.
Well, sort of. We’ll get to that later.
I was in the middle of a client project on Thursday and didn’t have time to read the detailed report, but I did have a second to look at the current results. Like other ongoing tests, NIST will update the age estimation and verification (AEV) results as these six vendors (and others) submit new algorithms.
This post looks at my three favorite questions:
Why does NIST test age estmation, or anything else?
One of NIST’s six research laboratories is its Information Technology Laboratory (ITL), charged “to cultivate trust in information technology (IT) and metrology.” Since NIST is part of the U.S. Department of Commerce, Americans (and others) who rely on information technology need an unbiased source on the accuracy and validity of this technology. NIST cultivates trust by a myriad of independent tests.
Some of those tests are carried out by one of ITL’s six divisions, the Information Access Division (IAD). This division focuses on “human action, behavior, characteristics and communication.”
While there is a lot of IAD “characteristics” work that excites biometric folks, including ANSI/NIST standard work, contactless fingerprint capture, the Fingerprint Vendor Technology Evaluation (ugh), and other topics, we’re going to focus on our new favorite acronyms, FRTE (Face Recognition Technology Evaluation) and FATE (Face Analysis Technology Evaluation). If these acronyms are new to you, I talked about them last August (and the deprecation of the old FRVT acronym).
Basically, the difference between “recognition” and “analysis” in this context is that recognition identifies an individual, while analysis identifies a characteristic of an individual. So the infamous “Gender Shades” study, which tested the performance of three algorithms in identifying people’s sex and race, is an example of analysis.
The age of a person is another example of analysis. In and of itself an age cannot identify an individual, since around 385,000 people are born every day. Even with lower birth rates when YOU were born, there are tens or hundreds of thousands of people who share your birthday.
And your age matters in the situations I mentioned above. Even when marijuana is legal in your state, you can’t sell it to a four year old. And that four year old can’t (or shouldn’t) sign up for Facebook either.
You can check a person’s ID, but that takes time and only works when a person has an ID. The only IDs that a four year old has are their passport (for the few who have one) and their birth certificate (which is non-standard from county to county and thus difficult to verify). And not even all adults have IDs, especially in third world countries.
So companies like Yoti developed age estimation solutions that didn’t rely on government-issued identity documents. The companies tested their performance and accuracy themselves (see the PDF of Yoti’s March 2023 white paper here). However, there are two drawbacks to this:
Enter NIST, where the scientists took a break from meterological testing or whatever to conduct an independent test. NIST asked vendors to participate in a test in which NIST personnel would run the test on NIST’s computers, using NIST’s data. This prevented the vendors from skewing the results; they handed their algorithms to NIST and waited several months for NIST to tell them how they did.
I won’t go into it here, but it’s worth noting that a NIST test is just a test, and test results may not be the same when you implement a vendor’s age estimation solution on CUSTOMER computers with CUSTOMER data.
NOW let’s turn to the actual report, NIST IR 8525 “Face Analysis Technology Evaluation: Age Estimation and Verification.”
NIST needed a set of common data to test the vendor algorithms, so it used “around eleven million photos drawn from four operational repositories: immigration visas, arrest mugshots, border crossings, and immigration office photos.” (These were provided by the U.S. Departments of Homeland Security and Justice.) All of these photos include the actual ages of the persons (although mugshots only include the year of birth, not the date of birth), and some include sex and country-of-birth information.
For each algorithm and each dataset, NIST recorded the mean absolute error (MAE), which is the mean number of years between the algorithm’s estimate age and the actual age. NIST also recorded other error measurements, and for certain tests (such as a test of whether or not a person is 17 years old) the false positive rate (FPR).
Many of the tests used a “Challenge-T” policy, such as “Challenge 25.” In other words, the test doesn’t estimate whether a person IS a particular age, but whether a person is WELL ABOVE a particular age. Here’s how NIST describes it:
For restricted-age applications such as alcohol purchase, a Challenge-T policy accepts people with age estimated at or above T but requires additional age assurance checks on anyone assessed to have age below T.
So if you have to be 21 to access a good or service, the algorithm doesn’t estimate if you are over 21. Instead, it estimates whether you are over 25. If the algorithm thinks you’re over 25, you’re good to go. If it thinks you’re 24, pull out your ID card.
And if you want to be more accurate, raise the challenge age from 25 to 28.
NIST admits that this procedure results in a “tradeoff between protecting young people and inconveniencing older subjects” (where “older” is someone who is above the legal age but below the challenge age).
NIST also performed a variety of demographic tests that I won’t go into here.
OK, forget about all that. Let’s dig into the results.
It depends.
I’ve covered this before with regard to facial recognition. Because NIST conducts so many different tests, a vendor can turn to any single test in which it placed first and declare it is the best vendor.
So depending upon the test, the best age estimation vendor (based upon accuracy and or resource usage) may be Dermalog, or Incode, or ROC (formerly Rank One Computing), or Unissey, or Yoti. Just look for that “(1)” superscript.
You read that right. Out of the 6 vendors, 5 are the best. And if you massage the data enough you can probably argue that Neurotechnology is the best also.
So if I were writing for one of these vendors, I’d argue that the vendor placed first in Subtest X, Subtest X is obviously the most important one in the entire test, and all the other ones are meaningless.
But the truth is what NIST said in its news release: there is no single standout algorithm. Different algorithms perform better based upon the sex or national origin of the people. Again, you can read the report for detailed results here.
NIST always clarifies what it did and didn’t test. In addition to the aforementioned caveat that this was a test environment that will differ from your operational environment, NIST provided some other comments.
The report excludes performance measured in interactive sessions, in which a person can cooperatively present and re-present to a camera. It does not measure accuracy effects related to disguises, cosmetics, or other presentation attacks. It does not address policy nor recommend AV thresholds as these differ across applications and jurisdictions.
Of course NIST is just starting this study, and could address some of these things in later studies. For example, its ongoing facial recognition accuracy tests never looked at the use case of people wearing masks until after COVID arrived and that test suddenly became important.
As noted above, the test used a Challenge 25 or Challenge 28 model which measured whether a person who needed to be 21 appeared to be 25 or 28 years old. This makes sense when current age estimation technology measures MAE in years, not days. NIST calculated the “inconvenience” to 21-25 (or 28) year olds affected by this method.
While a lot of attention is paid to the use cases for 21 year olds (buying booze) and 18 year olds (viewing porn), states and localities have also paid a lot of attention to the use cases for 13 year olds (signing up for social media). In fact, some legislators are less concerned about a 20 year old buying a beer than a 12 year old receiving text messages from a Meta user.
NIST tests for these in the “child online safety” tests, particularly these two:
However, the visa database is the only one that includes data of individuals with actual ages below age 13. The youngest ages in the other datasets are 14, or 18, or even 21, rendering them useless for the child online safety tests.
The mark of a great researcher is their ability to continue to get funding for their research, which is why so many scientific papers conclude with the statement “further study is needed.”
Here’s how NIST stated it:
Future work: The FATE AEV evaluation remains open, so we will continue to evaluate and report on newly submitted prototypes. In future reports we will: evaluate performance of implementations that can exploit having a prior known-age reference photo of a subject (see our API); consider whether video clips afford improved accuracy over still photographs; and extend demographic and quality analyses.
Translation: if Congress doesn’t continue to give NIST money, then high school students will get drunk or high, young teens will view porn, and kids will encounter fraudsters on Facebook. It’s up to you, Congress.
If you’re not a LinkedIn connection you missed this. Here’s something I wrote on the one year anniversary of losing my job.
(Part of the biometric product marketing expert series)
If your biometric firm conducts business in the United States, then your biometric firm probably conducts business in Illinois.
Your firm and your customers are impacted by Illinois’ Biometric Information Privacy Act, or BIPA.
Including requirements for consumer consent for use of biometrics.
And heavy fines (currently VERY heavy fines) if you don’t obtain that consent.
What is your firm telling your customers about BIPA?
Bredemarket has mentioned BIPA several times in the Bredemarket blog.
But what has YOUR firm said about BIPA?
And if your firm has said nothing about BIPA, why not?
Perhaps the biometric product marketing expert can ensure that your product is marketed properly in Illlinois.
Contact Bredemarket before it’s too late.
(Part of the biometric product marketing expert series)
When marketing your facial recognition product (or any product), you need to pay attention to your positioning and messaging. This includes developing the answers to why, how, and what questions. But your positioning and your resulting messaging are deeply influenced by the characteristics of your product.
There are hundreds of facial recognition products on the market that are used for identity verification, authentication, crime solving (but ONLY as an investigative lead), and other purposes.
Some of these solutions ONLY use face as a biometric modality. Others use additional biometric modalities.
Your positioning depends upon whether your solution only uses face, or uses other factors such as voice.
Of course, if you initially only offer a face solution and then offer a second biometric, you’ll have to rewrite all your material. “You know how we said that face is great? Well, face and gait are even greater!”
It’s no secret that I am NOT a fan of the “passwords are dead” movement.
It seems that many of the people that are waiting the long-delayed death of the password think that biometrics is the magic solution that will completely replace passwords.
For this reason, your company might have decided to use biometrics as your sole factor of identity verification and authentication.
Or perhaps your company took a different approach, and believes that multiple factors—perhaps all five factors—are required to truly verify and/or authenticate an individual. Use some combination of biometrics, secure documents such as driver’s licenses, geolocation, “something you do” such as a particular swiping pattern, and even (horrors!) knowledge-based authentication such as passwords or PINs.
This naturally shapes your positioning and messaging.
So position yourself however you need to position yourself. Again, be prepared to change if your single factor solution adopts a second factor.
Every company has its own way of approaching a problem, and your company is no different. As you prepare to market your products, survey your product, your customers, and your prospects and choose the correct positioning (and messaging) for your own circumstances.
And if you need help with biometric positioning and messaging, feel free to contact the biometric product marketing expert, John E. Bredehoft. (Full-time employment opportunities via LinkedIn, consulting opportunities via Bredemarket.)
In the meantime, take care of yourself, and each other.
Of all the technologies I don’t know about, jobseeker technology is the most important. Between July 2000 and today, I’ve spent over 30 months searching for full-time employment. So it helps to know how employers search for potential employees.
And a lot of those 30-plus months have been filled with self-styled experts advising people “how to beat the applicant tracking system (ATS)” (as if there were only one) and “how to access the hidden job market” (because of course employers don’t want anyone to know that they’re searching for talent).
So when Melanie Woods of CGL Recruiting offered 20 jobseekers the chance to see how their LinkedIn Profile appears to users of LinkedIn Recruiter, I really wanted to win one of those 20 slots.
I won a slot, and in our 20-minute session Melanie Woods imparted a great deal of knowledge, including the 7 LinkedIn Recruiter tips highlighted at the end of this post.
If I could boil all 7 tips down to 1, I’d emphasize that recruiters have limited time, and something a recruiter can understand in 0 seconds is much better than something that would take a recruiter 5 seconds to understand.
A few days ago, I ran across Melanie’s post that described her offer. It opened as follows:
Instead of spending money on advertising, my team is going to be taking a different approach and it involves YOU #jobseekers!
Sounds like a plan, since a word-of-mouth testimonial converts more effectively than copy written by a marketing hack. (But what if the testimonial is FROM a marketing hack?)
She then described the offer.
We are going to be offering 20 free 20 minute sessions to job seekers who are currently out of work. During your session I will pull you up live in LinkedIn Recruiter and test your profile to show you where you are coming up in searches and where you are falling out. I will help you adjust your profile so you can appear in more searches for the jobs you are targeting.
As you can probably guess, LinkedIn Recruiter is the premium-priced service that recruiters use to search LinkedIn for job candidates. The top-tier package (“contact Sales” for the price) includes:
A comparison of all of LinkedIn’s talent solutions is provided here.
If your company is recruiting more than 4 positions a year, the high-end version of LinkedIn Recruiter could be the tool for you.
But when recruiters use LinkedIn Recruiter, they don’t look at a candidate’s LinkedIn profile—they look at the LinkedIn Recruiter view of the candidate’s profile, optimized for their purposes.
Hence Melanie was offering job applicants the opportunity to see how their profile appears to a recruiter. Valuable information to have.
But remember that the lucky winners had to provide “advertising” to CGL Recruiting in return.
Here is where the advertising piece comes in….if you feel that the 20 minutes was helpful to you and your job search, we would appreciate you doing a review of our services on LinkedIn and one other social media platform (your choice), sharing how the time spent was useful to you and your job search.
After I indicated my interest, and after Dee Daniel provided a boost to my application, I was one of the lucky 40 winners. (Yes, they increased the number of winners due to high demand.)
Melanie and I met via Zoom early Monday afternoon Pacific Time (late afternoon Central Time), and I received a firehose of information during the 20-minute session. I’m not going to cover ALL the information she provided; instead, I’ll confine myself to the top 7 tips.
For my job search for a Senior Product Marketing Manager role, my Incode position is (maybe) more important than my current Bredemarket position. On my resume, I take care of this by listing Incode BEFORE Bredemarket. But because LinkedIn profiles are chronological, and Bredemarket is my current “employer,” I can’t reorder like that.
The LinkedIn Recruiter view of the profile doesn’t show all the positions, but only the top 3. And the first position takes great prominence.
In the default view, the recruiter can’t see my fourth position (Strategic/Product Marketing Manager from 2015 to 2017), but only the first three. And only one of those three positions is product marketing-related.
Melanie zeroed in on my “Sole Proprietor” position, which tells a recruiter nothing about what I actually DO at Bredemarket. Sure the recruiter could click through and read about the marketing and writing services that Bredemarket provides…but recruiters have limited time.
I thought about her advice after the call, and for LinkedIn (and resume) purposes I’m changing my Bredemarket job title to “Product Marketing Consultant.” My work for my clients is all product/service-related, so the job title makes sense.
Now recruiters will see that two of my three most recent positions were product marketing-related, which makes me more attractive to the one position that I’m targeting.
One position? Wait a minute…
That’s right. In my “job preferences,” my only listed job title was “Senior Product Marketing Manager.”
I can list up to 5.
Why not use all 5?
So now my job titles include the following:
So I have the position title from Incode, the consulting title from Bredemarket, two product marketing title variants, and a content marketing title for good measure (Bredemarket readers know why).
Now some people question why I’d list all these similar titles, since anyone who takes a few seconds can figure out that I’d be interested in a global product marketing manager position or whatever.
That’s the problem. Recruiters DON’T HAVE a few seconds. When hundreds or thousands of people apply for positions, recruiters need to get through the profiles as quickly as possible.
So Melanie wanted me to make her job easier.
You can control the visibility of your email address and your phone number on LinkedIn. While I don’t list a phone number on my LinkedIn profile, I do make my jobseeking email address (which is separate from my Bredemarket email address) visible. In fact, I configured my email address visibility for viewing by my 1st degree and 2nd degree connections.
But there was a fallacy in that tactic.
It became obvious in the session because Melanie (not a connection since her LinkedIn connections are maxed out) could NOT see my email address. Therefore, if recruiter Melanie wanted to contact me, she could ONLY contact me via InMail.
If you want ANY potential recruiter to see your email, increase its visibility to all connections. Obviously there are risks to this, so you need to judge what visibility is right for you. (Especially for phone numbers.)
While all of Bredemarket’s work is remote, and my previous work at Incode was remote, I’m not averse to on-site work. As long as it’s within driving distance.
To help local companies, I listed a selected five cities (the maximum) where I am available for on-site work:
But Melanie pointed out that I didn’t need to list Ontario, since my profile already states that I live in Ontario. That freed up one slot to add another city. I chose to list Riverside, although I could have listed Brea or Fullerton or Industry or Pomona or Corona or many other cities. (LinkedIn, your on-site locations feature needs work.)
Now let’s get into age discrimination talk.
If a company desires to discriminate against job applicants due to age, one effective way to do so is to look at the dates the applicants attended college. It’s pretty easy to quietly filter out the geezer applicants with no one the wiser.
Well, maybe.
For this reason I didn’t bother to add my college attendance dates to my LinkedIn profile. Why give the discriminating (in a negative way, not a positive way) firm the ammo they need to get the young, cheap workers they really want? (Of course those workers are inexperienced, but that’s another topic entirely.)
But Melanie pointed out one truth about companies that want to discriminate: if they don’t discriminate against you when they read your LinkedIn profile or resume, they can easily discriminate against you when they SEE you.
Oh, and there’s one more thing: if recruiters search for candidates based upon their graduation dates, profiles without graduation dates will never been seen by recruiters.
So I mulled over her advice.
But I did pencil in my 1991 graduation date from Cal State Fullerton’s MBA program. Since my LinkedIn profile includes ALL my biometric positions going back to 1994, this isn’t a shocking revelation.
LinkedIn profiles can include skills. I’ve listed near the limit of 50 skills, some of which are tied to particular positions, LinkedIn Learning courses, and other education and certifications.
Among many others, two of my listed skills are “identity & access management” and “sales & marketing management.”
Technology experts immediately see where this is going. So why didn’t I?
Melanie immediately noted that the ampersand character in those two skill descriptions can wreak havoc with some computerized systems.
I swapped out those skills for some new ones: identity and access management, and sales and marketing management, removing the problem.
Melanie’s biggest concern about my LinkedIn profile involved the very first sentence.
The one that appears below my profile, name, and preferred pronouns, but above my city of residence.
Senior Product Marketing Manager in identity/technology who is expert in describing why customers benefit.
She read that sentence word for word.
Regular Bredemarket readers are familiar with the last three words of that sentence, and realize that every one of those three words is critically important. Why rather than what, customers rather than producers, and benefits rather than features. I’ve devoted a post (plus another post) to those three words. If I only had a few seconds to explain the importance of those three words…
Um, yet again, recruiters aren’t interested in taking a few minutes to read everything I have written about Simon Sinek’s Golden Circle, customer focus, or benefits. They don’t even want to watch a short reel on the topic.
So I have to rewrite the last part of that first sentence. As I write this post, I’m still mulling over alternatives.
The 7 items above were just some of the tips that Melanie Woods imparted to me, all in the space of 20 minutes.
Again, the common theme is that recruiters have limited time, LinkedIn Recruiter lets them maximize that limited time, and jobseeker data also has to let recruiters maximize that limited time. If you can do something in 0 seconds, don’t take 5 seconds to do it.
I haven’t even explored some of the other features that CGL Recruiting offers, including Melanie Woods’ YouTube channel.
But the 20 minutes I spent with her were certainly valuable.
Now I just have to figure out another way to say “why customers benefit.”
Bredemarket