To Accurately Predict Who Will Thrive in Your Roles, Use People Analytics का पूरा ट्रांसक्रिप्ट

aestment and someone think of 55 00:04:28.370 --> 00:04:31.129 that number one draft pick in the NFL. You're going to lay out tens 56 00:04:31.170 --> 00:04:34.009 of millions of dollars. You want to make sure you're making the right decision. 57 00:04:34.009 --> 00:04:38.290 Or even if it's the counter at Bobby Brown, you want to make 58 00:04:38.329 --> 00:04:42.519 sure you're hiring the right beauty advisor that's going to give the very best customer 59 00:04:42.639 --> 00:04:46.519 service. So we take a scientific approach within the field and first is really 60 00:04:46.560 --> 00:04:50.839 understanding what is success look like in the role and before working with us a 61 00:04:50.879 --> 00:04:57.230 ladder, I had no clue. But applying scientific principles in terms of what's 62 00:04:57.269 --> 00:05:01.350 the knowledge, skills abilities that define success? When people have failed in the 63 00:05:01.470 --> 00:05:05.189 roll, what are the reasons why? And you start coming out with hypotheses 64 00:05:05.470 --> 00:05:10.500 like, oh, they need to have really high customer centricity and they need 65 00:05:10.540 --> 00:05:12.899 to be able to problem solve and they need to be able to have a 66 00:05:13.019 --> 00:05:16.819 team work and you come out with these characteristics and traits that would be ideal 67 00:05:17.060 --> 00:05:21.889 for successful performance. And then you go out, there's already hundreds of people 68 00:05:23.009 --> 00:05:26.370 in these roles, some are more successful than others, and you ask them 69 00:05:26.410 --> 00:05:31.449 to go through a battery of psychological assessments and you test those hypotheses. So 70 00:05:31.930 --> 00:05:36.240 is it true that the very best beauty advisors, who are selling more than 71 00:05:36.279 --> 00:05:41.680 anyone else, have the highest customer centricity? Do they have the highest teamwork? 72 00:05:41.839 --> 00:05:45.759 And out of all that noise you distill down what are those key factors 73 00:05:45.920 --> 00:05:50.149 that recipe for success, then you can apply to every higher going forward and 74 00:05:50.269 --> 00:05:55.949 make smarter decisions. Yeah, so you know the way that you describe it. 75 00:05:56.069 --> 00:05:59.470 And then more boil down senses that you're looking at data of all the 76 00:05:59.990 --> 00:06:02.750 candidates of are the last several years, let's say, so data and aggregate 77 00:06:02.790 --> 00:06:06.860 over time who were hired and turned out to be successful, comparing that to 78 00:06:06.980 --> 00:06:13.860 those who were unsuccessful and using that to understand those criteria. Yeah, and 79 00:06:14.019 --> 00:06:18.209 all likelihood you're already using certain criteria to make selection decisions and you can see 80 00:06:18.769 --> 00:06:25.569 under the people analytics microscope which of those factors are more indicative of success. 81 00:06:25.610 --> 00:06:30.170 So, as an example, one criteria that often has undue amount of weight 82 00:06:30.569 --> 00:06:35.319 in the selection equation as years of experience, and typically that's a very poor 83 00:06:35.439 --> 00:06:41.600 predictor of performance. So organizations are saying you need five to ten years experience 84 00:06:41.800 --> 00:06:45.160 and when you boil it down and start looking at the data, it predicts 85 00:06:45.240 --> 00:06:50.389 pretty low especially is compared to other criteria that would allow a much wider funnel 86 00:06:50.430 --> 00:06:56.110 of applicants to create a more diverse applicant pool, like all these other pluses 87 00:06:56.189 --> 00:06:59.509 that go along with it. So you know, my follow up is, 88 00:06:59.709 --> 00:07:04.420 what's a criteria that's super predictive? Yeah, so if we look at the 89 00:07:04.459 --> 00:07:10.939 last, call it fifty years of research and industrial organizational psychology, we look 90 00:07:10.980 --> 00:07:15.209 at cognitive ability, personality, interests to knowledge skills, like all these different 91 00:07:15.250 --> 00:07:19.970 aspects, years of experience, age, education level, mean, the list 92 00:07:20.050 --> 00:07:25.329 goes on and on, and one that routinely comes to the top is cognitive 93 00:07:25.370 --> 00:07:30.480 ability. It's probably the most predictive across all those criteria, but it also 94 00:07:30.519 --> 00:07:34.160 comes with a little heartburn as a result, because it creates something called adverse 95 00:07:34.199 --> 00:07:40.959 impact. So certain groups of individuals tend to score lower on average than other 96 00:07:41.040 --> 00:07:45.990 groups, not because there's any inherent differences, but just because of disadvantages in 97 00:07:46.110 --> 00:07:50.829 terms of equity standpoint. So we tend to supplement cognitibility with other measures that 98 00:07:50.870 --> 00:07:57.430 are also predictive but don't come with those biases like personality. So those in 99 00:07:57.589 --> 00:08:01.100 combination are very predictive and play well off of each other, regardless of the 100 00:08:01.139 --> 00:08:03.819 role. And you know what, maybe I skimmed over a little bit too 101 00:08:03.819 --> 00:08:09.740 quickly on this idea that years of experience is less accurate predictor tell me your 102 00:08:09.819 --> 00:08:13.689 sense of why that is cheese. Well, I think you're taking someone and 103 00:08:13.889 --> 00:08:18.970 there's transferable skills and knowledge, but you're plucking someone out of this experience and 104 00:08:18.050 --> 00:08:22.569 putting them into entirely different experience, even if it's the same industry, it's 105 00:08:22.649 --> 00:08:26.639 two different cultures, it's two different groups of people you're working with. So 106 00:08:28.360 --> 00:08:31.919 just because I have ten years of finance and experience in this one company where 107 00:08:31.919 --> 00:08:35.120 it's incredibly hierarchical, it

doesn't mean that it's going to make me a great 108 00:08:35.120 --> 00:08:39.919 leader in an organization that's more collaborative and people centered. So I think in 109 00:08:41.000 --> 00:08:46.429 large part it's because organizations are quite varied. What I would say is too 110 00:08:46.549 --> 00:08:50.590 exactly identical candidates on the cognibility, personality and like you go down the list 111 00:08:50.629 --> 00:08:54.740 of things, years of experience might give you a little incremental bump. So 112 00:08:54.860 --> 00:08:58.059 instead of being, you know, a success rate to fifty percent, maybe 113 00:08:58.139 --> 00:09:01.379 a bumps you up to fifty one percent by going with the candidate with more 114 00:09:01.419 --> 00:09:05.620 experience. But by no means would that be the first knockout that I ever 115 00:09:05.779 --> 00:09:11.809 look at when making decisions. Okay, so that reminded me of the Russian 116 00:09:11.970 --> 00:09:18.409 Roulette analogy you gave with and it was specifically in regard to hiring. Let's 117 00:09:18.490 --> 00:09:22.210 hear it. Yeah, so I think how most organizations approach selection is they're 118 00:09:22.250 --> 00:09:26.759 using old ways of thinking. So let's look at years of experience in the 119 00:09:26.840 --> 00:09:31.639 school they went to and their grades and all these things that can be scraped 120 00:09:31.759 --> 00:09:37.240 pretty readily from a resume. And imagine you're that hiring manager. In essence, 121 00:09:37.320 --> 00:09:41.029 you're spinning that roulette wheel hoping that you land on read and you get 122 00:09:41.070 --> 00:09:46.070 about just under fifty percent success rate because you have the to the zero, 123 00:09:46.269 --> 00:09:50.909 the Green Zero and the Green Double Zero. And with leveraging people analytics, 124 00:09:50.990 --> 00:09:56.659 imagine taking all the black spaces on that roulette wheel and now shading about half 125 00:09:56.700 --> 00:10:01.980 of those two read that's really the power of leveraging people analytics when making selection 126 00:10:03.100 --> 00:10:07.889 decisions and using science, because you're all of a sudden focusing on for that 127 00:10:07.049 --> 00:10:11.490 role. Here's what success looks like. Here's the recipe, are Algorithm for 128 00:10:11.690 --> 00:10:16.730 success. It's these types of personality traits, it's these types of interests and 129 00:10:16.809 --> 00:10:20.559 this type of background and skills. And now, when you're spinning that roulette 130 00:10:20.559 --> 00:10:24.799 wheel, by no means is it ever a hundred percent successful, because it's 131 00:10:26.000 --> 00:10:30.600 called science. It's not perfect, but it's far better than just going at 132 00:10:30.600 --> 00:10:33.399 it with that gut reaction like, Geez, I'm the hiring manager. I 133 00:10:33.559 --> 00:10:37.350 like this person because they're from Chicago to and they grew up rooting for the 134 00:10:37.429 --> 00:10:41.070 bears and wow, they're just like me and I love this candidate. As 135 00:10:41.110 --> 00:10:45.990 opposed to focusing on the things that really matter, there's too many biases that 136 00:10:46.110 --> 00:10:50.419 come into play when using the old way of thinking. Let's science do it 137 00:10:50.580 --> 00:10:54.740 for you and then have a final reaction as a hiring manager of all the 138 00:10:54.860 --> 00:10:58.620 candidates that meet that criteria so you're not making a bad decision. Then you 139 00:10:58.700 --> 00:11:03.299 can start going with more of that gut feel and reaction and how that synergy 140 00:11:03.460 --> 00:11:07.250 is between the hiring manager in the candidate. I know that they're maybe kind 141 00:11:07.289 --> 00:11:11.049 of myth floating around out there that people think people analytics is science, it's 142 00:11:11.090 --> 00:11:15.769 going to be one hundred percent accurate all the time and absolutely everything, and 143 00:11:16.250 --> 00:11:18.960 I think it's important to know that. It's going to make you more likely 144 00:11:20.039 --> 00:11:22.120 to win a Russian roulap, but it's, as you said, not a 145 00:11:22.200 --> 00:11:26.360 hundred percent accurate. So take down the house. Playing on the people analytics 146 00:11:26.399 --> 00:11:31.639 wheel, you would lose every time. Playing on the hiring manager with exactly 147 00:11:31.840 --> 00:11:35.230 exactly what it like, and with people analytics you're spinning the wheel with a 148 00:11:35.429 --> 00:11:39.110 seventy percent success rate versus the typical fifty percent success rate in terms of new 149 00:11:39.149 --> 00:11:43.429 hires. Yep, that's right. So imagine, thinking your mind the best 150 00:11:43.509 --> 00:11:46.940 performer you know in a role and you want more like him or her. 151 00:11:48.259 --> 00:11:50.659 You're spinning that wheel. You might be lucky fifty percent of the time. 152 00:11:52.059 --> 00:11:54.419 So they turn out to be a top performer and they stay long term in 153 00:11:54.460 --> 00:11:58.299 the organization. Now, coming to the people analytics wheel, you're spinning it 154 00:11:58.500 --> 00:12:03.090 and you'll get that person about seventy percent of the time. It's not perfect, 155 00:12:03.730 --> 00:12:07.970 but the cost of using the science pals and comparison in terms of making 156 00:12:09.129 --> 00:12:13.529 the wrong selection decision. You think of the miss of hiring the wrong CEO. 157 00:12:15.049 --> 00:12:18.799 It's estimated to be anywhere from ten to twenty times their annual salary, 158 00:12:18.120 --> 00:12:22.879 and it's only more in terms of like a fortune, five hundred, fortune 159 00:12:22.879 --> 00:12:26.399 one hundred organization. Even at an hourly role in an organization, making a 160 00:12:26.519 --> 00:12:31.389 Misshire at that level still cost the business tremendous amount of money, anywhere from 161

00:12:31.750 --> 00:12:35.990 twenty to fifty percent of their annual salary. So I would rather spin on 162 00:12:37.070 --> 00:12:41.509 the seventy percent success and look for opportunities to keep bumping that up. Yeah, 163 00:12:41.669 --> 00:12:45.379 me too. And Bright, you have the distinction of being the first 164 00:12:45.419 --> 00:12:48.860 gambling conversation we've have on technically people. I was waiting for it and now 165 00:12:48.940 --> 00:12:54.379 we've got it. So okay. So you've mentioned Dei. I would love 166 00:12:54.539 --> 00:13:01.330 to hear success story in terms of how you've helped an organization get better. 167 00:13:01.850 --> 00:13:05.490 Adi. Yeah, if I think of one healthcare organization that I helped, 168 00:13:05.690 --> 00:13:11.610 they had a big goal of how do we attract, engage and retain their 169 00:13:11.769 --> 00:13:18.200 diverse talent, and in the beginning that organization was very much soul focused on 170 00:13:18.440 --> 00:13:20.480 selection. So how can we put better practice? This is in place, 171 00:13:20.639 --> 00:13:26.120 so we're not looking through the manager Lens and more so looking through the Science 172 00:13:26.240 --> 00:13:30.549 Lens. So over the course of a few years, their percentage of diverse 173 00:13:30.590 --> 00:13:35.789 hires increased by nearly forty percent, which was outstanding. But over that three 174 00:13:35.789 --> 00:13:39.629 years span of time we got to see who stayed and who left. And 175 00:13:39.789 --> 00:13:43.940 although they are bringing on diverse candidates faster than they ever were before, they 176 00:13:43.980 --> 00:13:48.980 were also losing them at a much faster rate than they ever were before. 177 00:13:48.139 --> 00:13:52.259 So when you look at their population, diversity really didn't change at all. 178 00:13:52.539 --> 00:13:58.970 So they focused on diversity as the Noun as opposed to inclusion as a verb. 179 00:13:58.409 --> 00:14:05.210 And again looking at employee sentiment data. Connecting the DOTS will, we 180 00:14:05.330 --> 00:14:09.639 found was that diverse candidates were very much attracted to the organization and sold on 181 00:14:09.720 --> 00:14:13.600 this idea of a diverse environment, but when they got to their teams, 182 00:14:13.679 --> 00:14:18.320 their respective teams, they didn't see a leader like them. So the path 183 00:14:18.600 --> 00:14:24.669 of career growth and development was very much lackluster and they went and found that 184 00:14:24.830 --> 00:14:30.029 elsewhere. So they're almost sold a false vision or promise of what it was 185 00:14:30.110 --> 00:14:33.750 going to be like. So the organization immediately pivoted and started to put a 186 00:14:33.789 --> 00:14:39.139 lot of intentionality around how do we identify and promote diverse talent, and that 187 00:14:39.179 --> 00:14:43.620 all of a sudden had a cascading effect in terms of retaining talent like those

188 00:14:43.700 --> 00:14:50.220 leaders, because those fresh people they're bringing on with diverse backgrounds, they want 189 00:14:50.220 --> 00:14:54.450 to see someone like them that it's possible to succeed and go up the career 190 00:14:54.490 --> 00:15:00.129 ladder. So that organization is done wonders and their communities to reflect the demographics 191 00:15:00.210 --> 00:15:05.009 of those they serve in are really pioneering the way in terms of creating more 192 00:15:05.090 --> 00:15:09.200 of an inclusive environment and showing that path forward. A lot of conversations I've 193 00:15:09.240 --> 00:15:15.200 had in particular a podcast with Louren virgus towns, and her really strong point 194 00:15:15.240 --> 00:15:18.159 of view is that you have to start with culture. You can hire diverse 195 00:15:18.279 --> 00:15:22.389 workforce the day before you roll out your DII manifesto, but if your culture 196 00:15:22.509 --> 00:15:26.909 is not, as you said, doesn't live up to the verb of inclusion, 197 00:15:26.389 --> 00:15:31.110 then those people are going to mass exit. Is the BOTOM line. 198 00:15:31.149 --> 00:15:35.470 They'll find it somewhere else. So all right, nice segue mass exit. 199 00:15:35.710 --> 00:15:41.539 So we established this incredible accuracy right in terms of when you know a person 200 00:15:41.820 --> 00:15:46.059 may leave a company. But the pandemic, of course, has made retention 201 00:15:46.659 --> 00:15:56.090 tripli important. So tell me where retention and the pandemic intersect, Jay's well, 202 00:15:56.210 --> 00:16:00.769 first, in terms of our baseline models, in terms of predicting nutrition, 203 00:16:02.409 --> 00:16:04.759 like you said, they're eighty five percent accurate. So we collect a 204 00:16:04.879 --> 00:16:10.279 whole host of sentiment data from employees and we get a really good sense of 205 00:16:10.360 --> 00:16:14.200 which way the winds blowing for them in terms of are they committed to the 206 00:16:14.279 --> 00:16:18.149 organization, do they have this intrinsic joy at work? Do they intend to 207 00:16:18.190 --> 00:16:23.070 stay for the next twelve months or not? And those that do not are 208 00:16:23.269 --> 00:16:27.750 three hundred percent more likely to leave the organization in the next twelve months and 209 00:16:27.950 --> 00:16:33.500 we're able to predict with that eighty five percent accuracy if someone stays or leaves. 210 00:16:33.779 --> 00:16:36.220 Just being able to predict if they're going to stay or leave. That's 211 00:16:36.620 --> 00:16:40.700 that's a nice parlor trick or parlor game, being on the gambling hat, 212 00:16:41.460 --> 00:16:45.299 but the double click into that is also being able to just to tell leaders 213 00:16:45.379 --> 00:16:48.970 why. What's the reason? Why? What are the barriers that are getting 214 00:16:48.970 --> 00:16:53.169 in the way of People's success at work? Is it the relationship with the

215 00:16:53.250 --> 00:16:59.730 manager? Is it having more work life flexibility? Yeah, and when someone 216 00:16:59.850 --> 00:17:03.799 is hired fully remotely, there is not that honeymoon phase that you would have 217 00:17:04.240 --> 00:17:10.240 that if you're in person and getting the chance to drink the Koolaid and everybody 218 00:17:10.319 --> 00:17:14.160 seems so nice. You're spot on and again points to the importance of people 219 00:17:14.200 --> 00:17:17.109 analytics and why you should always be looking at data. So if you would 220 00:17:17.109 --> 00:17:19.910 have asked me and my biased mind, I would bet my bottom dollar on 221 00:17:21.109 --> 00:17:25.509 always seeing this notion of a honeymoon effect and the honeymoon effect and what we 222 00:17:25.630 --> 00:17:30.940 see in every survey administration are those employees that are newly brought onto the organization 223 00:17:30.980 --> 00:17:37.059 are the most engaged they're seeing their work lives through the rose colored glasses or 224 00:17:37.140 --> 00:17:41.779 the course of the pandemic, though, those that were hired and onboarded during 225 00:17:41.859 --> 00:17:48.289 the pandemic, they are acting, behaving, feeling more are like they've been 226 00:17:48.289 --> 00:17:51.930 in the role for three to five years, and that's the critical juncture where 227 00:17:51.970 --> 00:17:55.970 people tend to decide are they staying or leaving? They're also the most at 228 00:17:56.009 --> 00:18:00.440 risk for leaving right now, those that pandemic cohort is we've as we've termed 229 00:18:00.480 --> 00:18:03.319 it. And again, that's why we need to keep looking at data, 230 00:18:03.599 --> 00:18:08.039 because these things change with with so much pressure of an environment like a pandemic, 231 00:18:08.559 --> 00:18:12.309 on boarding look completely different. Even if you were on boarding in person, 232 00:18:12.910 --> 00:18:17.869 it's not like you're sitting in the break room talking to friends, sharing 233 00:18:17.910 --> 00:18:22.430 a meal. You're still very much in your isolation during the pandemic. So 234 00:18:22.069 --> 00:18:29.819 it looked completely different and employees didn't get that emotional attachment to their organization. 235 00:18:30.019 --> 00:18:33.339 They didn't fall in love with them and have that honeymoon period. Yeah, 236 00:18:33.420 --> 00:18:38.180 so so many people in the HR space are really rethinking how to on board 237 00:18:38.500 --> 00:18:41.930 and you know, in a way this is a good framework. You know, 238 00:18:42.450 --> 00:18:48.170 create some onboarding that replicates the honeymoon experience to whatever extent you can, 239 00:18:48.930 --> 00:18:53.329 and I would say one practical piece of advice. The single most important relationship 240 00:18:53.410 --> 00:18:57.680 that we're finding right now that mitigates many of the effects that we're seeing is 241 00:18:57.759 --> 00:19:03.279

the direct relationship with a manager, and we're seeing overall that's pretty strong and 242 00:19:03.440 --> 00:19:07.599 when it is the strongest, that's where we're getting that emotional attachment to the 243 00:19:07.640 --> 00:19:11.829 organization. But where employees are missing out as on those fringe connection. So 244 00:19:12.430 --> 00:19:17.150 they're really close with their manager, may be close with their direct a team, 245 00:19:17.269 --> 00:19:21.789 but when looking Cross Department or Cross area where you'd be bumping into people 246 00:19:21.950 --> 00:19:25.700 in the office, they're really missing those connections right now. So they don't 247 00:19:25.740 --> 00:19:30.059 have multiple connections, they're almost on this little island that's pretty isolated. Yeah, 248 00:19:30.180 --> 00:19:33.420 the weak ties that they're most Rada, which is a misnomer, of 249 00:19:33.500 --> 00:19:37.460 course, because those ties are not weak. They are very important for innovation 250 00:19:37.500 --> 00:19:41.970 as and, as you're saying, for retention in this case. Okay, 251 00:19:41.049 --> 00:19:47.369 so let's see. Let us talk about this concept. Office is m. 252 00:19:47.769 --> 00:19:52.920 So it is the idea that as we move into the hybrid world, leaders 253 00:19:53.039 --> 00:20:00.960 could view remote employees as less committed. As you know, maybe less productive 254 00:20:00.000 --> 00:20:03.200 right because you can't see them, can't see them other than you know are 255 00:20:03.240 --> 00:20:07.710 they logged on, and there are all sorts of inclusion implications around that. 256 00:20:08.390 --> 00:20:14.430 We know, though, that the majority of tech workers want flexibility. They 257 00:20:14.470 --> 00:20:18.430 want to choose how and where and when they work, and the smartest companies 258 00:20:18.470 --> 00:20:25.019 are giving people that flexibility. However, if office is m becomes a reality 259 00:20:25.420 --> 00:20:30.140 and workers who do have that flexibility face some form of discrimination, tell us 260 00:20:30.339 --> 00:20:34.220 what we can do about it. Well, let me paint the horrible picture 261 00:20:34.220 --> 00:20:38.089 first, and I think we're going to take decades step, words back in 262 00:20:38.210 --> 00:20:44.089 terms of inclusion in the workplace, and the reason why is when we look 263 00:20:44.089 --> 00:20:49.240 at right now, the delicate balancing active home work lives, what we're finding 264 00:20:49.440 --> 00:20:56.319 is that working mothers in particular want to remain working remotely more so than their 265 00:20:56.359 --> 00:20:59.599 male counterparts, and for those that do want to go into the office, 266 00:21:00.200 --> 00:21:03.279 they want to go in about one fewer day per week on average. So 267 00:21:03.750 --> 00:21:08.069 the post pandemic world, where we have this flexibility, where people can come 268 00:21:08.109 --> 00:21:12.390 and go as they want, you're going to see a far more male dominant 269 00:21:12.430 --> 00:21:17.349 work environment. Now you throw into the mix this notion of office is M 270 00:21:17.789 --> 00:21:22.619 we're just not leaders, but many of us harbor these feelings. It's pervasive 271 00:21:22.420 --> 00:21:26.380 that those that go to the office will have better relationships with their manager, 272 00:21:26.420 --> 00:21:30.299 are going to be the first ones to be thought about for developmental or promotion 273 00:21:30.420 --> 00:21:36.089 opportunities. All of a sudden you start seeing the glass ceiling that we've been 274 00:21:36.130 --> 00:21:40.210 trying to chip away at for decades arise again and, if anything, turned 275 00:21:40.250 --> 00:21:44.730 into a concrete ceiling, because the only ones that around the office, by 276 00:21:44.769 --> 00:21:48.640 large part, are heavily mail dominated. So I think that's the the picture 277 00:21:48.720 --> 00:21:52.519 that could go, that could get painted in terms of why this is such 278 00:21:52.519 --> 00:21:56.319 a critical issue to focus on right now and get ahead of it before it's 279 00:21:56.359 --> 00:22:00.880 too late. Yeah, and I think compounding that, I would add that 280 00:22:00.279 --> 00:22:06.029 there's estimates that as many as two million women could just split, could leave 281 00:22:06.029 --> 00:22:11.390 the workforce. We know they're downshifting their hours or leaving their job at much 282 00:22:11.390 --> 00:22:15.740 higher rates and also considering career changes, and it seems like it could be 283 00:22:15.980 --> 00:22:19.339 pretty pretty dire. So tell me how to fight this. Are there any 284 00:22:19.500 --> 00:22:23.220 any areas to look at? You know, from a people analytics perspective, 285 00:22:23.259 --> 00:22:27.819 I would consider that group. So, whether you're in the office or you're 286 00:22:27.859 --> 00:22:33.450 working remotely or hybrid. I would classify those is almost like a dei category. 287 00:22:33.809 --> 00:22:37.730 So for the analytics team you should be focusing on making sure that there's 288 00:22:37.769 --> 00:22:41.609 equity among those groups. So what do we look like in terms of pay 289 00:22:41.690 --> 00:22:44.920 for performance? What do we look like at promotion rates? What do we 290 00:22:45.079 --> 00:22:48.000 look at in terms of retention? So you want to make sure that you're 291 00:22:48.119 --> 00:22:52.400 creating a great experience for employees, regardless if they're going into the office every 292 00:22:52.400 --> 00:22:56.480 day or they're working remotely every day, and make sure that those outcomes are 293 00:22:56.640 --> 00:23:02.950 very equivalent to the best you can and have some intentionality around that and dispelling 294 00:23:03.069 --> 00:23:07.390 many myths. So the myths of the people are in this productive with all 295 00:23:07.509 --> 00:23:11.069 of the digital

exhaust that we give off in terms of workers for many roles, 296 00:23:11.109 --> 00:23:15.740 especially the roles that can be done remotely, you can back up with 297 00:23:15.859 --> 00:23:21.180 some pretty concrete evidence from a people analytics team perspective in terms of that productivity, 298 00:23:21.539 --> 00:23:25.259 that they're just as productive, if not more productive. So I think 299 00:23:25.299 --> 00:23:29.769 you can dispel those myths. Start Looking at it through a Dan Islands and 300 00:23:29.930 --> 00:23:34.130 the most progressive organizations are really thinking about the future of work. They're creating 301 00:23:34.210 --> 00:23:41.079 roles where they're just responsible for this hybrid so like directors of remote experience or 302 00:23:41.359 --> 00:23:45.880 directors of our extended community, or so they're really putting intentionality on. We 303 00:23:47.079 --> 00:23:51.000 know that how the work gets done is maybe a little bit different and what 304 00:23:51.200 --> 00:23:52.960 each group wants is a little bit different. Let's make sure we're meeting the 305 00:23:53.039 --> 00:23:57.950 unique needs, and that's where the employee listening strategy can play a large part, 306 00:23:59.309 --> 00:24:03.029 where your sense of seeing those groups task. What do they want to 307 00:24:03.069 --> 00:24:06.150 be? What do they need to be successful? What's getting in the way 308 00:24:06.190 --> 00:24:08.750 of their success? What are they worried about? And really catering to those 309 00:24:08.789 --> 00:24:15.299 individual groups is opposed to some blanket umbrella. The blankets statements or the blanket 310 00:24:15.700 --> 00:24:21.500 approaches rarely work because you have segments of groups that want different things and need 311 00:24:21.579 --> 00:24:25.730 different things to be successful. Yeah, so, Brett, I read your 312 00:24:25.769 --> 00:24:30.609 article in Forbes and you mention that you should move away or just totally forget 313 00:24:30.650 --> 00:24:36.329 annual performance evaluations where the goal is, to quote unquote, meat expectations. 314 00:24:36.529 --> 00:24:40.400 I thought that was interesting. So we're moving away from meeting expectations. What, 315 00:24:40.680 --> 00:24:44.880 then, instead should we do? And the rationale behind that is whenever 316 00:24:44.880 --> 00:24:49.319 I get performance data as a consultant, I start getting excited because wow, 317 00:24:49.400 --> 00:24:53.230 we have something tangible to predict. This is what the organization cares about. 318 00:24:53.349 --> 00:24:56.589 And when we get the data finally in hand, you find out that, 319 00:24:56.829 --> 00:25:00.430 you know, eighty percent of employees meet expectations, very few are far exceeding 320 00:25:00.470 --> 00:25:06.549 and very few are struggling. So it's meaningless for trying to predict anything. 321 00:25:06.789 --> 00:25:11.259 So instead it's thinking

about what does success redefining what success looks like in the 322 00:25:11.380 --> 00:25:15.619 role. How can we measure it more accurately with concrete things? Is opposed 323 00:25:15.660 --> 00:25:21.259 to abstract manager ratings, which are riddled with air and are not the most 324 00:25:21.259 --> 00:25:26.089 reliable indicators of performance. So rethinking performance in the hybrid world is going to 325 00:25:26.130 --> 00:25:30.890 be very critical and I think will dispel some of the officism. What many 326 00:25:30.930 --> 00:25:33.130 of us think productivity means is we go to an office and where they are 327 00:25:33.170 --> 00:25:37.759 nine and twenty five, or we leave after the boss leaves. That's impossible 328 00:25:37.880 --> 00:25:41.559 to do in the remote world and especially where employees are asking for flexibility. 329 00:25:41.880 --> 00:25:48.039 So instead let's redefine what productivity means and measure it that way and give employees 330 00:25:48.400 --> 00:25:52.589 what their expectations are to meet, regardless of where you're working all right, 331 00:25:52.869 --> 00:25:57.349 so, Brett, how about we move into our two minute takeaway and actually, 332 00:25:57.390 --> 00:26:00.390 you know what I want to share my key takeaway from this conversation, 333 00:26:00.630 --> 00:26:06.420 which is, you know, I was always fascinated by your concept of officism. 334 00:26:06.579 --> 00:26:07.819 I just think it's brilliant. But you know, when you paint to 335 00:26:07.900 --> 00:26:12.140 that picture and said that, you be taking a giant step backwards in terms 336 00:26:12.180 --> 00:26:18.339 of Dei and inclusivity and creating a sense of belonging and allowing people to be 337 00:26:18.500 --> 00:26:22.250 their whole salves at work. You know, that really struck me as something 338 00:26:22.369 --> 00:26:26.369 people are going to need to think about and are going to need to fight 339 00:26:26.529 --> 00:26:30.490 if they care in fact, about creating a great workplace of the future. 340 00:26:30.529 --> 00:26:33.599 So, anyway, that my key takeaway. So tell me a couple of 341 00:26:33.720 --> 00:26:37.400 things you want us to understand, and I think your spot on, and 342 00:26:37.359 --> 00:26:42.799 the I'll just extend your great insight and many organizations are probably feeling right now. 343 00:26:42.839 --> 00:26:47.390 We're doing exactly what our employees asked for. They want to work remotely 344 00:26:47.430 --> 00:26:49.829 and we have plans of doing that. But now they need to think beyond 345 00:26:51.069 --> 00:26:56.309 just meeting that need and how do we create equitable experiences for things that employee 346 00:26:56.390 --> 00:27:00.630 still value and want like career growth opportunities and promotion, and how do we 347 00:27:00.750 --> 00:27:03.980 make sure that office is M isn't getting in the way, regardless of where 348 00:27:04.059 --> 00:27:10.779 people

are working? Second one is just to remind people that they're sitting probably 349 00:27:10.980 --> 00:27:15.700 on a treasure trove of data and if they don't have a people analytics team 350 00:27:15.890 --> 00:27:19.410 today, there's still opportunities to go in and get what I think is a 351 00:27:19.410 --> 00:27:25.769 quick win. Start looking at your turnover data, start looking at your selection 352 00:27:25.930 --> 00:27:30.039 decisions, start mining and finding those nuggets, because I promise you they are 353 00:27:30.160 --> 00:27:34.359 there in terms of making smarter decisions. And then our final takeaway would be 354 00:27:34.640 --> 00:27:41.759 with so much change that we're experiencing and pretty severe gravity into the changes, 355 00:27:41.039 --> 00:27:47.390 with all of that going on, there's no better time to listen to employees, 356 00:27:47.430 --> 00:27:51.430 stop having the mindset of we think we know what our employees want and 357 00:27:51.589 --> 00:27:55.829 instead just ask them the questions. But then asking isn't enough. What we 358 00:27:56.029 --> 00:28:00.900 find in our research is that the organizations that are performing the best frequently ask 359 00:28:02.019 --> 00:28:06.859 their employees for feedback but, most importantly, they act on it quickly. 360 00:28:07.299 --> 00:28:11.660 So I can have a great conversation with my spouse tonight and she could tell 361 00:28:11.779 --> 00:28:15.369 me that, you know, I'd really appreciate it if you would put away 362 00:28:15.369 --> 00:28:18.450 the dishes more often, fold the towels, you know, etc. Etc. 363 00:28:19.130 --> 00:28:22.009 Maybe this is fresh in my mind exist what happened last night, but 364 00:28:22.650 --> 00:28:26.009 I need to act on that. I can't just listen and say, Oh, 365 00:28:26.089 --> 00:28:29.519 good talk, honey. I need to now today, act on it 366 00:28:29.799 --> 00:28:32.920 if I want to re engage her. And organizations need to do the same 367 00:28:32.960 --> 00:28:36.880 thing. It's no different in terms of a relationship they're trying to create with 368 00:28:37.119 --> 00:28:41.230 each and in every individual employee. So listen to your employees, but respond 369 00:28:41.269 --> 00:28:47.069 and help them connect the dots. We value your input. That's why we 370 00:28:47.150 --> 00:28:49.470 asked. This is what you told us and here's what we're doing about it. 371 00:28:49.950 --> 00:28:55.940 And not always will the organizational decision align with every single employee and what 372 00:28:56.019 --> 00:29:00.339 they want, but showing that kind of bread crumb trail for employees will incentivize 373 00:29:00.420 --> 00:29:03.819 them to share their voice more often. Maybe it wasn't the decision I wanted, 374 00:29:04.019 --> 00:29:07.059 but one time it will be. So I'm going to give my voice 375 00:29:07.099 --> 00:29:11.930 because I don't want to be forgotten about. So, okay, I think 376 00:29:11.049 --> 00:29:18.250 you've probably have a lot of people analytics converts after hearing your conversation. So 377 00:29:18.529 --> 00:29:22.289 if they want to get in touch with you and learn more about perceptics and 378 00:29:22.650 --> 00:29:26.319 you, how do they reach you? Yeah, they can follow me on 379 00:29:26.920 --> 00:29:33.759 Linkedin. So just search Brett Wells at perceptics and it's peer CEP ty x 380 00:29:33.279 --> 00:29:37.559 and you can see a lot of our latest research, whether it's on office 381 00:29:37.720 --> 00:29:41.029 is mm or turn to work under our resource library on our website. And 382 00:29:41.430 --> 00:29:45.869 we only got to skim the surface on people analytics. Anyone that would love 383 00:29:45.390 --> 00:29:49.910 a deep dive conversation, here's some case studies, success stories, or just 384 00:29:51.029 --> 00:29:53.380 want some help in terms of how would we stand up people analytics when we're 385 00:29:53.420 --> 00:29:57.339 more in that infancy or adolescent stage? Reach out. I'm happy to do 386 00:29:57.380 --> 00:30:00.500 a one on one. Fantastic. I think people will take you up on 387 00:30:00.660 --> 00:30:06.299 that and my email. I'll just throw out my email be wells at perceptixcom. 388 00:30:06.460 --> 00:30:07.930 Awesome. Well, I can't thank you enough. As you know, 389 00:30:07.970 --> 00:30:12.609 I really respect your work and your insights as so happy to have this conversation 390 00:30:12.769 --> 00:30:18.369 and bring some of what you're up to our listeners to thank you for being 391 00:30:18.450 --> 00:30:22.400 here. My pleasure, tiffany. Thank you so for our listeners, you 392 00:30:22.519 --> 00:30:26.400 will not be surprised to hear me encourage you to subscribe to technically people. 393 00:30:26.400 --> 00:30:30.680 All you need to do is visit our site, technically peoplecom. I hope 394 00:30:30.720 --> 00:30:36.430 you enjoy the conversation and we will talk to you next week. Whether you're 395 00:30:36.509 --> 00:30:41.349 looking to fill opportunities or find them, built in has you covered. If 396 00:30:41.430 --> 00:30:45.750 you're seeking to meet aggressive hiring goals, will help you attract sought after tech 397 00:30:45.869 --> 00:30:49.779 talent you might not otherwise reach, and if you're on the market for a 398 00:30:49.940 --> 00:30:56.019 career change, visit our site to explore exciting jobs with our customers and even 399 00:30:56.180 --> 00:31:00.500 with built in find talent, find opportunities built incom