What Sort of Questions Should You Ask of Big Data?

What Sort of Questions Should You Ask of Big Data?A&E has a popular show called “Hoarders.” If you haven’t yet had a chance to tune in, the premise is this: the subject of the show has a compulsive desire to collect and store something. (Old newspapers, hamsters, action figures, lunch boxes, doll clothes, etc.) This compulsion becomes so strong that it takes over the subject’s life, often ruining family life and harming the subject’s health. Here is the real kicker though: The very thing that once brought joy (newspapers, doll clothes) now brings ruin through sheer excess. Surrounded by stuff, the subject can no longer do anything useful with it. Sound familiar?

If you are like many caught in the throes of a Big Data campaign, you might have more in common with this popular series than you are comfortable admitting: Are you a Big Data hoarder? Do you constantly amass more and more data, while at the same time pulling your hair out because you worry about where you’re going to store it? Do you literally feel buried under all of your data, unsure of how to make it work for you?

If you answered yes to some of these questions, you are not alone: A recent Oracle study reports that a full 94% of executives in charge of companies who use Big Data note they are losing money because they are not leveraging their data to the fullest. Luckily though, all is not lost: Big Data Analytics can help you analyze and organize your Big Data.

As it turns out, it is not enough to “simply” collect Big Data. That data represents a huge intangible business asset, and as such, it bears paying attention to. Companies need to put their big data to work for them. The best way to do this is to approach your data with specific questions and goals in mind.

Below, we take a look at several pressing Big Data questions that can result in valuable insights about your business and customers:

1. What Do Our Customers Want?

The fact that companies should ask what their customers want is not exactly revolutionary. But, the idea that your data can predict whether or not your customers will like something based on a set of data points is pretty interesting, if not entirely revolutionary.

Let’s say you are a boutique pet food company with big plans on the horizon to roll out a new line of flavored dog foods. Sure, you could take a risk and whip up a huge batch of Mango-Pineapple-Elk-Surprise, but wouldn’t you rather have some indication going forward that you’re making the right decision?

Your data can provide the perfect window into your dog food conundrum — if you ask the right questions, that is: What ingredients are our biggest sellers? Do any of the ingredients in our new flavor list show up on the “most returned” list? Which flavors do customers rave about on our social media channels? If all signs point towards that fact that your furry customers love mango, pineapple, and elk, it looks like you’ve got yourself a winner of a recipe. Use Big Data to know what your customers want and react to it.

Related Questions:
• How much will customers want?
• How much will customers be willing to pay?
• Which market segment will like what we have to offer?
• How long will it take to get customers on board with a new product?
• Will customers stop buying an older product when our new product rolls out?

Case Study Example: Before Netflix optioned its wildly successful streaming series “House of Cards,” execs wanted to know if it was likely to be a success. If you are Netflix user, you know that Netflix is a master at storing data about your preferences and using it to make predictions about what you will and will not like. Having access to all that data allowed Netflix to make some shrewd predictions about whether or not the show would be a flop: Do audiences like Kevin Spacey? Check. Do audiences like dark series about criminals? Double check. Which shows are most likely to be paused? Watched all the way through? Netflix mined all of these data fields and gave “House of Cards” a thumbs up. Not surprisingly, the show was a tremendous success.

2. When Do Customers Want What They Want?:

As marketers know, the age-old adage “there’s no time like the present” is not always true. Sometimes, the present is the exact wrong time for something. Let’s say you are a non-profit organization and 2013 has seen you providing services to a record number of people. You can’t wait to tell all your donors about your doings, so you spend money on a large-scale mailer with lots of glossy photos and mail out in July. As it turns out though, many of your donors have summer homes out of state. They won’t even see your mailers till September, and by then it’s old news.

Had you done a bit more research, you might have known that while July is a bad season for contacting your donors via snail mail, it is a great season to email them, since they are likely to have fewer work emails coming in. Use Big Data to determine when customers will be receptive and when they are likely to want what you are offering.

Related Questions
• When are customers most likely to buy or need a certain product? And least likely?
• When do customers respond best to certain types of communications?
• How long should you wait between email blasts?

Case Study Example: Target caused quite a stir when it came out that the retail-giant was pretty good at predicting pregnancy. Statisticians at that company began their quest to develop a “pregnancy predictor” score with Target Baby registries, figuring that registering for baby gifts was a pretty good indication that someone was about to have a baby. Then, they looked backwards at the buying habits of those Moms-to-be, gleaning a couple of things: Women who were pregnant bought a lot of lotion, unscented beauty products, vitamin supplements, and hand sanitizers. These purchases seemed to cluster near the beginning of the second trimester, which made it pretty simple to predict a due date. Target used that due date to decide when to send out offers on certain baby-related products to these customers.

3. Why Are Our Customers Leaving Us?

No business or organization likes to be in the position of seeing customers walking out the proverbial door. Even worse though is not understanding why a customer is leaving. If your customers are leaving in droves, wouldn’t you do anything you could to stop them? Of course you would. Use your Big Data to tell you a story about why your customers are jumping ship and then make an end-run to keep them.

Related Questions:
• What behaviors do our customers engage in before they leave? (Making a complaint, missing a payment, etc.)
• What do customers say on our social media channels?
• What types of overtures are effective in reaching out to customers who may be leaving?

Case Study Example: T-Mobile wanted to decrease churn rates among customers. In order to do this, they used, you guessed it, Big Data. They knew that each quarter, they were losing a certain number of clients. When they analyzed the behavior of their defectors, they saw patterns beginning to develop across social media channels, billing data, and web logs. Having a better sense of who was about to leave allowed the company to make interventions, cutting the “defection” rate in half within a quarter.

4. How Can We Take Control of Things We Have No Control Over?:

In business, we can control lots of things, but lots of other things evade even our most herculean efforts to master them. Like weather. And natural disasters. And wars. So, maybe we will never be able to make it rain when we want it to or to snow (naturally) on the day ski season starts, but we can learn about how those Uncontrollables impact our business so that we can better plan. Use Big Data to learn from those vexing things that you can’t put a stop to. Sure, maybe you can’t make it snow as much as you want, but you can use data from the last time it didn’t snow much to understand how many chair lifts you need to have open this time.

Related Questions:
• Which events can we control?
• How do customers respond in given situation X?
• How much money do we lose every time X occurs?

Case Study Example: Point Defiance Zoo and Aquarium, a popular open-air attraction, is located south of Seattle in the rainy Pacific Northwest. One thing the administration can always count on is rain and lots of it. Given that no one in the PNW has yet learned to control the weather, what is an outdoor attraction to do? PDZA looked to the past in order to help them better prepare for the future. Using weather data from previous years cross-referenced with guest numbers, they were able to forecast how many people to plan for on certain types of weather days. The Zoo used those numbers to decide how many staff people to have, which times of day to open the most popular exhibits, how many concession stands to open, etc. Quickly, they were able to improve guest experiences, cut down on waste, and make smarter staffing decisions.

The Takeaway:

Now that you have seen some examples of the types of question to ask of your Big Data, do you feel ready to be a Big Data Whisperer? If so, congratulations, because you are about to embark on a fascinating journey. Gathering the data was the first step, but developing the right questions will determine how successful your Big Data journey will be. Hang on, it may very well be a wild ride!

image credit: usa.helpministriesinternational.com

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Innovative Data - Numbers Telling a BIG StoryRob Toledo is Outreach Coordinator at Distilled, aka marketing coordinator with experience heavily focused online. Technologically driven, with a love for SEO, outreach, link building, content creation, conversion rate optimization, advertising, copywriting, graphic design, SEO, SEM, CRO, Google Analytics, social media, creative content…you get the picture. He blogs at stenton toledo

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