From Cost per Hire to Revenue per Hire

From Cost per Hire to Revenue per Hire

Remember the old days when personnel officers made personnel decisions? Neither do I. And I am really old. That was so 19th century. Then came HR specialists who made talent acquisition decisions. But even that is last century. Then HR fell out of favor when executives realized that “people are our most important assets.” At that point, the company needed ‘business partners’ with a seat at the table and not HR clerks with pocket protectors. Now it’s a new century with a new terminology. We need ‘Talent Analytics’ and business partners with the word ‘analytics’ in their title.

But what can business partners or analytics experts do to inform better business decisions around strategic issues? Titles can be confusing. Instead, consider the types of analytics brought to bear on talent decisions. It all started with organizational charts and tables of workforce headcounts, salaries, accidents, and turnover by job title. Initially performed on calculators and then on spreadsheets, these are Descriptive Analytics— numbers or literal summaries of things that have already happened. They can provide context for business decisions but force the decision maker to subjectively weight that information to make intuitive business decisions. Objective business decisions require a benefit/cost analysis related to the future value of a decision taken today.

As the century rolled over, spreadsheets turned to cloud-based, mobile accessible SAS solutions. But most of the analytics remained stuck in Descriptive mode. The first versions of Visier, a leading Talent Analytics platform, focused primarily on charts, graphs, and info-graphics that described talent properties of companies and their business units.

Predictive Analytics

Not all, of course. There were a hardy group of workforce planning gurus that predicted where talent gaps would occur from retirement and turnover trends. There were selection specialists who predicted which candidates will perform best once hired. There were workplace safety specialists who would predict the impact of regulations and training on accident rates. These predictions drew closer to informing the present financial value of decisions to staff, select, or train talent to improve business outcomes in the future. But they don’t get all the way there. They lack a comprehensive dynamic impact model. It still begs the question, “What’s it worth to us?”

Prescriptive Analytics

A dynamic impact model defines the major forces that drive financial outcomes upward or downward. E=mc(2) is Einstein’s dynamic model for relating energy to matter. It is beautifully simple, yet contains all that needs to be known when converting matter into energy. There is a similar, if not as beautiful, model that projects the net business impact delivered by making hiring decisions with known levels of accuracy, screening power, and performance dollar impact (Cronbach and Gleser, 1965, Psychological Tests and Personnel Decisions). It is called the selection utility equation. In subsequent posts, I will get down and dirty with the equation, but not just yet.

It is a prescriptive vs. predictive model because it reduces the choice between staffing process A and staffing process B to two sums. The larger sum wins. There are no other contingent factors, with the possible exception that the owner’s favorite cousin owns the company that sells process A. That’s not relevant to which staffing option is better, but does relate to whether the VP HR keeps her job.

To boldly go where Business Partners have never gone before. Without a dynamic model captured in an equation, staffing business partners look for impact on the wrong end of the stick— the cost per hire end. They pin their pitches for investments in staffing technology on reducing turnover leading to fewer fills, reduced placement fees, and/or lowered spending on recruitment advertising. Sad, because if they even just looked at the selection utility equation they would see that while costs are additive, the performance dollar benefits are multiplicative. Higher accuracy gets further leveraged by larger screening power (more candidates per hire) and multiplied again by the difference in the annual dollar value of the performance of top, middle, and bottom third candidates.

From Prescriptive Analytics to EXTREME ANALYTICS.

EXTREME Analytics is a type of Prescriptive Analytics where performance can be measured directly in revenue or return dollars. Professional sales roles and business unit leaders tied to a P&L– even if managers of a convenience store– qualify for EXTREME Analytics.

The Talent Analytics Group, where I am Chief Scientist, recently completed consortium research involving 13 companies that provided three years of annual sales data and scores on our Sales Prediction Suite for 206 consultative sales professionals. From this data, we derived the EXTREME Analytics linking sales territory size to the increased annual sales per hire to be expected when hiring sales talent using our Sales Prediction Suite.

Here’s how it works. Column B applies if sales professionals were previously hired using resume sorts, screening interviews, reference checks and traditional final interviews. Column A applies if a sales personality screening test and behavioral interviews were added to the hire decision mix.

So even for Column A, hiring sales professionals into $1M territories produces an expected increase in annual sales per hire of $60,000. That may not seem like much, but when hiring 100 sales reps who stay and average of 4 years, that is a $24M shot in the revenue arm. At a cost of less than $1K per hire, there isn’t likely anything else the firm will do that can generate that kind of return.

So what do you say to an HR or hiring manager who doesn’t want to take discretion away from the awesome folks making subjective, biased hiring decisions? Who doesn’t believe in analytics because it lacks cultural sensitivity? I will leave that up to you, but my money is on the $24M boost in sales. And because we have the charts and graphs to prove it, the Talent Analytics Group (TAG) offers to put our fees on the line to guarantee that the sales show up on audit, or we proportionately refund the per hire fees. That’s what EXTREME Analytics empowers us to do. To make it fair, if the audit finds more than the guaranteed increase in sales per hire, the TAG earns a bonus.

For way too long, consulting firms of all stripes promised performance but invoiced for time and materials. EXTREME Analytics makes is possible for companies to finally know the life cycle benefit/cost analytics for talent consulting and pay for performance. It’s not for every situation, since performance value can’t always be quantified in dollars. And it requires that a mathematically provable model or formula quantify the relationship between talent method parameters and business impact. But that’s how history rolls, making decisions as objective and simple as possible, but not simpler.

Follow my postings for further details for those brave souls willing to grapple with the nuts, bolts, and the proof that underlies this column.

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