Understanding the extent and size of the problem and identifying a business leader to sponsor and benefit from the project allowed the people analytics team to clarify the intent of the project and frame the business questions (Step 1).
“One way to help HR on its way is to highlight examples of great work that the pioneers in this space are doing. This is not merely to imitate their peers, but rather to learn from and be inspired by the successes others have had.”
—David Green
Global Director, People Analytics Solutions, IBM
This chapter illustrates the methodology by examining five case studies through the lens of the eight steps. Although the eight-step methodology was not necessarily explicitly considered as these projects were unfolding, each case nonetheless implemented each step.
These successful cases were chosen to cover a range of business challenges, types of organizations, and geographic locations. The intent is to illustrate different organizational challenges and the value that can be achieved by addressing them methodically with workforce analytics. In addition to following the methodology, success requires strong project sponsorship. Each of these cases describes how the right sponsorship contributed to workforce analytics success.
Following are the case studies discussed in this chapter:
• Improving Careers Through Retention Analytics at Nielsen
• From Employee Engagement to Profitability at ISS Group
• Growing Sales Using Workforce Analytics at Rentokil Initial
• Increasing Value to the Taxpayer at the Metropolitan Police
• Predictive Analytics Improves Employee Well-Being at Westpac
Our eight-step methodology for workforce analytics is described in detail in Chapter 4, “Purposeful Analytics.”
The first steps focus on understanding why an analytics project has been initiated:
Step 1: Frame Business Questions
Step 2: Build Hypotheses
The next steps describe how the project will be conducted:
Step 3: Gather Data
Step 4: Conduct Analyses
Step 5: Reveal Insights
Step 6: Determine Recommendations
The final steps ensure that action will be taken as a result of the project:
Step 7: Get Your Point Across
Step 8: Implement and Evaluate
Nielsen Holdings PLC is a global information and measurement company headquartered in the United States. It has a presence in more than 100 countries, approximately 44,000 employees, and revenues of $6.2 billion in 2015. Nielsen measures what consumers buy (categories, brands, products) and what consumers watch (programming, advertising) on a global and local basis.
Nielsen was just beginning its people analytics journey in mid-2015 when Piyush Mathur was appointed Senior Vice President of People Analytics. He was tasked with building, developing, and growing the people analytics function and creating business value. Given the company’s heritage for collecting and studying data, Piyush was not short of passionate people interested in joining the team; he internally sourced a technologist, a compensation analyst, an HR partner, and a data scientist during his first few weeks. Later in 2015, Piyush and his team started a significant project focused on attrition.
Piyush explains, “Very quickly after I took the role, we realized a trend that had gone virtually unaddressed: the rate of attrition was rising, year over year.” Even without a standardized definition of attrition, the analytics team could see that the organization was constantly looking to hire externally while continuing to lose valuable associates in key business areas and roles.
The problem was difficult to focus on, for two main reasons. First, the team needed a standard definition of attrition to examine the problem. “We had counted over 16 different ways people were measuring voluntary attrition,” Piyush says. “We needed operational definitions, data governance, and robust analytical methods.”
Second, Piyush’s team did not have a sponsor for the project. To address this challenge, Piyush identified large businesses in the organization that had attrition higher than the company average. He approached the president of one such business (with approximately $1 billion revenue) and asked her about people issues. During the discussion, the leader recognized that retention was a problem and indicated that she wanted to do something about it. She became the sponsor of the analytics project. Piyush explains, “Sometimes we feel we need to find the solution and then approach the business leader. But it is also important to get the business leader involved early on, to recognize the problem before starting on the search for answers.”
With the sponsorship settled, Piyush initiated the analytics project. He clarified the business questions as follows:
• What factors make associates more likely or less likely to leave Nielsen?
• What could we do about it?
• What is the financial impact of people leaving?
Piyush was confident that his team could define, measure, and understand the factors causing attrition in a clear, predictable, and sustainable way. He was also convinced that his team could find insights and make actionable recommendations to address the problem. When reviewing the attrition problem, the team identified two specific groups of people they suspected were a high retention risk, and they tested this suspicion in the following hypotheses:
Hypothesis 1: Women and diverse employees have higher attrition risk than men.
Hypothesis 2: Employees who work remotely (for example, at a client’s location) have higher attrition risk than employees who work from a Nielsen office.
After clarifying the business questions and hypotheses and having the sponsor sign off on them, the people analytics team under Piyush’s leadership focused on four activities:
• Isolating the geographical areas to be studied
• Defining the time period to be studied
• Collecting the correct data
• Selecting the analytical model (see Figure 6.1)
Figure 6.1 Data and model used for the study of attrition in Nielsen.
The study was limited to people in the U.S. business, to avoid specific works council and data privacy challenges. Piyush explains: “We involved the general counsel and the chief privacy officer and their teams from the start. In doing so, they helped us by endorsing our project and recommending that we begin our project in the U.S. only to simplify the gathering of data.”
The team focused on a time period of five and a half years; this was the longest time period of consistent data, and it gave them sufficient data to complete a strong analysis.
The two predominant technology systems that the team used to collect the required data were the SAP Human Resources Information System (HRIS) and the Oracle Taleo recruitment applicant tracking system.
Choosing and gathering the right data is important for any analytics project, but Piyush outlines another caution: “Sometimes we wait for data to become perfect. I believe in ‘design thinking,’ where we imagine the future state but start building with what we have and keep improving as we go along.”
The next step was analysis. As Figure 6.1 shows, the team decided to use a Cox regression analysis, an example of the quantitative analysis objective of classification (see Chapter 5, “Basics of Data Analysis”). Although the team considered other techniques, such as logistic regression, it chose Cox regression because that method allowed them to study attrition over time as a function of the various predictor variables.
In its analysis, the team found no support for the first hypothesis: Women and diverse employees have no higher attrition risk. But the team did discover three core factors contributing to attrition: lack of lateral moves,1 being located at a client site, and recent hire date (tenure of less than one year). The second factor supported the second hypothesis: Attrition risk was indeed higher for associates working remotely from a Nielsen location.
These insights were clarified with a high degree of confidence. For example, someone given a lateral move was proven to be 48 percent less likely to leave than someone who was not given a lateral move. Interestingly, Nielsen had a very low percentage of managers offering lateral moves or associates asking for them; less than two percent of people received a lateral move globally. Although lateral moves might bring a small compensation change, the new role essentially has a similar responsibility level as before, but in a different environment (such as for a different business, manager, or function).
One of Piyush’s guiding principles is that business leaders will get more excited if financial benefit for analytics can be proven. His next step was to demonstrate exactly that. Piyush and his team, together with compensation, talent acquisition, and other HR experts, plus people from the financial planning and analysis group, built a cost of attrition model based on actual voluntary attrition data. This model included factors such as lost productivity and time (and associated cost) to recruit. Working with members of the finance department, they ensured that any model created would be taken seriously and be financially validated.
The financial impact analyses revealed that, for every 1 percentage point decrease in attrition, Nielsen avoided approximately $5 million in business costs. This analysis got the attention of the senior leaders, as Piyush elaborates: “We shared the model with our CEO and CHRO—they loved it. They really liked how we were using analytics empirically and financially.”
With the insights (the factors shown to contribute to attrition) gathered and the cost of attrition model developed and validated by finance, the team could focus on building very specific recommendations focused on talent reviews, lateral moves, and onboarding.
The first major recommendation was to focus on lateral moves as part of talent reviews. As part of discussions about talent and succession, leaders were expected to spotlight key individuals who would benefit from lateral movement to another part of Nielsen. This was embedded in the talent review process so successfully that it became part of discussions with the CEO beginning in late 2015.
The second recommendation concerned a program called Ready to Rotate that already existed but was poorly used. It was designed for associates to self-identify when they would like to be considered for a lateral move. It was originally created but not implemented because it lacked a committed sponsor. With the level of support Piyush had created, he was confident that a program like this could be reignited and implemented.
Finally, beyond programs supporting lateral moves, recommendations were implemented around onboarding. These were designed to address one of the other insights derived regarding the relatively high attrition among employees with less than one year of tenure. The onboarding actions included a buddy program and an “information all in one place” system for new recruits to allow them to more quickly feel connected and integrated.
Piyush remembers the president of the U.S. business and the sponsor of this project saying, “Insight without action is overhead.” As such, he wanted to make sure that the recommendations were implemented and communicated properly.
The analytics team partnered internally with Nielsen’s communications team to make the recommendations a reality. The communications team members had the expertise to make the Ready to Rotate program a success. They created a variety of materials, including a video2 about factors that contribute to higher levels of retention, an innovative approach to articulating the outcome of a people analytics study.
Together with the continued strong sponsorship of the business leader and the buy-in of the CEO and CHRO, Nielsen decided to implement the Ready to Rotate program globally, to shine a spotlight on talent that is asking or willing to make a move within Nielsen.
Piyush’s team proved the financial benefits of implementing retention programs through a systematic and methodical analytical approach with strong sponsorship throughout. By mid-2016, some impressive results had emerged:
• Nielsen identified 120 key individuals and, through lateral moves for 40 percent of this group, reduced the attrition rate to zero for the first six months after implementation.
• Through increased participation in Ready to Rotate and one-on-one engagement, the voluntary attrition rate in the U.S. business for the first quarter of 2016 decreased to half the rate it was during the same period in 2015. For the global enterprise, attrition was 2 percentage points lower in the first eight months of 2016. This translated to a benefit of more than $10 million.
• Following successful implementation in the United States, Nielsen rolled out the analytics project to another seven countries.
A methodical approach enabled the people analytics team in Nielsen to contribute to the company’s business success. However, for Piyush, this project was not just about improving the business. “At the end of the day, we are improving people’s lives by helping them stay in Nielsen through these programs. Moving companies is very stressful for people and it often affects their families, too. So we are not only helping Nielsen financially—we are helping people by providing them with rewarding jobs in other parts of our business.”
ISS Group was founded in Copenhagen in 1901 and has grown to become one of the world’s largest facility services providers. ISS offers a wide range of services, such as cleaning, catering, security, property and support services, and facility management. It has approximately 505,000 employees and local operations in 77 countries across Europe, Asia, North America, Latin America, and the Pacific, serving thousands of public- and private-sector customers.
ISS drives profitability through the productivity of its employees as they work to deliver services in client organizations. Given the central role of its employees to its success, a key component of the ISS business strategy is to ensure that all its employees are highly engaged. ISS takes the challenge of employee engagement seriously. This is illustrated by Group Head of Marketing Peter Ankerstjerne’s sponsorship of a workforce analytics project to explore the relationships among employee engagement, customer experience, and profitability at ISS.
Simon Svegaard, Group Business Analytics Manager, gives this background on the project: “While there is substantial evidence of a positive association between engagement and performance in the scientific and business literature, before making considerable investment in increasing engagement at ISS, we wanted to see if we could identify that association in our own organization.” In other words, ISS wanted to know whether it would see a return on investment from interventions aimed at increasing employee engagement.
To ensure collective agreement on the aims of the analytics work and strong management, a project team was established. The team included representatives from Group HR, Group Marketing, and an external consultancy. Of particular note was the external partner selected to assist the team: Morten Kamp Andersen is an experienced business consultant in the field of analytics and had already worked with ISS and several of its senior leaders on earlier projects. Simon explains, “It was critical that the right people, including the main stakeholders, were involved in this project from the outset. In many projects, collaboration across the business only begins when the results are presented, but we wanted to establish that collaborative approach from the start.” With such an approach the team could more easily get buy-in, support, and the resources it needed to deliver.
One of the project team’s first tasks was to clarify its hypotheses. Two clearly stated hypotheses captured the expectations about the project and guided future data collection and analyses:
Hypothesis 1: Employee engagement is positively related to both employee and customer experience.
Hypothesis 2: Customer experience is positively associated with contract profitability.3
Before data collection could begin, ISS had to identify the most appropriate measures of the variables relevant to the hypotheses. Specifically, the team needed to identify reliable and valid measures of employee engagement, employee and customer experience, and profitability. The best measure of employee engagement was the ISS global employee engagement survey. Workers take part in this annual survey covering all of the organization’s countries and operating units. The survey is administered in 52 languages using a combination of paper, email, and web-based questionnaires. The response rate in 2015, the year of this study, was 72 percent of the total population.
Employee and customer experience variables were assessed using the Employee Net Promoter Scores (eNPS) and Customer Net Promoter Scores (cNPS), which ask employees and customers whether they would recommend ISS to a friend or colleague.4 Contract profitability data also were available for analysis. Given the sensitivity associated with this information, the analytics team undertook careful communications regarding how results would be used and provided guarantees of individual worker anonymity to ensure that country managers were comfortable sharing the profitability data for the project.
By building a clear picture of the data required before gathering it, the team ensured that it was not distracted by other interesting but less relevant analysis opportunities along the way. The team’s confidence in the potential of this project was boosted by the global nature of the data it had. However, the team was careful to retain its focus on the two outcomes that the organization pays a lot of attention to: customer loyalty and contract profitability.
The project team conducted its analyses using three primary analytical strategies. First, team members used a data reduction technique (see Chapter 5) to limit the number of survey items in their final analyses. Next, they used a number of exploratory graphical techniques to examine the association between the variables identified in their hypotheses. They coupled these graphical techniques with regression-based methods such as partial least squares (this is an example of the prediction objective of quantitative analysis that Chapter 5 describes) to examine how well engagement could predict customer experience and profitability.
The analyses provided support for the first hypothesis at ISS: Employee engagement was indeed positively related to customer satisfaction. However, as Simon says: “While this finding reflected the existing external research suggesting that engagement is associated with performance outcomes such as customer satisfaction and profitability, the project team wanted to know more.” The real insight was revealed when the analytics team considered exactly what aspects of employee engagement were related to customer satisfaction as measured by the cNPS.
Three aspects of engagement turned out to be particularly strongly linked with customer satisfaction: motivation, capability, and purpose. In other words, cNPS scores were higher for business units in which employees were more motivated to do a good job, were well trained, and understood customer expectations. Moreover, as the second hypothesis predicted, eNPS and cNPS were positively related with contract profitability. Figure 6.2 illustrates the contract profitability (shown as a percentage) as a function of eNPS and cNPS. It shows that, as both eNPS and cNPS increase, so does contract profitability.
Figure 6.2 Links between eNPS, cNPS, and contract profitability.
The analytics team identified motivation, capability, and purpose as the key drivers of employee engagement, and these had the highest influence on the cNPS. The next step was deciding what to do about it. The team concluded that the findings about employee engagement had important implications for HR processes and strategic initiatives. As a result, the team had the following recommendations:
• Functional training programs to address the capability factor (for instance, skills training for facilities cleaners).
• A behavioral training program called Service with a Human Touch, to focus on understanding the emotional connection between workers and clients and delivering superior user service. The training was implemented for first-line managers responsible for contract delivery initially in Denmark before it was implemented globally.
• A manager education program to address the purpose factor. This would focus on ensuring that staff knew what both ISS and customers expected of them.
• A motivation toolkit to address the motivation factor. This would be part of an existing manager development program.
In addition, ISS was advised to conduct a managerial training needs assessment to determine where additional training was needed before delivering the training.
Not content to stop there, ISS wanted an even stronger research design to increase confidence in its conclusions. As a result, ISS implemented the recommendations intended to increase engagement among its staff at one of its customer locations, a financial services firm. This would enable the team to study the effects of the changes using a pre-/post-research design.
In the study, customer satisfaction was measured with a user survey both before and after interventions intended to increase the three drivers of engagement: motivation, capability, and purpose. The interventions included extensive training for all ISS managers, supervisors, and front-liners on how to manage and deliver a service against predefined behavioral standards. In addition, customers were asked to clearly communicate with front-line staff what they expected in terms of service quality and standards. Evaluation of this follow-up study revealed a significant increase in employee engagement following the training, as well as a significant increase in customer satisfaction.
Communication to stakeholders was critical throughout this project, as Simon stresses: “If you conduct analytics too far away from what the organization is doing, then nothing will be adopted. You need to link what you are doing to something that has relevance to the organization. Then when you present to your sponsors, you need to have a plan for what should happen next: Look at processes already in the company and build on and complement those. If you just come with a new HR process, people will not do anything.”
Simon communicated with the stakeholders before having conversations with C-suite executives, and he managed communications in the following way. First, he identified an existing manager communications platform that he could use for his communications. He then linked his communications to a goal that was also the focus for managers—in this case, contract profitability.
Second, he communicated with the core stakeholders individually. These ten people comprised the executive group management, including the ISS Group chief executive officer (CEO), chief financial officer (CFO), and chief operating officer (COO), plus the various regional CEOs. Simon had to make sure they each saw the benefit of the project individually, and he tailored his conversations to the different personalities and management styles of his audiences.
Finally, Simon, along with the external consultant, Morten, presented to the executive group management. Because each of the executives had already been involved throughout the project, the C-suite conversations were well received and the recommendations were readily accepted.
Encouraged by the findings from this analytics project and their ability to link engagement to business outcomes, ISS team members are now exploring links between engagement and sickness rates, as well as customer churn. ISS says that what made this project successful was the team’s ability to link HR practices (notably, efforts to increase engagement) with business outcomes outside of HR (customer satisfaction and, ultimately, contract profitability).
Founded in 1925 and listed on the London Stock Exchange, Rentokil Initial provides pest control and hygiene services across 60 countries with more than 30,000 employees. The company has three global brands (Rentokil, Initial, and Ambius) and several local brands, including Dexinfa in Lithuania, Calmic in Asia, and JC Ehrlich in the United States. Rentokil has revenues of approximately £1.8 billion, according to its 2015 annual report.
In the late 2000s, the company came under the leadership of Chief Executive Officer (CEO) Alan Brown, who started a period of examining operations, particularly sales expertise and performance. Workforce analytics came to the fore amid this atmosphere of scrutiny.
Sales results and turnover at Rentokil Initial were highly variable among the 700 global salespeople. Some regions were overachieving their targets easily, whereas others were consistently underachieving. Alan was keen to explore this using an analytical approach, in contrast to the anecdotal information he was receiving from the various managers and directors in the business.
Alan had no assumptions regarding the reasons for the varying sales performance. However, because everyone was highlighting people-related topics, he focused on the sales workforce itself instead of territory alignment, market opportunity, or competition. Because of his scientific background, Alan wanted a more methodical approach to assessing and improving sales performance, so he hired external business consultant Max Blumberg and his team to undertake an analysis. Internally, Max worked closely with Steve Langhorn, Director of Rentokil Global Academy.
As a first step in this project, Max interviewed sales leaders in different regions around the world in a bid to isolate a hypothesis that might explain the issue with sales performance. However, following these initial interviews, it was clear that different potential hypotheses existed, depending on who was interviewed:
• Effective sales training delivered at the right time will develop the technical confidence needed for successful sales performance.
• Better recognition tools will increase seller motivation to deliver higher performance.
• A globally consistent recruitment process for sales staff will deliver higher-performing sales people.
Max Blumberg set about gathering data relevant to his hypotheses in a multipronged approach. First, he conducted a detailed literature review of all the work that had been undertaken on sales performance in various industries. The intent was to understand whether examples of similar sales challenges existed elsewhere.
Second, Max undertook an analysis of the HR practices at Rentokil Initial. Given the range of hypotheses and ideas presented to him, Max wanted to clarify the various processes and policies that existed for each of the main HR functions, including recruitment, compensation, management training, and leadership development.
The final part of this initial phase was to collect new data from the workforce. A survey investigated employees’ perspectives of the HR processes in the company. People were asked to score the efficiency and importance of each HR process to sales performance. When the survey results were analyzed, it was clear that recruitment was viewed as the most inefficient yet most important HR process (see Figure 6.3).
Figure 6.3 HR processes plotted by inefficiency and importance.
This analysis allowed Max to focus on a single hypothesis:
Hypothesis: A globally efficient and consistent recruitment process with clear selection criteria will improve sales performance.
Max continued his investigations and next looked at whether the most commonly used selection tests correlated with sales performance. The analytics team found only small correlations between the two most frequently used tests and sales performance. As such, the team recommended discontinuing these tests. This recommendation was implemented.
Next, the team collected and analyzed another new set of data, gathered from surveying the sales force, to identify the specific attributes that most highly correlated with sales performance. These attributes were grouped into categories such as conscientiousness, interests, interpersonal skills, and cognitive ability.
The next step of the analysis involved looking for a selection test to accurately assess these attributes. The team undertook a literature review to source validated and relevant tests, and it also conducted an extensive review of selection tests that were available in the marketplace. In the end, Max’s team chose six externally sourced tests that appeared to meet the criteria needed to improve sales performance.
Using these six tests, Max and his analysts undertook a study among 270 sales people in the United Kingdom and the United States. Each person took all six tests, and their results were analyzed against their sales performance. This became a complex and sensitive exercise, partly because of the need to work with six vendors across the many global assessment platforms in Rentokil at that time, but also because of a significant level of concern among both the sales professionals taking the tests and their sales leaders about how the company would use the results.
Using this seller assessment dataset, the team undertook various statistical analyses to identify which traits could be linked to high sales performers. These analyses included logistic regression, an example of the quantitative analysis objective of classification (see Chapter 5). The analysis revealed that one test in particular, a personality assessment, had a strong relationship to sales performance. Max predicted that this assessment would identify an above-average salesperson with a high degree of accuracy. Using the U.K. sales population, Max then converted that into financial value. If the assessment were adopted and implemented, he estimated that the United Kingdom business alone would see a potential increase in sales of £1.5 million per year.
From these and further analyses, Max was able to make a very specific recommendation to implement one external test for the selection of all future salespeople worldwide. He also made detailed recommendations for the redevelopment and global standardization of the recruitment process, as well as the implementation of new induction programs and associated recruitment and induction training for managers.
Max and Steve jointly set up strong governance throughout the project. As part of that governance, regular checkpoints were established with three core groups of stakeholders:
• Global executive team and sales directors. This group required ongoing one-to-one meetings, as well as some team meetings at every stage of the project to ensure that they not only understood the insights from the analyses and associated recommendations, but also were clear about the decisions required.
• Works councils, especially in Germany and France. This audience was briefed and managed carefully. In particular, the works councils were made aware of the benefits of the project to those countries’ businesses and to the individual workers within them.
• The entire sales force. Communications with the entire sales force were handled through regular newsletters and emails. When needed, personalized communications were sent to salespeople asking for their participation in the surveys outlined earlier. These emails were privately addressed, to strengthen the message that the collection of personal data would be treated with a high degree of confidentiality.
Communication was only part of the story for the analytics team; implementing the recommendations required extensive planning within both the recruitment function and the associated HR functions, such as induction training and sales enablement. Specific elements of the plan included these actions:
• Procuring the selected assessment
• Implementing the technology needed to manage one global assessment test, which resulted in implementing a standard global recruitment process
• Training in interviewing techniques to align all hiring managers with the new recruitment process and selection criteria
Furthermore, the plans needed to be implemented worldwide. To achieve this, the team used a phased approach starting with the United States and the United Kingdom, moving on to Europe, and concluding with the rest of the world. The implementation plan took one year to roll out fully to all countries.
The entire project took more than two years to complete. The first year clarified the business problem and enabled data collection and analysis. The second year focused on implementing the recommendations. With the CEO sponsoring the project and effective stakeholder management and involvement of sales professionals throughout, the project had a high chance of adoption and success.
And it did succeed. In the year following the project, sales improved by more than 40 percent and the return on investment from the project was more than 300 percent.
Overall, this project demonstrated a clear and direct business impact in terms of increased sales. It succeeded because of its high-level sponsorship, effective stakeholder management, and strong methodical approach to analytics. As Steve summarizes: “This project demonstrated how analytics can shape the future through helping people secure the right jobs that will make them successful and bring benefit to business leaders and owners through increasing sales. It’s a win–win for everyone.”
London’s Metropolitan Police Service (the Met) is the United Kingdom’s largest police force, employing approximately 31,000 officers, 9,000 police staff, 1,500 Police Community Support Officers (PCSOs), and 2,800 volunteer police officers.5 It covers an area of 620 square miles and a population of approximately 7.2 million, and it is funded entirely from taxpayers’ money.
Robin Wilkinson, a board member and Director of People & Change, and Clare Davies, Director of Human Resources (HR), were responsible for implementing strategic workforce planning as part of a large transformation program. Although they had already set up a small project team, they contracted specialist analytics practitioner Martin Oest in late 2013 to expand their analytics and workforce planning expertise in HR. Collectively, they set about helping to deliver on the promise of this strategic transformation to reduce the overall cost of the organization while at the same time recruiting a more diverse workforce.
Sometimes undertaking an analytics project is extremely complicated not because the analysis is difficult or it lacks sponsors, but simply because there is no infrastructure for such work. This case study is an example of such a situation. It shows how to start from a low base and deliver successful workforce analytics.
The HR team faced a significant list of challenges and defined several priorities under the Met’s people strategy. The overall aim was to have a police force representative of the diverse population of London. The Met believed that a more diverse workforce would be better able to serve London by speaking the languages and understanding the various cultures that make up the city’s neighborhoods.
The team had to overcome other specific challenges, such as recruiting more police officers and reducing the overall workforce cost. It had to deliver this over a period of two years as part of the Met’s broader strategic transformation.
After identifying the challenges, Martin and his team set about clarifying the underpinning hypotheses to support this:
Hypothesis 1: By modeling scenarios for the future workforce, actions will be identified to enable new approaches to recruitment.
Hypothesis 2: Providing accurate real-time information to hiring managers will result in the hiring of more diverse candidates.
Workforce analytics capability in the Met was at a fledgling level at the start of this project. When Martin joined the project, he increased the focus on data governance that had already started with the team: “In the first phase, we delivered some simple quick wins, such as visualizing current headcount to achieve control of the data and to create one version of the truth for headcount.” In addition to data governance, Martin surveyed stakeholders about their requirements for analytics across various HR processes to provide the workforce analytics team with the right stakeholder input.
Also in the first phase of the analytics project, the HR analytics team established a steering group. This consisted of major stakeholders, including the finance department and a group called Portfolio and Planning. This latter group was responsible to the board for change management and the overall transformation program in the Met, so it had to ensure that any analytics activities were aligned to the overall transformation agenda. Martin’s participation added focus and clarity to the objectives of the steering group to enable effective decision making, governance, and alignment with the Met’s strategic direction. As Martin explains, “This steering group was needed to ensure buy-in and commitment across the Met.”
With the steering group, basic headcount metrics, and governance in place, Martin turned to the recruitment data in the second phase of the project. This was a crucial dataset for understanding ethnic diversity at the hiring stage. Lending visibility to the recruitment data was a critical step in increasing awareness of the diversity challenge among HR leaders, as Martin explains: “We delivered a recruitment dashboard that provided new insights to hiring managers. For example, we provided metrics for each stage of the recruitment process. This was the first time HR leaders had seen regular information about recruitment, so this was very well received. For example, it enabled them to spot the drop-out rate at each stage of the recruitment cycle for different ethnicities.”
The third phase of Martin’s work was to create a full-scale HR dashboard. If progress were to be made against the targets the Met had set, it was essential for HR and departmental leaders to have access to consistent and accurate information. This information included metrics beyond just headcount and recruitment, such as diversity and succession. Martin consulted carefully on the creation of the dashboard: “Scope and objectives were set and stakeholders interviewed. They were then involved in an ‘objectives and requirements’ workshop to ensure that the outcome met the business needs.”
With consistency and clarity around the Met’s current data established, Martin and his team set out to visualize what the future workforce could look like. They analyzed potential scenarios using “what if” models. Using Excel, they built an analytical model embedded with forecasting calculations to provide insights on minorities, gender, recruitment, and several more aspects of the entire workforce. Martin confirms, “This visual model was greatly appreciated and game changing for the Met. It was updated and distributed monthly to all stakeholders.”
Focusing back on the core topic of diversity and workforce costs, Martin was able to apply more analytical models to the work. For example, the team forecast attrition for police officers using historical data, predicted recruitment targets, and forecast year-end headcount.
The analytics methods gave the HR team at the Met significant credibility. Clare Davies had previously described a “we can’t rely on HR data” sentiment that had prevailed across the organization before this project. After just a few months, and with the implementation of the analytical models and methods, the mood changed. Clare explains: “We created a relentless focus on accurate data and insight, regularly using it to improve aspects of the operations. We worked hard to ensure that finance and HR data reconciled so that we had one version of the truth. We involved stakeholders along the way and gained credibility.”
Conducting accurate analyses and sharing the resulting data and insights was only part of the story. The analytics team had to reduce four recruitment databases down to two, to simplify data management and reduce costs. In addition, hiring had to be reengineered with input from many stakeholders to simplify the process for both candidates and hiring managers.
These changes revealed yet more insights, Martin says: “We identified a 20 percentage point drop in ethnic minority candidates between being ‘interested in applying to the Met’ and being ‘hired to the Met.’ This led to the creation of a dashboard to allow hiring managers to have insights of the data at every stage of the new hiring process.”
The continued focus on metrics, governance, and analytical improvement led the wider HR team to highlight several recommendations for action. One of these concerned the entry criteria for applicants to the Met. Martin explains: “We introduced a London residency6 criterion to encourage applications from London residents. That way, we would be more aligned to the London we serve.” Another result of the analytical approach was to challenge the selection tests administered by an external provider. These tests were changed after the Met’s analysis showed different selection rates across different ethnic groups for certain assessments. This finding was possible only because of the Met’s deep and methodical approach to analytics, the confidence team members had established in understanding the data, and their relationship with the external provider. The provider made changes to the selection tests as a result.
Successfully implementing these changes required strong messaging to both the workforce and the population the Met serves. The Met marketing and communications team were involved to provide expert advice on this. Communications tactics included a press release to the London population about a recruitment drive; the resulting media coverage brought about a sharp increase in ethnic applications to the Met.
The result of two years of implementing analytically driven changes and business recommendations was impressive. Diversity representation from new hires improved by more than 10 percentage points, headcount targets were achieved, and workforce costs for police officers in the fiscal year 2014–15 were under budget.
The legacy of this analytics project is noteworthy not just because it achieved those objectives: The Met now has both a strong platform for workforce analytics data governance and reporting and visualization technology that offers a single version of the truth, giving stakeholders access to the information they need to manage their operations.
Robin Wilkinson summarizes the achievement: “Our journey of workforce analytics has totally changed how HR operates at the Met. It is more trusted, delivers better-quality insights, and saves money. Its contribution to the Met and to the taxpayer is significant.”
Westpac Group is Australia's first bank, originally established in 1817 as the Bank of New South Wales. The organization has a portfolio of financial services brands and businesses, with a vision to become one of the world’s great service companies, helping customers, communities, and people to prosper and grow.
Damien Dellala, Westpac’s Head of People Data and Analytics Enablement, came to the role from a digital strategy and analytics background. Damien brought the perspective of treating employees similarly to the way customers are treated, that is, segmenting, analyzing, and acting on insights to create positive experiences at Westpac. With a love for data, an analytical mindset and digital strategy experiences to guide him, Damien successfully delivered new analytical capability to the HR function.
This example shows how Westpac laid the foundation for workforce analytics to support employee well-being.
Stress is a costly societal problem for individuals and the organizations that employ them. The Australian Psychology Society found in its 2015 Annual Stress and Wellbeing Survey that 45 percent of Australians experienced work-related stress. Depression and anxiety symptoms also have been on the rise since the organization began conducting the survey five years earlier. According to a 2016 Australian Psychological Society article, stress takes a notable toll on individuals’ physical and psychological health. Additionally, U.S. companies spend about $300 billion per year on stress-related healthcare and missed days of work as noted in a 2016 Business Insider article.
Concerned for their employees’ well-being, Westpac was ready to take on the challenge of better understanding the impact of these trends on its workforce.
To start, the HR Analytics team worked in partnership with the Health, Safety, and Wellbeing team to pose this question: Could we use the data we have and apply advanced analytics techniques to better understand the spectrum of well-being? Importantly, the intention was to support Westpac’s credo of “people always helping people” by developing an environment that equips people with skills to cope with stress or to maintain a positive balance. The project team suspected that certain events were leading to employees experiencing distress or, conversely, preventing them from thriving. With a renewed analytical capability to quantify or predict, the team tested hypotheses that challenged anecdotes, myths, and beliefs associated with the well-being of employees:
Hypothesis 1: Work flexibility is associated with well-being.
Hypothesis 2: Employees with higher team volatility are more likely to experience stress.
More than 10 traditional and nontraditional data sources were combined within an advanced analytics platform, which enabled sophisticated analysis and modeling of the datasets. Types of data spanned platforms, and more than 170 variables were assessed, including demographics, career history, leave history, work location, work patterns, business performance, collaboration patterns, technology usage, and employee opinion survey data.
The analysis assessed multiple predictors to identify both the propensity or likelihood of distress for employees and the specific drivers of stress, including reasons for leaving and team dynamics. This is an example of the classification objective of quantitative analytics, which Chapter 5 covers. This approach seeks to predict a discrete future event—in this case, a distress incident—by calculating the probability of experiencing the event for each employee, based on a variety of potential predictor variables.
The statistical models demonstrated that distress events were, in fact, predictable based on observed behaviors. The analyses revealed the factors associated with distress for specific groups of employees, setting the stage for a range of possible active or passive interventions that could be taken to reduce or prevent employee stress.
Given the variety of data and abundance of variables available, the data discovery phase became crucial to perform segmentation and understand early key drivers of the outcome variables. Damien explains, “This enabled us to start enriching the relevant data elements so they speak well to the outcome—in this case, did the employee raise a stress incident (yes or no)?”
Equipped with richer insights, the challenge was to translate the data into specific actions and interventions. The opportunity presented was to be targeted in intervention and to challenge traditional approaches within safety and employment. For example, one idea was to use the model to curate specific learning content for employees.
Tableau dashboards were prototyped to allow the specialist teams to filter the model results by different segments of the workforce. Armed with this information, the analytics team spent time communicating its recommendations to its key stakeholder, the health and safety leader. Together they communicated actions, including changes to policies, to the wider business management team and to individual managers, where needed. As a result, Westpac was able to rally support for these interventions and reinforce leadership fundamentals for well-being.
This methodical approach allowed the HR Analytics team, together with the Health, Safety, and Wellbeing team, to effectively address Westpac’s challenge and create an environment that helps its people flourish and grow. Damien says he and his team enjoyed being part of this feel-good project because they “helped solve a business problem to help employees be their best selves at work.”
The expert analytics professionals in these case studies undertook their projects with focus and intent. The organizational challenges and the techniques used to address them varied across the cases, but the following factors were common to all:
• The problem to be addressed was clearly articulated and linked to the overall strategy of the organization.
• Sponsorship of the project by an influential person positively impacted all stages, from initiation through implementation.
• Hypotheses were clearly understood and testable (and, in some cases, developed iteratively).
• Data gathered and analyses undertaken were appropriate for the project.
• Insights and recommendations were clear and precise.
• Communications to various stakeholders were well planned and developed with the help of expert communications professionals, when needed.
• Projects were designed to provide business impact and were evaluated for success and learning.
• Internal or external partners contributed expertise to the team to ensure success.
1 In a lateral move, an associate is given an opportunity to work in a different business but does not change either job band or corporate title. This typically occurs with associates who have been in the same role for 24 to 36 months with good or excellent performance ratings.
2 Retrieved at www.youtube.com/watch?v=h3S1bUhK3Fo.
3 The profitability of the aggregated contracts for each ISS customer was used as the measure for contract profitability.
4 Net Promoter Score (NPS) is a customer loyalty metric developed by (and a registered trademark of) Fred Reichheld, Bain & Company, and Satmetrix Systems, Inc. Reichheld introduced NPS in his 2003 Harvard Business Review article “One Number You Need to Grow.” At ISS, the eNPS (employee NPS) question was “How likely would you be to recommend ISS to others as a good place to work?” and the cNPS (customer NPS) question was “How likely is it that you would recommend ISS to a friend, colleague, or customer?”
5 As of January 2017 (https://beta.met.police.uk/about-the-met/structure/)
6 The London residency criterion was designed to promote applications from residents of London.