Amazon Coming to Town? Mitigate Talent Shortfalls with People Analytics

In January, Amazon announced its much anticipated shortlist of cities for its new second headquarters. While job seekers looking to join the industry giant eagerly anticipate the final announcement, companies currently operating in those shortlisted cities are dreading the impact that Amazon will have on their ability to attract and keep their top employees.


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With Amazon planning to hire upwards of 50,000 employees, this fear should be taken seriously.

Competition for talent is already fierce in these cities and when many of the employees who leave are the best and brightest, they take all their skills, knowledge, and connections with them, putting organizations at a disadvantage. So what can companies do to make sure their employee ranks aren’t decimated if Amazon comes to town?

A data-driven process is one smart strategy that business leaders can take to mitigate the risks—and find the opportunities—associated with Amazon’s big move. By using data and analytics, companies can create a tailored and realistic action plan aimed at retaining their employees.

The people analytics team at BBVA Compass, a U.S. banking franchise, discovered that turnover was highest for one particular revenue-producing role. By using an analytics platform, the team crafted a strategy targeting the branches with the highest turnover for this role. The results have been significant: Annual turnover for the revenue-producing job has improved dramatically at those branches, dropping by 44% since the start of the project.

When crafting your retention strategy, make sure you have the answers to these five questions:

1. Which Employees Are at Risk of Leaving?

By identifying employees who are at risk of quitting, you can be proactive about minimizing their chances of leaving and also take steps to manage any other employees that may be at risk as a result of turnover contagion (this happens when people quit their jobs simply because other people are talking about leaving, job searching, or actually jumping ship).

This is where predictive capabilities can be of benefit. Predicting risk of exit can involve some pretty sophisticated technology, but the process is straightforward: A profile of the organization’s talent who left in the past is created, which can then be used to identify similar characteristics in existing employees.

HR can then share this information with line managers and work with them to shortlist which employees will require a retention program that mitigates their chances of leaving when Amazon does officially come calling.

2. Which Managers are Most Likely to Lose People?

A recent Glassdoor study found that dissatisfaction towards their manager was a top reason why employees left a company. Look at metrics such as engagement score and high performer resignation rates to identify the managers receiving the poorest scores. Once you have this information, dig further to find out why, paying special attention to low scores in areas such as promotions and raises given out and absence days per full time equivalent.

By analyzing and monitoring manager effectiveness, you will be in a good position to identify where your management team is strong and where further insight is required.

3. Who Can We Afford to Lose?

When designing an employee retention plan, keep in mind that not all turnover is bad, as in the case of a toxic employee leaving. Just as you would use analytics to identify your critical employees who might be at-risk, examine the other end of the spectrum: who are the poor performers?

Identify trends in areas such as performance reviews, absences, time since last promotion, and training scores. Once you know which employees are underperforming, have a talk with their manager to determine if this employee has the potential to do better—and therefore, could benefit from a retention program of some kind—or if it might be time for them to spread their wings elsewhere.

4. What Will the Impact on Salaries Be?

Amazon’s entrance into the local market gives employees a chance to re-evaluate their salaries. When an employee receives a competing offer, their manager’s first instinct may be to match it. The better way to mitigate this risk is to look to the data and determine how the employee compares to the rest of their team and what the market is paying for a similar role.

Compare compensation profiles, incentive rates, performance ratings, and attributes of employees to others on the same team or in similar positions. If you have access to benchmark data that spans across industries, you can use this to also make a comparison into how fair your compensation practices are and determine if changes need to be made.

5. If People Do Leave, How Will We Make Up the Headcount?

When it comes to mitigating talent shortfalls, workforce planning is key. Case in point: Electronic Arts, a global provider of interactive entertainment software, used a people analytics platform to create detailed sensitivity analyses related to average hiring and termination trends—showing the probability of either exceeding or meeting fiscal-year headcount plans.

With the digitization of the entire employee lifecycle, from sourcing to offboarding, it’s now possible to aggregate data from multiple sources to model these kinds of scenarios quickly and accurately.

Look for technology and tools that enable you to create and compare different workforce planning scenarios (each with different workforce movement and cost assumptions) so you can choose the best option for your organization—and have contingency plans in place.

Data-Driven Practices Drive Organizational Success

A high risk situation such as Amazon’s presence in the local market shouldn’t be the only time HR takes advantage of its data. If companies aren’t regularly looking at their workforce through a data-driven lens and accurately predicting employee behavior such as voluntary turnover, they’re at a disadvantage in terms of retaining top performers, as well as keeping people-related costs to a minimum.

Data demystifies employee churn. The patterns vary: it could be a bad manager, a remote department that feels disconnected, or employees who have a long commute time. Workforce data identifies and addresses the biggest patterns we hadn’t previously considered through advanced artificial intelligence and machine learning.

By combining these identified patterns with the basic knowledge of organizational behavior, companies can implement systems and programs that truly incentivise employees to remain at their position—whether Amazon comes to town or not.

Ian Cook is recognized for his leadership and insight in the area of the area of people analytics and workforce planning. Cook currently works for Visier, and is responsible for continually enhancing the depth of insight available within this leading-edge application. Prior to joining Visier, Cook built Canada’s leading source of HR Benchmarking data. His knowledge and expertise comes from 10+ years of consulting to global companies. He holds an MA from Edinburgh University (UK), an MBA from Lancaster University (UK).