What exactly is data science? Think of an individual piece of customer data as a single jigsaw puzzle piece. It has part of a picture on it, but without being put together in the right order with its fellows, you can’t see the whole picture. And this puzzle piece doesn’t belong to a normal jigsaw puzzle. This isn’t your run of the mill, 1,000-piece, it’s-raining-on-our-vacation-so-let’s-stay-inside-and-do-this-instead puzzle. This is the ultimate, abstract, billion piece puzzle. And you’ve lost the top of the box with the picture on it.
If an individual piece of customer data is a single jigsaw puzzle piece, then data science is the act of completing that puzzle and seeing the larger picture. For years, businesses have been getting their hands on as much customer data as possible. The truth is, stockpiling customer data isn’t the answer to improved customer experience, better marketing or even advancements in industries like healthcare or pharmaceutical manufacturing; harnessing it with data science is. As Elea Feit, assistant professor of marketing at Drexel University put it, “In the end, the analytics won’t tell you the next big creative idea. It will tell you when the next big creative idea is working.” Here are four examples of data science done right.
TARGET: For years, Target collected volumes of customer data through every method they could and tied each of these data points to a unique code by shopper called a Guest ID. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our website, we’ll record it and link it to your Guest ID.”
But their mapping didn’t stop there. Linked with each shoppers Guest ID was their age, marital status, if they had kids, how far they lived from a Target, estimated salary, what credit cards they have, ethnicity, job history, where they went to college – the list goes on. Amongst the millions of Guest IDs, Target’s marketers wanted to know which of those IDs represented an expecting mother – but not just any expecting mother. Target wanted to know which of its customers were pregnant before the customer shared the information with anyone else.
Target surmised that if they were able to build brand loyalty with an expecting mother early in her pregnancy, then later she would stop at Target to buy not only the baby wipes and diapers that she needed, but paper towels, orange juice and everything in between. The key to connecting the Guest ID to the expecting mothers was – you guessed it – data science. Target’s data analysts developed a “pregnancy predictor” score which they tied to the Guest ID. While that may strike many shoppers as creepy, there’s no doubt that the project was a data analytics success as they are now able to estimate a shopper’s due date to within a small window, and time their marketing to specific stages of her pregnancy.
MCKINSEY AND COMPANY: For McKinsey and Company, it was data science to the rescue to make sense of some troubling lab results. The company was conducting a study using live, genetically engineered cells and simultaneously tracking 200 variables to ensure that its manufacturing process was up to scratch. Trouble came when two batches of the same substance, manufactured using identical processes, resulted in a yield variation from 50 to 100%. This meant big trouble for the company and potential losses of $5 and $10 million annually. The project team segmented its manufacturing processes into clusters of activity and then used data analytics to process “interdependencies and identified nine parameters that had a direct impact on vaccine yield.” With this new approach, McKinsey and Company were able to increase vaccine production by 50 percent. Thanks, data science!
NETFLIX: Gone are the days when Netflix was just a company that mailed you a new DVD of “Will & Grace” every other week. A huge part of the entertainment streaming giant’s business is to develop their own original movies and shows. In fact, Netflix plans to spend $8 billion over the next year on content. With that much money floating around, they aren’t leaving anything to chance. From the pre-production to launch, and every step in between, Netflix turns to data science to guide their creative process.
After the creative pitch is given and the business agreement is signed, the pre-production team begins trying to solve one question: given various attributes about a production, how much will it cost? How can they be true to the creative vision while also staying under budget? So, what’s the solution to answering these questions? Say it with us now: Data Science.
The same is true once production starts. The 1st assistant director (AD) is charged with creating the filming schedule. The 1st AD takes into consideration the day and time each scene starts shooting, the contractual constrains for each location and cast member, the logistics of transporting and setting up gear, and a million other factors to build the production schedule. Through data science, the 1st AD is armed with mathematical optimization that helps generate rough schedules to inform early-stage production planning.
After principal photography is completed on any Netflix production, hundreds of processes remain. VFX shots, daily film clips and final cuts bounce from department to department, all with their own timeline and needs depending on the type and scale of the production. With dozens of projects in the pipeline, data science helps Netflix identify bottlenecks and blocks in their post-production process. So, next time you’re cuddled up on the couch watching the latest season of your favorite Netflix Original, raise your glass to the data science tech that helped create it.
HEALTHCARE IN SINGAPORE: Data science isn’t just for big brands. The healthcare industry in Singapore has embraced data science as a way of improving treatment and saving lives. Patients suffering from diabetes often require extended hospital stays, which puts strain on both the patient and the medical infrastructure. With data science, healthcare providers are able to better understand each patient’s condition to create personalized treatment plans. If a patient is forgetful about taking their medication, the custom treatment plan, created with the help of data science, is designed to specifically address that problem. Following these healthcare providers example, others have used data science to “better understand that person’s history, genetics and even important demographic and cultural factors to more quickly and cost effectively diagnose patients.”
Whether you want to identify your customers or improve their brand experience, data science is your key to seeing the big picture. We want to hear from you! How is your organization currently harnessing data science or considering it in your business or marketing strategy? Tell us in the comments or share it with us via our social channels on Facebook, LinkedIn or Twitter (@OutlookMktg)! And keep an eye on our blog for more great content on key topics for marketers!
Author: Carolyn Kaleko, Senior Account Manager
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