Using data analytics to optimize employee engagement
Gone are the days during which CEOs and leaders of big, complex, multinational teams struggle to understand employee engagement. Say goodbye to menial HR surveys that weakly reflect reality and to post-damage exit surveys that raise issues very late.
In our AI era, it is possible to have a more robust approach to measuring and optimizing employee engagement. According to CrunchBase this “employee engagement” market is valued at USD 1 Billion. Peakon a Copenhagen startup is shaping up to be the leader in this space. They provide a B2B software that enables companies to measure and improve employee engagement. Whilst the most tech savvy and innovative companies of the sort of Google, Facebook and Amazon are deemed to have their own people analytics teams and solutions, most companies don’t. Peakon thus aims to enable those companies to upgrade their people analytics capabilities. By combining data science with organizational behavior and psychology it aims to elevate people management and bring it at par with the best finance and employee management techniques. Peakon estimates that more engaged employees will directly translates in the top lines of businesses. In fact, they estimate that highly engaged employees have 37% fewer sick days, 30% higher productivity and that every 1% increase in engagement leads to a 0.6% increase in revenues and 58% lower churn cost.
How does Peakon aim to increase employee engagement?
Peakon’s approach is to use simple data driven solutions to “understanding, engaging, and retaining staff”.
To understand staff and engage them frequently without burdening them, peakon carefully designed its data collection process. Its process is enjoyable and minimize time commitment. through smart surveys that uses algorithms to asks “the right questions at the right time”, the system distributes questions across the organization in a way that provides all insight a manger needs without burdening employees.
Real time data and analytics is provided by gathering continual feedback. This enables managers to take a preventative approach to people management rather than reactive one. Through smart algorithms, the platform pinpoints early on areas of risk and even suggests to managers tangible action plans. This is a major improvement compared to traditional way of doing yearly reviews. The algorithms work by understanding what motivates employees and what are common issues they are facing.
The platform also facilitates communication throughout the process and allows managers to respond directly to employees which proved to be very powerful: employees like to know that they are being heard.
The platform claims to be very secure, and uses latest encryption techniques to protect data.
Results are very encouraging: in 2017-2018 the companies’ revenues grew six fold. Their client portfolio includes top-notch companies such as Capgemini and BMW. And it is the fastest growing SaaS company in Europe. They recently raised a 22 Million Series B and plan to expand globally. Clients are also very satisfied with results with some clients witnessing 50% decreases in churn rates. In one instance, a client completely restructured its organization to act upon feedback received on the platform. Clients even compare the platform to Salesforce, as one said: “Every morning, I turn on my computer and check Salesforce to understand the real-time performance of our sales and pipeline What I love about Peakon is that it’s a very similar experience, but rather than looking at our sales figures, I’m looking at something more important: how our people are feeling.”
Will it be the next Salesforce?
Sources:
https://peakon.com/
Peakon Raises €4M To Bring ‘People Analytics’ To Employee Engagement And Retention
https://nordic.businessinsider.com/a-copenhagen-based-startup-turns-employee-feedback-into-a-science–and-it-just-scored-$22-million-to-take-on-the-us–/
https://www.huffingtonpost.com/julie-kantor/high-turnover-costs-way-more-than-you-think_b_9197238.html
Thanks for the great post! The rise of AI and data-analytics in the workplace means that managers can keep an infinitely better pulse on workforce productivity, engagement, and performance. However, such increased tracking capabilities comes with serious risks. Namely, with increased ability to surveil and nudge (aka control) their employees, corporate leadership risks overshooting and becoming a “big brother” that uses data to oppress their workforce to the point of stifling creativity, expression, and motivation. Last week, the Economist wrote a leader article on just this topic, explaining the pros and cons and how managers can thoughtfully employ technologies like the ones you described. You can find it here: https://www.economist.com/news/leaders/21739658-artificial-intelligence-pushes-beyond-tech-industry-work-could-become-faireror-more
Super interesting, Lama! I think you’re right in that a major benefit of something like this can even just simply be that employees feel heard. My biggest concern would how anonymous it is. It sounds like if managers can respond directly to employees so a lot of the feedback must not be anonymous. I’d worry about companies getting honest feedback this way. Also, I wonder if down the road it will cause any legal / ethical issues for example if an employee that is always complaining via surveys gets fired, can he/she claim that the only reason they got fired was because of honest feedback they gave?
Thank you for the post. Some of those figures are incredible, especially, “every 1% increase in engagement leads to a 0.6% increase in revenues and 58% lower churn cost.” That is a lot of real economic benefit that you can bring to your company by increasing engagement. I am interested to understand how consistent the methods of improving employee engagement are across companies. Understanding that would go a long way towards understanding how scalable this business is. In addition, do you know how effective they are at understanding causation for low engagement in an organization?
Thanks for the interesting post and Austin´s link to the leader article in the Economist! Although AI has great potential in almost any area of life I´m afraid that the greedy nature of human being may cause problems also in harnessing AI for ethically approved purposes. The vision of being treated like a robot in the future is not very attractive. It may turn out that the companies who do not survey their employees will attract the best people. The passion for work is a key driver for many experts and potential future innovators. It should not be risked by trying to push everyone in one size.
Great post, Lama! I am wondering how this platform confirm the identity of this person whether or not this person currently works for that company and how they will protect their anonymity. Also, seeing a very very great impact on just increasing 1% engagement, would you explain what they define and compose that one variable?
Really interesting post! I really like this concept. With time as Peakon signs more clients, it will be in a good position to see what motivates employees across companies and industries. It can then leverage this data to run its own training or hiring division. However, such oversight over the employees worries me, as if used incorrectly, can feel like a burden on an employee (think Big Brother oversight). This is unfortunately hard to predict. I also wonder how invasive are the surveys? How often an employee have to do them? It is hard to strike a balance between input frequency (amount of data) and the usefulness of the output.
Thanks Lama. It is very interesting as it looks like Peakon creates value for their clients and clearly capturing some of it through their SAAS model. It looks like the data collection needs to be done in an active way whereby the employees input the data. I would be interested to find out if there are passive ways to collect the data, because that will make the service offered by Peakon more meaningful, as it wouldn’t rely on the client’s employees. Furthermore, it would bring in more data, and with machine learning, you could create even more value for the client company. However, as per the comments above, there might be legal complications with privacy issues and possible employee backlash.