This week’s podcast guest is Patrick Coolen, Global Head of People Analytics, HR Intelligence and Organisational Design at ABN AMRO, talking about his journey, setting up and evolving his people analytics team at ABN AMRO, over the last eight years. 


I have known Patrick, for most of that time and one of the main things that has always stood out to me has been his laser sharp focus on delivering value to the business. Throughout this episode, Patrick and I discuss:

• His brilliant article written together with his colleague Jaap Veldkamp, “8 Big Ticket Items for People Analytics”

• Analytics for personalisation as well as analytics for value and analytics for evaluation

• Patrick's journey developing the people analytics team at ABN AMRO over the last eight years, which all started with a focus on advanced analytics and the business

• The excellent work Patrick is doing in employee listening

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You can listen to this week’s episode below, or by using your podcast app of choice, just click the corresponding image to get access via the podcast website here.

Interview Transcript

David Green: Today, I am delighted to welcome Patrick Coolen, Global Head of People Analytics, HR Intelligence and Organisational Design at ABN AMRO, to The Digital HR Leaders Podcast. Patrick, it is great to have you on the show. We have known each other for several years, so I am particularly excited to have you on the show. 


Can you provide listeners with a brief introduction to you and your role at ABN AMRO?  


Patrick Coolen: Sure, I can. I have been working in HR and analytics now indeed for seven years. We have a team of 12 by now, but we started, of course, with less people. We started with two, focusing pretty much on the people analytics part, so the advanced analytics, and on-boarded a few products as we went along. So, we are also now responsible for serving management dashboarding, strategic workforce management and currently also thinking and talking about productising organisational design. 

So that is in a nutshell, my team and what we are doing. 


David Green: Brilliant. And I know as we shared on several occasions, we won't talk about today because it is a sore point for both of us, but we are big football fans, you with PSV and me with Liverpool.

 You alluded to the journey that you have been on with people analytics, over the last seven/eight years at ABN AMRO. It is quite inspiring. I often point people to your articles on LinkedIn because it provides a lovely timeline of how you have grown the function and the value that you have delivered, both to the business, but also to employees as well.

 We are going to talk quite a bit today about your new article that you recently published, 8 Big Tickets for People Analytics. Before that though, I thought it would be really great if we delved in a little bit deeper to that journey that you have been on. If you could talk through the journey of evolving the team, over the last eight years?  


Patrick Coolen: Well, like I said, we started in the beginning really with people analytics. So, for us that is using advanced analytic techniques, or statistics, or machine learning, at that time it was predominantly statistics for us to improve decision-making. What we did, and it is not the only way to go I guess, but what we did in the beginning very specifically, is focused on that advanced analytics. So, we didn't include metrics, descriptive analytics, in our departments because we wanted to have our focus and a steep learning curve, if you will on advanced analytics. So that was maybe one thing we were deliberately focusing on in the beginning.

Another thing is business focus. If I was to prove very quickly that people analytics, how we just defined it, is beneficial for our business, it also is a good selling point within HR because they like that, of course. So, understanding what drives the business? What keeps them awake at night? What opportunities do they see? What language do they talk? It is very important to know and to understand. And I always like to tell the story that when we walked into a management team, in the early days of our journey, I just asked, would you like to understand how your workforce is influencing your business outcomes? And nobody says no to that question, of course. Although I haven't found people who said no to that question. And that is a good starting point to start going into the discussion and go, how can we support that from our people analytics practice?

So, in the beginning it was, start simple. Focus on your business. Focus on advanced analytics and go from there. And only later, to finish a little bit of the journey, we on-boarded strategic workforce management. Which is sort of more of a gap analysis on your workforce, but it is a kind of an overarching service within HR, and we do that together with our business partners, of course, the HR business. And it needs a lot of support from analytics and more technical tools like dashboarding, survey management were on boarded later. Survey management is an opportunity for us to collect new data and also to control a little bit the surveys that are sent out on employee themes throughout your organisations, so to manage that as well. Dash boarding is all about visualising our insights, either coming from your descriptive or predictive analytics. 

And like I said in the introduction, we grew from 2 to 12. I don't expect it to grow very much further. I also don't think that is a scalable model. I think the rest of HR, and I am very pleased with within our company, should start to work in a more data driven way, and it is not a scalable model to bring people in my department to help others. I think other people in HR should have that DNA of a data savvy employee, so to say.

So that is kind of the journey that we have had over the last 7 years. 


David Green: It was really interesting. We will talk a little bit about that stakeholder led approach, I think is a good way of calling it. So, a fun fact for listeners, the first people analytics conference I chaired, which was back into 2014, Patrick was one of the speakers. And Patrick was quite unique in the fact that he really was having that business led approach exactly how he said there, would you like to understand how your workforce affects outcomes? Anyway, so there were a couple of others there, I think Thomas Rasmussen who was at Shell at the time. Patrick used, as you said, two people to start off with and rather than focusing on cleaning data and improving data, you actually went out to find, what are the big challenges that the organisation is facing that we can potentially apply advanced people analytics to? By doing that and obviously delivering value I guess, you have been able to grow over the last seven years. Rather than focusing, as a lot of teams do initially, on data and building a team, you had two people and I think you leveraged expertise from outside the organisation to actually help you with the analytics, but you made sure that you and obviously the person that was working with you, were making sure that that work was directed on the right business challenges for the organisation.  


Patrick Coolen: That is correct. And I think it is an interesting perspective. If you look at the wall of Boudreau, I hope that is familiar to your listeners, but the way on the left you have descriptive and on the right, you have predictive analytics. I always said, you can start on the right side of that wall. You don't need to be mature in all aspects of HR IT, HR data quality, you can start with a simple data set and do predictive analytics. So, by saying that, I am more believed that people analytics should be approached as a research organisational capability than a technical, IT driven, data-driven capability, if that makes sense. Otherwise, you are going to wait for maturity in those areas and maybe you will wait forever. If you look at it as a research approach, then it is easier to see where you can start. Does that make sense?  


David Green: Yep, it makes perfect sense to me. And I don't know if you remember all the way back to 2014 Patrick, but the number of presentations we saw that had the Bersin maturity model for people analytics in it. Now I am not saying that that maturity model was necessarily wrong, but it did imply that you couldn't do predictive analytics, as you said similar to the wall of Boudreau, the predictive analytics unless you have got your descriptive analytics, right. And obviously your experience or those and others suggest that is not the case, just focus the predictive analytics or the advanced analytics work, on the right business problem.  


Patrick Coolen: That is right. What you do need to have, of course, is the capabilities to do predictive analytics. So, our goal today, data scientist skills, people who do the number crunching, and build models based on statistics and machine learning. And I think I missed that in the journey I described at ABN AMRO. We didn't have in the beginning, in our first year or two or three, data scientists within our company that wanted to work within HR. So, we decided again, not the only way to go, but we decided to go with a niche company, Inostix in our case which was taken over by Deloitte at a later stage. Just to have, again, that steep learning curve. We didn't want to bother about quality of our models or statistics or whatever.

So, well that was a joint effort between ABN AMRO and Inostix in the first two or three years. And only then we started to hire data scientists. To be honest, if you are able, from the beginning, to have your own data scientists, I think that is the best way to go. But if you have problems recruiting those talents, or not the budgets, there are other ways as well. Also, universities, PhDs, for instance, it maybe helps you out with data science role. 


David Green: Yeah, I think as you said, that is a great way to do it. We talk about one option is to borrow data science skills from elsewhere in the company, another option as you did, is to use skills from the outside to create that momentum, prove the value of people analytics and then you can grow and build your own. 


Let's turn to the article now, The 8 Big Ticket Items for People Analytics. So firstly, and this is maybe something particularly for other practitioners that might be listening, what motivated you to write the article?  


Patrick Coolen: Well of course, the main reason for writing this article is that I didn't want to be missed in your annual review of the best articles on people analytics. No, but kidding aside, we always like sharing our best practices and our thoughts, and maybe at some point at an early stage, even. We like it because we always learn as well. So indeed, 8 Big Tickets for People Analytics, all over the world people luckily appreciate it, but also gave feedback. And immediately a week after we published it, we have actually new ideas on, maybe it should be nine, or we should re cluster them, or we learn something on one of those topics. So, it is always helpful, besides fun, it is always helpful to share as you also get feedback.

It was a year ago, I think, that we wrote our last article so together with Japp Veldkamp we wrote, The 8 Big Tickets, a little bit forward looking to next year. 


So, answer in short is, it is fun, but we also learned from it.  
 

David Green: And perfect timing for the new annual collection of articles, which I think have featured you in every year since we started it. But seriously Patrick, I think practitioners like you, and there are others out there, that share what you are doing I think there is a real benefit to the wider community because I think this is an area of HR that people are thirsty for information about. What they really want to hear about is practitioners like you and organisations that are doing the work.

It is interesting, I had a conversation with one of your peers a couple of weeks ago, and they told me that actually sharing the work that they are doing externally has really helped them. You alluded to one that has helped in terms of you learn and you get feedback, to maybe you iterate. What are some of the other things that you have seen? They said that by sharing externally, had a bigger impact internally within their organisation, than whether they shared the same story internally. I would love to hear your take on that?


Patrick Coolen: I like that one. Well, I wouldn't say bigger, but it definitely helps. Because you build a brand, I guess, externally as well by sharing. Which is also very beneficial if you want to extend your team and you want to recruit people, or you need anything else so to say, so the brand helps. And the brand externally also helps with your internal brand, but I wouldn't say that it even more helps than what we do internally, but it definitely supports what we do internally as well. 


David Green: As I said, I know everyone is very grateful that you have regularly shared insights from the work. So, let's focus on the article as such now. We are going to talk through two or three of these big-ticket items.


Patrick Coolen: Can I make on remark by the way, because otherwise I will forget. We were talking a few minutes ago about using a vendor, right, when you don't have a data scientist at work. I also said that if you could, it is good to have your own data scientists. And the reason for that, I think this is an important point to make, we personally don't like black box analytics, so you really need to understand how the data is structured, how the data should be interpreted. What data transformations really mean, in terms of benefits and maybe threats. Biases in that data, you should be very aware of that. Our team really believes that the best way to take all the opportunities and challenge the threats is to really touch the data yourself and do the modelling yourself. In the beginning, the first two or three years, that was the reason why we were sitting very close to our appreciated colleagues of Inostix, so they maybe did the modelling, but we had a lot of extensive conversations about the data and what to do with it, and which attributes to choose for the models we were trying to make.

So just a point I wanted to make, black box analytics, I wouldn't suggest that to anyone. 


David Green: And actually, I think that leads to another good point that is related to that. You have always openly advocated and explained that you work very closely with your legal team at ABN AMRO, so before you even start a piece of work, you get legal involved and make sure that, not only is it the right thing to do, but how can we do it to make sure we legally comply, but also that we can provide value to employees. Which is what we are going to talk about in a minute. And then again, before you communicate any of the results you have got that checkpoint, which is just good practice really, isn't it? Because as you said, you have got to be careful because it is people data at the end of the day. It is the data of our workforces.


Patrick Coolen: Absolutely. So, I guess, way before GDPR in Europe legislation, we were already very careful, not only what law tells us to do but also ethical, looking at what are we doing and is this okay? So, we always tried to be as transparent as possible and share whatever we do with employees, both specifically with compliance and legal, and actually per case, we checked if you were allowed to pursue the case that we wanted to investigate. Nowadays, we have more like a framework that has been worked on annually actually to improve it. Specifically, when we go in a minute maybe to bringing insights to production, so personalisation. But nowadays we have a framework where we fill in some questions and based on those questions, it is green, amber, or red. Green, we go ahead and the other two colours, we would have to talk to compliance and legal. But it is definitely very important, absolutely.  


David Green: And particularly if we look at the first of the big ticket items that we are going to look at, analytics for personalisation, So tell us a little bit more about that and why it is considered a big ticket item for you and Japp?

Patrick Coolen: Well, I think for us it is the big next step. We have been doing a lot of research, which was delivered in a dashboard or in a PowerPoint or in a memo, if you will. And for a year, year, and a half, we have been thinking and working on, okay, but how can we use our insights at scale? So, for all of our employees?

 In the last year and a half, we had one case. I guess you are familiar with it, it is in the book, Excellence in People Analytics. It is about employee experience. So, we ask in a monthly survey, in a sample of our organisation, what is ABN AMRO doing well? And what can we improve on related to ABN AMRO, as a good employer? We used a lot of topic detection, text analytics, and the results are of course a list, or a visual, of all the topics that people talk about. Either very happy about, or they give suggestions, or they have some complaints. And we shared that via a BI dashboard with the whole organisation. So that was an example of where in the last year and a half, we productised in a dashboard, the insights.

But the next step is also to productise our insights coming from models in software or in processes. So now we are working on vacancy recommendations for instance, learning recommendations. The difference in our case is that learning recommendations is pretty much driven by our learning system, so we are aligned but not driving that project. Whereas with vacancy recommendations, we are. So, we are looking at what data can we use, within GDPR again, that we have of our employees and how can we create a sort of skill profile based on machine learning. And that skill profile can be used to match against vacancies, and it delivers you a sort of matching percentage, if you will. And if you have that, we are able to show you the five best matches of internal vacancies or the five best matches of re-skilling vacancies, or even further in time, the five best matches of external vacancies for people who are at risk, for instance, that could be a very beneficial insight as well.

So, these are just two examples, one a little bit more down to earth, dashboarding driven in terms of delivery. And the vacancy recommendations will be incorporated in our HR IT landscape.  

David Green: And of course, we talk about analytics and personalisation, and I see, you may agree with me, that this is the opportunity to really provide value to the workforce as well. Because if you are using employee data to provide relevant recommendations for them, just like we have as consumers, looking at Netflix or Amazon, then that is much more relevant than just looking at what, training courses can I do? That is where you are really providing that fair exchange in value to the employee, for providing their data. And as you said, a real opportunity to just scale that. Really interesting that you are looking at extending it to any employees that may be at risk in the future, around how their skills match with external vacancies as well, I think that is really good. 

Patrick Coolen: Yeah, that is the future, by the way. So, to be honest, we just embarked on this vacancy recommendation project, but it looks very promising. We are validating the skill profiles we are creating. We are validating the matches we have against vacancies. But we have still have to bring it to production. I want to be transparent about that, but it is very promising. You are right, it is like our iPhone, we get a lot of nudges on a daily basis, and we think it is very interesting, it is trying to help you and me to have a more effective life, so to say. Maybe I'm exaggerating here. But what we are trying to do with the same sort of techniques, is to maximise the careers that our employees have to make sure that they have the best possible career they can have, by nudging in on those topics that are relevant for them.

We have one rule, if we do personalisation, it should be beneficial for the employee and not because we have to collect some data or whatever, no, it should be beneficial for the employee. That is the golden rule.  


David Green: Yeah, I think you are right, and that takes you back to one of your previous articles, 10 Golden Rules. But yeah, that is a great golden rule to have.

So, the next two of the two big ticket items we are going to talk about are almost like the bookends of people analytics, and certainly line up with the way we think about delivering that value through the Operating Model that we recommend at Insight222, that was in our recent research. So, you have analytics for value and analytics for evaluation, and this is really about putting the business front and centre of the work that the people analytics teams does. That is right, yeah?  


Patrick Coolen: That is right, yes. And this is immediately the feedback I talked about; I think you are right that you placed them together.

Analytics for value and analytics for evaluation, they both are related to the value we create for our business.  


David Green: Which is always the approach you have had. It is kind of that evolution, isn't it?

In the article, and again we will reference this in the material that comes out with the podcast, so people don't need to go and find it. Although it is quite easy, just go to Patrick's LinkedIn homepage, but we will do that anyway.

Can you tell us a bit more about the workshop that you designed to articulate the value of a people analytics project? I would love to understand how that was received and what was the impact of holding those workshops with businesses?  


Patrick Coolen: Yes, of course. And it sounds maybe very basic, you could argue it is just a workshop. But in our case, so I am not saying that it is as powerful for all other organisation as well, but in our case, we really liked it. It is difficult in these times, but it is a physical meeting, so not digital. We have a lot of offline tools and support elements and things we can use. We are trying to create a circle in the thinking of the participants. First, what are your products? And are you responsible for everything you just mentioned when you described your product? Or is it just parts of it? Who are your clients? And can you pick the most important ones? Because normally when you ask people to list clients, they come up with a very extensive list, and it kind of blurs into stakeholders as well. But in the end, you have to be very clear who is the client which is, in our opinion, something different to a stakeholder.

If you have your products, your responsibility, and your clients, then we end the workshop with, okay, when is your client happy with your product? So, it is starting to go into the area of success measurement, KPIs, and things like that. And then at the end, we link them back to the activities, are these KPIs that you just articulated, logically connected to the products you described in the beginning?

That is kind of a circle, and it is a lot of fun, but it is also very, very important because it allows us to really pinpoint the value for, for instance, diversity. I forgot to tell you, but for instance we have diversity people, learning people, recruitment people, or businesspeople in the room, looking at a specific domain of course. And if you have the main KPIs, it really drives your portfolio on research.

A nice additional benefit is that sometimes we use these workshops also for instance, articulating the KPIs for learning department, what should they go for? Regardless of if they do the research or not. So, it is a good way of driving your portfolio and research, helping your internal customer articulating their most important KPIs, and in the slipstream, creating a data-driven culture as you go along. So again, it is just a workshop, but in our case, a very much appreciated and powerful one. 


David Green: I guess it is so important because, as you said, it directs the research in the right direction and it also acts, from what you are telling me, as a nice checkpoint that you are measuring the right things, the right KPIs. Because we can measure lots of things with analytics and we certainly measure a lot of things in HR, but it is far more important to measure the right things rather than many things.  


Patrick Coolen: Absolutely. That is true. So, the circle rationale I just explained, is also very important because if you are asking me and I go into a workshop like this and I will be responsible for a department, it is very easy to forget to connect all the dots. So, in that sense it is a powerful workshop, that really helps to pinpoint what they are really doing and what should be the main measurements of success.   

David Green: It would be great to hear a couple of examples of where you have seen analytics for evaluation, done really well?  


Patrick Coolen: So, that is another big ticket. 


Evaluation is for us, if you look at people analytics, maybe one step back, you are looking at the impact of your workforce on your business outcomes. But there is another type of research we do in the area of evaluation and that is where we look at workforce interventions. So, leadership programs, other skill related programs, diversity programs, actually the whole basic bunch of services that HR provides. And not for all of them, but for the strategic ones, the bigger ones, the more expensive ones, or the more strategically important ones, we run an analytics project where we evaluate.

So, an example, which is I think in the article itself. The use of skill coaches within our retail branches, skill coaches are supposed to help the sales organising teams to improve, for instance, client satisfaction and sales etc.
And we found out, so it was already a bold move by retail to do the research to really check, do these skill coaches really help boost our sales?

We found out by just using analytics, that they do. The teams that were using and making better use of the skill coaches sell more mortgages. The model doesn't explain to you immediately why that is the case, but it is an important signal, in terms of evaluation, because we invest in skill coaches. In this case it is a success story. 


A little bit longer ago, we looked at some training programs, also with the intention to improve eNPS. It was a large program within the bank and also within retail. There we found out for instance, that a few modules of that whole learning intervention that weren't very efficient. So that helps our clients to make decisions to maybe exclude a few of those modules.

We also saw that some modules were particularly powerful for a specific group of users but not everyone. So, this is maybe a smaller example, but it gives you an idea of how you can use analytics to fine tune your programs, not so much to stop our start, but also to fine tune the interventions you are doing to make sure that you go for the right KPIs, so that they influence the right KPIs. 


David Green: Yes, really important that you use the analytics to direct the evolution of the programs and whether you should invest more, for example, in skills coaches at bank branches. From what you have told me, it suggests that justifies that investment. So yeah, very interesting.

So, let’s sort of distil it down now, what would be your number one piece of advice and if you want two or three, that is fine. What would be your number one piece of advice for people analytics teams looking to deliver more value to the business?  


Patrick Coolen: Well, I think I mentioned a few. Start small. Be business focused. Be very specific on your outcome, so related to what we just talked about, what is the real value? Ask 10 times the question, why? Why is this? All that effort in the beginning of your program pays off at the end. So be very specific about the value. Understand your business and if you don't understand the business, involve colleagues who do have an understanding. So, I would say business focus start small. You don't have to be mature, like I already mentioned in all those different areas, like IT, data quality. It is important, but it shouldn't block you from starting it.

And I guess, in a more generic sense I would almost refer to, The 10 Golden Rules of Analytics, on LinkedIn. 


David Green: Well, we will help people by putting a link to that article as well, but I remember that one, at the time, being something, a lot of people learnt from.

One of the areas of work that you have spoken a lot about, you kindly contributed a case study to Excellence in People Analytics, and I think a lot of people have learnt from your approach to, is employee listening. We won't go into huge amounts of detail now, because obviously I would love people to buy the book and read the case study, but also, I think you have shared on a number of occasions, the steps that you have taken around that employee listening program. But can you give us an update because obviously the book came out in July. Also, employee listening has been a big theme of course, over the past 20-21 months or so.

Can you give us an update? What is the latest on employee listening at ABN AMRO?  


Patrick Coolen: Yeah, sure. I think from a technical perspective, we improved our models significantly. When we talked about the case around the time that the book was created, we had an 84% precision rate. I have to explain that. That means that we were able, in 84% of the cases, to take a text that somebody is writing down and place it in the right bucket. So, for instance, manage equality, or diversity. And by the way, we have 120 of those buckets or topics. So, in 84% we were able, with our modelling, with our topic detection, to predict the right topic. Now, we have reached beyond 90.

So, we kept on improving our topic detection modelling, that is one thing. 


What we did recently, because now we have enough data, is we have built our force model already, but we are continuing to look at, okay, but people talk about a lot of topics but which of those topics are driving client satisfaction, or driving our employee satisfaction, or any other business outcome, if you will. Because of the year and a half, two years, that we have been collecting this employee experience data, we created an extra source of data that we can include in all of our research.

So that is what we are looking at now. I think in the book, you also show a diagram with bubbles, which represent the topics. And those are very relevant because this is what people talk about, but it doesn't show you immediately yet if a bubble is big, if it is also very important for client satisfaction. So those types of models we are adding on the concept of employee experience, which is very relevant because it is focusing on where you could do your intervention, based on the targets you are pursuing.  


David Green: And if I remember rightly from one of your previous articles, the employee experience team at ABN AMRO, works very closely with the customer experience team anyway. So, you have got that opportunity, I guess, to see if this is important for employees, and as you said is it important for customers as well? Obviously not every topic is going to be important for customers and employees, but you were able to do that linkage. 


Patrick Coolen: You are right. And again, I think a huge benefit is having this extra data to be able to deploy it in any research we do, regardless of if it is customer satisfaction, or fraud detection, or average handling time in call centres, it is an extra data set that previously we didn’t have. So, the sentiments of our employees can be used to explain some of our business targets as well. 


David Green: Unfortunately, Patrick, we have come to the last question, so we will have to continue this maybe not on a podcast, but over a beer hopefully, whenever we get a chance to meet again.

This is the question that we are asking everyone on this series, and you have touched on it already so you might just want to provide a summary here.

How can HR help the business identify the critical skills for the future?  
 

Patrick Coolen: Well, of course HR has a huge role in identifying critical skills. I think that is one of the core businesses. HR business, learning experts, performance experts, you name them, I think they have extensive dialogues with our business to identify critical skills. Also, people within my team who are dealing with strategic workforce management and re-skilling, have and support those discussions with the business, the critical skills for the future. So that is HR, I guess.

People analytics, I think we can help in a few ways and maybe I will mention two. One is, what is the skill distribution? Can we visualise that? Do we see areas where there are a surplus of talents and where do they have a lack of specific talents? Can we connect that? So that is, I guess, very interesting data and information for, for instance, re- skilling. But on a more granular level, I think we can test hypothesis as well. Of course, that is what people analytics does, I guess.

Let's take the recruitment example, where we have a recruitment profile on specific skills. We hardly ever investigate, and we did that a year ago, if these skills are the right skills. We came up with them by having discussions, right, and then we do an assessment, and they score high on those skills, and we hire a person. But what we should do is after a year or maybe two years, see if the person is still here. Is she/he successful? What are their characteristics and also skills, and then maybe adjust profiles? So, it is also a purifying mechanism, people analytics, to test if the skills you came up with are the right skills, on a more granular level. I hope that made sense. 
 

David Green: Makes perfect sense and I think linked to what you said earlier around the work you are doing around creating skills profiles to support employees around the personalisation bit. So, helping employees with either learning opportunities, or re-skilling opportunities, or internal opportunities, is analytics acting as a thread that helps link together talent mobility, workforce planning, and learning, together. So maybe that is a big role that people analytics can play in these skills top question, which is such a big question for organisations at the moment. People analytics is a thread that connects some of those traditional silos together, perhaps.  


Patrick Coolen: Yes, and the case we discussed about vacancy recommendations, you can also relate that to skills. I mean, how do you get the person from A to B, you can have projects and communication, which you can also mass customise it and bring it to the individuals themselves, to see the opportunity. So that is also where people analytics can play an important role.  


David Green: I agree. Well, we could go on, I know we could, but Ian will cut the tape at some point. So lastly, Patrick, thanks for being a guest on the podcast. It has been great to have you on the show. Can you let listeners know how they can stay in touch with you, follow you on social media or find out more?   


Patrick Coolen: Well, that is an easy answer, that is LinkedIn. If listeners think, oh, that's an interesting topic we discussed today, feel free to connect on LinkedIn. Like I said, I am always happy to share and learn.  


David Green: Brilliant. Patrick, thanks for everything you do for our field and for your partnership and support over the last seven, eight years. So, thank you very much for being on the show.  


Patrick Coolen: Thank you too David, and thanks for having me.