Global disruptions, economic uncertainty and the high cost of capital weighed down M&A activity for most of 2023. However, 2024 has seen renewed activity, especially in the U.S., buoyed by a stronger-than-expected economy and employment rate.
As before, M&A success will continue to require thorough due diligence, thoughtful integration and deal value capture. However, traditional approaches to talent retention – if only to avoid disruption to a target’s operations – are under pressure in today’s still cost-conscious business environment.
For example, acquirers historically have used cash bonuses to retain a target’s key leadership members and employees. Typically, these time-bound (vs. performance-based) retention bonuses are awarded over a period of at least one year and as a percentage of base salary. They also can be materially significant (2% of deal value on average) and even cause unexpected harm (e.g., disenfranchising employees who don’t receive one).
However, as organizations keep a close eye on expenses and seek alternatives to cash, HR and compensation professionals are moving up the maturity curve with artificial intelligence (AI).
Several key learnings about the state of retention agreements emerged in our 2024 M&A Retention Study:
With this in mind, we can harness the power of data science to inform retention agreement decision making. Experienced HR professionals are working across different domains of AI and machine learning to retain the key talent required to move the combined organization forward.
Skill scans use machine learning to collect vast amounts of data on the prevalent and emerging skills in both the external market and the internal organization. Prevalent skills are those skills most historically required for a job or work area as listed in job descriptions or postings.
Emerging skills are the new skills cropping up within these same sources. Collecting and comparing these digital data efficiently during M&A stages can help an acquirer focus recruiting efforts, become more strategic in the use of cash retention bonuses where needed, and better direct training and personalized career management efforts during integration.
A talent flow analysis uncovers the places where organizations are gaining talent as well as to where they are losing it. Here again, HR-led AI methods use web crawling to collect market data on talent movement based on a range of sources, such as hiring or new role announcements and job postings across organizations.
When the acquiring company understands these talent flows, they can identify back-up talent with relevant institutional knowledge and skills. Acquiring organizations also gain important insights into the pace of talent movement, and what the competition for talent is like beyond their typical industry and revenue competitors.
Using external labor market data and job postings, machine learning can help an acquirer explore talent options. For example, for one energy company a review of local labor markets determined that the target organization was the main employer in the region. In fact, they were consuming all available talent in that region! In this case, sourcing alternative back-up talent meant the acquirer had to go to a different U.S. state. The good news was that key talent was available in these other states and at a better cost.
With this kind of data in-hand, acquirers are in a better negotiation position with individual offer letters or can choose to abstain from committing to lucrative retention payouts.
It’s important for organizations to recognize that pay is not the only lever when it comes to retention. WTW’s December 2023 Global Salary Budget Planning Report found that 96% of organizations increased their compensation spend – the most seen in nearly two decades – due to employee retention concerns. When you add in M&A activity, these increases may climb, as more than one-third of acquirers are using base pay increases to retain leaders and salaried talent.
However, organizations can look beyond pay to actions such as enhancing career opportunities and professional networks. A talent marketplace, where new work assignments and skill building opportunities are transparently listed on a tech-enabled platform, can help activate careers for merging talent.
HR’s use of a work architecture – the building blocks for how work and skills are managed – also identifies new career paths and skill-based networks. A work architecture that defines key skills for work and job levels can help acquirers identify skill-based pay premiums versus general pay retention agreements.
Tracking how employees experience an integration and how it affects their engagement, productivity and presenteeism are critical to being resilient through the process. Cultural misalignment is the most cited reason employees leave during an integration. Capturing and linking data on engagement, wellbeing, careers and performance can identify areas for change management, communications and even new total rewards and wellbeing initiatives in demand through the M&A implementation.
Organizations can mitigate other talent risks by collecting data on emerging jobs and titles. This HR-led data science method uses public data acquisition through web scraping over time, comparing the jobs and titles of today versus those most common over the last few years, to identify what’s emerging.
This data can be used to develop new career propositions. For example, one company used new emerging titles and job descriptions as a key retention tool through integration to the final state. In another instance, a merging company’s leader positions were below the management level of the acquirers. To effectively transition these leaders in the new career framework, new job titles and accountabilities were used based on identified emerging work and market skills.
This review effectively reset the role for incoming leaders and created a new focus for their careers. This helped ease the path from the former organization and established a future-oriented mindset.
A successful M&A process has an early and intense focus on people. Data science brings new capabilities to this process, effectively addressing the people factors and transaction goals while providing cost-effective alternatives to finding and retaining talent.
