Sales Compensation Analytics: The Next Big Thing… Again

Analytics is hot and growing hotter.  More specifically it can revolutionize the way you make decisions, which could make you and your company more successful.  Analytics is the Holy Grail of Sales Performance Management.  If you work in the realm of SPM you already know this because the term “analytics” has been a buzzword around SPM solutions since the dawn of time (or at least 15 years).   It’s amazing how the latest and greatest concept can stay “latest and greatest” for a decade or more.  The curious fact is analytics has been re-revealed as a new milestone every couple of years.  I suppose that means nobody is ever getting there.  It’s right up there with El Dorado and Atlantis.

So what is “Analytics”?

Analytics (according to Webster’s)

[An-l-it-iks] noun. The science of logical analysis.

Clear?  So it turns out that the term analytics is more of a discipline than a destination.  More specific to sales compensation:

Sales Compensation Analytics (my definition)

The science of dissecting sales compensation data in an effort to forecast, model and detect patterns with the ultimate goal of discovering methods for optimizing the ratio between sales and compensation.

Analysis is done everywhere.  I suppose there is a critical point at which analysis becomes “analytics”.  It’s the scale, granularity and robustness that vary from organization to organization.

At one end of the spectrum you might have The Stone Company; they operate in the proverbial dark ages of compensation management.  They manually calculate commissions and pass them around on spreadsheets.  HR does pay analysis by requesting a dump of data from the payroll system.  The only reports upper management have is very high level.  They basically have payroll history in a sort able spreadsheet.  They can tell you how many sales reps made more money than the CEO, but they can’t tell you why.  They’re forced to manage by gut and not surprisingly, they do alright because they’ve been doing it this way for years and there’s not a lot of risk taking.  Many businesses operate this way. 

At the other end you have The Glass Company; they’re a world class outfit when it comes to compensation management.  They get fresh reports daily from an automated, online system.  Sales and compensation can be sliced and diced at the most granular level.  They can give you the global average cost of sales for widget X on Tuesdays.  They can tell you how many times a customer bought accessory B with widget Y.  They can tell you how many sales reps are on pace to hit quota and they can model the effects of the proposed new marketing campaign.  They still use their gut but they back decisions with data.  The business feels empowered to try new things and innovate. 

Notice that the difference between the two companies is not the talent of management or the insight into the value of strategic compensation.  The difference is in the tools available.  Many, many companies make do with very limited access to data and analysis tools.  They make do by working with what they have and by filling the gaps with assumptions and experience.  However, they would use more data if they had it.  The thirst for data and insight is unlimited.  That’s a universal truth.

Why Doesn’t Everyone Have Tools?

A developing theory I’ve had over the years is that most companies stuck in the dark ages are there because they can’t decide where to go.  Imagine that we were running an experiment where we put a group of people down into a deep tunnel with several unmarked passages to lead them out.  Nobody in the group knows which way is out and they must decide what to do.  I think we’d find several outcomes.  Some groups would follow a strong leader who would decide, rightly or wrongly, the direction.  Other groups would split up and go in several directions each with their own goal in mind.  Some groups wouldn’t be able to decide on a common direction and the debate would paralyze them, they’d just sit there. 

These outcomes are analogous to what we see in the SPM world in regards to analytics.  Some companies are stuck in paralysis.  They agree that something must be done but they can’t agree on how to do it, so nothing is done.  Some companies have followed a direction set by a strong leader.  Other companies have developed independent capabilities in several places within the organization.

Macro-Analytics vs. Micro-Analytics

The reality is it is very unlikely that you will be able to build a monolithic analytics environment that gives everyone what they need in one rollout.  There are too many perspectives to satisfy. 

The most common divide is a concept I like to call macro vs. micro.  When putting together an analytics tool the most important element is the data and how it’s structured.  Often times you need to tweak your source systems in order to produce data at the level desired for your analyzers.  Here’s where the conflict occurs. 

Let’s take a Global Compensation Director.  She would like to look at her data at a summary level based on role, country, etc.  She wants to reconcile compensation data from the SPM system with data from the accounting system and the sales reporting system.  Global Directors want to view metrics like a captain of a cruise ship.  Cruise ships can drift a few feet off course as long as the captain can avoid the ice burgs.

Now let’s take the Compensation Analyst assigned to the Northeast New Jersey territory in the retail business unit.  He wants account level and product level metrics.  Analysts need to know the specific rate paid on specific orders.  They are looking to find deviations at a micro-level.  They need to answer questions related to the setting of quotas and the effect of vacation time.  Analysts deal with individuals and their paychecks, there is no place for estimations, averages and summaries when it comes to paychecks.

Take the First Step

So where is the “analytics” City of Gold?  Every company needs to draw their own map.  Certainly there are many ideas out there that can be reused as starting points or templates.  This isn’t a case where you buy a tool and it triggers a wave of game changing analytical analysis that you’ve never heard of.  What a nice analytics tool gives you is built-in adaptability.  You might start with one vision and change it later after a period of use. However, in order to get started, every stakeholder needs to identify what they want to see.  Priorities can be set based on the value each capability provides.  Perhaps its Finance’s ability to do better forecasting that takes the top spot.  Perhaps is Sales’ ability to analyze the impact of spiffs.  Perhaps you choose to provide the capability that is cheapest to implement.  Whatever the case, any capabilities are better than none.

My favorite analogy to use here is the construction of the New York subway system.  The intricate web of tracks we see today all started with a single line.  At the time nobody envisioned what is it today and I would bet there was great debate over where to start.  Thankfully, they started somewhere and New Yorkers have been benefitting ever since.