Interview with Timo Schönenberg, People Analytics Manager at HelloFresh
How can People Analytics help organizations to stay agile, to reach People OKRs and to grow and retain talent? Our People Analytics Lead Yannik Leusch discussed this question with Timo Schönenberg, People Analytics Manager at HelloFresh SE. He provides insights on how the use of data can, for example, aid the reduction of employee fluctuation and the identification of talents in his company.
What role does People Analytics play in the HR department of HelloFresh?
Timo Schönenberg: If you think of HR work as “People work” (which I very much prefer), you’ll soon notice that it’s inevitable for an employee centric and high performing People team to have a profound, strategic approach to it. To achieve this, our People Analytics work plays a key role in delivering the fundamental insights needed to outline a data-driven, successful People strategy.
So how does People Analytics achieve this?
Timo Schönenberg: This can be well explained by first shedding some light on the initial roots of People Analytics within our HR department. Back when I started at the end of 2018, the first team making use of People Analytics and further demanding a more sophisticated approach to this topic was our HR Business Partner team in alignment with People leadership. This resulted from a very clear understanding that we needed a data-driven approach to be able to
- know where we stand concerning the People OKRs defined
- identify key areas for development and thus define targeted actions to reach these OKRs
- tie our People strategy & work close to the needs of our employees
That makes sense – can you give an example of how this worked for a specific People OKR?
Timo Schönenberg: A very simple example to illustrate this is our churn OKR: If we want to decrease churn by a certain percentage, we first need to know where we currently stand (x %). As a next step, we need operational insights to tell us which groups are churning the most, i.e. functional teams, age/tenure/career step groups etc. We then rely on engagement data to uncover the drivers behind their churn: What are the predominant reasons for leaving in this group? What are they typically dissatisfied with? Let’s say a lot of employees in a specific department are leaving due to dissatisfaction with their team’s organizational setup. This learning would enable the HR Business Partner team to initiate structural change with the department heads in order to increase engagement and thus prevent future churn in their department. That being said, People Analytics supports our HR team to reach our People OKRs by simultaneously addressing the needs of our workforce – catching two birds with one stone!
Were you able to extend this approach to other People areas?
Timo Schönenberg: Yes, over the past two years, this setup has been extended to further collaboration with other HR sub-teams such as Talent Acquisition, Learning and Development or Employee Experience. Following the same logic, People Analytics infuses data into the daily work of these sub-teams, allowing them to boost their performance even further. Should, for example, offer acceptance rates or participation rates in training sessions decrease, Talent Acquisition or L&D can specifically tackle dynamics leading up to these patterns and observe the benefits of respective actions taken by tracking these KPIs. With an outlook on the rest of 2021, the essential role People Analytics already plays in our HR department will become even more important, along with a more diligent application of predictive analytics and the implementation of new formats to encourage a data-driven collaboration across HR sub-teams.
Where and how do you set your focuses and priorities?
Timo Schönenberg: The way we define our priorities is primarily shaped by two factors
- The People OKR set
- The current situation we are facing as a company
Consequently, we can ensure that the data we collect and analyze generate meaningful, actionable insights and are aligned with what we need to know in order to reach our targets and act promptly in case of unforeseen challenges. At the moment, we are very keen to develop an in-depth understanding of talent (employees with high performance/potential) and key drivers behind churn.
A good example of a situation which forced us to act swiftly is obviously the pandemic. It prompted us to collect additional data points on how safe and supported our employees feel during these debilitating times and given a setup of primarily working remotely. Another topic which has gained more and more importance over the last couple of months is the comprehension of employee productivity including its blockers and drivers. The aim here is to be able to support our employees in successfully meeting the challenging and changing demands of our rapidly growing environment
What difference can People Analytics make in the challenge of retaining talent and how does HelloFresh apply People Analytics for this particular purpose?
Timo Schönenberg: In order to retain talent, we first need to identify and understand talent. While the identification is something covered by Performance Management initiatives led by our HR BP team, understanding the identified talent is a task in which People Analytics can make a real difference.
In a first step – similar to the churn example mentioned above – we combine operational and engagement insights to answer the following questions:
- Where does our talent lie? Which demographic groups have a higher talent pool than others?
- In which demographic groups did we lose the most talent in the past? What were the reasons for that?
- What specific needs do our talents have? How do they differ from those of other employees?
The insights resulting from this analysis can then be infused into the work of different HR sub-teams:
- L&D can align existing talent development initiatives – such as our Future Leaders’ program – to the talents’ needs discovered in this analysis
- HR BPs can proactively implement career development initiatives for talent pools with a high churn rate due to dissatisfaction with career opportunities
- People Analytics can track the success of these initiatives and deliver actionable input for future initiatives
As a next step, we are currently trying to identify predictors, such as specific competencies, for spotting talents at HelloFresh, with a high interest in how these predictors might differ between different teams given the respective types of work they do. This knowledge will not only allow us to optimize our talent identification processes but will also have implications for Talent Acquisition concerning the question who to look for or for L&D in evaluating which skills to focus on.
In our recent study on People Analytics, we found that many organizations recognize the potential of analytics in HR, yet struggle with implementing more data-driven approaches. Which challenges do you see in the organizational implementation of People Analytics, for example with regards to governance and acceptance?
Timo Schönenberg: First – what appears quite intuitive, but is indeed worth mentioning –in order to drive meaningful analyses, you need to have access to meaningful, high-quality data. In order to connect multiple data points, you also need to have a consistent set of data across various sources. This may sometimes sound easier to achieve than it actually is. Particularly in our tech unit, team structures and names are constantly evolving, which can make it hard for data producers to always stay on top of these updates. Bearing in mind that different sub-teams are often responsible for producing different sources of data, the likelihood of ending up with an at least somewhat inconsistent data set can be quite high.
Thus, the first basic challenge for People Analytics is that it relies on clear processes which guarantee high data quality and which need to be aligned with other sub-teams who might have different priorities. That being said, I’m truly happy about HelloFresh’s internal campaign concerning data literacy led by our BI and L&D team. Another challenge in “getting the car on the road” lies in the focus definition of People Analytics. To generate the most impactful insights, you need to be well aligned with business stakeholders and/or other HR sub-teams in terms of what you measure. People Analytics won’t make a real difference if what you analyze is nice-to-see, but cannot be translated into actions.
Do you see any limitations when it comes to data integration?
Timo Schönenberg: There is a fine line between maximizing data-driven People work and crossing ethical boundaries. For example, we would be able to drive the most impactful regression analyses between churn and employee engagement data if the latter available was on an individual level. Yet, we always ensure high anonymity standards such that those data are only accessible on an aggregated level. This approach can sometimes limit the sophistication and impact of such an analysis, but is undoubtedly necessary.
HelloFresh has grown massively in the past few years. Nonetheless, you are well known as a highly agile organization. How can People Analytics support an organization in becoming more agile or in remaining agile despite a rapidly growing environment?
Timo Schönenberg: Part of becoming or remaining an agile organization is to receive in-time feedback and measure the success of actions taken. This applies as much to general business initiatives as it applies to People initiatives, with both potentially affecting employee performance, churn or engagement. People Analytics represents the perfect way to track and measure the effects of such initiatives.
Can you give some further examples for this track and measure approach?
Timo Schönenberg: Let’s assume you would like to implement a new career development program including a redefinition of career tracks, employee development programs, etc. In addition to that, you continuously track satisfaction with career and personal development. If you saw rising satisfaction scores and less churn related to this topic after implementation of the program, you would be able to tie this pattern back to the actions taken. Hence, you would know that you are on the right track, allowing you to further investigate what can still be improved and define potential next steps.
To maximize our agility in this respect, we made the decision to extend our engagement survey format from only one major survey and three minor checks per year to four in-depth surveys a year. Thereby, we want to ensure to not only continuously track the engagement of our workforce, but also ask for timely feedback regarding potential key drivers such as career & personal development, collaboration, leadership and others.
In a nutshell, staying agile as a company when it comes to its People is highly dependent on People Analytics, as it delivers insights on where you stand and which specific actions you need to take. One example of what this practically means for us is our monthly People Analytics meeting with People Leadership, HR BPs and Employee Experience to discuss relevant key KPIs (such as churn, engagement, growth, etc.), collaboratively identify patterns and define relevant actions.
Do you see any specific challenges or opportunities for People Analytics in agile organizations?
Timo Schönenberg: Based on my personal experience, the biggest challenge resulting from an agile environment is the frequent structural change in some parts of the company that I have addressed earlier. In the daily business, this can lead to recurring time-consuming data cleaning efforts.
Staying agile can also imply having to rethink your strategy and with that, which new data points you might want to look at and which you might need to replace. In some cases, this can result in a lack of comparability of insights over time.
Can you elaborate?
Timo Schönenberg: For example, we have a couple of items that constitute our “Employee Engagement index”. In line with best practices and changing interests from our side, we have now added two new items to this index, making it less comparable to our previous engagement scores. Thus, while People Analytics is an enabler for agile People work, an agile environment also forces it to be able to adapt quickly.
As the new year has just begun, what are your main goals in People Analytics for 2021?
Timo Schönenberg: One main goal we have is progressing to a more diligent use of predictive analytics, for example through further identifying significant predictors for individual/team performance or churn and infusing these insights into our People work. Increasing the sophistication and quality of insights generated along our engagement survey landscape is another goal on our list.
As we will be moving to a new HR system, a challenge on our agenda will be maintaining our high standards whilst transferring to this new platform, for example in building a data infrastructure for an automated data pull into our dashboards.
In our recent study on People Analytics, we have learned that many organizations are just getting started on their way to making data-driven People decisions. What are your recommendations for getting started with analytics in HR and how may these differ for small vs large or agile vs stable organizations?
Timo Schönenberg: First of all: Start with the basics and take a step by step approach. Really think carefully about: What makes us think we need People Analytics? Based on that, what do we really need to know in the first run? Is this an internal insight or is the business or another HR team supposed to work with it? If the latter is the case, my top recommendation is to speak to your relevant stakeholders. They are the best source of information concerning the question which insights might help them do their People job better. This does not mean you should leave it all up to them, you just need to first identify the relevant questions to be answered together.
Furthermore, don’t bite off more than you can chew: At the beginning, your dataset will very likely not be extensive or clean enough in all parts to run off and build the fanciest dashboards and run the most advanced analyses. This circumstance will require you to work with MVPs first and build upon them.
The last point might be even more relevant in small organizations, as they are more likely to miss clear processes on data governance which tend to be more established in larger organizations. Besides that, the dataset itself might be changing more frequently in more agile organizations. Another difference may lie in the role you need to take on as a People Analytics team. In smaller, more agile organizations you have an even higher chance to act as an agent for change and identify what should be analyzed in the first place as your environment is still evolving. However, I fundamentally believe that People Analytics has the potential to initiate a highly demanded, progressive change of making today’s working life more employee centric, even if it is rooted in the most stable organizational setups.
Timo, thank you so much for this interview!
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