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Sylvain Rochon, June 1 2022

Anupam Dasgupta - Psychometric Analytics in the Hiring Process and Predicting Attrition

A CykoMetrix Spotlight production

Every week, the Spotlight shines on an amazing professional with a story to tell and lessons to teach.  Welcome to the CykoMetrix Spotlight.

The following is an adapted transcript of the exchange between Sylvain Rochon, CMO at CykoMetrix as host, and Anupam Dasgupta, Innovative Marketing and Strategy Leader.

Sylvain Rochon: Welcome. This is CykoMetrix Spotlight. My name is Sylvain Rochon. I'm the chief marketing officer at CykoMetrix, a SaaS-based platform that allows consultants, training and staffing companies to generate continuous development within their business and with their clients. We provide pulses of psychometrics assessments allowing changes and interventions, and then measuring again, keeping track of the progress of clients over time. We help generate all sorts of really cool data, so you can check us out in the links below. 

For now, we are not here to talk about CykoMetrix. We are here to talk about psychometry because our guest, Anupam Dasgupta is an innovative marketing and strategy leader. He is a senior marketing and strategy leader with extensive experience in driving growth for software products and services businesses. He was recognized as the Most Influential Martech leader at the World Marketing Congress in 2017, 2018, and 2019. He has also served as a member of the editorial board for Airport Focus International, a UK-based magazine, and has been featured in leading newspapers and wrote a few fiction novels too. So, a bit of a writer like myself. Thanks for joining us, Anupam. 

Anupam Dasgupta: My pleasure, Sylvain. Thank you so much for the kind introduction. It's great to be here in conversation with you. 

Sylvain: Well, thank you so much. It's really great that we have a bit of a colleague in you because your space is marketing. Typically, in this series, we've been talking to psychometrists and people in HR. They are naturally used to psychometrics in those pieces, and sometimes the consultants that use the tests. But now, you are in marketing. Marketing is very data-driven and you are very much aware in marketing of things like corporate culture, organizational leadership because it's part of the task of marketing to take care of messaging and branding and what a company looks like. So, you are involved, in fact, or at least you should be involved, in hiring processes and choices of what's happening inside with people. At least, that's from my own experience in marketing. 

So today, when we were talking, we were thinking, we should probably talk about, at least, it was your recommendation about the psychometric analytics and how it's used in the hiring process as well as how we could predict attrition and other parameters that are undesirable to a business. So, let's start with the basics of how. How often are you, involved in marketing, in basically an HR space, like hiring processes, thinking about attrition and thinking about soft skills, and psychometry? What is your general involvement and how do you see the place of marketing in all this? 

Anupam: Interesting question, Sylvain. I think you already mentioned about marketing being responsible for employer branding, and internal communication. So, whether you're throwing out the brand of the employer as an employer to potential talent, that's where your employer branding comes in or you are communicating the right way to your folks, your employees who are already on board. So, marketing does, as you rightly said, have a strong role to play in the entire talent acquisition, talent retention, and talent engagement because of the compelling piece, which is the internal communication, which could be compelling in a nice way or compelling in a bad way, if not done properly. 

So now speaking about psychometric analysis per se, it's a wonderful way of profiling, as all of us know, the person to see if they are a fit with the organization. I think very often used by a lot of organizations, very data-driven process wherein you get to figure out whether a person fits into the organization. The way I would put it is that being a senior leader in the organization, there is always kind of a contribution and input which goes in from marketing saying that, what kind of weightage or what kind of importance to give to the person's aptitude or his leaning towards the organization, his kind of do's and don'ts in life. 

So that kind of effort comes together also because of the entire internal communication piece, the messaging which happens as part of the employer brand when you're scouting out in the market. You're telling the market that "Hey, we are one of the best employers." So, I think intuitively, there is a link between what you would take as ideal psychometric profile because I think that ideal employee DNA is what we send out in our messaging as well that you are an ideal employee if you are A, B, C, D. Those A,B,C,D is what we expect as outcomes from the psychometric profiling. So, I think there is a very fundamental connection. So that's how I would react to how marketing relates to the entire employer branding, internal communication, hence talent acquisition, retention, engagement, and specifically on psychometric analysis. Because like I said, the outcomes of an ideal psychometric analysis is nothing but the ideal DNA of an employee which we keep messaging on platforms outside the organization when we are acquiring talent. Even within the organization, that kind of behavior is kind of encouraged “through internal communication”, where we say that, "Hey, this is what a brilliant and ideal employee looks like." 

Sylvain: That's really interesting, the ideal employee because I would submit different organizations have different ideas of what that looks like for a variety of reasons.  What do you think is an ideal employee for the company you're currently working for?  Can you give us examples of tangible examples of what that looks like? 

Anupam: Sure. Fortunately, in the organization that I work for now, I think one of the key traits of an ideal employee is transparency and the ability to be candid. So, the extent of being candid is such that you can say anything to anyone. A fresh entry-level employee or someone at the so-called management trainee kind of level can go and tell the CEO, for example, what he exactly feels about the CEO and what he is doing, right or wrong according to that person. So, I think the best thing here in my current organization is the ability to speak what is on your mind. Which sometimes is not the case for some other organizations where people have a little bit of hesitation as to, "Hey, should I say this?" or, "Should I sugar-coat this?" or, "Should I kind of avoid this and be a little diplomatic." 

I think that's the first stellar trait, which I've seen in this organization having been with them for around a year. I can certainly say that when we speak out anywhere in external forums, or when we are speaking to employees, we always look out for employees who exhibit the trait naturally. Well, I did mention you encourage certain kind of behavior. It cannot be explicit encouragement otherwise, it looks like you're kind of forcing someone down an alley but you always observe indirectly or directly that these guys are bold by nature. They just come up and they talk. They speak their mind. They may be right, they may be wrong, but they speak their mind. So, I think that's the first trait and I think closely followed by a very keen, strong sense of innovation. 

I think the people who get hired in my current organization are inherently innovative. So, they always ask questions. Why is something the way it is and why not something else? So, I think top two traits would be the ability to speak your mind, to be bold, and to be innovative. Versus some of my earlier organizations where the ability to speak was not always 100%. There was a little bit of hesitation also because of the way the management was structured. Every organization has its culture, there's nothing good or bad about it but the positives were slightly different. For one of the previous organizations I have in mind,  there was a lot of focus on sincerity, hard work, the ability to run whenever, wherever you are to kind of meet the finish line. So, a lot of focus on hard work and sincerity and diligence versus necessarily a spirit of innovation. Yes, but not that high. Being 100% candid, not always. 

So, I think, like you said, every organization has a different DNA and hence, the employees who over time actually identify with the organizational DNA would have different qualities in different places and you can't really consciously encourage that behavior. You would always keep observing who is falling into that and who is not. You'd be happy about some and about others you think that it's okay if they are not organically fitting into it, then probably they're a better fit somewhere else. So, this does not mean that you let them go but it's just an observation that this is how it is. So that's my response to your question. 

Sylvain: So, what you're saying is that you can, through psychometric measurements as a part of hiring, you can make sure that the person that's coming in has matching characteristics and soft skills, and values to the existing culture in order to maintain the culture the way it is. Because it's part of a DNA of a company and engenders certain behaviors internally like at your company, for example, honesty and being able to speak your mind and things like that. So, you're also perhaps attracting these types of employees to the company as well. That falls into the messaging marketing like who are we as a company and branding. It kind of goes all into one. 

Now, something that came up which is kind of the reverse thinking of this in some of my other discussions internally here, is that when you're doing a measurement checking for culture, the values inside of an organization, using psychometric tests, one may find that the actual culture or values of the company through the evaluation may not match what the company is advertising in the world. Like our value is this, they may write it down on the board, but internally, there's a mismatch. Have you ever encountered this? 

Anupam: Yes, it happens because it's in a way you would say that what is the brand proposition in reality and what does the brand aspire to be? So, it's like this. In the mind of, let's say the founder or the CEO, the brand is something. That's his or her perception, but the brand may not be there yet. So, it may be where the brand aspires to be, but the founder or the founding team thinks that it's already there. So, it may not be a conscious misrepresentation of what the brand stands for. So, if someone is a founder, he or she does say what they believe, his or her perception is that the brand stands for A,B,C,D  but the brand may not have reached there. So that's where someone, a third-party, independently assesses it. They say "Hey, but this is probably where you want to aspire or you want to be but your brand proposition today is not that." 

So that's where the dissonance might come in for people who join and they evaluate objectively. They might think "Hey, this is not what I signed up for." Yeah, that could happen.  Not because of deliberate misrepresentation; it could just be a gap between perception and reality. So, yeah. 

Sylvain: It happens. Now let's talk about something that's important to you, but also to almost every organization that's relevant to what we're talking about: attrition. How many people do you lose? When we were talking earlier, we even delved into how many people do you actively want to lose sometimes, to maintain dynamics, and for all sorts of reasons? How can we manage attrition in multiple ways? How can we predict attrition rates? What are the tools for that, psychometric assessments being one of them. 

Anupam: Yeah. Very interesting topic of discussion. So how do you manage attrition from a qualitative point of view? Of course, we know the factors, it could be better projects, and better opportunities. It could be better rewards, and higher compensation. It could be a friendlier workplace. It could be career progression or even job titles in cases. Those are the qualitative factors. To your point on what are the tools or how do we actually measure this. So, psychometric profiling, of course, goes a long way in determining the person's individual profile to find out how likely is he to fit in with the organization. Is he likely to have a longer tenure? Is he going to get bored in two years or not fit in with the rest of the folks, etcetera? But if I look at it from a purely data-driven angle, I would talk about a slightly broader perspective where once you are in the organization, you have been working with an organization for let's say, four or five years, then there'll be so many inputs which act as factors, factors that would affect my behavior or, which would lead to my behavior. 

Let's say you have a few segments of employees. For example, you have your A Grade employees, which are like your superstars and B which are good, but not great, and C, which like you said, you might want to let go. Now, if you actually had done a cluster analysis, sorry to get into technicalities, but if you had done segmentation, segment A is actually driven by A,B,C,D factors. Segment A is possibly your superstars who don't care about compensation because they know the way they are, they'll anyway get the best compensation. So maybe they are more worried about getting the kind of opportunities, which will keep them on the fast track. So, your data analysis would actually lend you those kinds of insights. 

So you find out that category A, the maximum concern is around the quality of work they get. For category A, you want to retain them certainly. So, then you need to make sure that these guys get the best kind of work and high-quality work, most challenging work so that they are engaged, and they don't kind of look out for a change, etcetera. Category B is probably somewhere in between, and it is going to be the most voluminous segment. So, they are not on either side of the spectrum. The maximum employees would probably be category B where the factors could be slightly more varied, for example. It could be some of them are looking for better compensation. Some of them are looking at a more structured career progression path because they may not be that sharp and brilliant that they can chart their own way. They want to have the organization detail it out for them, "Hey, after two years, you need to do this, after five years, you need to do that." 

So there could be more hand-holding required. They might be looking for more compensation. These are all hypotheses. Data has its own funny way. The actual factors might be totally different but this is just an example. Then category C could be people who are extremely insecure about losing their jobs all the time. They may be short of skills. They may need training, extensive training in their functional area. They may even need interpersonal orientation. So, there could be a bunch of things that the organization can then consider whether it's worth to actually invest in all of those areas. So, are we really looking at retaining segment C? Or it's okay if they find better pastures for themselves somewhere else? They could become, who knows, a grade-A employee in some other setup. 

So, to answer your question, I think a lot of data analysis, which is your typical machine learning-driven analysis, would typically work for larger organizations because machine-learning only works with a significant volume of data. But at least, do some significant statistical analysis, even if you are not reaching machine learning. Significant predictive analysis to say, "Hey, these are the factors which we need to look out our retain our superstars and so on for the category B and category C." 

So attrition is a very interesting problem to handle. An interesting topic to speak about. That's how I would sum it up in terms of how you can measure or kind of predict who is going to churn out. 

Sylvain: Well, to summarize what you said, if I understand correctly, you have your superstars, you have your average employees. They're fine. They're doing a good job. Those that are lower performers, for example, and for whatever reason, they each have a perhaps different set of criteria that would help keep them or have them motivated to leave because of their effectiveness or their productivity and their mentality. They're kind of different beasts from what I can understand. Category C, which is the lower performers or perhaps troublemakers or whoever, whatever the issue is, in regard to why they're providing less to the company. 

In those cases, the thing is that you prefer if they would be better performers, that they would be type A or B. That's just the honest truth. So having a conversation with another at some point about such a topic, especially here at least in Canada. The rules are often pretty strict and it's hard to fire someone because it can often be perceived as either bias, discrimination, or things like that. So, you can't just offhand fire. Employers are a bit skittish about it. They want to have actual tangible proof. So often what happens is that they'll try to change the condition so that they'll leave on their own to avoid the liability of having a reason and things like that. It becomes sometimes an uncomfortable situation. But, yeah, from a business perspective, that category of employees that are more of a drain, in some cases. You are spending the salaries. So, it's kind of a difficult situation. 

In your experience gathering data as part of the hiring process, can we often predict the emergence of category C individuals in the business? Because there could be a variety of reasons why they are low performers. It's not necessarily because they're dumb. It's usually not the case. It’s other reasons than that. It is sometimes family issues or other reasons. Is there some kind of predictability so that employers can avoid the drain, the liability and the kind of uncomfortable situation of having to deal with people that just aren't doing well? 

Anupam: Yes, again, interesting question. So one is, you could get some insights from the psychometric profiling, but then, it's a debatable topic. There are some smart folks out there who can actually beat the psychometric test. I mean, to some extent, that's another topic. I think at the least when you appear for a psychometric test, there could be a bunch of folks who are not really honest and trying to be well prepared and trying to give some ideal responses, which a smart psychometric test would anyway beat it out and find out that you're contradicting yourself. But having said that, there could be other interesting pieces like social media. 

So people are at their casual candid best most often unless they're bragging about something on social media where if you have a lot of data on a person on social media, and if you could do some analytics around it. So, it could give you interesting trends like we spoke. I mean, you never know what data springs up. It could be absolutely wacky. It could be if someone uses a particular word in a sentence more often than not, he is found to be more likely to churn out in the first year of his work of his or her employment. If a person is posting at let's say 4:00 a.m. on Facebook, then probably they aren't the most productive at work, or maybe they are the superstars. So, you never know what data churns out again. The most common inferences which human mind would draw,  data often stumps them completely. Data has its own way of doing things. 

So, long story short, I think in summary, there are ways of collecting social media data and it's done in different cases. For example, one thing I am aware of is when underwriters determine the insurance premium, they look at social media data. Now, of course, there is that entire question of privacy, how much can you gather and how much can you use and where does the line blur, and where is the line distinct? So not getting into that zone of how do we actually define the privacy bit, technically, purely in data terms, if someone had the data about people on how they behave on LinkedIn, how they behave on Facebook and Instagram, which social media do they use more often than not, versus which ones they don't, all of that could actually be a wealth of data with the right text analytics to tell you that "Hey, this is a warning alert for you. This is not the right employee." or, "Here, this looks like a superstar employee in the making." So those are other apparently intuitive ways of finding out at the initiation when you're trying to interview an employee, is this what we are looking for? 

Then again, I think a long way to go there, one is privacy, and two is the entire bias that might creep in because of the entire thing or perceived to have crept in because of the profiling bit which we were discussing the other day. People might think that this is only because it's a certain profile of a person and hence this kind of prediction is being encouraged and all of that. This comes in from the fact that you are allowing a lot of confidential data about the person including his, or her personal details which make his or her profile kind of out in the open for analysis. 

So privacy and profiling are two challenges, I guess. Other than that, had you had this data then, probably it would have really given another way of analyzing. 

Sylvain: Yeah, that falls into the area that I'm passionate about. Technologically, which is how do you look into text using artificial intelligence. It's not a person sitting down and reading people's emails. It could be but it's very onerous and a very expensive way to analyze data, and the reader's bias kind of comes into play. But it is, we are using, in some cases, artificial intelligence trained to identify patterns in speech or in text to infer behaviors and look for causal patterns and behaviors with the massive amount of data. Things like that are actually a thing I'm very excited about. 

Because especially in the psychometrics field, the traditional way to gather data is through self-assessments. To your point, you ask a person some questions, the people, if they’re smart enough, they can game the question and gain a system to get a better profile than they would usually get otherwise, if they were absolutely honest. Then there's that risk of that whereas if you have an AI sifting through a person's social media and emails, people can't be constantly readjusting and adapting for that to try to game that. I can't say it is impossible but extremely difficult. Way more difficult than gaming an actual assessment that's you sit down for half an hour. So, how do you see the use of such tools like artificial intelligence in social media? 

You dabbled a little bit regarding privacy and where privacy is going with GDPR in the world and the adaptations.  It's an excellent tool to gather behavior data but the downside is that the world seems to be moving towards really keeping people's information private as well. So, you have two forces that seem to be going in an opposite direction regarding use of that useful for hiring process. So, what do you think? 

Anupam: Yeah. It's a bit of a dilemma, but before I go there, I just want to step back on the entire use of AI and the controversies that would come out of it. For example, to make it less controversial, let's take a particular country. Let's not take the entire world. Let's say in a particular country, there are north and south. North is perceived to be more educated and more prosperous, and the South is perceived to be backward. If AI is genuinely for some reason giving a model that South is not giving the better kind of employees and if you start using that trend, then probably there could be a lot of outrage that, "Hey, this is pure bias because everybody in the country believes that North is better, South is not great, so your AI is something I don't believe in. It's just your bias." 

So that's another piece that ties up to the profiling piece we're talking about that your AI would, of course, have no bias, but people might still react by saying that "We don't believe in your AI models. This is your very own individual bias, which is showing up in your conclusions." To your following question now on how does privacy versus the use of technology come in? It's a difficult question because the world is becoming more and more conscious about privacy like you said. 

So, I'm just thinking about that... I'm just thinking aloud. If there was a way to have anonymous segments without really having individual data. So, someone, somewhere down the line has to have some indexes or some quantitative data, which will make sure that this anonymous segment comprises of, let's say people from the North of that country I was talking about. 

So somewhere, it's more of a matching. It's not really an individual's exact data. It's probably some representative data, which will kind of match an anonymous segment. So, what I'm trying to get at is, is there a way to kind of dilute the person's actual data and yet, have some representative value that kind of matches with one segment so that we come to a meeting ground between having the absolute data out there in black and white, versus having a somewhat representative segment, which may not be as accurate as the absolute data would be. You're using technology without actually bringing the person out completely in the open. But I'm thinking out loud, I'm not 100% sure if this is practical or feasible, but I'm just thinking about a meeting ground where you could actually anonymize to a certain extent, yet use some leading characteristics of a person's profile, which kind of associate him or her with that segment. 

So, I don't know where the future will lead us but, to some extent, I am leaning towards the use of technology. I am not insensitive to privacy or to a person's need for privacy. I am somewhat leaning towards technology because the power and the outcomes from there, especially in the context of HR and a person's productivity in the organization, his future in the organization could have an immense impact on these pieces. So, yeah. 

Sylvain: Yes. There are no actual solutions right now. It's more of a balancing act. I think we will for a number of years still struggle with what is the correct balance or the pros and cons for the individuals. Honestly, the whole debate around the use of AI, in general, like we can think about more broader companies where that are often in discussion like Google or Amazon or you're doing your shopping. There's a lot of use of AI and use of personal data. Even though we criticize either they're using our data for free, the consumer actually gets a lot out of it, and that's really the trade-off. That's like, "Okay, I don't like that Google and other companies are using data and I'll get compensated for it." Well, we are because we're getting excellent searches. If we did give that personal data, we would have the same kind of search results we used to have in the 90s or in the early 2000s, which I do remember. It was very frustrating at the time because it was hard to get excellent results. 

So, the point is like we were saying, it's a bit of a trade-off. How you're using AI to analyze so that you can do better hiring and you do, using psychometrics and by analyzing texts. So, you want to have a good result so that the employee is a good fit and therefore happier in the workplace. It's good for everybody. But then you are delving in a person's private data. So, yeah, we balance that by using as much anonymized data as possible. So, there's privacy coverage there. It's all a balancing act. 

It's not perfect.  We know exactly where this is going to go. It's exactly this way. Nobody's going to be annoyed at it. It's a bit like the analogy I sometimes use: taxation. If you get a higher rate of taxes in the nation, but your government provides a lot that you care about, you don't mind the high taxes. It's a negative but it translated into a positive. There's a balance. But if you don't get a good trade-off from what you're providing it is otherwise very frustrating. 

Anupam: Actually, to your point, when you mentioned the benefits. Post-COVID, we keep hearing about the great resignation and all of that. So, post-COVID, there are certain areas of work where there's great demand and less supply. So basically, it could be beneficial for a potential job seeker as well. If he is in one of those occupations which are in great demand after COVID, you don't have enough supply with the great resignation and what not and certain set of people saying that "We are masters of our own lives. We don't want to be slaves, we want to do what we want to do." So, in those cases, probably let's say there's a person who's appeared for ten interviews and got eight job offers. He or she would actually get to know from these AI algorithms. "Hey, you know what? If you go for company E versus company C, you're likely to gain by x amount over a period of three years. This person actually gets a decision support system for himself. 

So on the trade-offs, I completely agree with you that there's a kind of concession in terms of submission, in terms of private data that I'm giving up. At the same time, I'm getting a decision support engine that tells me, "Hey, this is where you should join and you should go for it. This is your career path." I mean, on the face of it, you're going for C versus A. Maybe they're paying you something more, but A is going to be your dream job. Maybe you don't know today but AI knows. Because AI has so much data to talk about, which you don't have. 

Sylvain: Yeah, and also like that part of the process, a person has a choice. Then you can also look at, what about the culture, does it fit me? Then, will I be happy in the environment? Do I know the people? They get to be a bit more discriminant. They don't want to go and that's a bit what's happening during the great resignation according to data, at least here in North America. In most cases, it's not because the person doesn't need money, it's because they want to make a choice. They go back to school, taking loans out, and taking risks, and it's very unusual. It's very confusing for a lot of the markets, especially in the service industry at this time. But it kind of points to me the need to do proper analysis. To look. Yes, let's look at the culture, values, and purpose inside companies, which falls into all the marketing stuff that you and I work with all the time. Alignment with the employees where employees are no longer line workers, just doing a job clocking in and clocking out, and being exhausted. It's like, "Whew! I finished my day grateful." No, they want to be engaged in their work and they're taking decisions accordingly, which is a sign of the times perhaps. It's interesting. 

So, our work and also for psychometrists, assessments, marketing and HR. They need to work hand-in-hand to develop great environments for everybody involved. Everybody tries to get a win from the situation, all the parties. 

Anupam: Yep. Absolutely. With HR or marketing or, whichever you call it, strategy or top management. All those groups of folks would definitely be looking out for people who add value to the organization because like you said, it's no longer the age of line workers. People want to add value and be valued. So the kind of people who are out there and who are not just taking up any job, are also not doing it out of the very fact that they want to be sure that A: they will add value to the place they're going and B: they will be valued for the value they add. 

So I think the entire paradigm shift from just having something because you want to earn that dollar versus something which gives you the kind of self-actualization or the satisfaction that I could add value and I was valued because of that, is something which is a change in perspective. I think it's a good way to go. A lot of the employer branding and the messaging, etcetera, would, of course, have to resonate with that to call out "Hey, we are, of course, not here to sound like slave drivers or anything. We are here to collectively add value towards our objective.” We see people who believe in that - the person has honesty and integrity that I will only take up a job if I see myself added by adding value to that trait and being valued for that. 

So yeah, I agree with you. HR, marketing, and all the pieces of an organization, and senior management, must be looking out at that in terms of employer branding, messaging through internal communication to kind of engage people on those lines. 

Sylvain: It's all ecology. We all have to work together. Well, Anupam Dasgupta, everybody watching, I think you heard it from the man that's how marketing, HR, psychometrics are all kind of playing together to create better workplaces, better branding, better sales, competition, trying to create win-wins for everybody involved. If you want to get in contact with Anupam, there's some information below in the description and links you can check out. I'm sure he's going to be happy to engage in conversations after this video. Thank you so much, Anupam for being here and being in the Spotlight. 

Anupam: Hey, Sylvain, thank you so much for having me here. It's been an absolute pleasure and all the best to you and all the best to all the audience of this as well. 

Sylvain: That's the sentiment. Thanks a bunch.

About Anupam Dasgupta – www.linkedin.com/in/dasguptaanupam

Anupam is a senior marketing and strategy leader with extensive experience in driving growth of software products and services businesses. Anupam specializes in business planning, marketing strategy, new product launch, product marketing, demand generation and branding. He has been instrumental in enabling consistent improvement in revenue numbers across organizations, ranging from startups to MNCs. Anupam was recognized as “Most Influential Martech Leader” at the World Marketing Congress in 2017, 2018 and 2019.

Anupam has been featured as a speaker and panelist in global conferences. He has also served as member of the Editorial Board for Airport Focus International, a UK-based magazine. Anupam is also an author with a couple of fiction novels to his credit, that have been featured in leading newspapers.

About CykoMetrix - www.CykoMetrix.com

CykoMetrix is a leading edge combinatorial psychometric and human data analytics company that brings the employee assessment industry to the cloud, with instant assessments, in-depth analysis, trait measurements, and team-based reporting features that simplify informed decision-making around recruiting, training, and managing today’s modern workplace.

Written by

Sylvain Rochon

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