The new frontier of workforce listening and why it matters for people analytics
Tim Offor, Co-Founder & CTO, Plaetos Group
Lowest ever unemployment rates. War on talent – keeping them, attracting them. Millennials/Zoomers (customers and employees) “shopping” on values alignment. The Great Resignation. Quiet (and noisy) quitting. Employee burnout. Shareholder activism. ESG.
These are the rips and currents that buffet companies managing large workforces. Cack-handedness by company leaders on any of these issues can leap, like a hot ember, into the tinder of social media. It’s how history is made.
This is why workforce listening is a thing. And it’s why companies that lead the pack (top quartile performers) are Listening. Very. Carefully.
Workforce listening is a core business process driven by the people analytics function. It’s the active and intentional process of gathering and analyzing formal feedback and informal content from employees to improve organizational outcomes and improve employee experience.
The goal of an effective workforce listening process should be to quickly (speed) and efficiently (effort) source useful (relevant, generalizable) information about employee attitudes, emotions and behaviors to inform important people-reliant strategies.
In this article I wanted to set up some guideposts for moving from a primarily survey-based, quantitative workplace listening approach, to one that makes use of the growing mass of qualitative data already being generated in your organization to deepen and broaden your listening program and its value for people insight.
Think Yammer, Slack, Teams and Webex chat (public only). Growing every second of the day with every keystroke across the world. It’s where employees talk, where the subtle signals about their real experience of work live. So, for this piece, words are “the window on the (organizational) soul”.
Workforce listening is evolving – fast
Until the turn of the century, listening was really just survey, maybe with some (analog) interviews and focus groups thrown in for the most progressive organizations. Today, workforce listening is a sophisticated, multi-channel process that ties into the business goals, and is judged on the quality of its insight.
But it’s still largely based on actively sourced, metric data from surveys, but increasingly extended through content analysis of text data.
Over the past 10 years, we’ve witnessed a widening spread between the leaders and the laggards in the workforce listening space. Laggards (the “pack” in the diagram below) are still primarily listening through (better) surveys. The leaders are breaking away from the pack in the search for better, faster insights to drive business performance.
What’s splitting the field?
1. Workplace listening is now strategy
A key difference we’re seeing is the shift from workplace listening as a collection of activities (with surveys at the core) to an integrated, strategic process that seeks to harness all relevant data to inform the key people-related business strategies.
Support from the office of the CIO is now essential as workplace listening, people analytics and business intelligence teams are seeking to put to work the mass of human data within the enterprise.
The prize for this increasing investment is better employee engagement and productivity, which means greater efficiency and profitability. And the more intangible outcome of no public brush fires and, indeed, the virtuous circle of improved reputation/brand.
2. Big data and metadata make more possible
Big organizations are awash with unstructured data – it’s growing at 3X the rate of structured data. The digital traces of employees going about their day-to-day business are a rich resource for understanding what they think, how they feel and how they are interacting.
Aggregating, organizing, curating and preserving this data so it’s accessible and useful for people insights is no small task. Doing it in a privacy preserving way without sacrificing the metadata (data about data) that gives it organizational context makes it even more challenging. Consequently, the owners and beneficiaries of organizational “people insight” are clamoring to get in early to inform and shape the organization’s data strategy.
...the owners and beneficiaries of organizational “people insight” are clamoring to get in early to inform and shape the organization’s data strategy.
3. Social sciences shift to study the “community of the organization”
The well established suite of research methods and tools that were honed in the social and behavioral sciences are now being harnessed to understand the community of the organization, to great effect.
When workforce listening was little more than the annual engagement survey there was little need to think beyond simple designs and analysis techniques for this readily tabulated, primarily metric data. But with the rapid growth in the amount of text content from chat and collaboration tools has come a dawning that finding the gems amongst the vast amounts of unstructured data needs new methods and tools, and a more disciplined approach to their application.
What this means is that traditional and largely analog qualitative research, where intensive listening on specific topics is done to understand very deeply, is being digitized. It opens up the exciting opportunity to combine the “traditional” quantitative approaches dominating workforce listening today with new, large-scale qualitative research methods.
Mixed methods (qualitative and quantitative) research, computer mediated content analysis, conversational analytics, organizational network analysis – all can have a powerful impact on the quality of people insights to support decision making. [See: What is Qualitative Data and Why it Matters More Than Before]
4. The new AI stack of Cloud, GPUs, Transformers, APIs
It’s not just the growth spurt in NLP text analytics solutions driving new workplace insights, but the increasing sophistication and availability of the enterprise cloud stack.
If it’s the explosion of text data – combined with workplace metadata (demographic, organizational) for context – that’s creating the opportunity, it’s a new generation of cloud AI services that’s providing the tools to unlock the insights.
And the pace of change is explosive. 1 million ChatGPT users in 5 days, Microsoft and Google announcing chat front ends for their search engines in the same week. Large language models – for understanding and generating language (and images, code…) are the future, now.
If “harnessing AI for business value” is referenced in your business strategy, better people insight from your existing data is the low hanging fruit that will yield you quick wins (and impress the CEO!).
So, how do you get your People Analytics function ahead of the pack?
Some questions to get you thinking:
If your organization is stuck back in the pack and you aspire to work your way into that breakaway group up front, here are some questions to help you chart your way forward.
Business case: Which business strategies would benefit most from better people insights? Culture transformation [What are the underlying attitudes that are holding us back from alignment, and why are they there?] Retention? [Find out early what will keep your best talent from leaving.] DEI&B? [What’s your people’s real experience of Equity, Inclusion and Belonging that might be affecting your Diversity targets?]
CIO: What’s your CIO’s view on tapping into your digital people data? Does their team have the bandwidth to support some experiments? Do they have budget? How can CIO and HR collaborate to make HR more data-driven?
CPO: What’s your CPO/CHRO’s view? Are they interested in new insights beyond what their surveys are currently yielding? Do they have budget for some experiments? What would it mean to reach targets sooner and reduce that social media risk?
Collaboration & chat tools: Where are the most useful (group) conversations happening? Who owns the tech tool data and would need to approve access to the data? Are the data already being collected or shared?
Privacy: What’s your organization’s privacy stance around workforce data? Are they open to discussing how you could use anonymized data for some people insight experiments? Are there regulatory or contractual barriers you might need to navigate?
Employee attitudes: What do you think your workforce would make of a passive listening program? If you focused only on team/group communications and anonymized sources (not 1:1 comms) – and explained what you’re wanting to achieve – do you think they’d be supportive?
Internal communications: Are you on good terms with your internal comms people? Can they help you craft the messaging? Do they “own” data they could share for your listening experiments?
Allies: Who are your allies with a shared interest in getting better quality workforce insights? People analytics? Line managers? Data and analytics? AI/NLP? Org psychs? Transformation leaders?
Further reading: If this article piqued your interest, you can go a bit deeper by reading Jeffrey T. Polzer’s recent paper “The Rise of People Analytics and the Future of Organizational Research” [open access].
Tim Offor is Co-Founder and CTO of Plaetos Group, which builds qualitative people insight software. The PlaetosEQ workforce listening platform specializes in analyzing and visualizing qualitative data - what people think, feel and believe about what and why - so large enterprise customers can get a more nuanced understanding of their company culture and get to alignment faster. Prior to founding Plaetos, Tim consulted to large companies on social research, stakeholder strategy, social impact assessment and building stronger relationships with stakeholders.
People Analytics refers to the use of data and statistical methods to drive HR decision making and improve business outcomes. It involves collecting, analyzing, and interpreting data on workforce-related issues such as talent acquisition, employee performance, turnover, and engagement. The goal of People Analytics is to provide actionable insights to HR and business leaders to make informed decisions that drive business results and improve the overall employee experience.