Using quantitative and qualitative data for evidence-based talent decisions

Unleash the power of people analytics for evidence-based decisions in professional services. Discover insights, predict success, and gain a competitive edge.

June 20, 2023

Ray D'Cruz
Performance Leader

Professional services firms know how to analyse financial data to gain insights into people and performance. Yet financial metrics form just one part of the ‘people analytics’ picture, and can often be a lag indicator of market, performance and people issues. For example, a drop-off in monthly financial metrics may indicate a latent performance issue or a drop in demand. Narrowly-based financial metrics can also leave firm leaders needing to rely on their ‘gut feel’ to identify areas of concern. For example, a surge in personal hours might reflect demand, it might indicate a supply issue, such as team structure or delegation, or it might be a well-being red flag.

A better approach is to use a people analytics program, which taps into both qualitative and quantitative data to produce meaningful, evidenced-based insights about people and performance. Information can be sourced from financial data, as well as manager feedback, peer feedback, client feedback, engagement scores, diversity measures and well-being indicators.

Despite its potential, our research suggests that a minority of professional services firms use people analytics, and only medium-sized and larger firms consider partner performance analytics are valuable.

The value of evidenced-based talent insights

People analytics can be used to:

  • discover firmwide or group-based performance issues or improvement opportunities
  • make balanced, evidenced-based talent decisions, such as promotions or de-equitisation
  • identify predictive indicators for successful lateral hires and other key recruitments
  • recognise and reward behaviour that contributes to firm success
  • identify partners and staff who are at risk of leaving, and
  • identify partners and staff who are at risk of burnout or stress.

The benefits of a well-deployed people analytics program can be significant. IBM’s former CEO, Ginni Rometty, stated that the company saved nearly $300 million by using people analytics to identify the people most at risk of leaving and prescribing actions.

Strong leadership is vital

Leadership receptiveness and engagement is critical for a thriving people analytics culture. Allied leaders provide much-needed cultural acceptance and support for the program and the professionals who run it.

Another key role leaders have is in making it safe to fail. This is important given analytics necessarily involves experimentation.

An evidence-based model

The following diagram is an adaptation of Bernard Marr’s Evidence-Based Model for People Analytics. This five-stage model provides a conceptual framework for applying people analytics in professional services firms.

Stage 1 - Clarify objectives and key questions

The first step in rolling-out an analytics model is to set objectives for the program. These usually relate to the internal and external forces impacting partner and staff engagement, retention and performance.

Many objectives are shared by professional services firms, including retaining key talent, making good lateral hire and recruitment decisions, implementing sustainable remuneration outcomes, and enhancing well-being. Other objectives are firm-specific.

The next step in applying a people analytics model is to consider the key questions you want answered, such as:

  • Who are our top performers?
  • How do we reward top performers relative to others?
  • What are the early signs of future partner or staff flight risk?
  • Who is a flight risk?
  • How do we predict promotion success?
  • How do we predict lateral hire and recruitment success?
  • Who is at risk of excess stress or burnout?
  • What support mechanisms are best for struggling partners or staff members?

The answers to these questions should be informed by evidence-based insights, which can help offset decision-making problems such as entrenched views, groupthink outcomes and unconscious bias.

Stage 2 – Identify information sources

Common quantitative data points include:

  • financial metrics, such as utilisation, realisation, fees, hours and profit
  • non-financial metrics, such as marketing, learning and development, and pro bono activities
  • HR information system data, which may include demographic and tenure information
  • client feedback scores
  • collaboration points or tokens
  • engagement scores and turnover
  • peer ratings
  • performance scores, and
  • diversity, equity and inclusion key performance indicators.

Qualitative data includes:

  • formal reviews, which have extensive comments that are often rich and thoughtful
  • feedback from managers, peers and clients
  • well-being indicators, and
  • individual use of social media, which may demonstrate aspects of engagement.

Stage 3 – Extract and analyse data

Most data can be easily retrieved from firm systems using business intelligence platforms or off-the-shelf products.

Initially, turning data into relevant insights is an experimental, exploratory process. Being able to explore theories using different ideas and data points is a positive process for participants.

Most medium-sized and larger firms have data analysts who understand statistics and spreadsheets. Smaller firms can usually rely on the analytical capabilities within their finance department. In certain situations, where the significance of the hypothesis is great, it may be worth engaging external expertise to validate conclusions.

Stage 4 – Present and communicate ideas

It is important to engage firm leaders in considering performance theories and evidence. Unfortunately, we know from our many conversations with HR professionals that they fear being strongly challenged or even sidelined. This fear is reasonable given the significant power gap between partners and non-partners in many firms.

To make this stage in the process a success, firm leaders need to provide a safe space for their HR team. This is not an issue that is unique to professional services firms. For example, when Google investigated what makes teams successful, it found that the top dynamic of an effective team was psychological safety. Appropriately, it was the people analytics team at Google that made this finding.

There is a subtle shift that HR can make to ensure the presentation of ideas goes smoothly. Instead of advocating a position, HR can advance ‘loosely held ideas’ and shape those ideas into a firmer proposition in collaboration with firm leaders. Ideally, HR will present these theories using a clear performance story narrative, and simple, compelling data visualisation tools.

This approach serves two purposes. First, it avoids a binary debate over whether the theory is correct. Second, it allows firm leaders to the discuss ideas presented and shape the insights that flow from them. This results in consensus around the final decisions, rather than an exercise in management endorsement.

Stage 5 – Make decisions and evaluate

With data-based insights, firm leaders are positioned to make informed, balanced talent decisions. The feedback loops from this process should include an ongoing review of the data sources, IT infrastructure, reporting tools and the HR skills set.

HR teams need to build a range of new skills to deploy analytics with success. In Excellence in People Analytics: How to Use Workforce Data to Create Business Value, the authors set out nine skills for the HR professional of the future:

  • analytical thinking
  • workforce planning
  • data analysis
  • human-centred design
  • employee experience implementation
  • digital literacy
  • organisational acumen
  • stakeholder management, and
  • storytelling.

Tech enablers

Firms can underpin effective people analytics by implementing technology that enables:

  • aligning, agreeing and progressing objectives
  • tracking performance against agreed measures
  • managing the formal review process
  • facilitating ongoing feedback conversations, and
  • analysing and reporting on all aspects of performance.

There is a correlation between the size of the firm and the perceived value of this technology. Firm leaders from medium and large firms are more likely to see technology as important or essential. This may indicate that firm leaders think of software as an efficiency tool, more than as an effectiveness tool. We see it as both.

For example, text analytics and sentiment analysis tools can be used with qualitative data. A typical review in a 150-partner firm involves approximately one million words of text. Sifting through that data manually is a difficult, time-consuming task. In contrast, text analytics and sentiment analysis tools can code and provide analysis with the click of a button. Beyond the efficiencies they offer, these tools can increase the catchment of information.

An approach worth considering

Given the high-stakes nature of partner and staff engagement and retention, the advantages of a people analytics program are worth exploring. Done well, people analytics can challenge the status quo, illuminate blind spots, promote innovation, counter significant business risks and give firms a competitive edge.

We explore these concepts in more depth in Chapters 19 and 18 of our book The Partner Remuneration Handbook: A comprehensive guide to partner compensation and contribution management in professional services firms. You can buy a copy of the book here.


[1] Michael Roch, Maria Georgakopoulou, Polina Pavlova and Ray D’Cruz, R. “Evolving Performance in the Professions” (Performance Leader, 2016),

[2] Jonathan Ferrar and David Green, Excellence in People Analytics: How to Use Workforce Data to Create Business Value (Kogan Page, 2021), p4.

[3] Bernard Marr, The Intelligent Company (John Wiley & Sons, 2010), p14.

[4] Ferrar and Green (n1), p283.

[5] Ferrar and Green (n1), p274.

[6] Ray D’Cruz and Michael Roch, “Partner Contribution & Reward Survey Report: Current Trends in Partner Contribution and Compensation Management in Professional Services and Advisory Firms” (Performance Leader, 2018), p7,

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