Analytics is Fast Becoming a Core Competency for Business Professionals

McKinsey’s Big Data report lists shortage of talent in the big data space. Interestingly, the shortage of business professionals who can work with data (1.5M by 2018) is about 10X that of data scientists (140K by 2018). In my experience this shortage ratio exists even today. For every 10 business professional (product, marketing, operations folks), there is perhaps one or fewer data scientist (professionals with analytics or advance analytics skills).  So the way I see this gap being closed in the future would be that, the business professional would also be part analyst. Let me explain.

Analytics as a word conjures up images of complex algorithms and code; thereby most business professionals can’t imagine themselves as part analyst. But there is part of analytics that is simpler and more importantly, successfully used by business professionals today, in driving data-based decisions. Let’s take a common decision almost all of us have experienced – buying a new car and let’s see how it can be approached analytically.

Analytical (data-driven) approach to purchasing a car: You start by nailing down your constraints – time, money etc. and your feature set – “must haves” and “good to have”. Perhaps good mileage is a “must have” for you and low emission is “good to have” for you. Based on all of these criteria (which is unique to you), you narrow down your choice to a select few as finalist. You test drive the finalists and choose the best one you like based on criteria you have pre-decided. This is analytics. There is a process by which you came to the best and most appropriate choice of car based on your needs and car facts. Analytics is fact driven decision making. 

Non-analytical approach to purchasing a car: Non-analytical process would perhaps start by test driving cars, irrespective of any criteria, and you either discover the criteria as you go along rejecting cars to justify the rejections or buying the first car which “feels” right.

What is the advantage of analytical over non-analytical approach? I was recently talking to a friend who was complaining about the mileage of his new car. He seemed unhappy with the $100/week spend on gas.  I asked him, if he had changed jobs, so his commute was longer than expected. He confirmed, that was not the case. I asked him, if the car is giving him lower mileage than expected. And that was also not the case. Finally I asked him, why he bought the car with low mileage when he knew he was going to use that for his long commute and when he knew cost was a constraint for him. He answered by saying, he didn’t know the cost would be this high, and that it would bother him and most importantly, he really liked the feel of the car when he drove it. Could he have gotten a car, which he liked the “feel” of while still making sure it met his “must haves”? You bet! But that requires the analytical approach to buying a car. Using data to drive decision gives you significantly higher chances of making good, long lasting decision over non data-driven approach.

Can most of us envision ourselves as using such kind of analytical approach to buying a car, or buying a house, or choosing a career, or choosing a school for our kids? Yes, most of us do. Is the process of making data-driven decision in our day-to-day “business” life a whole lot different?  No, it is not. Let me explain by taking an example.

Let’s say you are a marketing manager at an eCommerce company selling shoes (imagine

 Zapatos).  Spring is here and for this quarter you have a marketing budget of $100K. You have 1M+ customers and prospects, and you have to decide where to spend that $100K to get the best ROI possible. Should you spend that towards acquisition i.e. driving new traffic to your site, or should you spend that towards engagement of current base or both? If you focus on acquisition, which channel or combination of channels should you choose? If you focus on engagement, should you go out to the entire base, or a subset? Should you customize your offering by segments and if so, how? At the end of the day, you want to make the choice which aligns the most with your company/ department’s priorities and gets you the best ROI. But the question is how to make the best choice NOW?

A non-analytical approach to marketing may look like doing what was done last spring (status quo) or choosing projects from last quarter or going with projects which you believe to be the best. Just like in the car buying example, unless you make a choice by keeping ROI (success criteria) in mind, you would likely not get the best ROI from your effort. You will execute some marketing campaign, probably not the best ones.

An analytical approach to this marketing campaign would be to learn from the past campaigns – what worked, what didn’t, what gave the best ROI. Let’s say, you find that your organic acquisition is at par with competition and you decide to invest in re-engagement of the current customer base, and habituation of the prospects or light users. Now, you would go back to past campaigns and see what worked. Let’s say, you find certain customer segments (loyalist) buy irrespective of marketing to them (you know it because you used a control in the last campaign) and you also find other segments that respond to marketing. Now you have clues as to which segment to not market, which segment to saturate towards optimizing the ROI.

Can you or any business professional do this? Yes, I think so. As long as you understand and practice a data-to-decisions framework like BADIR, you can use simple techniques to optimize your day to day decisions. These simple methods don’t need complex tools. As long as you have access to data through some data tool (like Tableau, Spotfire, Pentahoe, Splunk, Microstrategy, Business Object etc), you can download the data into excel to analyze. You can also do the analysis in the data tool itself (if available).

Currently, business professionals may depend on their analytics counterpart to help make those decisions- analyze past campaign, find the best target segment etc., but those analytics resources are increasingly scarce. There by, many business professionals are finding themselves having to either learn how to optimize those decision using data or make decisions based on gut. And we know, gut based decisions don’t show long term results.  And I see this learning of analytical skills by business professional accelerating and becoming a core requirement in the future.  This is the way we will close the projected gap in the McKinsey report.

If you are a business professional – marketing manager, product manager, sales professional and operations manager; in a role where you are making decisions in a day to day workflow, then it is imperative that you equip yourself with skills to solve a problem using data i.e analytics. Make sure you are not left behind.

Forbes.com