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Using Mentoring Technology To Measure And Maximize ROI

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What’s Mentoring Return- on-Investment (ROI)? How do you maximize it? You may be thinking: my company struggles to define, collect data, and estimate ROI much less maximize ROI. How could I possibly manage my resources to maximize ROI? First, let’s define it: Mentoring ROI = Value Earned ($) in Employee Development / Retention (Cost of Mentoring Technology) + (Cost of Program Administration)


Here are a few examples of some ROI objectives when it comes to mentoring:

●  Gain greater leverage (higher production as a result of higher employee engagement)

●  Improve retention (reduce turnover costs)

●  Create more human capital capacity (usually measured by promotion rates)

For the purpose of this article, we’ll use retention as our example. As you know, everything in life is a matter of allocating scarce resources: capital, hours in the day, physical resources, etc. You can think of the availability of mentors as a scarce resource. Most people who have managed employee based mentoring programs will tell you: a critical limiting factor for program impact is mentor availability. Mentors are your gold. Now the question is, how do you want to allocate or manage that gold? Let’s identify a few things you’ll need to calculate mentoring ROI.

First, you’ll need to be able to compare those people in the program with those not in the program (as well as compare people in the program with others in the program). That means you need to link your population via Human Resource Information System (HRIS) technology. Post-it® Notes on a whiteboard won’t cut it.

Clorox, a Fortune 500 household products company, found that depending on what information you include in your HRIS feed, you can measure pretty much anything you want in a much simpler way. It allowed them to fine-tune their program for future iterations. Secondly, you need to be able to evaluate the results of this vs that mentoring approach (A-B testing of sorts). By mentoring approach, think matching and engaging methods.

Let’s take an example of an onboarding mentoring program. The classic approach would be to match the joining associates with someone who has two to five years of experience. That way, the mentor knows enough about the organization to guide the new employee but is also new enough themselves to remember what it was like when joining the organization. Makes sense, doesn’t it?

Now let’s extend that example into a world where you can adjust your mentoring approaches and measure the results with technology and the HRIS data set. Let’s say you match your onboarding employees with a wider spread of mentors (in term of tenure in the organization):

●  Some mentors are more senior, greater than five years

●  Some are more junior, less than two years

●  Some are the prototypical two to five years.

So now the question is: which matching methodology is most effective at lowering turnover or increasing time-to-proficiency of the onboarding associates? Spoiler alert: it’s not a one- size-fits-all answer. Consumer products company, Clorox, is seeing a 41% decrease in turnover in their mentoring population versus their non-mentoring population. By their calculation, they’re getting a 19x ROI for their mentoring program.

Another example is a large professional services business. By large I mean greater than 20,000 employees. They do a lot of onboarding. In a high-volume onboarding situation, new employees can feel like a number rather than a name. In this example, the company found that when matching onboarding employees (mentees) with mentors, the population of mentees with mentors of less than two years of tenure enjoyed significantly lower turnover rate in the critical first year as compared to the higher tenure mentor segments. In retrospect, why was that? We found that it was because the onboarding mentees were looking for a ‘buddy’ type of relationship – a friend at work.

Conversely, in another organization – a medium-sized law firm. They found that onboarding associates paired with more senior mentors, greater than five years, fared best. The law firm believes this is due to a different desired dynamic. The onboarding mentees, associate attorneys in this case, were more interested in time-to-proficiency and having access to more senior partners in the early days helped facilitate this.

So indeed, one size does not fit all and when allocating your scarce resource – your mentor gold. You may read the examples in this article and say: ‘Well now that you point it out, I see the logic of the matching styles and it seems somewhat intuitive or something one could have gleaned by interviews or focus groups. But what other bits of perspective are out there in your data set that you haven’t thought of or noticed?

You can design your programs with the best of intentions; that’s a start. But if you can measure the results and adjust your methods accordingly, you can manage your resources to maximize mentoring ROI. Dalia Ballesteros-Torres of Clorox summed up this dynamic: “With technology we can expand on a more global level. Mentoring will still have its personal, human component, but the way we facilitate mentoring will evolve.”


Paul MacCartney is the Chief Learning Officer at MentorcliQ, an award-winning mentoring software solution. MacCartney has been in the learning and talent development field for over 30 years. During that time his programs have inspired over 20 million learners.

International Mentoring School is the Spanish business partner of MentorcliQ.




Atesora Group

Atesora Group

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