Unlock the Potential of Lead Segmentation for HR

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HDMI we'll kick off with a brief introduction of human Capital Management Institute and then I'll introduce our speaker today gra I thank you Moon and welcome everyone to the webinar I'm Grant cooperstein VP of analytics for hcmi been with hcmi for a little over six years uh my primary focus here is Workforce analytics and planning engagements for clients product development and also training my background is in economics compensation and recruiting analytics and ultimately started my career on the practitioner side uh leading a Workforce analytics group uh for a financial services company here in the Greater Los Angeles area and I've also worked uh for a company by the name of inum U doing Workforce analytics and planning um in a Consulting role for them as well today we're going to be focusing on how to improve your Workforce analytics efforts uh with Workforce segmentation and really using and optimizing data uh to drive insights at your organizations uh a couple of housekeeping items so as we go through we're going to have a couple of polling questions um so we would appreciate uh your participation in those and uh as you have questions feel free to enter them in uh the chat box and we will either uh field them as they come in or more likely um take care of those at the end so if you do have burning questions uh we encourage you to answer to to put them in as we would love to answer so a quick background on hcmi so what we do all we do is focus on Workforce analytics and planning uh we bring a very data driven approach to analytics uh many of us at hcmi are former practitioners um and we help quantify the impact of human capital so what that means really we put dollars to the people uh help HR speak the same language as Finance which I'm sure many of you have experienced at times it feels like you you're not speaking the same language um but really how to drive insights and link to to a quantifiable Roi um with your Workforce analytics and planning efforts how to turn uh data into uh into decision making and insights so H how do we do it hcmi uh methodology really in many ways starts with questions um so question driven analytics um and how to link metrics up to those questions uh so taking uh data from across the talent management life cycle um and really a big part of what we do is how to standardize position clean data to maximize uh the value and insights that you're able to to glean now as I'm sure many of you have experienced um Workforce data is not uh always clean in fact it rarely ever is um and it's it's certainly very complex particularly when you you know dealing with uh differences in timing issues reports don't line up uh are we including all employees full-time part-time contractors regular employees uh there's many different facets and and uh furthermore depending on the particular question that you're trying to answer uh there can be reasons to in ude or exclude or or create slices of of the population and that's really what we're going to be talking about here today is how to segment the workforce um to really get the most value and to uh make sure that um you're answering the question that you set out uh set out to um in terms of your your Workforce analytics efforts and what metrics and methodologies U will get you there fastest and ultimately um give you the most the most bang for your buck the best results for your analytic efforts um and ultimately linking to you can see down below um that in clients that we work with we typically uh find U rather large uh Workforce Roi or value creation cost savings um outputs in terms of uh dashboards our proprietary human Capital financial statements or um custom Workforce analytics projects um to focus on a particular issue or question so since we're talking about key Workforce questions and um what what questions kind of link up to metrics that are critical at your organizations uh we wanted to kick off with our first polling question around uh which questions are most critical at your organization so this brings us to our first polling question can I hope everybody sees the question up on their screen can you select the human capital question that you think are the most important important to your company so option A can we model our Workforce to optimize cost and productivity option b what are the predictive indicators of turnover option C what is our total cost of Workforce as a percent of Revenue and expenses option D is internal Mobility a source of value or turnover in cost and the last option what is the financial impact of Employee Engagement please make your selection and we'll share the results shortly I think we're just about ready to go ahead and close out the poll so as you guys can see with uh 30% the first question was selected can we model our Workforce to optimize cost in productivity productivity has definitely gained a lot more interest over the last few years um Grant do you have any additional comments yeah no that that uh looks looks about right um it looks like it's a three horse race between uh optimizing uh the workforce uh to for cost and productivity predictive indicators of turnover um and interestingly uh third place is the financial impact of Employee Engagement um and certainly that is one um that for some roles can be rather difficult um whereas others that are either customer facing uh or uh product touching um it can actually be quite a an impactful and clear linkage um so you know if there's any questions around uh you know as we get to this question session later in the presentation around that I'd be happy to talk about that a little more all right so when we were talking about the process of standardizing uh integrating and and really cleaning data to optimize your your analytics and planning efforts and create robust models for analytics uh we have what we call our Workforce data blueprint really a a comprehensive uh methodology and process for integrating data um but the the message here is really that um you need a a strong Foundation uh in terms of your your data to do workfor analytics really the only Gap that you can't work around is the lack of data um and then um it's really a matter of your your approach um to integrating and assembling your Workforce analytics model that is going to uh yield the best results and and give you uh the insights that you're after um in in terms of Standards just to talk about that for a minute um it is not by any means um the exciting sexy part of analytics uh but it really is a prerequisite to do a successful Workforce analytics and so when we talk about standards we're talking about how you're classifying um your jobs um which creates uh the ability to um segment the workforce uh successfully and um create cohorts for analysis uh what types of transactions are being processed and how are is classified what counts as a promotion what counts as a transfer um how how you're looking at uh you know capturing things around uh supervisor changes pay changes all of those things um it's not that that data is typically wrong um it's just frequently in many organizations inconsistent um and so it's a matter of creating clear definitions um not only for for uh grouping and analyzing that data uh but for getting that data processed and entered into the HR systems that your your organizations are are utilizing um and then um in terms of timing I mentioned that um as one of the complexities uh in doing Workforce analytics U many a time I'm sure has there been perhaps a report from finance and a report from and surprise surprise uh the numbers don't match up uh it's for a couple reasons uh you know One Finance typically counts FTE uh full-time equivalents as opposed to headcount um but oftentimes uh due to transaction backdating um those uh numbers particularly for past uh month end counts or quarter end counts um can be off just simply due to transactions that weren't entered into the system yet um getting entered in later periods with with an effective date for the prior period um it's something that um is I think all organizations uh struggle with and have to have a a a means to deal with um but really um having clear uh cut offs um in terms of your recoring as well as your processing of transactions um will will make your lives much easier uh in terms of having um a a cleaner um data set for analysis when you're looking to do uh and build models and and and complete uh your data collection process one of the key components um and this goes for uh integrating data as well as your analysis in terms of how you are reporting metrics and Al Ely linking human capital data back to financials back to the business um is segmenting it across what we call the talent management life cycle um and you can see here we have elements um of different types of HR data uh recruiting performance compensation uh Mobility turnover uh and ultimately the more aspects and the more elements that you are able ble to integrate into your Workforce analytics uh models and Reporting uh the the greater in your insights the more robust your model becomes uh there was an example of a project that we were working on U where we were trying to quantify the impact of employee referrals um so as a recruiting uh Source uh employee referrals uh look to be uh the source where the highest percent of high performers uh was was get gained from from uh new hires so uh much higher than just uh the direct sourcing or agency or website um and it it makes sense um you know employees uh that that are referred by um current employees uh or or I should say new hires that are referred by C employees um would you would think would naturally be higher perform um the other side of the coin uh was when we looked at turnover those were actually the group that also had the biggest problem with retention they turned over at the highest rate um and so um looking at the results uh separately um we we figured that you know both both uh findings couldn't be right there had to be something that we were missing um and so as we went through the process of combining uh data what we actually found was that high performing current employees that were doing employee referrals uh the employees that they referred had a much higher likelihood I think three to four times as likely to both be a high performer and um have significantly higher retention whereas uh average rated employees um not not high performing current employees that did uh employee referrals um their referrals um were the ones that actually turned over at a much higher rate and didn't necessarily have as as a strong likelihood of becoming a high performer so the message is really um as you go through um your analysis and you're building out your models and reports don't just focus on one area or one uh particular slice of data um really the more uh different pieces the more different puzzle pieces you have the more uh informative and really robust um your analysis becomes so when we talk about Workforce segmentation U really we're talking about creating cohorts for analysis um these can be anything from looking at different tenure groups looking at different job groups um but the key is looking at at uh Eng grouping employees uh by uh their common traits and um characteristics so um whether it's um a retirement analysis that centers around um you know different different age groups or looking at um employees that are uh in different functions um this is really key to being able to go from the 30,000 foot view of the whole organization and drilling down into specific groups that may have very different behavior and characteristics um than the rest of the organization so we're going to go dive into a few case studies um that show examples of this and then we're also going to look at um some uh I I guess more advanced um ways to segment the workforce Beyond uh just what uh most organizations typically focus on so in this case study the the organization was looking at commute distance as a key driver of turnover um and in fact um as some may attest to on the call as well um the way in which uh some policies and um requirements are are um made at or organizations isn't necessarily always from a data driven basis um so in this case the CEO actually um had said that you know he felt that you know they should do no hiring Beyond a certain distance uh from any of their locations uh which of course uh recruiting was just delighted to hear that their uh applicant pool got uh cut in in half or or even less um so so uh this was due to exit interviews that cited uh employee uh Comm distance as being a a particularly uh you know high high likelihood of why if someone would leave um the key really was that as we broke it down between different Workforce groups um surprise surprise uh they behave differently in fact um the group with the highest feet um and lowest overall turnover was Senior Management um and when the they heard that it was it was kind of like you know hitting in the M okay so um you mean it doesn't apply equally to everyone no it doesn't um so what would have been a very bad decision um and bad policy to to make um was avoided just simply by showing uh differences in Trends uh for different groups and you can see on the graph that you know the commute distance quartiles first is the lowest and fourth is the highest and there certainly are some groups um that that uh policy would have been good for particularly those um that are the more entry level and lower paid positions um you can see the the yellow line skilled and semiskilled um Trends up quite significantly as the commute distance increases um but it's not a one Sho size fits-all approach similarly uh looking at compensation and uh turnover um it what uh is again um something that U applied across all uh job groups um doesn't necessarily um show a trend uh but as you break it out uh what you see is that uh again um anyone below the second cortile is likely to have a significantly higher turnover rate as you move beyond the second quartile however um turnover rate stabilize and so what this says is actually two things um that for some groups uh compensation is a stronger driver of turnover than others uh but for all groups uh once you reach a certain threshold of compensation it doesn't have to be necessarily 100% of Market or above uh the turnover evens out and U becomes less uh compensation becomes less of a driver of turnover once you're at a a certain level so one of the most uh important important areas uh to look through your your uh analytics efforts in terms of how you're segmenting the workforce is what we would call um our a job framework or Workforce categories job families um typically uh not down to the job title or job code level but uh really focusing on creating cohorts not based on where the uh employee sits but what it is that they are actually doing um here we have what we call a decision Band Method really stacking uh employees based on the level of responsibility and decisions that they make um all the way up to establishing vision and strategy which would be at a senior manager or director CEO level um and within those um you would have different grades um delineated um across each level to go into a little more detail so ultimately uh what you're looking to do is start from a high level looking at um management or professional staff sales and then as you see drill down uh into what we would call a job family so looking at um all the analysts across the organization um beyond that kind of going down to what where it includes a level and then ultimately um something around a a jaw code so this really allows you to uh if you think of like a Rubik's Cube um you can uh cross-section the organization uh by a number of different uh criteria and really create uh views that you otherwise couldn't uh the other benefit of this is as as many of you probably have experienced as well um just uh trying to group uh by job titles um many uh organizations have um a mess of job titles inconsistent uh too many um in other times um they may not be detailed enough to tell what it is that the uh position or the employee is actually doing um so as as you apply this type of methodology um your analytics efforts will be become uh so much easier um in terms of doing job level analysis and really uh job an starting your cuts and your reports um at the job level um is a great way to quickly find uh insights uh for particular slices of the organization so one more example uh we were talking about analysts and you can see that they they um are likely uh distributed um in your organizations as well across many different functions um but if you were for example to um go into a report or or look at an extract of your data it might not be easy to exactly pinpoint how many analysts do we have um and this is uh one of the ways in which um that becomes much easier um furthermore um you know you see for example trainers and recruiters um you know oftentimes for companies that have uh decentral HR operations um those May sit in different uh business units so you may have recruiters that are dedicated to it um you may have um trainers that are in fact perhaps maybe they're out in a sales group um and if you were to just segment the workforce by different lines of business or fun um you would be counting what our really HR staff as part of that function or business line um so the other uh way in which this U alleviates um a lot of the the difficulties that companies have in grouping and uh even just counting how many people we have in HR is it's a very clean and easy way to identify not just your central HR St which um is not so much of an issue but that decentral HR staff that fits out in the business um it can sometimes be difficult to count or cure um so with that um it also enables the ability to uh pinpoint and uh flag what we would call critical job roles um really these are roles that if you have uh you know just one more dollar or if you could hire just one more employee uh what you know what where would you dedicate that that money or or what employees would you hire um they are folks that um are uh hard to replace long time to H productivity in terms of training um difficult to Source um and those that really either Drive strategy or um are integral component of the operations of a company uh so I think we have our next uh polling question coming up so we'll go ahead and start that now so our second and last polling question how are critical job roles identified in your company the first option is strategic and non strategic positions the second option strategic critical and operational critical positions the third option uh critical job rules are determined by subject matter experts and lastly no critical job roles are identified within the company please make your selection and we'll close out the poll in just a minute we have a few more votes just trickling in okay I think we're ready to close out the poll so the results look like uh option b strategic critical and operational critical is the leader with 43% nearly half of the attendees that's that's a great percentage um however the second answer that came in with the 30 9% uh selected no critical job rules are identified Grant do you have any other comments yeah it's actually I'm very pleased to see that um option b the Strategic critical and operational critical one out over uh strategic and non-strategic not to say that um strategic and non-strategic um is not an important um you know filter or or way to um start on the process of identifying which job roles are truly critical but oftentimes that operational critical group gets overlooked um you know focus is easy to go to for example Senior Management um but um you know for for those roles that are touching product and really um kind of the day-to-day uh roles that are needed um are sometimes an after um and um you know also in terms of having you know a difficulty 39% uh know critical job was identified uh it's not an easy process to go through because you know organizations um many times it's tough to to uh exclude certain job roles from that critical population I mean everyone wants to think that um you know their job or or um know that a lot of jobs across the company are critical um and it's not to say that they aren't important but this it's really an exercise in being able to um apply points and weightings to the most critical and um you know kind of having that mindset where um you know you can't give extra extra points to one without taking away from others um is it's a useful exercise for organizations to go through um So speaking of strategic critical and operational critical here we have um an example um from um the airlines um that uh you originally uh in going through the exercise um everyone uh flaged uh Pilots as being strategic and fight uh crew as being non-strategic um and well that's not untrue um in work uh that we've done particularly with uh Jet Blue um they view their uh flight staff as the ones that actually drive uh customer satisfaction what they would call Net promoter score um and what that means is that they actually have the ability to influence uh customer experience on the plane more than say the pilots do and uh for that reason uh they drive uh not not only uh you know Employee Engagement uh among the crew but also customer engagement and customer satisfaction which ultimately links back to revenue um and so this is a case study um that uh we have I believe on our website uh for those of you that haven't seen it um but actually very powerful in Shifting the Paradigm a little bit as to which job roles are truly critical um and um you know moving away from just focusing on strategic positions to those that truly impact and drive customer uh and employee engagement so one of the ways that you can use uh a job framework uh this is an example of a case study focusing on on Career paths um but being able to segment and create uh unique and like groups of jobs allows you to H not only improve your level of analysis but also create uh what we would call you know custom cohorts or career paths um and um the particular uh interesting point here is that uh this organization was looking at the progression of their accounting staff and looking at the differences between those that they hired externally um and the market rate that they had to pay for new talent coming into the organization versus the cost of promoting and growing Talent through their career path um so you can see the dashed lines are the kind of that growth uh rate for each different leg in their career path um the solid orange line is kind of that market growth rate and the Shaded area between is the cost savings that they get from promoting uh internally and creating uh a a defined career path for their employees to move through now it's not just that their internal rates are uh lower than the market growth rate it is but really what they're doing is they're able to FastTrack and Target high performers and move them through the ranks at a higher rate uh than uh would otherwise be the case and so what that means is that for example um if you look out at the senior accountant um that starts with about eight years of experience on average um if you were to go out uh into the market they're actually able to move their accountants into that role um much faster and maybe they get there with only five years of experience um so the employees benefit because they get uh fast and and good job growth for for those that are high performing um and the company benefits because um if they were to lose that employee and have to go out and replace it uh with a a new hire uh on the market they would pay much more so they they actually achieve um a a lower overall total cost a wood fortun growth rate um and they also create um a a environment for their employees where there's a lot of opportunity for career growth and Mobility so moving beyond uh some of the uh ways that we were talking about to slice index the workforce primarily around jobs um and looking at uh some other ways uh more more perhaps Advanced or unique ways to uh create cohort so we have hiring classes um Mobility categories but really that's you know any any uh rate or result um can be grouped and banded um into a a category to uh analyze unique characteristics of that population for example um is there something about those that have uh a a very high turnover rate um that is different um than others maybe uh around education maybe around um you know other other characteristics that um are pre-joining U the organization so um and then looking at um you what we call cor tiling um based on U those results so we're going to look at a couple case studies that showcase um some of these methodologies as well so in this case study focused on you know how effective are our leaders at managing Talent um the top part uh with manager a and manager B is hypothetical uh the bottom table um is actual results uh from a company um and so I won't read through all of the characteristics of each uh but you can pretty quickly see um that while they both exceed their business goals manager a um is is really um you know the the a type uh I guess personality that um you know is is hard driving um you know they actually um you know don't invest a lot of time in training and developing their people but they still get results manager B um act you know invests in uh junior employees invests in training um actually has a low turnover uh more more transfers and promotions and so um what's surprising is that organizations often can't tell the difference between these two really they're just looking at that first PLL they both exceed their business goals um one manager a does it by creating uh really almost a wake of Destruction um the other does it by uh really doing what uh management should be held accountable for um is uh growing developing and ultimately um creating um a an environment for employees to move through the organization really uh what talent management should be all about um so what does that really mean so if we go down to the table um you can see that at this company um the the analysis was done uh by uh managed turnover so looking at the terminations that were attributed to um either the direct manager uh or looking at determinations within a more senior manager's overall organization hierarchy and you can see that over 80% of the turnover um was attributed to about a third of the managers of the organization uh and these were not just managers that say for example were over a sales function um that naturally would have a higher turnover this was actually um a very even distri distribution between the manager groups um so what really you know what we want to figure out is what are the low turnover managers doing um that the high turnover managers are not um so uh just an example of how you can um segment the workforce um in a bit of a different way um and really see uh surprising results that um so much of the turnover is attributable to um you know really just a third of the managers of the company this case study focused on really uh leadership impact and competencies uh for um a a company that I'm sure most uh everyone on this call has received a package from at one time or another uh so their question was do leadership competen these matter um and if so what is the impact to the business um so this was focused around what they would call center managers so their distribution centers the uh the manager for each uh Center and uh what leadership competencies were really important um so by focusing on those uh you can see the legend at the bottom of the graphs um that basically scored in the the highest quartile and those that scored in lowest cortile and again these these were not for business results these were for a leadership uh score uh review that they did annually um those in the top cortile had um a much much uh lower cost per piece as well as uh less damage so they really got more of the good and less of the bad out of these uh managers uh for their centers um and you can see you know the savings opportunity is absolutely huge um almost so much so that it looks unrealistic but this is really just due to this their size of their Workforce um and so of course the larger your organization the more potential Ro or savings you have at uh you know from from doing this type of analysis and really linking um your uh whether it's leadership or engagement uh but being able to link it back to operational and business results um is extremely powerful okay one more case study uh this is focused on training and sales performance um so what uh this particular training was was what we would call a manager job shad in uh basically uh in this uh business uh their most of the Salesforce U was was fairly disperate uh spread across uh you know many locations and um out in the field um some managers would uh run a course where they would take their uh sales staff out and actually uh show on the job training show them tips and tricks um the the most surprising part about this was that this train was not actually uh required uh this was an optional training that some managers did and some didn't um and the results uh speak for themselves but certainly um for those that complete uh that training and that go through that training um are uh I think they were about three times or four times more productive in terms of their sales um than those that didn't um and uh what was most surprising in fact is that it's not that the the sales group that tracked performance didn't know who their top performers were they absolutely knew but they had never layered that data against uh training data so they had no idea who was who wasn't completing the training um and which courses mattered which courses didn't it was only until that data was combined um that this uh Insight surfaced so uh it's another case uh both for the uh importance of training uh but also um for integrating and combining data um and so this one again focused on you know creating cohorts based on what type of training they took based on how much training they took um and so as you're going through your efforts to segment the workforce um it's important to think outside the box a little bit in terms of the cohorts and the analysis that you're doing and that'll will drive um you know a lot of insights in how you should be segmenting um your employee population okay with that um we're gonna actually go into a quick uh demo of uh how you can use a dashboard to uh do some work more segmentation and actually um also do a little bit of um future scenario and and predictive uh modeling out into the future so this is what we call solve dashboards um really a focus on a couple of key uh questions or areas across the talent management life cycle uh the one that we're going to be looking at is focused on training and the ROI of training um so give me just a moment here um and I will get that up all right so what we would call a learning Effectiveness score um and really um a big part of the value um of of this uh dashboard and Analysis is being able to clearly uh Define and measure something that is quite difficult so how how effective is our training um and um how what's the impact that our training has this dashboard gives you a clear score and also links uh as you can see below to a financial impact of training um so the overall score here is broken down um into these metrics and so really this is what we would call one of our Advanced index metrics um this one focused on learning uh but there are those that that really spam the across the talent management life cycle um over here you can actually see the trend of how the organization has done as well as a projected forecast out into the future along with the projected impact um and you also get uh a list of critical insights as well as predictions um that let you know specifically where to focus um for uh improvements um or to keep doing the good things um that you are doing um perhap the most exciting part uh of this dashboard is the ability um as I mentioned to go in and segment uh on particular uh areas of the workforce so say for example if we wanted to focus on service staff um we can do that and U the dashboard uh updates automatically um if we wanted to actually go in and look at some of the scenario modeling capability um for example um if we upped our training performance differential uh which uh by the way is uh you know really the the pre looking at the pre and post training performance um we can actually see uh that the future uh trend has updated as well as the projected uh impact um also if we wanted to say for example go and a drill into any one of the individual metrics um we can get a uh individual trend for those as well um with uh benchmarks um that can be um either displayed or not displayed and we can go in here um and for example look at um increasing satisfaction 10% um and we can see that um update as well so just wanted to give everyone a quick overview um and also show some of the functionality in terms of slicing and dicing the work force but also uh being able to uh model uh out into the future um and how that U kind of integrates as part of our our solve dashboards so with that Moon I think we will open up for question sure thank you grant this concludes the content portion of this webinar if you would like to ask a question for the Q&A portion please type in your question into the chat box on the right hand side of your screen uh we'll wait a few minutes to collect any questions you may have if you have any additional questions after the conclusion of this webinar feel free to contact Grant directly or you can also send an email to info at hcmi nst.com so we do have one question Grant what is your recommendation to calculate cost of turnover where is a good place to start that's a great question um and really what you uh want to do is start by breaking it out into categories um so you have your direct expenses um which would be uh you know cost of of processing separations um you know exit interviews all of that you would have um your um you know productivity portion um both pre and post departure um you have your replacement costs um etc etc and certainly we can provide um an overview a more detailed overview of of kind of what those buckets are and what might be included in those buckets um but one key thing that I would say is don't forget to include the benefit of turnover which is that you're not paying uh salaries uh bonuses etc for employees that leave um it's one area that uh I know from experience that Finance likes to shoot down in a lot of cost and turnover models that that HR and organizations er um there still will be a significant impact to turnover in terms of cost but make sure to include that particular portion as a a savings um that negates some of the cost to turnover U but really just creating those uh buckets and then coming up with the applicable uh portions for example replacement costs um you can allocate um a portion of recruting costs um to those uh that that are specifically for replacement items great thank you uh we have one another question how can um sorry I read the wrong one what metrics would you include to determine quality of hire it's a great question in fact we do have a quality of higher index um and so I I I don't have that up in front of me but I'll just go off the top of my head um you would want to include certain a a metric around new hire retention or turnover um depending on the type of position or the uh typical um you know level of turnover at your organization that might be a less than oneyear turnover rate that might be a less than 90day turnover rate so there is some flexibility in terms of uh gauging and customizing that metric to your organization uh but definitely new hire turnover is one um you would want to include something around uh performance uh the one caveat to that is you don't always have a uh recent or or uh you know it can take a while to uh have someone go through the performance process and actually get a review rate um so either performance or what we would call high potential um if there is a new hire survey uh set rating by manager um if there's a 90day performance review for new hires um you would want to count capture in some way that feedback um to flag U you know is the new hire a potential to be a high performer um you know you would also include areas around um the uh percent of qualified applicants and as well as the percent of job requirements met um so often times what happens is that uh in the absence of uh you know a better better data or um you know looking for better Talent uh job requirements tend to get inflated over time uh that doesn't always mean better job fit uh so what we found um is that you know in some cases um new hires or applicants that meet only 80% or 85% of job requirements actually tend to do better uh for some roles than those that exceed uh or or need 100% requirements um so kind of gauging that job fit based on the job requirements uh for each uh role is is another important aspect L did we have any additional questions come through yes the next question is do you think EEOC groups are sufficient for data segmentation that's all our company currently has uh it's a good question so um they are a great start um and in fact the EEOC groups do align fairly closely with what we would call Workforce category so they the highest level um of of job segmentation the one thing I would say is that in some cases um those groups are more aligned by function um than they are by the actual JW so what that means is they're focused uh for example on sales when perhaps um the job may actually be more of a support role or maybe a management role um so they they are a good start um but but I would recommend um digging into that a little deeper particularly to um clean up some of those inconsistencies as well as driving down into what we would call the job family level so most of your insights uh in doing analytics when you signment by uh job will be at the job family level um so um for example in the slide that we showed kind of grouping all analysts regardless of if they were a senior analyst associate analyst you know lead or mid-level um typically because um they again kind of follow the same career path they also tend to uh behave fairly similarly that job family level is a great one to focus on um alternatively um you could use the uh critical job approach where um you did analysis at the EOC level but then also um focused on just a few uh critical rales to dive into deeper as a start and so that might be a good middle ground um that you could get to rather quickly and find some low Aang fruit great thank you grant I think we have time for one more question the next question is around turnover how can turnover be controlled to retain local talents well that that sounds like at least a million dollar question if not more um so I I'm not sure I can answer definitively but I I'll take a shot and so certainly um location plays a role in turnover whether it is because there are competitors nearby and it's easy for employees to say jump ship sometimes they don't even have to uh leave the building um in in the case some cases we've seen at companies they just move to a different floor um but uh again focusing on uh segmenting the workforce by either location or by job um you may find uh very different Trends um you may also find different drivers of retention and so um the key is really identifying which of those make the biggest impact U and that that will drive your strategy um happy to talk about that little more uh I realize that that's that's a great question and um one that um you know and some ways um it might well be worth uh building um a a you know analytic project around um either internally or um you know happy to talk about um how we might be able to assist with [Music] that great thank you I think with two minutes left I think that that's all the time we have today for questions and answers if you have any questions about the information that was covered today or you would like to discuss your company's Workforce analytics planning needs please contact H my and one of our Specialists we'll be happy to talk to you this concludes today's presentation have a great day