Laying down an analytics framework for Employee Retention

Employee retention is becoming an increasingly important proposition for many companies (check out this article from Josh Bersin of Deloitte that presents a nice short succinct view of the growing demand; Employee Retention Now a Big Issue: Why the Tide has Turned). Even companies that don’t have an immediate crisis on their hands are recognizing the need to lay down a framework that will allow them to proactively address employee retention in the future. This is a very sensible approach to take since in order to effectively predict attrition risks across the business the #1 thing you will need is lots of historical data. So if you think you may have a requirement to run a retention management program in the next few years, then you’d better start capturing and storing the appropriate data TODAY. If you do nothing more than capture the data, you are at least moving one step in the right direction. Which brings us to the next obvious question “What data should I be capturing?”. Before we can answer that question, we need to ask ourselves “What is Retention Management?

Retention Management refers to the proactive and systematic effort by employers to create and foster an environment that reduces the amount of unwanted voluntary attrition and encourages high-performing and high-value individuals to remain with the organization.

As such it needs to be considered as an holistic endeavor which is an integral part of the employee life-cycle and not something that is limited to end of year compensation planning. This has some key implications for building and consuming the analytics.

  • A variety of Analytics will be required:
    • Predictive Analytics to capture patterns within the data that effect attrition risk; maybe it’s career velocity, compensation, or churn?
    • Content Analytics to find patterns of activity across the business to find root causes of attrition; maybe its employee sentiment, project involvement, skills, or experience.
    • Social Network Analytics to identify patterns across the social network that help effectively action attrition risks; from finding hot-spots, identifying attrition influencers, and factoring in social network effects.
  • Incorporating a variety of drivers:
    • Internal; such as employee sentiment, engagement, projects, compensation (relative to peers), career progression, skills alignment, experience, performance, or work environment.
    • External; such as compensation (relative to market), market demand (for skills), economic, or personal factors.
  • From different data sources:
    • HR databases, CRM solutions, enterprise social networks, and associated content.

  • It needs to be broadly consumable by a variety of solutions across the employee life-cycle:
    • For example; if skills alignment is the #1 risk factor for a specific target group, that fact needs to be available to the learning system so that it can impact training plans or the project management system to adjust job assignments.

  • Retention Management is a highly collaborative process which involves participants from across the entire organization; HR analyst, brand manager, people manager, compensation analyst, recruitment strategist, or corporate development. This means the framework needs to combine collaborative workflow:
    • Identify Risk of attrition across the organization, determining the employee attrition risk and drivers through analysis of relevant data captured from HR systems, business processes and applications, social and collaboration systems, and external market and industry data.
    • Assess Impact to the business by analyzing and evaluating factors ranging from; total cost of attrition (replacement, productivity, training, salary inflation), impact to operations (employee impact, skills, influence, expertise), impact to revenue (customer or partner relationships, external influence, current projects), impact to strategy (skills gap analysis, skills alignment, growth plays), etc.
    • Build Action Plan incorporating a wide range of levers (salary increase, promotion, job transfer, project assignment, skills development, change in contract, adjustment of company policies, organizational changes) to mitigate risk and prevent unwanted voluntary attrition.
    • Evaluate Performance by assessing impact of past actions in order to improve decision making over time and optimize retention strategies across the business.

Admitting that I may be a bit biased :-) IBM is doing some very cool work on the retention analytics front and has recently announced a new offering in this space. Here is a link to an article by Doug Henschen of Information Week where he talks about IBM’s holistic view to workforce analytics, specifically calling out their retention analytics services offering, The IBM Way On Analytics.

3 Responses to “Laying down an analytics framework for Employee Retention”

  1. Great post Marie. Do you think that this framework is applicable for different organizational functions as well ? (e.g., for a center of excellence, for looking at the business from a product portfolio level, etc.?)



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