People Analytics in Diversity, Inclusion & Belonging – André Bezemer, Nestlé



I hope everybody awake we're getting towards the end of the day um I'll talk about two things so what I want to do is basically share with you how we necessarily have taken to specific instances in the diversity space and I'll talk a little bit about diversity and what we're doing in that space but concretely how has analytics been able to do basically contribute to the business and trying to resolve some of the issues and give insight into things that we didn't know before um I spoke a little about that in the morning I'm kind of an odd guy cuz they don't come out of HR actually originally IT guy I've been an SP consultant for many years and via that I got fired a back door into HR and by all kinds of other routes I got into people analytics but I've basically spent the last seven F years of my time in corporate talent management managing Talent succession of performance with top management in Ursula group and that in that 7 1/2 years are basically started analytics as a side hobby so one of the first people in the group that actually introduced dashboards at the time there were actually only six so I'm gonna give you a bad example after it and that kind of evolved over time and then in the end I joined people analytics last year November so that's in a nutshell me I'm Dutch born in Rotterdam live in beautiful switz in a little tiny village in the middle of the vineyards Nestle for those of you who don't know us we are a three hundred and eight thousand people company the interest interesting fact is if we add all cocoa farmers and coffee farmers that are for most of their life depending on Nestle you can add about a million people to the cut to that list so we have a huge footprint on on the face of this earth we are about a little bit over 90 billion turnover wise we operate 413 factories in 85 countries so we are an company that is very much rooted into operational excellence one of our former CEOs always likes to say that we're actually just milk farmers that turned milk into chocolate that's it well not fancy we're not Giggy very basic and that's one of the things we always have to keep in mind still today that's still very much rooted in the company 2000 brands are saying over the coffee break to someone I still find brands that I've never heard of and I'm with the company for about 14 years we have us on the sixth floor we have a demonstration the floor and I'm surprised if it's happen again which is kind of cool but it tells you also how difficult to keep up with what we're doing actually 1 billion products sold every single day so for I could be you all of you in the room it's very difficult to avoid as as the reality and some of you might have heard well we are a company that's very much rooted in to M&A so we do we by definition follow two paths one is internal development we have a very large R&D footprint in the world employ about 3,000 R&D people globally but we also do a lot with M&A now there's two things on this list you can see a little bit and you I'm sure you recognize lots of brands and for some of those you're probably not aware that was actually Nestle because we don't always say Nestle on the label very big that's the skin health is something that was as you see quieted 2014 and as some of you might have seen in the press releases last few weeks we are about to sell that again so we're going through what we call a portfolio cleanup and we're really trying to get back to core what we believe is is what we should be doing and skin health doesn't fit into that on the other hand you might have seen a little bit earlier star Starbucks so we have acquired the licensing for Starbucks coffee and then I mean the bit that sits outside the then the Starbucks shops basically so it's buying capsules etc and interesting that's something I'm proud of is that it's a fairly big machine enos then it comes with all the governance it comes with all the bureaucracy the silos and all of that we are having and since this week I think we have Starbucks on the shelves in 10 countries and that M&A was closed q4 last year so we are actually if you really want we have a CEO that is an outside hire which is also an interesting thing which is very non nestle actually it's the first external height zero 120 years but he's also very keen in hitting the accelerator in the company we're too slow our route to market is too slow we need to step up and that is an example of that where we've been able in in a very short period of time actually get products to in a super market basically talking about gender then gender if you talk to our CEO there's this there's business cases around gender you've all seen them you've all read them particularly in the Nordics that's a topic that where you guys are a little bit ahead of the rest of europe i would say but if you ask our CEO is very simple everybody it's just the right thing to do everybody deserves equal opportunity whether that's in the gender space in LGBT space everybody deserves equal opportunity and the story it's not a debate and so with that in mind we have actually signed an eyelid pledge that basically states that so we have externally committed to that and as part of that we've started tracking gender pay and equal pay across the company systematically since 2018 and in that space then of course the magic question once you commit to these things i don't know if you know there's lots of gri index associated shared value indexes all these external bodies that ordered nestle on what we've committed to so once you've done that you got to stay foot you got a really deliver so the magic crusher we've tried the answer is we want to pay men and woman equally for the same work but in do we actually do that that's the big question and it's a very sensitive topic is it's a topic that triggers emotions perception around i think women are underpaid by x percent certain gentlemen going very much account against that there's a very sensitive topic by definition just to set the different straight when we talk gender pay is basically an average or mean of the of the workforce and you look at those salaries equal pay is the more from my perspective the more interesting one because that is actually comparing equal so if some of there's two people man or woman having the exact same job what is the differential if there's a differential that's what we talked about this one in some countries is legally required UK I believe is one of them where by law require to report that every year that is not yet there but we are basically tracking both and we're doing this in a basically every when we run a salary review cycle every year and the precursor to that we're going to start driving equal pay analysis at country level all right so we're gonna really systematically drive that in order to close gap so how have we set that up the approach really is standardized approach right we don't we're like any other company we're short on budget even though there's almost 100 billion but we have pressure for cost and not everybody out there can do that so the people that let this group is organized with the corporate team I'm part of that corporate team then there's hubs in the different parts in the world Sao Paulo we sit in North America we sit in Dubai so we have people all over the world and basically what those things can do they can work with the markets and do these analysis and then come up with concrete action planning so it's really standardized approach where we have some options to go further it is all about defining and get the data set build the capability across the company project structure wise it's an interesting thing so we have a steering committee but the other thing is to also kind of reflecting the commitment of the company this stuff is actually reported to the Executive Board so we do look with the Executive Board in a certain frequency on this topic and see where are we right so the tricky thing with this topic is that you can it's it's a it's a local thing by definition I can't compare Brazilian reais with Swiss francs is impossible and-and-and the labor market works very different but what we really trying to do is come up with a local approach that we can still compare in some form globally and what we're really trying to do is that once we do the analysis identify do we have outliers where we stuff needs to happen or is it anti groups of the workforce where we see different differences that need to be dressed so that's what we're trying to do so marketing analysis but we still operate and we can compare between markets so again no so this is gender pay gap is basically as an average it's a fairly simple thing but then on the other side here you have the more complex stuff now I'm not a statistician particularly the next slide but we're basically trying to do we get all the variables age gender talent ratings years with the company etc and all of that gets basically jotted in statistical models and I can't read those I can tell you but we have data scientists we have statisticians in in the company that do understand all of that what I do understand is the outcome though so the outcome of that analysis will then in this dummy example will tell you that females are earn on average at one point zero one three more than a guy right so and that's kind of what we want to get out and where does that then sit and what do you do about it because that's all all that matters in the end is that number is fine but if you don't do anything you might as well not waste your time it's about the subsequent actions that you drive of that and that's really what we're trying to show here is if there's individual people you can very much take care of those people individually right you might have someone that just got in the company lagging behind salary for example you address that you can compensate these things but we find in some case we find that there's bigger parts of the workforce where you require much more systematic approach in structure approach to kind of fix the gap that could be justing scales that could be adjusting policy why certain things there's many things that you can do in that space to do to correct but the really ideas get that inside on the table define an action plan with the market and by the time we then get the salary review cycle rolling you can then say oK we've identified this group or we've identified these individuals so what are we going to do in the salary round to start closing that gap and that's really what we want see and that is also what is being reported back to the to the board before continuing to the dashboard bid other questions and just interrupt me if you have questions so that's really been trying to do and we started with this as a project and it's now been basically at the point where we start rolling it out and every market they can start using it and do actually a project myself right now where we're looking at at senior management because that's obviously as you talk of senior management level all about these topics there is the question what about so we're looking right now at c-suite level people and do that comparison ourselves as part of did Market Analysis we were focusing are we lifting those specific bits out because they follow also a slightly different different reward approach um dashboard one of these as it is so important DNI in the company what we found is that people don't always have the data available people ask what about gender balance for this what about gender balance for that so there was a kind of a pretty big need to a get all those metrics on the table get them easily accessible but it was also need to standardize stuff because if you're operating across the entire planet I can tell you that attrition and the calculation is not the same everywhere for some reason so that also led to misunderstandings right to people sitting looking at what I thought was the same matrix but in the end not being the same metric so in those space spaces I've mentioned here so in headcount attrition hiring talent and succession we've standardized all those metrics and what we've really been trying to do is get that data on the table now when I say succession management one of the ways to get your gender balance improved is hiring externally but a very traditional that's the thing is the higher up you go the lower our rate is for external hires which we look at c-suite level our internal movement ratios about 94% it's very very high and that's very traditional Ness I think was built we develop from within and I don't think it's the right metric that think it should be a little bit lower because there needs to be a healthy movement with the outside market but that's why we are so the other way to do there's a succession plan plan for succession that's right and the higher up you go the thinner the percentage of female gets in Nestle it's like in most of the companies in the case so what we're doing is giving people inside on succession metrics to start driving and planning in the longer run to also allow for example women to develop get them ready for roles right this dashboard and that's a thing too bold bit this dashboard is globally accessible to everybody so if you have that link you can access it it's very simple so if your production production worker in Brazil in Casa bhava factory you can access this if you have probably on your computer alright so this is that's a very bold move in Nestle terms even guess when I propose when we propose this first we go to a rolling in return but then we start in the end is if this is available you generate also a form of it's transparent in the end if we say that this is so important then what's the problem with showing those numbers nothing now the other thing is obviously GDP are my talk data privacy's we cannot access any form of individual data here there's also a power those of you who know power bi know that you can click and sub filter very easy by using the interactive functionality of the of the of the graphics we've switched that off in many cases to also avoid people being able to filter in such a way that you if you end up with the highest grade we all know who that is or who that is typically so that has all been logged down it's fully mobile enable I can look at it on my iPad my phone so that's all there so that's what it looks like now to report early made these are too many metrics now what are my learnings with working on dashboards over the years and we consciously decided to put that many on to start knowing that we very quickly would go come back and revise it and trim down following the way it's being used so this is gonna be revised next month already and I think what I'm trying to say is that when you create dashboards they you have to keep a dashboard relevant – will have to be reviewed systematically and it's very rare in my experience that a dashboard stays the same for more than 12 months the company moves priority shifts what is an issue today might not be an issue anymore tomorrow so keep that in mind so just as it and this is dummy data so what you see here is basically it's you need to read it from left to right so we're looking at holder information versus successor information and then what we do is we look at different slices of the organization so the top is what we call corporate key positions which is our the top management of top management this is only about two hundred and twelve people they're hand-picked so there's a very precise and a population the next one is our management committees in market which is about a thousand jobs in Nestle we then talk a to e so she senior leadership that is about four thousand if I remember well then we get management a to H is about thirty five thousand and the rest of the workforce now the nice thing you can see here is you see one aunt you see okay holders X percent successors and what you try to achieve is obvious that this is always higher than that one because if you want to move the needle you got to make sure that you have sufficient women identified to take those roles one of the other things we've committed to to ensure some of this and that has also to do to do with dealing with unconscious bias or let's be realistic partially conscious bias because that's the reality is what I find is the average amount of successor female successors on each of those positions versus up in ready now so how many women do we actually have and that gets very relevant for for senior management how many do we actually ready to take the job now if it gets wakened and what this allows us to do is in in terms of succession planning people days we can give people exact detail and we have a separate view for a child as where they can go in all the required details that really tells you when you have your people day we're discussing 50 jobs but out of 50 jobs there's actually 15 where we don't have any women on the list or we have them maybe on the list but the only scene is ready in three five gives really six to ten so you can start challenging very concretely and start pushing them and really start having the conversation we see two names on the on the list one male one female are they really different and then start pushing that and we see I've done some of this in my previous role with with talent management and it's a very good way in a very pragmatic way to actually start driving that agenda and get more women on that list so we've been able by using similar data at that time I wasn't automated like this but we were able to actually move the needle in the senior lays the organisation by just getting that insight and giving a business partner than that ammunition that also requires that you have to equip the business partner with having those challenging conversations and the higher up you go the more difficult to get ya know this that in the other I'm sure you face these things as well but this is very very it's very sorry I think it's very good and it helps you think of my own product but it's been very very helpful in the conversations and that's what I've seen that's it I'm not sure wanted to share with you a thank you and Ray you're welcome love your present

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