We are thrilled to have THE Chris Kunze -- Founder / President of Kunze Analytics, LLC -- as our guest! Kunze Analytics uses data-driven analysis and statistical review to help companies identify weak spots in their hiring process, and to root out potential job candidates who might cause liability to your company. Engaging and Informative!
Many businesses engage in discriminatory practices or unreliable hiring practices that prevent unproductive employees from being hired. Chris Kunze will tell us how the use of data can help improve a company’s employee retention rate. He has helped hundreds of companies across the country by providing them with customized and interactive applications that have the greatest validity. Certified consultant and navigating psychometric testing. Chris states that the psychometric model is able to detect hidden and latent traits the interviewers do not often spot, which allows the company to make more accurate personnel decisions.
When using statistics, typically this is done for a company with a minimum of at least ten employees in the same role. In the past, more people would have been needed but with modern advanced analytics there is greater statistical power with fewer people. Good communication between Human Resources and frontline Managers allow Chris’ company to collect useful performance data and create an accurate model. It is very important that companies are mindful of who they promote to a team leader position so that fairness and job relatedness is tied to their personnel decisions. Not only are these people responsible for who is promoted and included, depending on their biases they may exclude candidates and prevent capable employees from receiving certain training.
Eric notes that the psychometric test seems to have a personality model and asks if they are connected to other known personality tests. Chris believes that those tests are good for understanding an employee but not ideal for screening employees, other models are used for screening. With AI technology, they are able to apply multiple performance metrics to the application process and make sure that they are compatible. Chris emphasizes the importance of the human element when utilizing this technology. A human being is able to make sense of it, apply the most useful metrics, and customize the applications for each company.
In the midst of this pandemic, some biased managers have utilized this pandemic to let go of employees that they did not work well with despite their high performance, others will have to adjust to a growing demand from remaining employees to remain home. The relationship between employers and employees is changing dramatically throughout this pandemic. Chris notes that in the past companies have let go a lot of employees then overworked their remaining employees without compensation. Chris explains the six step method his company Kunze Analytics uses to help employers open up their applicant pool. If you would like to utilize Chris Kunze’s services for your business you can find him at kunzeanalytics.com.
00:00:50.100 --> 00:00:58.020 Eric Sarver, Esq.: Good evening, welcome to employment law today i'm your host Eric solver i'm in employment law and business law attorney.
00:00:58.410 --> 00:01:10.950 Eric Sarver, Esq.: At the law office very again solver and i'm here Tuesday nights at 5pm where I have guests each week and we discuss the most prevailing and prominent issues around employment and Labor law law.
00:01:11.340 --> 00:01:18.660 Eric Sarver, Esq.: That particularly effect and impact small to mid sized companies and business owners and so in that spirit.
00:01:19.740 --> 00:01:30.000 Eric Sarver, Esq.: Our topic tonight is data driven employer success using statistics and analytics in hiring practices and I have a very interesting guest tonight.
00:01:30.330 --> 00:01:40.410 Eric Sarver, Esq.: With a very unique and interesting method and methodology and i'd like to welcome them to the show Chris coons of coons analytics welcome to employment law today.
00:01:40.860 --> 00:01:42.540 Chris Kunze: Thank you were great to be here.
00:01:43.320 --> 00:01:51.510 Eric Sarver, Esq.: Wonderful i'm Chris i'm going to give you a more proper fitting introduction in just a moment, but before I do, I thought I would take.
00:01:51.870 --> 00:01:58.380 Eric Sarver, Esq.: A little bit to describe to our guests, a little more detail about our topic typically the data driven employers success.
00:01:58.860 --> 00:02:10.680 Eric Sarver, Esq.: And so, in that vein many businesses as we many of us know struggle with problematic patterns in their hiring practices, I see this quite often as an employment law attorney.
00:02:11.340 --> 00:02:18.900 Eric Sarver, Esq.: They hire managers and supervisors who contribute to a discriminatory workplace environment for as an example.
00:02:19.770 --> 00:02:29.910 Eric Sarver, Esq.: They also may have employers who fail to create solid hiring criteria to weed out poorly performing employees who may lower the company's productivity.
00:02:30.360 --> 00:02:38.220 Eric Sarver, Esq.: And the same patterns may persist for employers during succession planning organizational changes or an employee retention.
00:02:38.610 --> 00:02:53.220 Eric Sarver, Esq.: So during the pandemic with companies undergoing these great and seismic changes the need for reliable hiring practices, the need for solid employee retention and succession planning practice is critical.
00:02:54.000 --> 00:03:06.510 Eric Sarver, Esq.: So the question becomes, how can one use the statistical data or analytics How can that be helpful to an employer to increase their chance of reducing discriminatory hiring practices.
00:03:07.260 --> 00:03:13.590 Eric Sarver, Esq.: How can data and analysis of statistics help a business to increase their employee retention rate.
00:03:14.220 --> 00:03:22.710 Eric Sarver, Esq.: So this episode of employment law today my guest Christopher Collins, and I have coons analytics and we'll see we'll discuss this topic in more detail.
00:03:23.070 --> 00:03:31.440 Eric Sarver, Esq.: And before we do as I promised you Chris i'd give you a little more of a proper introduction, because you certainly have an impressive background, I think our viewers should be aware of.
00:03:33.150 --> 00:03:42.180 Eric Sarver, Esq.: Here goes my guest tonight folks is Chris coons Chris is the founder and President of coons analytics llc, as I mentioned.
00:03:42.630 --> 00:03:50.670 Eric Sarver, Esq.: Chris is a multi lingual data scientists with 20 years of experience predicting employee performance safety and retention.
00:03:51.360 --> 00:03:57.000 Eric Sarver, Esq.: Assisting hundreds of companies across the globe, to implement screening assessments and leadership surveys.
00:03:57.420 --> 00:04:07.440 Eric Sarver, Esq.: Chris coons uses machine learning algorithms to build and validate predictive models so that new candidate and employee that it can be scored efficiently.
00:04:07.800 --> 00:04:25.440 Eric Sarver, Esq.: and accurately through customized and interactive applications as a Phi beta Kappa graduate of marquette university with a liberal arts degree, Chris also completed studies and philosophy at the Gregorian University in Rome, Italy, he was a recruiter throughout Europe.
00:04:26.790 --> 00:04:37.140 Eric Sarver, Esq.: And he worked for three years as a student counselor at the albertus Magnus university include and the higher much higher university if I pronounced that correctly in this'll dorf Germany.
00:04:37.860 --> 00:04:44.130 Eric Sarver, Esq.: Chris Chris recently worked for years and R amp D at john wiley and sons workplace learning solutions division.
00:04:44.730 --> 00:04:56.460 Eric Sarver, Esq.: Through iTunes analytics and while there he leveraged governmental workforce and client data to publish a library of 139 job related performance models for pst select.
00:04:56.910 --> 00:05:02.220 Eric Sarver, Esq.: Certified partners and clients Chris coons is a certified consultant with frontline systems Inc.
00:05:02.790 --> 00:05:10.260 Eric Sarver, Esq.: The premier creator and distributor of optimization simulation and data mining software and finally.
00:05:10.560 --> 00:05:20.430 Eric Sarver, Esq.: He is a future solve advisor assisting HR professionals to navigate the waters of psychometric testing psychometric testing so Chris you gave me some tongue twisters there when you describe it.
00:05:20.430 --> 00:05:22.950 Chris Kunze: yeah and it sounds like you all right, some of those words.
00:05:23.820 --> 00:05:26.160 Chris Kunze: spoke German pretty well they're convinced me.
00:05:26.370 --> 00:05:34.680 Eric Sarver, Esq.: Oh, thank you well great Thank you so much well it's seriously a pleasure to introduce you to the show, and so this topic, really.
00:05:35.100 --> 00:05:41.640 Eric Sarver, Esq.: strikes a chord in me as an employment law attorney I often have clients come to me for compliance.
00:05:42.060 --> 00:05:51.390 Eric Sarver, Esq.: With Labor and employment laws or for Defense when haven't complied and i've seen clients who tragically make the same mistakes in their hiring they fail to properly vet.
00:05:52.260 --> 00:06:04.620 Eric Sarver, Esq.: Their employees, they don't have a sound method for evaluating data for their employees, and so they have the same issues over and over again with discriminatory managers and so forth, so i'm really glad to have you on the show.
00:06:05.910 --> 00:06:18.690 Eric Sarver, Esq.: I was wondering, I know I said a lot about you, but can you tell us a bit more about yourself and your company, you know how you how'd you get to the place of founding coons analytics and what would you describe is your company's main mission.
00:06:20.430 --> 00:06:27.810 Chris Kunze: Sure Eric Thank you well, I found myself as an employee of a very large publishing company.
00:06:28.890 --> 00:06:51.660 Chris Kunze: They had purchased a smaller psychometric assessment publishing company and I went into research and development as a consultant, my task was to look at over 10 million data points from client results over a three and a half year period to see.
00:06:52.830 --> 00:07:00.930 Chris Kunze: In those data patterns that we could use to build a completely new library of models for our clients.
00:07:01.380 --> 00:07:08.760 Chris Kunze: And so, this is the type of an assessment that's administered during a screening process for job applicants.
00:07:09.180 --> 00:07:18.690 Chris Kunze: They log online these applicants are typically finalists or sometimes they're it's earlier in the procedure, but they take an assessment online.
00:07:19.230 --> 00:07:27.810 Chris Kunze: When they're done it's automatically matched to a pattern or a performance model and the applicant tracking system.
00:07:28.440 --> 00:07:35.460 Chris Kunze: Will sometimes receive the data and the recruiter will see an overall fit score or job match score.
00:07:36.120 --> 00:07:54.900 Chris Kunze: And reports are delivered to those who are going to interview the candidate well as I was doing that I found that well i'm going to need to write some code some software development and I was working in a statistical program from IBM spss.
00:07:56.130 --> 00:08:02.970 Chris Kunze: And up until then the company had only was only scoring one assessment at a time.
00:08:03.570 --> 00:08:10.920 Chris Kunze: But in order for me to analyze my data I need to be able to score hundreds and thousands of assessments at at a time.
00:08:11.460 --> 00:08:18.780 Chris Kunze: get their math scores to whatever model I was building, so I could analyze it and then to see.
00:08:19.620 --> 00:08:27.750 Chris Kunze: I figured out well let's write a program that would create the optimal model, based upon the performance data from a client.
00:08:28.050 --> 00:08:34.410 Chris Kunze: And a large data set of people in that company would complete it let's see if we can get in the computer.
00:08:34.710 --> 00:08:46.470 Chris Kunze: To basically find the optimal model with the highest correlation the most value the greatest validity and that led me down a path where I knew I had to get back into consulting.
00:08:46.830 --> 00:08:56.310 Chris Kunze: Because that was all about customization and the company, I was working for were basically catering to boilerplate and.
00:08:57.690 --> 00:09:20.520 Chris Kunze: Another use of the product that wasn't for mid size or larger companies in the way I was customizing and so that's how that happened in 2018 but they gave me word and I knew, so I basically started my company up in the end of at the end of 2016 as a consulting firm and was able to.
00:09:22.380 --> 00:09:26.820 Chris Kunze: begin a search for even better ways to build models.
00:09:29.310 --> 00:09:36.870 Eric Sarver, Esq.: Center thing a lot there to say again and to really focus on, I know you mentioned that the important than having a large.
00:09:37.320 --> 00:09:46.830 Eric Sarver, Esq.: pool of applicants are people to use to evaluate certain patterns and I, one thing I wonder, too, is when you are talking to a prospective.
00:09:47.130 --> 00:10:03.720 Eric Sarver, Esq.: let's say customer of yours, a client a company that is coming to you and saying hey we're having these issues we keep hiring people who are not performing well or you keep hiring same sort of bad apples, if you will, bad actors, is there a way that you sort of.
00:10:05.340 --> 00:10:12.930 Eric Sarver, Esq.: succinctly put this in a nutshell, for them like, how do you explain it in a way that captures their interest.
00:10:14.550 --> 00:10:23.250 Eric Sarver, Esq.: Yes, is there, like a short like a primary some generally curious I know because I know my clients who might need this or people, the same tonight, want to know.
00:10:24.480 --> 00:10:25.110 Eric Sarver, Esq.: How they would.
00:10:25.410 --> 00:10:44.790 Chris Kunze: Right, so I would I would tell them that, out of all the information that they gather during a job candidates screening from the applicant the application phone screening questions with their answers background check information.
00:10:46.500 --> 00:11:00.840 Chris Kunze: And references that the test the psychometric assessment is the most valid and it's The easiest way to look at hidden or latent traits.
00:11:01.500 --> 00:11:15.480 Chris Kunze: That is very difficult for an interviewer to get a grasp on people are so practice and coached in interviewing they read books, they go to courses they they put on their best.
00:11:16.290 --> 00:11:25.110 Chris Kunze: persona and clothes and they just but then sometimes when they're hired the next day you see them at work and it's like a different person.
00:11:26.490 --> 00:11:43.530 Chris Kunze: So it's basically we're going to take a deep dive into this person's comfort zone in different psychological characteristics cognitive abilities and occupational interest to get different dimensions of who they are, like their DNA.
00:11:44.010 --> 00:11:45.630 Chris Kunze: And that's going to allow us to make.
00:11:45.630 --> 00:11:54.000 Chris Kunze: Much more accurate personnel decisions, because we have more information about who they are, and if they're going to finish.
00:11:54.000 --> 00:11:54.480 Chris Kunze: The job.
00:11:56.310 --> 00:12:04.050 Eric Sarver, Esq.: It makes a lot of sense, Chris when you think about it, because you're right people, people who might say, well, why are people who might ask the question well why do I need.
00:12:04.620 --> 00:12:13.230 Eric Sarver, Esq.: This deep dive the analytical data when I can assess people through interviewing who conversations second interview.
00:12:13.530 --> 00:12:20.520 Eric Sarver, Esq.: Well, I agree with you that people these days are very much like they be coached in and taught and trained how to properly.
00:12:20.820 --> 00:12:30.570 Eric Sarver, Esq.: And some people are better than others and said disguising are hiding their bias right we've all if we've ever entered for jobs, read about you know tips on line of what the same would not say.
00:12:30.990 --> 00:12:40.170 Eric Sarver, Esq.: So it's interesting that your test and target what I heard you say was phrase from the implicit perhaps biases are subtle maybe say character.
00:12:40.710 --> 00:12:52.050 Eric Sarver, Esq.: Flaws or trades, or just indicators of some kind of issue that says this person this employee would not be a good fit for this company.
00:12:52.590 --> 00:12:59.010 Eric Sarver, Esq.: So that's really I think it's a great way to explain i'm hearing you talk about right the the complex aspects you know the the.
00:12:59.310 --> 00:13:08.730 Eric Sarver, Esq.: How you sort of works you're like the the making the software, if you will, the back back behind the scenes and how you would present it to someone might be interested in your services.
00:13:09.780 --> 00:13:19.890 Eric Sarver, Esq.: i've certainly seen the need for the deep dive and you know a lot of times we'll see in employment law and people out tonight who are listening might notice that there.
00:13:20.610 --> 00:13:27.870 Eric Sarver, Esq.: It is truly difficult to assess, as you mentioned, from implicit biases or challenges, so I really appreciate hearing this.
00:13:29.400 --> 00:13:35.970 Eric Sarver, Esq.: And I am curious about you know we talked about this and analytics and such i'm.
00:13:36.450 --> 00:13:42.690 Eric Sarver, Esq.: wondering, a question that might be best suited for when we come back from the commercial break in a moment, but the question would be.
00:13:43.230 --> 00:13:57.840 Eric Sarver, Esq.: What types of businesses, so you can think about it during the break, what do you think best benefit from your method some statistics analysis to improve the hiring practices in terms of the size of the business or it's a certain industry or field.
00:13:59.130 --> 00:14:08.310 Eric Sarver, Esq.: So that's something I want to ask you to come back, we are here tonight on employment law today on talk radio nyc station around each week.
00:14:09.120 --> 00:14:25.860 Eric Sarver, Esq.: i'm the host Eric savoured and my guest is Chris coons of coons analytical see we're speaking about data driven employer success using Stat and analytics in the hiring practices so stick around we'll be right back.
00:17:24.660 --> 00:17:36.540 Eric Sarver, Esq.: Welcome back to employment law today i'm your host erick solver here on talk radio dot nyc i'm here with my guest tonight, Chris coons of coons analytics llc Chris it's great to have you on the show.
00:17:37.500 --> 00:17:47.670 Eric Sarver, Esq.: When we left off before the commercial break I left you with the question what types of businesses do you think most benefit say fun statistics analysis.
00:17:48.210 --> 00:17:56.940 Eric Sarver, Esq.: To improve the hiring practices i'm referring to, of course, both in terms of size of businesses and of any particular industries that jump out at you.
00:17:58.080 --> 00:18:17.430 Chris Kunze: Sure great question of when we use statistics typically it's for companies that have at least 10 employees in the same role now they could have 10,000 in the same role or 100 in the same role, but the minimum sizes 10 and.
00:18:19.110 --> 00:18:31.890 Chris Kunze: In the past, we had to have 30 or 40 people in the same role, but that was with a different type of linear analytics today with advanced analytics we could actually have.
00:18:32.700 --> 00:18:43.380 Chris Kunze: Significant power statistical power to predict with even smaller samples down to 10 people so that's kind of our cut off, now we look for companies that.
00:18:43.830 --> 00:18:57.150 Chris Kunze: have relatively good communication between the human resource department and the frontline managers, either in the sales service administration operations.
00:18:57.480 --> 00:19:08.190 Chris Kunze: So that we can get the performance data, the real performance data that's used to make personnel decisions, there has to be that communication and that's a.
00:19:09.360 --> 00:19:19.440 Chris Kunze: Basically, a criteria for us to to be able to work and to do statistics now different industries, there are a whole bunch of them so think of the educational.
00:19:20.220 --> 00:19:27.270 Chris Kunze: industry which is huge after the military you've got education right we think of all our schools and our our post.
00:19:28.170 --> 00:19:35.880 Chris Kunze: Post secondary and graduate schools, the government, the US Department of Education now requires.
00:19:36.300 --> 00:19:52.260 Chris Kunze: These institutions to publish their placement rates after students go through a college the there are people who have the job of helping them find work, and so that those are numbers that are very important to any.
00:19:53.700 --> 00:20:12.480 Chris Kunze: Academic academic institution, and so we can help them find that right type of person who is going to play students help them get financial aid and recruit them into the proper program and subject matter of their interest, then think of manufacturing.
00:20:12.840 --> 00:20:26.400 Chris Kunze: So we help a company that is going to hire several hundred or over 1000 employees and we do this in an efficient manner by building a very accurate model.
00:20:26.880 --> 00:20:37.530 Chris Kunze: Then, after that, who is going to be chosen to be promoted into that team leader position, so this is very important that companies do that correctly.
00:20:37.830 --> 00:20:49.320 Chris Kunze: And that not only the model to which the entry level employees are matched against, but also the the succession planning the other levels so that fairness and.
00:20:49.620 --> 00:20:58.530 Chris Kunze: Job relatedness can be directly tied to those personnel decisions, because it is a personnel decision, not only to hire someone.
00:20:58.950 --> 00:21:16.260 Chris Kunze: Or to promote someone but also legally any decision that would exclude certain employees from getting training or coaching or mentoring and so we have to be fair right, we want to reduce bias, as much as possible favoritism.
00:21:16.650 --> 00:21:17.070 Eric Sarver, Esq.: and
00:21:17.850 --> 00:21:21.690 Chris Kunze: You know, basically, what would be any quality.
00:21:22.920 --> 00:21:38.790 Chris Kunze: Then other other industries would be, for example, the this is interesting transportation right, I had two very large transportation clients, one of them manufactured and operated locomotives and Canada and the US.
00:21:39.300 --> 00:21:58.650 Chris Kunze: And so we were able to detect with a very short assessment that on one scale people who scored low on that scale were four to five times more prone to have injuries and accidents each injury and accident.
00:21:58.890 --> 00:22:06.870 Chris Kunze: could cost the company between 55 and $65,000 and some of those were fatalities so.
00:22:07.140 --> 00:22:09.720 Chris Kunze: They were very careful about using.
00:22:09.720 --> 00:22:14.310 Chris Kunze: psychometric data to create a safer workplace then.
00:22:15.510 --> 00:22:18.210 Chris Kunze: Recently i've done some modeling for.
00:22:19.290 --> 00:22:31.050 Chris Kunze: Transportation companies they they drive buses commercial buses and now with all of the sensors throughout these vehicles were able to detect what's called.
00:22:31.530 --> 00:22:38.970 Chris Kunze: A telemetric data, so how fast do they break are they safe when they make turns do they.
00:22:39.780 --> 00:22:53.820 Chris Kunze: drive in such a way, accelerate too quickly, where then they're graded as risky drivers so we're able to find the type of personality of a safe driver, especially for you know driving in in the inner city.
00:22:54.540 --> 00:23:04.440 Eric Sarver, Esq.: um you know there's a lot there yeah really important stuff i'm going to hear from you saying to is that this is not just about assessing someone's.
00:23:05.040 --> 00:23:10.890 Eric Sarver, Esq.: personality for the sake of having an employee that's a good fit for a company and productive I mean sure that's part of it.
00:23:11.490 --> 00:23:17.700 Eric Sarver, Esq.: You want employees that have good morale that are going to be a good fit that a month later, you won't look at them and say who is this person.
00:23:18.090 --> 00:23:24.450 Eric Sarver, Esq.: And they're very different than how they appeared in the interview, but what I hear you saying a few things one I hear you saying that.
00:23:24.840 --> 00:23:36.690 Eric Sarver, Esq.: Your models and the importance of proper screening using data analytics can help a company with liability issues around safety if you have it's a worker to take unnecessary risks.
00:23:36.930 --> 00:23:44.640 Eric Sarver, Esq.: Perhaps they should not be that's a job of you know, we had poor conductor driving certain large operating large machinery.
00:23:45.000 --> 00:23:54.480 Eric Sarver, Esq.: or even if they're hiring and training folks right if they have an implicit bias that could lead to disparate impact discrimination where you look at a company and suddenly.
00:23:55.380 --> 00:24:02.700 Eric Sarver, Esq.: There are very few minority faces, there are very few people of color and you know the company's scratched his head and when something does happen.
00:24:03.420 --> 00:24:10.170 Eric Sarver, Esq.: I know when I get, for example, a client that comes to me and to being super discrimination there's usually a mix right there's some sort of.
00:24:10.950 --> 00:24:28.620 Eric Sarver, Esq.: Direct over bias and discriminatory behavior or conduct or comments, but often with that, if you look at the company's culture this be the more covert right the underlined the implicit bias let's say you say gee, why is it that and this company went from you know say 10% or 20%.
00:24:29.700 --> 00:24:37.920 Eric Sarver, Esq.: You know people were African American and Latino to 2% African American Latino and then the 8% Caucasian you know folks like me.
00:24:38.550 --> 00:24:50.760 Eric Sarver, Esq.: You know why is that so so certainly I also here to talk about just the that your models can be important when it comes to sustaining certainly staving off discrimination certainly.
00:24:51.210 --> 00:25:01.080 Eric Sarver, Esq.: Safety right in the workplace, that has to do with a lot of occupational safety and OSHA violations that a company could be expressing themselves by using your company so.
00:25:01.590 --> 00:25:16.680 Eric Sarver, Esq.: And i'm interested in what you said about how this is not just for one industry is not just for tech it's, not just for that say manufacturing, but it can be a manufacturing company or a school and educator.
00:25:16.710 --> 00:25:32.730 Chris Kunze: and your school, and I even had a chance to build models for elementary middle and high school teachers leveraging gains in learning in math and English from the actual students, we had.
00:25:32.760 --> 00:25:51.600 Chris Kunze: Two years of data, and so we were able to find the personality of those type of teachers that we had good evidence would be beneficial for the students and to create that learning atmosphere, if I want to, I want to make an analogy, so do you play tennis.
00:25:52.650 --> 00:25:53.370 Chris Kunze: Do you play tennis.
00:25:53.460 --> 00:25:56.550 Eric Sarver, Esq.: And I don't that does ping pong count, you know table.
00:25:56.970 --> 00:26:00.570 Eric Sarver, Esq.: Count okay sure, and then I play some things on the.
00:26:00.660 --> 00:26:01.860 Eric Sarver, Esq.: table okay okay.
00:26:02.580 --> 00:26:03.990 Chris Kunze: Do you remember Bruce Lee.
00:26:04.740 --> 00:26:06.690 Chris Kunze: Yes, Lee was a karate guy right.
00:26:07.020 --> 00:26:10.440 Chris Kunze: And there's some videos going around the Internet that he could take a nunchuck.
00:26:10.710 --> 00:26:14.100 Chris Kunze: Which is a steak on a chain, and he could play ping pong with it right.
00:26:14.490 --> 00:26:21.960 Chris Kunze: Now now he's probably the only person in the history of mankind that could do that right there was so skilled and it's probably a fake video but.
00:26:22.830 --> 00:26:34.950 Chris Kunze: Normally, you want to play ping pong with a paddle surfing So if you don't have a paddle and you just have a stick you're probably going to lose right, you might hit the ping pong ball every once in a while, but it's going to go all over the place, you have no control.
00:26:35.700 --> 00:26:45.060 Chris Kunze: Right, so what is that what is that paddle that paddle is very important, so basically do you think of its its length.
00:26:45.660 --> 00:26:48.810 Chris Kunze: Those are all of the key performance indicators.
00:26:49.140 --> 00:26:53.430 Chris Kunze: That are objective so let's say we're taking a sales position.
00:26:53.730 --> 00:26:58.860 Chris Kunze: Right and we're going to look at all of the sales people in a certain role and they're.
00:26:59.250 --> 00:27:13.260 Chris Kunze: Now they're remote selling their around the country making sales so we're going to look at their gross revenue their quota attainment their profit margin their volume their account growth year over year their their.
00:27:14.520 --> 00:27:24.210 Chris Kunze: Account retention we're going to have all of these sales metrics and then that these basically will know where everyone is in the role.
00:27:24.630 --> 00:27:27.090 Chris Kunze: Whether they're below average average above.
00:27:27.120 --> 00:27:40.050 Chris Kunze: wherever they are in all of these metrics and then we'll have another set of metrics that go the other direction, how wide that paddle is because you just don't want to fall narrow and you want some with do so you can find the sweet spot when you hit the ping pong ball.
00:27:40.350 --> 00:27:42.300 Chris Kunze: All of those are going to be survey.
00:27:42.570 --> 00:27:45.900 Chris Kunze: survey results pulse survey results.
00:27:46.920 --> 00:27:53.970 Chris Kunze: Annual performance appraisals different types of gathering of information about how they're doing the job.
00:27:55.020 --> 00:27:57.510 Chris Kunze: So that's a little more subjective but it's very.
00:27:57.510 --> 00:28:06.810 Chris Kunze: Critical because you want to protect the company culture it's not just about that they get the job done, but they do it the right way.
00:28:07.200 --> 00:28:17.760 Chris Kunze: And so, when we have done that study we hand you that ping pong paddle and now you're able to play and you're able to place the ball well i'm going to put this way on the end of the table that's an entry.
00:28:18.090 --> 00:28:31.200 Chris Kunze: level employee or i'm going to just hit it barely over the Net, so you have to you know, I have to reach forward to get it that's a manager or wherever we want to go on this along the edge that's a supervisor so now.
00:28:31.560 --> 00:28:39.720 Chris Kunze: When you start having balls come at you and, and these are candidates they're sending in their resumes they want to be interviewed they want to work for your company.
00:28:40.050 --> 00:28:56.250 Chris Kunze: You get to match them to all of those models and they're well built, so you get you get an understanding of what their performance will be in different roles and of course now you're the winner because you're ready for any type of serve they give you.
00:28:57.750 --> 00:29:06.840 Eric Sarver, Esq.: I bet now G Kristen i'm sure people listening, who are attendance fans, is that the to a tennis racket for the paddle but I thank you for shooting for me.
00:29:07.530 --> 00:29:12.630 Eric Sarver, Esq.: But that is something I hear about you know, so these models that you're building these.
00:29:13.200 --> 00:29:21.150 Eric Sarver, Esq.: Using statistical data analyzing and right breaking it down during a certain conclusions and then using that to analyze the current.
00:29:21.630 --> 00:29:31.800 Eric Sarver, Esq.: Applicants or current employees very good moving up, but so you talked about succession planning can be helpful in terms of say predicting employee performance and.
00:29:32.580 --> 00:29:37.860 Eric Sarver, Esq.: and longevity as well as routing out negative employees or I should call them problematic employees.
00:29:38.430 --> 00:29:48.480 Eric Sarver, Esq.: And you know, essentially because these are often the underlying issues that if companies know this before they can screen to win out in advance.
00:29:49.140 --> 00:29:58.620 Eric Sarver, Esq.: Who might end up being a perpetrator of discrimination or might end up being someone who is a full that's a self.
00:29:59.340 --> 00:30:05.490 Eric Sarver, Esq.: Former then they can save themselves a time, money and energy and effort and having to rehire.
00:30:05.820 --> 00:30:10.380 Eric Sarver, Esq.: You know, one of my colleagues and jenna fox on my shelf stocks about her PR and marketing.
00:30:10.650 --> 00:30:22.890 Eric Sarver, Esq.: And a lot of what she does comes down to 10 or services help you to either make money right or save money and to free up more time and increase productivity, so I think it's great that you're talking about that.
00:30:23.880 --> 00:30:30.690 Eric Sarver, Esq.: We have to take our second commercial break, but so we're at the halfway point when we come back and folks I have more of.
00:30:31.440 --> 00:30:41.760 Eric Sarver, Esq.: Chris coons here from coons analytics llc we're talking about drivers for employer success using statistics and data analytics.
00:30:42.150 --> 00:30:58.560 Eric Sarver, Esq.: And models that Chris has built and designed to help your company to succeed in hiring employee retention and succession planning, so if everyone could just stick around to talk her down yc employment law today stick around we'll be right back.
00:31:00.270 --> 00:31:03.630 Eric Sarver, Esq.: are listening to radio nyc.
00:31:05.250 --> 00:31:05.790 HP.
00:33:48.630 --> 00:33:55.260 Eric Sarver, Esq.: Welcome back to employment law today i'm your host erick solver here with my guest tonight, Chris kenzie from kenzie analytics llc.
00:33:56.580 --> 00:34:07.860 Eric Sarver, Esq.: and Chris it's so good to have you on the show this evening I we were talking a moment ago about the different models, you have different programs and how your services can help.
00:34:08.340 --> 00:34:16.170 Eric Sarver, Esq.: Small to mid sized businesses out there with their hiring and retention and their employees productivity and safety and so forth.
00:34:16.800 --> 00:34:26.190 Eric Sarver, Esq.: So interesting stuff that were just describing tonight talking about here, I was curious to hear actually two things one thing jumped out at me before I asked my next question.
00:34:27.270 --> 00:34:36.630 Eric Sarver, Esq.: It seems that these tests, the psychometric tests have a bit of a so they are connected to a personality profile so i'm wondering does your do your models.
00:34:37.020 --> 00:34:49.920 Eric Sarver, Esq.: Have any connection to any type of the those personality tests that some of us are familiar with, such as the see the the minor scripts or some of those other tests out there, that you hear about my.
00:34:49.980 --> 00:34:55.830 Chris Kunze: fingers the myers briggs, which is a 16 quadrant or a disc tool for quadrant.
00:34:56.940 --> 00:35:10.020 Chris Kunze: Those instruments are best used for understanding the personalities of your employees, but they're they're not designed for personnel decisions so.
00:35:10.710 --> 00:35:25.170 Chris Kunze: If you do have a more robust tool every once in a while a scale from or some information from those instruments could be could be used to add some greater richness, but normally.
00:35:26.730 --> 00:35:33.180 Chris Kunze: You would use a tool that has been designed for pre pre screening and pre hire assessing.
00:35:34.320 --> 00:35:47.310 Eric Sarver, Esq.: hmm, so I think interesting because I have colleagues who, as I know, and you might know to perhaps use and utilize these assessments and this has happened, for example, or the myers briggs test.
00:35:48.270 --> 00:36:00.960 Eric Sarver, Esq.: Which when I hear you saying is can be helpful, perhaps insane knowing your employees personality style what they must respond to you, but for more or perhaps in depth, as you mentioned earlier in the show deep dive.
00:36:01.440 --> 00:36:02.190 Chris Kunze: yeah so.
00:36:02.760 --> 00:36:16.410 Chris Kunze: The difference myers briggs had 16 different personality types and you can slice and dice those personality types in many different ways and find a lot of insight, but with the type of tool that i'm talking about.
00:36:17.010 --> 00:36:36.900 Chris Kunze: When we look at the possibility that possible number of patterns that we could create for a client it's somewhere between 6,000,000,001 with 20 zeros after it, so our software cycles through all of these.
00:36:37.950 --> 00:36:48.090 Chris Kunze: different patterns, to find just the right one for the client, and so the other assessments, while they're valuable for why they were designed.
00:36:48.540 --> 00:36:58.470 Chris Kunze: They don't have all of this capability of customization and predictive quality, the US Department of Labor actually publishes.
00:36:58.860 --> 00:37:10.470 Chris Kunze: validity guidelines and the correlation between performance on the job or it could be tenure and days, if that if the company has a lot of turnover.
00:37:10.800 --> 00:37:24.120 Chris Kunze: And there's poor morale because you know it's just a revolving door, then we have to build models, not so much on performance, but you have to stop the bleeding on the wound to retain.
00:37:25.440 --> 00:37:28.710 Chris Kunze: Employees and that's a little bit different from a performance model.
00:37:29.820 --> 00:37:38.730 Chris Kunze: So we're looking at 10 year old days we'll build a model that will increase that until the client is is satisfied that retention has been stabilized.
00:37:39.120 --> 00:37:51.420 Chris Kunze: And then we'll continue to have retention tenure and days of the employee average tenure with performance criteria criteria and then we'll build it out that way so.
00:37:52.590 --> 00:37:53.130 Chris Kunze: yeah it's.
00:37:53.160 --> 00:38:05.910 Chris Kunze: it's We really need to go beyond and use the modern technologies and the power of the computer and artificial intelligence is really automated reasoning, you know it's not.
00:38:06.240 --> 00:38:07.260 Chris Kunze: Something that's a.
00:38:07.350 --> 00:38:13.830 Chris Kunze: Little scary or whatever it's me doing a task that would take me 600 years to do in a couple of minutes.
00:38:14.160 --> 00:38:27.480 Chris Kunze: Right right and I could probably make a lot of errors and finding a needle in a haystack well, you have to use computing power to do that, but the machine doesn't understand the ramifications I do as the as the data scientist and as.
00:38:27.480 --> 00:38:40.110 Chris Kunze: The as the consultant so it's the proper use of these technologies to help our clients really have a solution, and these are complex so let's say we're hiring a loan officer and.
00:38:40.500 --> 00:38:47.160 Chris Kunze: They have to produce so much mortgage premiums, they have to retain their clients.
00:38:47.400 --> 00:38:50.340 Chris Kunze: They have to have good performance appraisal reviews.
00:38:50.640 --> 00:39:07.830 Chris Kunze: And we want to retain them so maybe the assessment will find models that are good predicting two or three but that fourth metric that's when we the machine learning then looks at all the possibilities to find one where all the metrics are going in the right direction.
00:39:08.310 --> 00:39:16.110 Chris Kunze: And that these are highly complex problems nonlinear and you need the help of of technology.
00:39:17.220 --> 00:39:27.480 Eric Sarver, Esq.: Yes, and you know people out there listening if they can identify with what you're saying, as it relates to their own business and their own practice the profession in that.
00:39:28.020 --> 00:39:42.390 Eric Sarver, Esq.: Data in of itself doesn't always tell a story unless the person who, who has a data can make sense of it, making interpreted and provide an explanation provide zaps some.
00:39:43.050 --> 00:39:49.140 Eric Sarver, Esq.: Some context and talk about what the implications might be and how to then use that data.
00:39:49.620 --> 00:39:58.170 Eric Sarver, Esq.: To make decisions, and you know this, I think, is true, whether you're a website design company, you know a lot of us design companies will say.
00:39:58.590 --> 00:40:08.280 Eric Sarver, Esq.: give you a report of your website performance and it's all certain data certain metrics and if you don't understand the terminology being used or.
00:40:09.000 --> 00:40:14.910 Eric Sarver, Esq.: The precepts a perspective of what a certain number of units or visitors, you know, is, I mean what is that.
00:40:15.420 --> 00:40:23.190 Eric Sarver, Esq.: In relation to other websites it doesn't really mean much you know, likewise, I can let's say I have clients of mine is an employment law attorneys.
00:40:23.910 --> 00:40:34.920 Eric Sarver, Esq.: I can find perhaps some issues where they might be out of touch with with the law requires, and I can talk about the liability and damages and maybe there, for example, not paying overtime properly.
00:40:35.370 --> 00:40:41.880 Eric Sarver, Esq.: But if I don't explain to them what that means and how to fix it and how to do so in the correct way.
00:40:42.330 --> 00:40:48.690 Eric Sarver, Esq.: Then it's just the data alone information is not going to help and that's why you know a lot of people in different fields we have.
00:40:49.410 --> 00:41:00.420 Eric Sarver, Esq.: In law, and I think in medicine of the field of course one of the bugs and love seeing recently it's a mug and it says, you know, please do not mistake your Google search my law degree.
00:41:00.750 --> 00:41:11.490 Eric Sarver, Esq.: And you know I seen variations of that with please not mistake your Google search my fill in the blank right to my medical degree from my computer programming degree so.
00:41:11.790 --> 00:41:28.350 Eric Sarver, Esq.: You know, we need what I hear you saying is, we need the power of artificial intelligence and computer automated processes, but we need the human element, you know that's the brain power that's you know that's your Crispin D and kenzie analytics don't see to make sense of this.
00:41:29.160 --> 00:41:30.000 Chris Kunze: Right Eric and.
00:41:30.030 --> 00:41:30.930 Chris Kunze: Data you know.
00:41:32.760 --> 00:41:33.240 Chris Kunze: I wanted to.
00:41:33.330 --> 00:41:37.230 Chris Kunze: Just make a comment that we've been talking about performance metrics.
00:41:37.410 --> 00:41:59.220 Chris Kunze: And then we spoke about retention employee retention or turnover, but also when you apply a model, you want to make sure that there aren't any protected classes that are negatively impacted right adverse impact, and so the we're looking at the four fifths rule to make sure that there.
00:41:59.250 --> 00:42:01.500 Chris Kunze: are sufficient hiring of.
00:42:01.710 --> 00:42:18.150 Chris Kunze: Protected classes and when you put all of these criteria together that's what we solve for right a couple companies in particular litigious industries will need to look at adverse impact, and I have.
00:42:18.870 --> 00:42:26.580 Chris Kunze: fun projects every once in a while working with a company that creates skills tests, they have over 300 skills tasks.
00:42:27.060 --> 00:42:28.650 Chris Kunze: And the last couple of projects.
00:42:28.650 --> 00:42:42.030 Chris Kunze: i've done they've asked me to do these adverse impact and they'll say, can you shorten the test and then also make it more predictive and make sure that we know where the cutoff is so there's no adverse impact well that's that's fun.
00:42:42.420 --> 00:42:43.440 For a data science.
00:42:44.910 --> 00:42:46.080 Eric Sarver, Esq.: Like a challenge for you, I.
00:42:46.140 --> 00:42:48.510 Eric Sarver, Esq.: know I like to challenge as well as or.
00:42:48.570 --> 00:42:57.930 Eric Sarver, Esq.: You know client that's important people to understand that these statistics are very crucial and critical in terms of having the right.
00:42:58.470 --> 00:43:07.350 Eric Sarver, Esq.: let's say again disproportionate hiring practices, if you don't have a protected class for those who might not know people who might not be an employment field.
00:43:07.830 --> 00:43:20.610 Eric Sarver, Esq.: Mainly referred to under the law when it comes to employment people, based on the race, gender national origin in states and cities X orientation, which is now being pushed towards federal protection as well.
00:43:21.810 --> 00:43:33.480 Eric Sarver, Esq.: Things like familial status religion able bodied disabled so people get the classes can't be discriminated against, either overly think about you know over harassment or comment or.
00:43:34.290 --> 00:43:43.200 Eric Sarver, Esq.: exclusion from the workplace but also cannot be impacted adversely in a sort of covert way right where the numbers reflect.
00:43:43.800 --> 00:43:49.800 Eric Sarver, Esq.: say again if everyone in the company, I like to say they tell my clients if everyone looks like mean you know.
00:43:50.700 --> 00:44:01.080 Eric Sarver, Esq.: Just as a white male a certain age, gender, etc, then that can be problematic so so it's great that you have all these different.
00:44:01.950 --> 00:44:09.000 Eric Sarver, Esq.: methods and also all the different angle ends and the goals that you accomplish you know, for your clients, we are.
00:44:09.930 --> 00:44:21.750 Eric Sarver, Esq.: heading towards a break, but I just wanted to again remind folks though that joined us late and i'm here today with Chris candy from kenzie analytics and oC and Chris is a data scientist and his company.
00:44:22.680 --> 00:44:29.850 Eric Sarver, Esq.: Basically, uses this text analytics and models that Chris build algorithms and such to.
00:44:30.390 --> 00:44:37.230 Eric Sarver, Esq.: To help companies with their employment base with their hiring practices retention succession planning to really weed out.
00:44:37.710 --> 00:44:48.150 Eric Sarver, Esq.: Perhaps implicitly biased or problematic employees and also to attract the the top gun be the high performers, the people that play well with others, etc, so.
00:44:48.540 --> 00:44:58.860 Eric Sarver, Esq.: We listening to us here on employment law today and a targeted nyc, we have to take commercial break so folks stick around we'll be right back.
00:45:02.100 --> 00:45:02.940 Chris Kunze: Talk radio.
00:45:03.150 --> 00:45:03.630 And my.
00:45:05.550 --> 00:45:06.720 Education and.
00:47:15.150 --> 00:47:24.570 Eric Sarver, Esq.: Welcome back to employment law today i'm your host erick solver here every Tuesday night 5pm to 6pm Eastern standard time I know that Chris you're in.
00:47:25.260 --> 00:47:30.630 Eric Sarver, Esq.: The central time zone so it's actually four to five for folks watching by you and.
00:47:31.470 --> 00:47:39.690 Eric Sarver, Esq.: An interesting question came to mind I was wondering we've been in this pandemic now for almost a year and I wondered if that's i'm wondering.
00:47:40.110 --> 00:47:57.750 Eric Sarver, Esq.: To myself if that's enough of a timeframe to gather data, so my question for you is has your data review analytics will be able to any COPA 19 based changes let's say in the rate that companies are having poor hiring practices or fails succession planning.
00:48:00.030 --> 00:48:04.290 Chris Kunze: Eric so what i'm seeing is that and we're reading about it in the news.
00:48:05.370 --> 00:48:11.430 Chris Kunze: That because of the closing of schools and children being at home and many parts of the country.
00:48:11.730 --> 00:48:15.180 Chris Kunze: Right, one of the parents or caregivers has to be home.
00:48:16.560 --> 00:48:24.510 Chris Kunze: So employers are deciding well who's going to have that flexible schedule and go home.
00:48:25.260 --> 00:48:34.140 Chris Kunze: And then they're going to get used to in this long period of time people working remotely or people in a hybrid situation where.
00:48:34.500 --> 00:48:48.270 Chris Kunze: At some point they'll be working maybe one or two days at home and the rest of the days in the office and then, who are the employees, that will be required to be at at brick and mortar office so.
00:48:48.540 --> 00:48:53.790 Chris Kunze: A lot of people are going to be vying for that privilege to maybe be more flexible.
00:48:55.050 --> 00:49:09.990 Chris Kunze: And and stay at home they're going to like that, but then the employer has to probably say no to some people if they're not able to do that because functions have to be fulfilled, they have to have representation and going back.
00:49:11.070 --> 00:49:21.150 Chris Kunze: we're seeing that you know that's a big issue with employees being furloughed and also many companies have let go employees so who did they let go.
00:49:21.450 --> 00:49:32.520 Chris Kunze: This was an opportunity for managers, maybe to you to get into their bias or favoritism and get rid of people that they themselves didn't get long to get a get along with but.
00:49:32.580 --> 00:49:33.300 Chris Kunze: These could have been.
00:49:33.330 --> 00:49:38.370 Chris Kunze: Excellent performers that should be rehired back as soon as possible.
00:49:38.580 --> 00:49:42.360 Chris Kunze: So all of these issues of letting people go and hiring them back.
00:49:42.660 --> 00:49:52.200 Chris Kunze: And then, once they're there in a remote situation working the employer might say, oh my gosh well why don't we record them as an independent contractor.
00:49:52.440 --> 00:49:55.170 Chris Kunze: Instead of an employee that we don't have to pay benefits.
00:49:55.560 --> 00:49:58.470 Chris Kunze: And they might start thinking of other ways to.
00:49:59.790 --> 00:50:11.220 Chris Kunze: You know, restructure the whole relationship like as if they were a gig gig employees right so there's a lot of opportunity here, and this happened 10 years ago after the.
00:50:12.210 --> 00:50:31.140 Chris Kunze: The 2008 2009 economic crisis, Dr Peter capella who runs the Center for human resources at the wharton school at university of Pennsylvania, has written a lot about this topic and employers, they got used to they let a lot of 10 years ago, a lot of employees go.
00:50:31.740 --> 00:50:33.900 Chris Kunze: And they didn't hire them back right away.
00:50:34.140 --> 00:50:46.080 Chris Kunze: So the people that were working were overworked and they had all of this, this load put on their shoulders and a lot of them didn't receive increases in their compensation.
00:50:46.830 --> 00:50:56.490 Chris Kunze: So we hope that doesn't happen again that employers as soon as they're able to rehire their people and give them training invest in them.
00:50:56.910 --> 00:51:03.060 Chris Kunze: So that we have an equitable real relationship between both parties.
00:51:04.560 --> 00:51:14.940 Eric Sarver, Esq.: Right and it's mentioning data there you know, Chris what we're talking about now with the coven 19 applications it kind of takes us back to what you and I were discussing earlier on the show right about.
00:51:15.870 --> 00:51:23.940 Eric Sarver, Esq.: let's say these managers or employers need to either bring some people back or leave some people remotely and they don't want to be.
00:51:25.260 --> 00:51:42.480 Eric Sarver, Esq.: Say inequitable or have disparate impact in terms of are they bringing back say all the occasion, workers and not not people of color are they bringing back people will save a certain age group, and not a different age group and is that going to be statistically relevant or you.
00:51:42.540 --> 00:51:44.130 Chris Kunze: Are the women 2.5.
00:51:44.160 --> 00:51:49.050 Chris Kunze: million women have lost their jobs since since February of 2020.
00:51:49.560 --> 00:52:00.660 Eric Sarver, Esq.: Yes, excellent point and I have a colleague Simone sloan she does HR consulting and diversity inclusion work and she is on my show we talked a lot about this about the pandemics.
00:52:01.380 --> 00:52:07.230 Eric Sarver, Esq.: Basically disparate impact right disproportionately impacting employees of color and especially women of color.
00:52:07.560 --> 00:52:13.920 Eric Sarver, Esq.: And you see that, with people who are single parents are homeschooling or you see that, with people in terms of their resources so.
00:52:14.340 --> 00:52:22.110 Eric Sarver, Esq.: So it's interesting that you talk about that, and some of the data you seen a wondering can can the analytics can one of your programs perhaps be.
00:52:22.920 --> 00:52:33.120 Eric Sarver, Esq.: used to help employers to to sort of take these tests and see hey maybe we should bring back employees of this personality type maybe we should give more.
00:52:34.020 --> 00:52:41.550 Eric Sarver, Esq.: more leeway to employee staying home or this personality type is that something that you were programmed might be able to predict for our listeners out there.
00:52:41.670 --> 00:52:47.460 Chris Kunze: Yes, it is and i've been working on it for years and, and now we have a six.
00:52:47.580 --> 00:53:00.240 Chris Kunze: step method to be able to help employers to really open up their applicant pools and hire many more people that they can have confidence in So how do we do this.
00:53:00.780 --> 00:53:20.670 Chris Kunze: Step number one is there are over 200 different assessments out there that are used in pre hire So the first step is to choose a reliable assessment that has been designed for pre screening of job applicants and if you don't have one, I have a very favorite one that I like to use.
00:53:22.320 --> 00:53:32.400 Chris Kunze: Then step two is for us to use all that fancy automated reasoning, to build a very accurate success pattern for each one of the roles in your company.
00:53:32.970 --> 00:53:45.630 Chris Kunze: Step three is we build scorecards and these scorecards then have all of your metrics in them, as well as the employees or the candidates.
00:53:46.620 --> 00:54:05.040 Chris Kunze: Assessment results and each one of the key performance indicators, the metrics has a different type of statistical model behind it, one that's very powerful instead of having 10% accuracy, it can have up to 90% or more accuracy.
00:54:05.610 --> 00:54:06.450 Chris Kunze: And these are.
00:54:06.600 --> 00:54:16.710 Chris Kunze: Lengthy under the hood you see these thousands of lines of code 10 different what we call regression trees and so forth, but.
00:54:17.550 --> 00:54:22.260 Chris Kunze: We build scorecards and then the company can start to tinker with all the different.
00:54:23.100 --> 00:54:37.950 Chris Kunze: People and figure out how to coach everyone to greater productivity, when we find the insights then we put them in an action plan, so the manager and the direct report can work all year on these slight behavioral changes.
00:54:38.400 --> 00:54:47.040 Chris Kunze: The fifth step is the revolutionary one we simulate all the possible combinations of someone's assessment results.
00:54:47.460 --> 00:54:58.980 Chris Kunze: So that you have a table for each KPI you'll never have to guess what someone's assessments out remain mean in your performance metrics or 10 year and.
00:54:59.430 --> 00:55:08.580 Chris Kunze: that's for the hiring method we call it a cheat sheet Finally, we have a visualization tool on the web, and this, these are dynamic dashboards.
00:55:08.970 --> 00:55:27.210 Chris Kunze: Excuse me, and these dashboards allow the manager in front of the employer to put in their scores and to play with that one behavioral trait or two that can significantly increase productivity, so it visualizes the modeling in six steps.
00:55:28.440 --> 00:55:28.950 Chris Kunze: that's.
00:55:29.370 --> 00:55:32.160 Eric Sarver, Esq.: Really, something I appreciate your sharing that with us tonight.
00:55:32.880 --> 00:55:47.430 Eric Sarver, Esq.: we've got a couple minutes and I want to give you the chance to truly share with our with our listeners tonight, how they can find you how they can contact you, I would say one thing, before that, though, one thing that strikes me about you, you know, Chris and about kenzie analytics.
00:55:48.450 --> 00:55:52.470 Eric Sarver, Esq.: Certainly right your expertise or knowledge, you really seem to know your stuff.
00:55:52.920 --> 00:56:04.470 Eric Sarver, Esq.: and your passion for it, you know I find that combination is so crucial, I mean I love, what I do as well, when I represented clients and employment Labor law matters, you know hybrid class actions that complicated stuff.
00:56:05.790 --> 00:56:15.420 Eric Sarver, Esq.: You know I tell people that it's even if you don't understand let's say how something is being done, if you trust that the person you're hiring him that he or she or they know it.
00:56:15.780 --> 00:56:21.720 Eric Sarver, Esq.: And they understand it, and they can deliver what you need so in your in our case here people hiring.
00:56:22.050 --> 00:56:33.750 Eric Sarver, Esq.: Chris kenzie and cons the analytics can really get a sense of that you would deliver for them in terms of employee retention and practices we've got about a minute left, I want to before I sign off, I want to say.
00:56:34.230 --> 00:56:40.650 Eric Sarver, Esq.: To our listeners tonight, I want to hear first how they can How can people find you if you tell us a quick you're.
00:56:40.860 --> 00:56:51.720 Chris Kunze: Sure just go to the analytics COM and you'll have our contact information you'll even be able to see my schedule and schedule a meeting.
00:56:52.440 --> 00:56:56.250 Eric Sarver, Esq.: Great and that's K U n G analytics one word.com.
00:56:56.280 --> 00:56:57.810 Chris Kunze: No space between the words.
00:56:58.050 --> 00:56:59.610 Chris Kunze: Okay Lindsay analytics account.
00:57:00.510 --> 00:57:05.580 Eric Sarver, Esq.: In the last 20 seconds, though, to say again once again i'm here tonight with Chris kenzie of the analytics llc.
00:57:06.420 --> 00:57:14.190 Eric Sarver, Esq.: For your hiring and retention needs folks thanks once again for joining us on another evening employment law today.
00:57:14.730 --> 00:57:27.780 Eric Sarver, Esq.: we're here every Tuesday every Tuesday at 5pm with different guests, to help your business to thrive and survive this pandemic so stick around come back next week, and thank you so much, and Chris, thank you for joining us this evening.
00:57:27.810 --> 00:57:28.920 Chris Kunze: you're welcome Thank you were.
00:57:30.210 --> 00:57:30.600 Chris Kunze: A pleasure.