HR’s Big Data Moment - IEDP
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HR’s Big Data Moment

Big Data will transform how we evaluate, motivate, hire, and retain people in future


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The hype around Big Data has largely been focused on consumer-marketing. But according to Stanford Graduate School of Business’s marketing professor Harikesh Nair, Big Data will also transform how we evaluate, motivate, hire, and retain people in future. In this abridged version of a recent article he discusses his research:

Designing a Better Compensation System

In one study, conducted in collaboration with a Fortune 500 contact lens manufacturer, we looked at internal data to understand how to design better compensation incentive systems within a large sales force.

Although there is a large amount of academic theory on the question of whether and how incentives like compensation work, much of this theory has been just that: theory. The availability of data on contracts and outcomes on employees has now provided unprecedented access to help us see which theories work and which don’t.

At the initial stages of our collaboration with the firm, the company used an incentive plan that involved a salary and a commission that paid commissions on sales if the agent’s sales per quarter crossed a quota and fell below a ceiling.

Like most firms, the company faced significant challenges in formulating and optimizing the right quota-plan for its needs. A quota that is too low is always ‘beat’, providing little room for incentives. A quota that is too high demotivates agents because they feel it is unattainable.

The company also needed to fine-tune the ceiling, which helps the firm from paying out large commissions due to reasons unrelated to the sales agent’s efforts. The challenge is that if the ceiling is set too low, the company reduces the scope for incentive pay; if the ceiling is too high, the company may end up paying out too many commissions.

A third challenge was determining how often commission awards were paid out. Commissions were paid out based on the total sales achieved during an entire quarter. The result is a potential inefficiency: If quotas are very low and easy to beat, agents may find it optimal to shirk in the early months and make up sales later in the quarter. The shirking may be high for the most productive agents, as they know they can easily make up the sales in the last month. This suggests that paying out commissions based on a weekly or monthly sales achievement cycle may reduce shirking. But how much the improvement may be was hard to predict.

Still another aspect relates to the broader question of how to design incentive systems that do not create their own distortions in behavior. Incentive systems have hidden costs because smart agents can game the system. If a sales agent feels he or she has no way of making the quota, or has already beaten it, he or she will tend to reduce effort. Or the sales agent may push customers to buy at a time when it suits the agent, which may result in a lost customer.

One insight from our recent empirical work is that such distortions may be so large that they could in some instances actually overwhelm the gains from incentive provisions altogether.

A New Plan Yields Strong Results
A new plan was selected in consultation with senior management, sales managers, salespeople, and legal and human resources teams. The plan that was implemented featured low quotas and no ceilings. It also included a monthly incentive based on a straight commission (a scheme where there is no ceiling and the commission rate does not change with the sales achieved).

The results were extremely strong. The companywide effect of the new compensation scheme was about a 9% increase in overall revenues.

The results from the new plan showed that the old plan’s effect was not simply to shift sales across months of the quarter, but to also reduce the overall sales in a quarter. In the new plan, sales went up in every month of the quarter compared to the old plan. This shows that the shifts across the months seen in the old plan were also accompanied by a net reduction in total achievable sales. In other words, the old plan was inefficient.

The new plan eliminated the large swings in sales in the old plan. These had been driven by the incentives the old plan induced for agents to change their effort when they are close to or far away from quota. Importantly, eliminating this volatility also reduced inventory holding costs and streamlined supply-chain and capacity planning. Finally, data from surveys conducted at the firm showed that employee satisfaction with the new plan was high, arising primarily from the reduction of quotas and the subjective assessment of productivity under the old regime.

How Data Aids Decision-Making
We believe, this approach has the potential to significantly improve the practice of compensation design. It is rigorous and practical, utilizes internal databases, and is built on sound theory. It also showcases the value of combining models with large datasets for improved decision-making.

This kind of research also leads to another key theme in organizing workplaces: the alignment between functional areas within the firm; in this case, between sales, marketing, and hiring and retention of employees. In many instances, incentives are not balanced within a firm because these decisions are split across various units, and each has different goals.

The advantage of being close to the data is that we can quantify the extent to which such alignment helps improve outcomes for the firm.

As these studies have demonstrated, analytics and data have the potential to transform the study of work.

Further Information

Executive Education at Stanford Graduate Business School

Read the original article in full

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