Studies have shown that the return on investment on people analytics can be as high as $13.01 for every dollar spent. On LinkedIn, the number of professionals with "People Analytics" listed as a skill has increased by >60% over the past 12 months and the Harvard Business Review identified big data as the next "management revolution".
This shift and its impact on the human resource function promises to be as significant as the data revolution has been for the marketing function. With increases in the velocity of data transfer, the volume of data collected, and the variety of data types collected, marketers have been able to orchestrate highly personalised customer experiences.
The application of such analytical techniques to HR would allow companies to create extremely targeted and personalised employee engagement programmes. Volker Jacobs explains that providing HR and employees with effortless and seamless experiences impacts business value significantly. Organizations with a highly engaged workforce experience a 19.2% growth in operating income over a 12-month period.
The point is not whether it's important to engage employees in a meaningful way, but "how to start this journey successfully".
A New Era for Engagement
In light of these developments, the landscape of employee engagement is changing fast. In fact, the ability to monitor and improve employee engagement through the employee lifecycle with continuous listening tools and advanced analytics is making the concept of engagement almost indistinguishable from the employee experience.
Josh Bersin summarises the evolution of employee engagement in three major maturity phases. Employee Engagement phase 1.0 referring to traditional annual engagement survey, phase 2.0 implying pulse surveys and intelligent action plans and finally phase 3.0 with even more data, intelligent nudge engines for everyone.
Even this phase of Engagement 1.0, in Bersin’s classification, has yet to become ubiquitous. Still only about 66% of companies survey their employees regularly, with just 22% reporting positive results from the practice. These developments collectively point to a higher degree of resolution and relevance in the use of data around employee engagement.
Improving the talent value proposition is an ongoing process and needs to be applied across milestones of the employee experience: Onboarding, Engagement, Development, Performance, Transition, and Separation. To build robust employee profiles, this can also be cross-referenced against, among other variables, the employee’s stage of life: Generation Z, young parents, talents approaching retirement and others.
Within the past decade, more progress has been made in this space than in the previous 50 years. New methodologies and tools have proliferated, within web and mobile applications in a far more agile and responsive infrastructure. Augmenting the annual survey with a structure of specific-purpose pulse surveys allows leaders to meaningfully track and get proactive about different dimensions of their team’s experience.
The Connections programme at Amazon is one extreme version of this, with daily surveys with different questions sent to different segments of its massive 566,000 employee base globally.
Having some version of this programme in your company to "understand the sentiment, provide actionable insights in real time, and enhance the work environment”, as described by Krish Krishnan, the head of Amazon’s Connections team, would go a long way to bridging the actionability gap in most survey strategies.
Additionally, data points about the employee journey and sentiment across that journey increase by orders of magnitude over running less frequent but longer surveys. This creates a much richer view of the employee experience across their lifecycle in the company, with the ability to proactively flag out points in that journey that unintentionally sabotage employee engagement.
The movement in this direction is gathering pace. Bersin reports that 30% of companies are already pulsing employees monthly or more. The result of this would give leaders the ability to highly personalise their management style in the best way to improve both employee motivation and performance, tied to ‘moments of truth’ within the employee experience.
Employee Lifecycle Insights
Putting data from across the employee lifecycle through continuous listening channels can clearly yield powerful talent insight. Whether this is a simple matter of slapping together the relevant charts on a single dashboard, or having a more scientifically robust way of normalising the data across stages, establishing a single source of truth for employee experience data is the first step towards strategically managing the employee lifecycle.
Rather than having your data strewn across different silos and increasing the friction of data analysis, using a single tool can give you the power of scalability. Ideally, the tool should also allow you to run analytics across lifecycle stages. For example, it may become apparent that the reasons candidates apply for jobs at the company are vastly different from why they continue to stay at the company. You may also realise that employees who are more engaged at work learn better and faster than their less engaged peers.
When crafting HR policies for different stages of the employee journey at a company, this data can be invaluable in determining what skills to train managers on to help them engage and retain their staff better, or deciding between investing in free staff lunches or subsidized gym memberships.
Making these decisions using insights from Continuous Listening and Real-time Sensing reduces ramp time, increases maximum output potential and lengthens the stay of each employee at the company. Overall, programmes of this nature allows the creation of personalized employee experiences - a “one-size-fits-one” approach to the workforce that promises to be more engaging for employees and more productive for employers
Beyond Employee Engagement 2.0
The advancement in technology - mostly in aspects of artificial intelligence (AI) like machine learning and natural language processing - has made it possible in theory to combine data sources from multiple silos and paint a coherent picture of reality today, and what it might look like tomorrow.
What is clear is that with continuous listening systems maturing, and data flows between silos become more flexible, it is very likely that HR will rapidly transition across the value chain from Descriptive to Diagnostic to Predictive and eventually Prescriptive Analytics within the next few years. The impact on the personalisation of the employee experience across their lifecycle is profound.