In it for impact – what People Analytics told us about McKinsey’s people

One of the biggest challenges and critiques about employee engagement and satisfaction surveys is that the results can be overwhelming in volume but

People Analytics helped us discover that McKinsey’s people are consistently driven by the firm’s mission – to have lasting, positive impact
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Keith McNulty

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Top Voice 2017 | Human Capital Leader | People Analytics, FoW, Psychometrician, Data Scientist | @dr_keithmcnulty

One of the biggest challenges and critiques about employee engagement and satisfaction surveys is that the results can be overwhelming in volume but low on insight. In larger organizations in particular, developing a single survey which caters for all of flavors of what people do can lead to a somewhat convoluted and unstructured set of questions. And when the results are reported back to the business with no effective synthesis or summary, it makes it challenging for managers to know what to do with them.

This year my team and I had a chance to help rethink how to generate meaningful insight from our company-wide satisfaction survey. The objective was clear: better understand what it is that makes our people satisfied – what they are in it for – and ensure that this is understood in how we report results back to our business leaders.

Reducing the complexity of survey results using factor analysis

It was a great opportunity to apply some powerful statistical techniques to cut through the noise in the data and narrow in on the most important insights. Even more amazing were the conclusions we drew: that no matter what part of the organization someone is in, or what kind of work they do, there is a remarkable consistency about what keeps people excited and engaged at McKinsey.

Satisfaction surveys have the habit of becoming more complicated and less organized over time. Managing an ever-widening group of stakeholders in the organization can become a more and more demanding affair.

One common consequence of this is that survey questions get added, tweaked, adjusted as different stakeholders seek to get more insights on aspects of the employee experience that are important to them. Before you know it, surveys start to lose some of the original structure behind them and results become more convoluted and challenging to interpret.

Rather than try to obsessively centralize control over survey content, which can be frustrating for stakeholders, analytics techniques can be applied after the fact that can reduce the complexity of the results and hone in on the key themes covered by the mass of questions.

This year we used a dimensionality reduction technique called factor analysis to create more sharp and intuitive analytics of our survey results. The concept behind factor analysis is that all measurable survey responses (usually responses on a Likert scale) are a function of a (smaller) number of latent variables. By analysing the statistics of the survey questions, identifying groups of responses that relate strongly with each other, and applying a dash of expert judgment from a psychometrician, these latent variables can be unearthed.

Finding the things that matter most

We ended up identifying 11 latent variables that were represented by different combinations of survey questions. Examples of these latent variables were Opportunities and Incentives and Work life balance. Being able to represent survey results lined up against this smaller set of latent variables added considerable value in terms of making them more straightforward to interpret.

Most surveys have a single outcome question which asks the responded to provide a final, overall opinion on the most critical construct being measured. For example, ‘To what extent to you agree with the statement ‘I am happy in my work’?’ or ‘How would you describe your overall level of satisfaction currently?’. If we regard this single question as the outcome variable, we can use statistical techniques to understand how much of the response to that outcome can be explained by each of the latent variables indentified using factor analysis. Is it all about incentives? Or does the manager you work for drive your satisfaction the most?

Relative importance analysis is a technique used to estimate the importance of individual predictors in a regression model. In our case, we wanted to work out the importance of each of our latent variables in determining the overall satisfaction of our people, and to understand how much of their satisfaction is explainable by our latent variables (versus general unpredictability, which manifests itself as error in the regression model).

We applied relative importance analysis to the survey results of a number of subgroups of our people: those that work directly with clients, those that work purely internally, those that work in technology related disciplines, those that work in traditional strategy consulting, and many more.

There were many differences between these groups, paricularly on how much of their satisfaction could be explained by the latent variables. However, one thing was remarkably consistent across all our people. The most powerful factor predicting their satisfaction was Role Impact. This factor is broadly defined as ‘playing a meaningful role in a group or organization that drives lasting, positive impact’. Every single group we analysed had this as the dominant explanatory factor in their overall satisfaction.

This resonated strongly with me personally – my prime motivation orients around having impact and achieving lasting positive change. But both philosophically and analytically, it was so satisfying to see in the numbers that all of my colleagues were of a similar mind.

I lead McKinsey’s internal People Analytics and Measurement function. Originally I was a Pure Mathematician, then I became a Psychometrician. I am passionate about applying the rigor of both those disciplines to complex people questions. I’m also a coding geek and a massive fan of Japanese RPGs. All opinions expressed are my own and not to be associated with my employer or any other organization I am connected with.

McKinsey is often quoted as a great place to work, and many reasons are given for why that is the case. But perhaps the most important reason is this: despite our changing organization and our increasingly diverse talent, our people all thrive on our mission: we are all in it together and we are in it for impact.

#InItTogether #PeopleAnalytics #EmployeeEngagement

Top Voice 2017 | Human Capital Leader | People Analytics, FoW, Psychometrician, Data Scientist | @dr_keithmcnulty

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