Using Predictive Analytics to Power Up Your Compensation Programs
After working for two decades building compensation software for global customers, the way most companies manage their compensation programs haven’t changed much over time. Most organizations use the same traditional models when it comes to budgeting: Merit Matrices based on Salary Surveys.
Merit Matrix for Budget Planning: Is there still any merit in it?
You can build different merit matrix models, but your underlying employee indicative data remains the same.
A what-if projection is typically built on current performance ratings, current salary and market pay ranges. The budget alignment is then driven by an iterative increase, to arrive at an acceptable budget.
A model that is built using this model has an indigenous flaw: It discounts the fact that we are building a merit matrix based on current data.
With the advancement of predictive analytics and machine learning, the ability to build more accurate budget programs that aligns with your organization is what HR should be looking at. The magic 3% budget should be exposed in a way, its usage is more actual than accrual.
The merit in Merit Matrix can still be retained using the traditional method of building budgets only by applying modern heuristic algorithms.
Enough talking – where am I going with this?
Let’s take any traditional Merit matrix and its components, Performance rating, Compa-ratio (driven by Mid Pay) or Quartiles or Range penetrations. This data is currently the most common method used to plug employees into a matrix and provide an iterative method of applying increase percent guides to build budgets.
What if we have the ability to use the future data and build our budgets on top?, That means our budgets can more accurately reflect reality based on internal data points. Let’s for a moment assume we have the power to predict employee pay increases, then we can build bottom-up budgets using increases predicted by machine learning algorithms.
Using regression algorithms, we can predict salary increases to a fair degree of accuracy (up to 99%) This gives you a fixed budget that is more accurate and closer to the actual requirement vs the 3% estimate.
Using the predicted pay increases, based on a more accurate model, HR can place employees into the appropriate quadrants in the matrix based on the predictive distribution. Now HR can build guidelines and roll out a more accurate set of guidelines to managers. This level of predictive accuracy also has the added benefit of finding ways to set budgets below generic guidelines without impacting pay for performance strategies. What would shaving a fraction of a percentage off your budgets mean to your organization. Thousands of dollars saved, or more flexibility in retaining holdbacks for top performing teams. Certainly a good news story for executives looking for better insight.
Once you complete the merit increase cycle, you can also compare the predicted increases vs manager input increase. A bias report, an outlier report and more finding, they are of immense value to HR.
Does this really work? The proof of the pudding is in the eating
We have built a few models for different organizations with varying organization sizes from 300 employees to 30,000 employees. The organizations are from different verticals ranging from manufacturing to services to specialized jobs. For each organization, we used various data points to ensure our models were unique by nature and was specific to each organization. We validated our logic on the data sets compared to actual results. For example, our predictions were cross-referenced to historic information of increases. What we confirmed is that we could arrive at a high degree of accuracy of forecasting that we’ve never seen anywhere else. We are now in the process of intervening user experience to influence the dependent variables used in the predictive analytics. I am working on to build a model that can be shared by the customer but still has uniqueness in delivering specific value.