Pay reviews; the Compa ratio magic. Strong Analytics V

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Introduction

We are, in most organisations, in pay and bonus round season. I have been involved in running pay and bonus rounds for over fifteen years.  One of the most helpful ratios and presentation tools is the compa ratio.  It is an incredibly powerful analytical tool.  At its most simple the compa ratio is the role is the position salary divided by the market salary.  This gives a ratio.  The magic is the amount of information contained in that number.  A compa ratio of 1 indicates that the position is paid at the market rate.  A ratio of less than one show the position is paid at less than the market rate and by what percentage and a ratio of more than one shows the position is over paid against the market and by what percentage.

By building graphs and visualisations of the compa ratios you have a powerful tool to assist management in making decisions on where to spend the limited salary increase resource.  Compa ratios can also be derived from total cash or even total compensation figures; although please see the methodological warning below.

What is it?

Most of us have salary data information from salary surveys.  We use this data to see how various positions sit in our labour market.   If I work in an insurance company I may have the excellent Mercer survey on insurance pay; if I work in banking I may very well use the methodologically sound McLagan survey.  Provided the jobs or roles have been correctly matched we will have a mass of market data on most of the roles in our organisation.  We will also have the average salaries for the same roles in our own organisation.

Here are some examples of comp ratio calculation:

Position salary Market salary Comp ratio
100,000 100,000 1 (Salary at the market position)
100,000  90,000 1.1 (Salary 10% above the market)
100,000 110,000 0.9 (Salary 10% below the market)

By using the simple compa ratio we will be able to see how our roles fit to the market.  Here is an example from a data set:

Role Average of Current Base Salary Average of Salary Compa
Actuary

$370,000

1.03

Management   Team

$370,000

1.03

Analytics analyst

$36,000

1.03

Analytics

$36,000

1.03

Analytics Manager

$100,000

1.01

Analytics

$100,000

1.01

Asst Trader

$47,648

0.94

Commodities

$41,603

0.95

EM

$29,347

0.96

OTC

$57,696

0.93

Special   Sits

$44,335

1.02

Treasury

$31,333

0.63

Here we have roles categorised by department with the compa ratio.  We can immediate see that there is an issue with the Assistant Trader role in Treasury.  At 0.63 we are clearly paying well below the market.  At best this warrants further investigation; at worse we have an immediate problem that should be prioritised in the pay increase distribution.   The concept becomes more powerful when we convert the data in to a graph

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In this example I have produced a graph showing both compa ratio and the attrition rate.  There is a strong negative correlation between compa ratio and attrition rate.

Getting clever

Using compa ratios it is possible to compare departments against one another as well as roles within a department.

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This shows the compa ratio by department; again illustrating where our pay round fire power should be concentrated.

The analysis can be extended to looking at sex discrimination, for example.  In this graph we look at the differences between males and females by compa ratio.

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This chart again gives an indication of areas that will require to be considered when carrying out the pay review.

Making connections

Another very useful application of compa ratios is to compare department compa ratios against a range of business analytics.  So, in the table below I have compared compa ratio with return on risk capital.  The concept is to focus our pay increases on to those areas that give the best return for the business.

Department Average of Salary Compa Average of RORC
Political Risk

0.93

32.00%

M&A Advise

1.00

28.00%

Treasury

0.89

18.00%

EM Debt

0.99

14.00%

Special Sits

1.00

14.00%

Derivatives

0.97

12.20%

Swaps

0.95

8.20%

OTC

0.95

7.40%

FX

0.95

7.23%

EM

0.96

5.50%

Vanilla

0.94

3.50%

Grand Total

0.95

11.60%

This approach shows a low correlation between market position and return on capital of 32%.  Depending on our reward strategy we may wish to focus our pay budget on, for example, Political Risk which has the top return on capital but has a compa ratio below one, showing we are paying, on average, below the rate for the market.

Thinking bigger

A similar approach can be taken when using a compa ratio for “total cash” – that is salary plus annual cash bonus.

A word of warning

I will talk of some of the methodological issues later in the article; but of particular note is that great care must be taken when looking at total cash market survey results.  Survey organisations use different methodologies so be sure you are comparing like with like in terms of cash bonus definition and the timing of the payment of the bonus.

Combining data

One of the most powerful ways to use total cash compa is to compare base salary compa, total cash compa and, for example performance ranking or even better, a business KPI to ensure alignment of bonus payments with outcomes.

A common reward strategy is to place salary at the median of the market place but to pay bonuses at the upper quartile, or better, for upper quartile performance.

He is an example of a table of salary compa ratio, total cash ratio and return of risk capital.

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This is a very powerful analytic graphic.  It shows that there is a major mismatch between the areas achieving the best return on risk capital and the market position for both salary and total cash.  It further shows that two areas with very similar RoRC have different compa ratios for both salary and total cash.

We can carry on with this type of analysis with almost any business metric and any mixture of KPI’s and compa ratios.  It is a really powerful way to think about pay and bonus analysis.

Methodological warning

A major consideration when thinking about this type of analysis is that salary survey data relates to positions, not individuals.  Further, accurate job matching is essential to ensure a good “fit” to the data.  Salary surveys are best viewed as not absolute numbers but as indicating relativities in the marketplace.  It is more important to look at the relative position of a role than the absolute salary level.  This is because roles are different between organisations as are the people who fill them.

To use the compa ratio approach well requires a good understanding of the statistical methodology underlying the raw numbers, it advantages and its limitations.  We need to understand both the size of the data population and its stability.  Even quite large populations used for data can cause issues if that population changes year on year.  This applies both to the organisations taking part in the survey as well as the roles and the individuals within the roles.    Survey data is averages of samples; good statistical approaches can ensure that the samples closely resemble the total population; but in many cases there are no more or less than a sub-set.

This applies still further when looking at total cash survey data.  The definition of total cash and the age of the data are essential consideration when manipulating the analytical outputs.

Conclusion

When we are analysing data in preparation for the pay round the compa ratio is a very powerful analytical tool.  Used effectively it can give a great deal of data in a simplified format that is amenable to graphs, diagrams and info graphics.

Used in conjunction with business data it can create meaningful business insights that will shape and direct the nature of the pay and bonus round in your organisation.

If you would like to understand more about data analytics and the pay round please contact me at idavidson@rewardresources.net

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Pay round visualisations – Strong analytics III

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Introduction

An important part of any pay review is reviewing pay.  That is looking at pay modelling, outputs and outcomes.  My experience says that the 80/20 rule applies.  80% of the pay round outcomes will be straightforward.  What will be of interest is the 20% of the population that comprises of exceptions and outliers.  So a good analysis will be layered to provide details on the total spend by department or area and the identification of outliers and exceptions.

The most effective way to provide this data is to do so using graphical data and info graphics.  Human beings assimilated graphical data far faster, in most cases, than vast spread sheets of data or even summary data in tabular form.  We like to look for patterns and at pictures when going through the sense making process.

The other very important piece of the presentational jigsaw is to show, wherever possible, the link to business metrics and key process indicators. (KPI’s).  It is very useful to show correlations between our reward outcomes and business metrics.  We must use the data to show our “bang for the buck”.  That we are spending shareholder money to best advantage.  This approach should be supported by reference back of the pay outcomes to our reward strategy.  So if our strategy is to pay our top performers at the upper quartile of our pay market we must show that correlation in our presentations.

Getting pay visualisation right saves time, effort and increases the credibility of the reward team.  It aligns the reward analysis with that of the organisation and its management.  Having a cohesive pay narrative, linked to business outcomes with make the “sell” of the pay round easier and faster.  Anticipating the questions of our stakeholders is both simple and powerful.

Exceptions and outliers

If the pay round is well structured management will have a focus on the exceptions and the outliers.  Identify the top and bottom ten per cent of your pay proposals.  Clearly identify those staff who are being rewarded outside the policy or in a different way to their peer group.  DO NOT provide pages of spread sheets or tabular summary data. (Unless specifically asked for by a stakeholder).  For most managers pages of data are difficult and time consuming to read and difficult to interpret.

This graph shows a correlation between revenue ranking and market position.  It is immediately oblivious that there is an outlier.  The reason for that person’s position on the graph can be explained and a recommendation made as to how to correct the anomaly and increase the correlation between revenue ranking and market position.  (The underlying assumption is that this is part of the pay strategy).
Revenue
 

Develop the pay narrative

As reward professionals, working closely with our HR business partner colleagues, we should have developed a coherent pay narrative.  A story of what our pay round is trying to achieve and what it has actually achieved.  The reason for this is that it makes explanation, presentations and data analysis much easier if we have started off with a basic, clearly expressed set of principles and assumptions.  This may include foreign exchange rate decisions, key metrics including the budgets and a clean set of data as a starting point.  Time spent cleaning pay data is never wasted and can save a vast amount of time and trouble later in the process.  Data is never perfect.  I have frequently come across situations where the headcount I was using for the pay review and the information in the Finance department was different.  Agree and reconcile the approaches and numbers before the pay round starts.

There is never enough time or resources to process a pay round perfectly.  By undertaking the data cleansing, agreeing the pay narrative and assumptions and any reconciliations in advance (and appreciating that is not always possible) will save time and lead to a better pay review process.

A picture is worth a thousand words, or ten spread sheets

Producing high quality, clear info graphics and visualisations of reward data is a very efficient use of resources.  Returning to the 80/20 rule it allows management to focus on the 20% of the pay review that is important or of interest to our stakeholders. Graphics such as the one below can be used to answer questions before they are even asked.  Using this approach highlights our exceptions and the extremes of our pay distribution.

The supporting data is of course available behind the graphics.  But, returning to the theme of a good pay narrative, we can illustrate and support both what we are hoping to achieve and what we have actually achieved.  A good graphic is a “smack in the face with the obvious”. A crude but accurate comment on what a good graphic should achieve.

Business metrics and KPI’s

It is no longer enough just to present raw pay data.  We have to put the information in to the business context.  We must illustrate the connections and correlations between our limited pay and bonus budget and business outcomes.  Reward the performers and the revenue generators.  Pay outcomes can be used to give a clear message as to what behaviours and activities will be reward and those which will not.   Many organisations, even those in financial services, are looking carefully at the “how” something is achieved as well as the “what”.  Balanced scorecard approaches are very common; it is still possible to focus on the financial outcomes by giving it a high scorecard weighting; but we can nuance the approach by giving smaller weightings to cultural, behaviour and approach.  A well-constructed balanced score card will be measurable and give another basis for our graphics to show appropriate correlations.
Blog pic 3

In an earlier post (https://iandavidson.me/2013/08/23/pay-round-processes-a-big-data-approach-including-the-add-on-benefits-to-recruitment-training-and-development-and-succession-planning/) I showed how it is possible to run a pay round based almost entirely on those factors that lead to business success.  It is not easy and arguably it removes “discretion” from managers.  But, it is the use of that very discretion that often leads to upset and even legal challenge.  A robust process backed by robust data is the way forward.

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Conclusion

The pay round in the vast majority of organisations is resource and time constrained.  It can be made easier on all stakeholders by presenting a solid reward narrative illustrated and supported by appropriate and timely visualisations.  This allows the focus of the reviewing stakeholders, be they the Remuneration Committee, Executive management or line management, to be on the 20% of the population that requires attention rather than the 80% that does not.

A strong story, answering questions before they are asked and linkage with business metrics will be both appreciated as part of the alignment of HR and business strategy and as an efficient way to manage a pay round.  Providing good graphics saves time and increases focus when resources are, like high pay increases, very rare.

Pay round processes – a “big data” approach. Including the add-on benefits to recruitment, training and development and succession planning

Introduction

The key to using data intelligently in HR is to start with the business numbers.  This article is about how to structure a pay round driven by business results.  It focusses on data rather than the normal subjective judgements and gaming that goes on in the vast majority of companies at pay increase time.  The objective is to reward what matters to the business.

This is a long post; but it is not going to give the full detail of the approach.  This will differ in each organisation.  It is a new approach in thinking about the pay round process and the article gives the broad concepts and approaches to the subject rather than a detailed “Dummies guide”.  (I can provide one of these for a reasonable fee).

Effective and efficient

This approach is based on good use of data and behavioural psychology.  It generates rewards for behaviour that is important to the business.  By doing this it sends out a clear message both on culture and on what behaviours are rewarded in the workplace.  This creates the “virtuous circle” of reinforcing profitable behaviour leading ultimately to high performing teams.

You should use this approach.  It gives hard statistical evidence as to why pay increases were, or were not given.  It gives a better return on your pay increase investment.  You are rewarding behaviour that benefits the organisation; not a managerial whim or some perception of employee merit based on last week’s conversation.

In the beginning

Like all good science, start with a test group.  Select a discrete group of employees where business and HR data is available.

Key business success metrics

The key business success metrics for this group need to be clearly defined.  As an example, if looking at sales staff then consider sales revenue, the conversion rate of sales calls to sales, repeat sales and so on.  It is advisable to weight these business success metrics from the most important to the least important; always focussing on the bottom line impact of these factors.

Rank the employees

The next step is to rank the employees against the business metrics.  This must be undertaken strictly against the business metrics.  It is difficult, but is an essential part of the process.  It may well be that your “best” employees are not those who score highest on the metrics.  Stick to your original business metrics.  Do not change them because the employees in the list do not fit your perception of “good”.

What makes these employees “good”?

This is the most difficult part of the process, but the most important.  This is where the power of big data starts to prove itself.  Now take the HR data on each of the top employees to see what common factors make these employees perform better against the business metrics than others.  This could include:

  • Time in role
  • Education level
  • Personality profile
  • Supervisor
  • Training courses
  • Previous roles
  • Previous employer(s)
  • Outside interests
  • Social network size
  • Email activity – internal and external
  • Sales calls length and frequency
  • Time and attendance data
  • Daily newspaper and magazine reading
  • Social profiling (you can use postcodes for this)

As long as you have the data and you should have the data, you can include it as a factor.

You will now need some strong statistical knowledge to undertake a regression analysis to identify the common factors for your high-ranking employees.  I am aware there are a number of statistical techniques that can be used at this stage.  You pay your money and you take your choice.

The outputs from this exercise will depend both on the richness of the data you hold on employees, the type and location of your organisation and your company culture.

It is important to note that this technique is not limited to revenue generating activities.  We can build success factors for HR, cost of hire, attrition, benefit spend, payroll costs and so on.  Or much the same in Compliance, for example.  External audits passed, compliance costs, compliance checks carried out – you can fill in the blanks.

Results part one

What you will have, if the process has been carried out correctly, is a list of individual factors that predict behaviour that support business success.  Some of the factors will appear not to be relevant; and I am aware that correlation does not imply causation.  Some of the factors will be surprising, do not rule them out or ignore them.  GO WHERE THE DATA TAKES YOU.  Human beings are programmed to look for patterns where none exist and make choices based on often faulty heuristics.   The data may not always take you in the right direction – but normally it will.

The ranking

This is the easier part.  You rank the employees by the factors.  This process is already part carried out by the earlier steps.  The exact nature of the ranking will depend on the analysis.  One approach may be to rank the employees by the factors with the highest correlations to business metrics success.

The pay increase allocation process

In an ideal world you would allocate 80% of your budgeted increase to the top 20% of employees.  That is because it is statistically likely that 80% of your revenue comes from this top 20% of employees.

This process largely removes the subjective elements and gaming that goes on around pay allocation in most organisations.  Decisions can be justified and supported by the data.  A clear signal is sent out to employees as to what is being rewarded.

Extra benefits to recruitment, training and development and succession planning

By having identified the factors that are correlated to business success (provided you have chosen the business metrics correctly) you have a powerful dataset to aid recruitment, training and succession planning.

Recruitment

You have a list of factors that predict business success and effective employees.  Using these factors a template can be developed to quickly and factually identify those applicants who are most likely to do well in your organisation.  It may not be the only measure; but it will provide an excellent screening tool.

Training and development

The factors that lead to success have been identified; thus you can train and develop employees based on those success factors.  A provable bigger bang for the training buck.

Succession planning

From the analytical process you will have identified both the success factors and those supervisors who have the most successful teams.  A variant on this exercise can be used to identify what factors make up the most successful supervisors and managers and build your succession plans accordingly.

Power of big data

The above discussion shows how HR data can be used to drive business success.  One of the tenants of big data is to automate the analysis of the data.  With a little work it is easy to automate the data scrapping processes to allow the identified factors to be ranked against employees and allocate the pay increases once the basic rules have been formulated.  Having the data available and categorised allows for very powerful management information reports and data visualizations.

Warnings and alternatives

The above process is, for the majority of organisations, new and perhaps frightening.  It will not work the first or second attempts.  However, the very process of data scraping and analysis will yield a honey store of good things.  The process can be changed and refined to fit the organisation.

This is different HR.

It is data driven and business focused.  Some will argue it takes HR away from its traditional routes; why not?  HR has not yet earned a full place at the board table with its current approach.  Finance, IT and other support functions have a greater claim, because they have the data and facts to support cost activity.

Conclusion

This concept is fairly new for most organisations and will take:

  • A change in mind-set
  • A robust data store of employee and business data
  • A strong understanding of the underlying statistical processes to carry out the appropriate analysis
  • HR working with Finance, IT and data professionals, statisticians and the business to get the clear benefits from the approach.

When this approach is fully working it provides a rich and effective way of spending the salary budget as well as providing a firm “big data” base for HR strong analytics.

Working this way gives credibility to HR and builds up a subjective data bank of HR information with which to support business decision-making.  Implemented appropriately it is a win win for all parties in the annual pay round process as well as for the wider HR community.

Rewarding Reward Podcast http://www.idavidson.podbean.com/

Ian Davidson Reward podcast

I have produced the fourth podcast in the series “Views over the City”  http://www.idavidson.podbean.com This podcast covers pay and reward issues on a global basis.  This podcast includes:

For more on Banking remuneration see: https://iandavidson.me/2013/06/12/rebuilding-trust-in-the-city-of-london/

“I was at a recent meeting in the City of London to launch the document “Focus on rebuilding trust in the City” a Chartered Institute of Personnel and Development (CIPD) survey of staff in financial services in the City of London on trust and their employment relationship”

For more on Executive pay https://iandavidson.me/2013/06/18/balance-of-power-executive-pay-and-shareholders/

“There is considerable controversy over levels of executive pay.  There are a multitude of stakeholders or would be stakeholders pugnaciously striving for influence.  Remuneration committees are supposed to control executive remuneration.  However, as the MM&K recent survey shows, FTSE CEO Remuneration increased, on average, by 10% in 2012.  Why are shareholders allowing this to happen?”

For more on strong analytics:

https://iandavidson.me/2013/05/30/strong-analytics-3/

“As a reward specialist I am asked questions like, what is our pay inflation going to be next year?  I used to go away, do research and say 2.4% – having used the historic average.  Of course it was never exactly 2.4% so my boss would turn round and say – “but Ian, you said it was going to be 2.4%, you’re fired”.  If asked the same question now, I respond with an answer; “there is a 50% probability that it will be 2.4%; but there is as 10% probability it could be 4%, so we should factor that in to our budget.”

My new reward podcast give a wide view over the reward landscape as well as a fascinating conversation with innovation guru and author Peter Cook.

http://www.idavidson.podbean.com

If you would like a guest blog post or to guest blog post on this influential reward blog please get in touch.

blog@mauritius.demon.co.uk

Why thinking in averages is below average thinking – Strong analytics II

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Introduction

As a reward specialist I am asked questions like, what is our pay inflation going to be next year?  I used to go away, do research and say 2.4% – having used the historic average.  Of course it was never exactly 2.4% so my boss would turn round and say – “but Ian, you said it was going to be 2.4%, you’re fired”.  If asked the same question now, I respond with an answer; “there is a 50% probability that it will be 2.4%; but there is as 10% probability it could be 4%, so we should factor that in to our budget.”

The problem is that point data estimates, like, pay inflation is going to be 2.4%, have a high possibility of being wrong.  Using a probability approach gives more information about outcomes and new ways of thinking about those low probability, high impact events in our lives – our “Black Swans”.    Dr Sam Savage, a pioneer in work on probability, tells the story of the mathematician who drowned in a river that had an average depth of three inches – that average hid a deep trench right across the centre.

Average forecasts are wrong on average

Taking point averages and using them to forecast is a common fallacy.  House prices have gone up, on average, $20,000 per year, the forecast is that they will go up $20,000 next year – oops, they fell $50,000.  Using a probability approach tells us that there was a less than 40% chance of a $20,000 increase and, a 10% chance of a $50,000 fall.  

Playing with Monte Carlo

So how do we get to the “50% probability that inflation will be 2.4%”?  My favourite method (but not the only one) is to use a Monte Carlo Simulation.   This is a statistical technique that allows me to account for volatility in numeric analysis.  It does this by producing a probability distribution for any factor that has variable outcomes and by producing a large number of random samples.  What does this mean?  Well, I took some UK National Health Service (NHS) quarterly sickness data over five years.  The average percentage absence was 4.2%.  I ran one million random trials (it took about twelve minutes) against the data distribution.  It showed that while there was a 50% chance of absence being 4.2% there was a 10% chance of it being 5%.  That may not seem like a big difference but when you are dealing with the biggest workforce in the UK, 0.8% is a large number of doctors and healthcare workers off ill.    I used a different set of NHS data on the number of sick days lost per employee.  The 50% probability of days lost was 6.1 – but there was a 10% chance of 9 days, a large difference. 

As an HR professional it is better to say that we have a 50% probability of wage inflation at 4.2%, which clearly gives a large range of other probabilities than saying it will be 4.2% with a very high probability of being wrong.

Probable advantages

There are a lot of advantages to using the probability approach.  We can show what might happen and also the probability of each outcome.  One example of this that I have used is to look at the probability of different performance measure outcomes in an organisation if they were normally distributed.  I then compared this with actual outputs and was able to show the CEO which departments were “outliers”; had produced markings that were higher or lower than forecast.  That allowed us to talk to the line mangers to find out why the department was marking higher or lower than was predicted if the performance was normally distributed – which is what you would expect.

Using probability analysis is invaluable for “what if” exercises.  How many times have we been asked to model what would happen if you cut the budget by 3%? Using one number you get one output.  Using the probability approach you can give a range of possibilities.  I have looked at death rates in an organisation against the probability forecast.  On one occasion, using a probability approach, I suggested increasing insurance cover just in time for a sad increase in the number of employees dying. (The increase was, of course, entirely random, but I had forecast that probability). 

Another important use of probability is that of project planning. One statistical quirk in project planning is that if all the tasks are completed, on average, on time, then the project will be delivered late!  (Think about that for a minute….) 

Fooled by Black swans while thinking fast and slow

You may have read “Fooled by randomness” by Taleb or “Thinking fast and slow” by Khaneman.  Both make the same point. As humans we are programed to apply heuristics and biases to problem solving: dismissing or ignoring the unlikely in favour of what we think we know or what happened recently.  Yet unlikely outcomes are more common than most people would guess; also the outlier outcomes tend to be extreme by definition.  Of course, in HR we work with people, who act in quite random ways sometimes……

Bombs and gas masks

When in investment banking I worked with an outstandingly good business continuity manager called Stuart Dunsmore.  He talked about the possibility of a bomb in central London being extremely small; but the effects would be highly disruptive. Sadly, he was proved right, but the upside was we came through the London bombings with our UK business unharmed due to his preparations.

When in the City of London I carry an emergency gas mask.  Why, well, the chances of needing to use one are small, but I only need to use it once to save my life!  The probability of a biological or chemical attack in London is tiny; but the chance of death is high.  Low probability events with high impact; do not let them take you by surprise. 

The dark side

Now for a public health warning.  First, those of you who have a statistical background (unlike me) will spot holes in my argument.  There are issues with Monte Carlo simulations or even using probability approaches.  But, they are better than point averages for forecasts.  It is a continuum, yes, there is better mathematical or statistical approaches available but even starting to think on the basis of probability is a game changer.  Second, probability often depends on the future being similar to the past – but it will not be!  However, using the probability approach makes us more aware of both that factor and that highly unlikely events do occur with surprising frequency.

Conclusion

Most of us in HR are not statisticians  Using the probability approach does involve some understanding of statistics and how to use the programs that are available, be they Microsoft Excel add-ins or programs designed specifically for this work. However, taking the time to understand probability both as a mind-set and as a set of techniques is a major game changer for HR. 

I would urge you to give it a try; you have little to lose.  The gains are large; greater chance of producing “better” forecasts, certainty of being wrong, on average, less often.  Increased credibility and perhaps a more open mind set to when those outlier events do occur.  Enjoy!