Forum: Problem solving with data
Many collect data, few know what to do with it. The core skill business professionals need to know.
By Piyanka Jain
McKinsey’s Big Data report, published in May 2011, lists a shortage of talent in the big data space. Interestingly, the shortage of big data business professionals (1.5 million by 2018) is about 10 times that of data scientist (140,000 by 2018). Although the shortage prediction is for 2018, this 10-1 ratio exists today. That means a lot of people are collecting data, but few really know what to do with it. This huge gap between business professionals and data analysts is paralyzing the business world.
Now imagine a world where marketers, product managers, sales people and operations folks actually know how to use the power of the data they collect on a daily basis. In this world we remove the knowledge deficiency and provide an amazing amount of competitive power to the business with the data.
As business professionals, we naturally have some ability to dissect the data we collect, but when it comes to more detailed analysis, most business professionals conjure up images of complex algorithms and code – and then tune out. Business professionals need not be afraid of what big data can deliver. There is so much to learn with the information we collect on our potential and current customers. Those insights enable us, as marketers, to be the best at what we do.
Let’s go through a basic decision making process most of us have or will go through at some point in our lives – buying a car. I want to provide an analytical and non-analytical approach for you to gain perspective.
Buying a car
The analytical (data-driven) approach: You start by nailing down your constraints – time, money, five feature requirements (must haves) and five wish lists (good to have). Perhaps good mileage is a high priority in your features list while low emission is a lower priority for you. Based on all of these criteria (which are unique to you), you narrow down your choices in vehicle options to a short, final list.
You test drive the finalists, consider the biggest priorities on your requirements list and begin to grade each vehicle with a 1-5 score on each requirement and wish. The requirements are graded with an extra point and the wish lists stay at the numbers they were given. Then take the average of requirements and the wish list. You now find out what your next vehicle purchase will be. 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.
The non-analytical approach: This process may start by test driving cars, any type whatsoever, irrespective of any criteria, and you either begin creating your own criteria as you go along – maybe rejecting some car and loving others, based on what you “feels” good.
So what is the advantage of analytical over non-analytical approach? I was recently talking to a friend who was complaining about the mileage on the new car he recently purchased for his long commute. He seemed very unhappy with the $100 a week he spent on gas. That didn’t seem unreasonable to me. So I felt compelled to ask him if he had changed jobs and if his commute was even longer after buying the car. That turned out to be not true. Then I asked if the car is giving him lower mileage than expected or advertised – something that may be feeding his complaint. Well, it turned out that was also not the case.
Finally I asked him why he didn’t buy a car with higher mileage instead of the one he bought, especially since he was going to use it for his long commute and when he knew the gas cost was a constraint. He answered by saying he didn’t know the cost would be this high or that it would be so burdensome to him. Most importantly, he said 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 decisions delivers a significantly higher chance of making a good, long-lasting decision over non-data-driven approach.
Can most of us envision ourselves using this kind of analytical approach to buying a car, buying a house, choosing a career or choosing a school for our kids? Yes, most of us do. Many of us base our decisions on facts. The process of making data-driven decisions in our day-to-day “business” life isn’t a whole lot different.
Say you are a marketing manager at an e-commerce company selling shoes (imagine Zappos). Spring is here and you have a marketing budget of $100,000. You have a million or more customers and prospects, and you have to decide where to spend that $100K to get the best ROI possible. Should you spend it toward acquisition, i.e., driving new traffic to your site, or should you spend that toward 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 a choice that aligns best with your company/department’s priorities and gains you the best ROI. But the question is, how to make the best choice now?
A non-analytical approach may look like doing what was done last spring (status quo) or choosing projects from last quarter or going with projects that you believe to be the best. Just like in the car-buying example, unless you are specifically focusing on maintaining ROI (your expected success criteria) in mind, you would likely not get the best ROI from your effort. You will execute some average marketing campaign, but not the one that makes the most sense for your goals.
An analytical approach to this marketing campaign may be gathering insights from 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 the competition and you decide to invest in 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 that certain customer segments (call them loyalists) purchase irrespective of marketing to them (you know it because you used a control in the last set of campaigns) and you also find other segments that respond to marketing. Now you have clues as to which segment not to market to and which segment to saturate toward optimizing the ROI.
Can you or any business professional do this? Most definitely! All it takes is a little understanding and practice of a data-to-decisions framework (BADIR, for example). 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 (such as Tableau, Spotfire, Microstrategy, Business Object, etc.), you can download the data into excel to analyze it (or analyze it within the data tool).
Today, many business professionals depend on their analytics counterpart to help make key product and marketing decisions, analyze future launch and past campaigns, find the best target segments, etc., but those analytic resources are increasingly scarce and in high demand. But marketing professionals don’t need to find themselves in a lurch without these analytics professionals. Learning some of the basics of the data game will help marketers derive some of the best tactical options for future campaigns through past consumer behavior.
If you are a business professional 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 problems using data analysis. Make sure you are not left behind.
Piyanka Jain (firstname.lastname@example.org) is president and CEO of the analytics consulting company Aryng. She often keynotes at business and analytics conferences on in-sourcing of analytics via developing internal organizational capabilities – people, process and tools. Her prior roles include head of NA business analytics at Paypal and senior marketing positions with Adobe. She is an INFORMS partner. This article is based on a blog of Aryng’s analytics tips series for business professionals.
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