Datafication of Compensation Distribution

Is your data science providing you enough indications that challenge your existing compensation strategy?  Does it reveal that the art of compensation distribution performed by your managers is not in accordance with your compensation strategy? Old habits die-hard, so you need to make sure that your plan for data-driven decision-making is not getting overridden by compensation managers’ belief system and they are not ignoring data science recommendations.

Effectively distributing compensation using limited budget in your disposal is always challenging. Your strategy can be to give performers much higher increases than rest of the people as it may still result in improved engagement and retention as concluded by some research results.

Your compensation system should empower you with actionable insights by providing sharper data analysis on some of the critical decision points like performance, potential, risk and social score of your employees. Performance analysis primarily depends on internal data, potential and risk analysis depend on internal as well as external data and social analysis (how person is perceived or represents company outside workplace) mainly depends on external data. While internal data remains structured, external data (e.g. tweets, social media, blogs etc) combines both unstructured and semi-structured data. Your data scientists need to extract insights and value by combining these internal and external data sources to generate a real value for your business and to datafy your compensation process.

By unlocking the true value of your data, effective data analysis provides you unambiguous decision points. As a result, you may even automate compensation distribution process in cases where you are not looking for any diversion from your planned strategy and where there is no scope for your compensation managers to add value in the process.

In a nutshell, you have to leverage your employee data, available inside and outside your organization’s firewall, to enhance data-fueled decision making as it will surely result in improved business performance and will also help you to attract and retain best talent.

Inject market data to address external compensation inequalities

Ever changing labor market conditions and supply demand fluctuations make sure that organizations don’t design employee compensation strategy in a vacuum. Success of your compensation strategy not only depends on your system’s ability to provide market data analysis during decision-making but also on how well you analyze the external data during your planning phase.

You start this exercise by deciding on the source of market data as per your industry benchmarks. Relying on multiple survey sources for market salary data will be more beneficial than depending on a single source. In case you opt for multiple sources, you need to decide on mathematical techniques you are going to use to combine multiple sources into an easily comparable source to make comparison with the internal data a smooth exercise.

Getting your current job descriptions updated into your internal system is a must have requirement before you proceed any further. While analyzing the survey data, you need to make sure that you are matching your detailed job description with benchmark job descriptions as matching just a job title or a brief summary can result in wastage of money, time and resources. You also need to have a plan in place for the cases where you don’t find job match in benchmark data.

How you utilize survey results after your internal analysis depends on your current or desired market position. Either you can decide to pay as per market dynamics or you can decide to lead the market. It’s very unlikely that you will decide to pay below market standards after putting so much effort in analysis. You can also opt for total compensation offerings to match or lead the market rather than focusing only on base pay.

It’s easier said than done, you need real experts for a detailed market data analysis internally as software in place will not help if you fail to analyze the data correctly.

Seeking feedback on your compensation process

Organizations put a lot of efforts in choosing the people who can award compensation to their workforce. They may even decide to have a different set of people for different type of compensations awards (i.e. Salary allocation or Stock Grant) as per their business needs. But the question is after giving compensation allocation responsibility and various tools to make informed decisions to these people; do you seek their feedback on compensation allocation process in a planned manner?

It is critical to seek feedback from your compensation decision makers as it will:

• Help you to overcome any shortcomings in your existing compensation policy.
• Help you to understand good and not-so-good things about the tools provided for compensation planning.
• Help you to discover how determined your decision makers are to voice their opinion for betterment of your compensation policy.

Every compensation round has a theme or a predefined objective that needs to be fulfilled and it varies with the type of compensation you are dealing with. It’s best to collect feedback and keep it associated with specific compensation round. This association will not only help you to closely analyze context specific feedback but also to take the corrective actions. In case you need collective and only one set of feedback you can always combine compensation round specific feedback into one.

You should decide on feedback questionnaire if you are planning to collect specific feedback and can request people to rate things you want to be rated. However, it will be good to provide some free hand where feedback provider can share feedback not related to questionnaire.

Finally, you need to analyze the collected feedback; work on to resolve the highlighted problems (if possible) and follow-up with feedback providers (if required).  Publishing corrective actions or changes incorporated as a result of this exercise will convey that you value feedback and will motivate people to participate in future. You can expect some real value addition to your compensation strategy by this exercise as you will be working with the people important to your business and compensation process.

Metrics for Compensation Allocation

Simon would like to allocate performance bonus to his workforce who got performance rating of 5/5 and 4/5. People who got 5/5 rating will get (X) % of their base salary as performance bonus where as 4/5 will get (X-1) %. He would like to define compensation metric for the same so that he can apply it, with his decided % amounts, on his entire workforce rather than allocating to each one of them. He would also like to share it with his peers or subordinates in case they would like to use same metrics with their own numbers.

Neither the use of organization wide published metrics is new to compensation process nor is the desire of a manager to build his own metrics for distributing compensation. Only question is whether your current (or potential) adopted compensation solution supports it or not.

As a manager, you would always like to create metrics that you can use to allocate compensation to your team as it will facilitate a smooth process and help you in well-informed decision-making. From ages, managers are using off-line tools to flag and store decision attributes which helps them in making compensation as well as other decisions. What they actually need is a system which not only allows them to build their own metrics based on various person related attributes but also allows them to share it with their peers or subordinates. Manager owned metrics supplements the organization wide metrics (aka HR established Metrics) and not really replace them.

Some managers will be happy if system supports basic attributes like performance rating, work location, Job/Grade whereas others may need more specific attributes like years in Job/Grade, compa-ratio and grade ranges. It will be beneficial to have embedded support for all the possible decision attributes as well as custom attributes (an extension to store business specific values) so that managers can build robust metrics for compensation allocations with great ease. It will result in compensation allocation process to reach the next level.

Compensation budgeting to retain your best talent…

Compensation budget is an organization’s financial commitment towards compensation awards for its workforce. It’s a lifeline for compensation management process.  A performance linked compensation budget for your managers to work upon will help you to retain your best as top talent always have more options and  you may always run into a risk of losing talented workforce to your competitors.

If your compensation allocation objective is to attract, retain and motivate best talent than performance linked budgeting is the key.  Adoption of merit linked budget over the fixed budget will provide that required extra room for distributing compensation to your best talent, to achieve your organizational goals.

Be it a fixed number or a number based on some prorated value (such as on base salary), a manager with more talented people should have more budget in his disposal to allocate workforce appropriately. Use of organization or division wide performance linked budgeting guidelines will ensure consistency as allowing discretion beyond a point may result in derailment from original purpose. To enforce consistency, variation beyond an accepted threshold should not be permitted.

Choosing performance linked budgeting over the fixed budgeting gives an extra boost to your pay for performance system.

Is your compensation hierarchy flexible enough?

 

Deciding on who will get involved in the compensation allocation process varies from organization to organization and there is no fixed formula or hierarchy which suites all of them.

We usually have leads in organization structures to provide work direction to others. However, you may not want them to be included in compensation decisions. Hence, existing supervisory or position hierarchies may not work as your compensation allocation hierarchy for various reasons.

Who will get what in an employee stock options plan is decided by one set of people, usually middle or higher management, whereas how much performance bonus needs to be given to an employee is decided by a different set of people, usually immediate manager or lower management. Different types of the compensation decisions are taken by different people in management chain and it’s not your immediate manager who will always call the shots.

It’s likely that once you start allocating compensation to your people, you will figure out that you don’t have enough information to allocate compensation for some people and it would be better if a better suited manager decided their compensation allocation. You definitely cannot afford to create a new hierarchy from scratch in this scenario as the system is already live.

To summarize, a fully flexible compensation hierarchy is very much needed to meet your business requirements.

Does your compensation system provide this flexibility?

A checklist which can help you to perform a readiness check on the flexibility of your compensation system includes:

•  Ability to support a compensation hierarchy similar to or different from your existing HR hierarchy.
•  Ability to further customize the compensation hierarchy by including or excluding identified people or roles.
•  Ability to realign people within the customized compensation hierarchy on a real-time basis with zero downtime.
•  Ability to have different customized compensation hierarchies for different types of compensation.

Compensation is a fundamental reason people work and the above checklist will help you to put the right compensation distribution responsibility into the right hands, which will result in a robust compensation system.

Seven keys to a robust compensation system

The seven keys to a robust compensation system that will help you in attracting, retaining, and motivating the workforce are:

1. You should have a fair compensation policy in place and it should be easily accessible to your workforce.
2. Your compensation policy should be context sensitive. Market data should be used wherever appropriate to keep it fresh and competitive.
3. Your compensation revision schedule (e.g. salary revision cycle) should be communicated to the workforce in a planned manner and in advance. Open communication will help your workforce to focus on their duties instead of wasting energy and time in waiting and guessing about it.
4. Compensation should be linked to the performance. It will keep performers motivated and will help you to retain them.
5. Policy execution should be monitored regularly to make sure policy has “Buy in” at all the levels and execution is not diverting from the planned objectives.
6. Your compensation policy should be open for feedback. Proper and industry accepted channels should be established to achieve this.
7. You should have a built-in audit capability to detect and correct any compensation discrimination. It will save your brand and increase employee engagement.

Bell-Curved Pay-For-Performance

Yes, it sounds weird, but it’s very present in most of the companies today in some way or the other, thanks to the economy and tight budgets. I don’t have any intention to start a debate on whether it’s good or bad, but to check the readiness of your compensation system to handle this. Does your compensation system have the capability to accommodate a forced bell-curved system? How ready is your compensation system to absorb this if it comes as a shock?

Information that can help your decision-making to be systematic rather than random includes:

1. Current as well as past performance ratings for all employees.
2. Ranking (1…N) for all the employees. This will be useful for employees sharing the same performance ratings as well as should you take out one of them out of the compensation round.

People should not confuse ranking with rating. Ranking should be used along with performance rating to meet the assigned x% penetration target for compensation distribution.

Your compensation system should allow managers to rank all employees in their hierarchy along with the capability to use rankings given by managers reporting to them. It will result in a system for higher management where all population is ranked from 1 to N and each ranked employee has associated performance rating information. It will allow applying any formula for compensation distribution as rating and ranking together will best indicate the importance of any selected employee.

Having your compensation system ready with the above-mentioned information will also avoid having to involve all managers when the target % of penetration changes during the compensation distribution cycle for any reason.

To conclude, let me ask you “If only 60% of your workforce could get performance bonus and 80% of your workforce could get salary increase as per published guidelines, how would you handle this?” Would you prefer to use ranking along with performance rating or are you already equipped with something better? Please share your opinion and suggestions.

Thanks for reading.

Photo by c r i s

It’s all about Compensation…

Compensation is not the only reason for employee’s motivation but improper and random Compensation Policy is often the biggest reason for employee’s dissatisfaction.

When we ask someone, does your organization value the people who really contribute towards “Achieving Organization Goals”? ; Most often answer is “Yes, We do. We have Pay for Performance Compensation system in the place”.

A Pay for Performance Compensation System is a fashionable thing to have.  It is important to remember that, It’s not about having a system in place but how system is getting executed, it’s about adoption by the people (Is it really open for feedback ? ).

Nothing is perfect, but a compensation process should be a system for the people and by the people.  Parameters used as decision variables in implementing Pay-for-Performance (P4P) affect employees motivation and are therefore very important.

Three critical decision variables in this process include:

• Individual Performance: It’s a key decision variable from which the whole “P4P” system is derived. Contributors will we rewarded and it really motivates the individual. While it’s great to have criteria specified, many times individual criteria can kill the “Team-Sprit”.  Human nature to perform better than others for better rewards can be a dangerous thing for team goals. Often it is the collective goal that is critical to the organization. It doesn’t mean we should not have individual criteria, but expanding its scope (read Team Performance) can help resolve the conflicts it creates
• Team Performance:  Addition of “Team Performance” as a decision variable with “Individual Performance” is the best way to convey that “we need to deliver as a team”. Proper guidelines on team size, scope of work and qualifying criteria should be published to make this decision variable clear and avoid unnecessary debate.
• Company Performance: If your organization is doing well, it can decide to share its happiness with its employees too. Most companies make sure to include a component of company performance into the mix.

Compensation (salary revision, bonus payout    etc) depends on budget, and the above three parameters should be factored into how the budget is allocated.  Using a combination of the above three factors, added to clear communication strategy will result in improved satisfaction.

The above three guidelines are “Context-Independent” Pay-for-Performance  factors but “Forced Parameters” cannot be ignored while talking about any compensation system; so let’s talk about these “Forced Parameters” and their influence on the P4P process in next post.

Stay Tuned…

Be Careful with Averages (Especially with Compensation)

The Department of Labor put together this chart that compares the average amount spent on compensation and benefits between private and public sector employees.  At first glance, you might think that those interested in high paying jobs should look to public sector employment, or that public sector employees are overpaid.  However, drawing such conclusions from a simple average is premature.  In this case, the problem is that we aren’t looking at pay for the “average employee” across these two dimensions; we’re looking at averages calculated from entire groups of very diverse people.  Nancy Folbre, an economics professor at University of Massachusetts breaks these numbers down into a distribution of earnings in an effort to discredit initial interpretations of these averages, and to come up with some meaningful takeaways from the data.   

Comp is much more polarized in the private sector, where private sector employees are over-represented in lower and higher income brackets, while most public sector employees fall in the middle ranges.  43% of private sector workers earned less than $25k per year and more of them are part time (26%).  More public sector employees are college educated (45% of public sector workers have a college degree v.s. 29% of private sector workers).  The data suggests that employees performing similar jobs in the upper end are paid significantly more in the private sector than they are in the public sector.  And if you’ve got a lower skilled job, then it’s probably better for you to work for your local municipality.

Averages often offer poor and sometimes misleading insight when it comes to compensation reporting.  Too much is lost when data is aggregated.  The fact that the average salary for a US subsidiary is lower than a Mexican one may or may not be a problem; if they are the same, it may or may not be a problem; or, a problem may exist when the average salary in the US is higher than it is in Mexico.  Then you ask yourself, who cares about salary averages broken out by country, or business unit, etc..  I see too many compensation reports that just offer these higher end aggregates and don’t allow someone to look deeper into the numbers to draw meaning.  If you’re going to show an average, then be sure to allow someone to cut that average across multiple dimensions to get to some level of granularity; otherwise, an average is just a tease.