A CRM Agency’s Secret Weapon: The Data Scientist
“Mars, we have a problem.”
That could very well have been the message sent in 1999 when the Mars Climate Orbiter erupted into flames and exploded. What should have been a celebration for NASA engineers became a disaster. All because of data, in this case, the incorrect measurement of units.
Who or what was the culprit? The navigation team at the Jet Propulsion Labs used the metric system. millimeters and meters. Lockheed Martin Astronautics, which designed and built the spacecraft, provided data in inches, feet, and pounds. While the team sounds like a scene from Iron Man, there was no Jarvis to reconfigure the units in milliseconds. The result was catastrophic for the Orbiter.
Everyone has data. It is what you do with it that really matters. Data can get lost, obscured, inaccurately analyzed, or ignored. Add numerous parties into the equation—multiple vendors, agencies, business stakeholders, data sources—and the potential for data delinquency accelerates. So how do you optimize these most prized assets?
The answer is as elegant as the discipline itself—data science. Data science is the art of transforming existing data into meaningful insights that businesses can use to make decisions. Core to the data scientist’s toolkit is artificial intelligence, which strives to enable machines to replicate human intelligence to execute reasoning. Memorable examples of AI onscreen are 2001’s HAL 9000, Lieutenant Commander Data from Star Trek and Arnold Schwarzenegger in all the Terminator movies. Machine learning, a subset of AI, can drive value for your CRM by applying predictive analytics to drive meaningful growth. This is where the true advantage exists. CRM agencies leverage this advantage to segment, be predictive, personalize, and optimize channels.
Think about how much data exists in the digital age: smartphones, the Internet of Things, connected vehicles. Then factor in social media. There are 500 million tweets daily. Five billion videos are viewed on YouTube daily; 500 terabytes of data are shared daily on Facebook. Billions of data location pings originate from smartphones every month. Insights derived from artificial intelligence are not only helpful; they’re essential to weaving through gazillions of data points to partition sentiment, propensity, advocacy. Old-school research firms and focus groups would survey sentiment toward a product or campaign. In the new world order, the data scientist is your personal wizard.
But, at the end of the day, data is all about understanding people. Here are a few examples of brands that utilize data science to leverage information to their advantage.
In predictive analytics, there’s the infamous Target case study. An angry father confronted a Target manager with a flyer for baby clothes and a crib. The flyer was sent to his teenage daughter. The father was purported to ask, “Are you trying to encourage her to get pregnant?” Several days later the father apologized as his daughter confirmed the pregnancy. What came into play here was the brilliant ability to consume data, compile behavior, and predict a life event—a pregnancy. Not only could Target predict the pregnancy, but their algorithm could also signal the stage of pregnancy based on purchase. Prenatal vitamins at 20 weeks; unscented lotion at six months; scent-free soaps, jumbo bags of cotton balls, and hand sanitizers just before the due date. What’s better than knowing your customer’s needs? Receiving coupons for the very items you need when you need them. The data science could not have been any more precise than if the customer purchased What to Expect When You’re Expecting.
Amazon has set the standard for personalization based on Big Data. Consumers can buy anything on Amazon, but the choices can be staggering. To reduce churn and help the consumer get what they really want, Amazon adopted a collaborative filtering engine. CRMs strive to attain a 360-degree view of the customer. And Amazon takes this holistic view seriously. The collaborative filtering engine analyzes the items previously purchased, online shopping cart or wish list, products reviewed and rated, and frequent searches. Then the personalized recommendation system offers products that people with similar profiles have purchased. This method generates 35 percent of the company’s sales annually.
To enable faster purchases and decrease distraction, Amazon will tell you everything about ordering the item. Does it qualify for free delivery and on what date? Is it in stock? And it offers speedy one-click ordering. Amazon also packs the shopping experience with customer-submitted images of the product along with Q&As and reviews by verified purchasers. And if a review appears in a different language, you can translate into English. Merci for suggesting to buy one size bigger!
Peloton was storming the fitness world well before COVID-19. Several factors make up this $4 billion start-up’s success. Time: lack of time to hit the gym. Unused fitness equipment: stationary bikes stored in the basement. Competition: remote spinning that ranks you against a virtual exercise community. Social media: bragging rights on social channels.
Homing in on the spin craze, Peloton was founded in New York City in 2012. The revenue model consists of two elements: the actual stationary bike plus the monthly subscription for virtual classes. The inspirational factor comes into play by using the customer’s own data against his or her rivals. Joining the community can be an initiation. Users create their exclusive screen name, by which they are known to the virtual community. Fun, pun-driven names are encouraged while alpha-numeric ones are not. (Who wants to yell out, “Congrats on ranking number ten, MZG1991!”?) Patrick Mahome’s Peloton name is 2PM. Spinners supply stats such as age, gender, and weight, and while this might seem intimately intrusive, it pays off later as it measures caloric burn more precisely and ultimately leads to the coveted Leaderboard ranking. Bikes track performance and remember preference settings. Instructors yell out during class to give props, recognize birthdays, and galvanize their students.
Then the power of competitive data takes effect through gamification. Spinners are invited to race each other and track live progress. It’s like playing a video game—all the adrenaline, visuals, and challenge except you’re not sitting on your couch, you’re actually busting your ass. You can also send high fives and video chat during workouts. Excuse me, my little cousin is burning more calories than me? Talk about primal motivation. And loyalty. The more the spinner becomes entrenched into the Peloton community and feels included, the more they use the service. Retention rate is 95 percent. Word-of-mouth referral is one of the most lucrative ways Peloton acquires users.
Peloton is showing how data innovation can succeed even during extreme crises: confined at home with your own sanitized bike, while you connect to people just like you.
This brings us to data cleanliness. Part of the beauty of understanding the more technical portions of data science is the foundation of data hygiene. Data integrity is always a problem. It makes absolutely no sense to add AI or machine learning if the data itself isn’t clean. And what does that mean? No duplicate records. Unique customer ID. Continuously fresh data.
Data cleanliness is the path to data godliness! What does this mean?
First, it is critical to have your data successfully identify a unique, distinct person regardless of how they enter your data sphere. Is the Alex Garcia who browsed your website on Monday the same as the Alex Garcia who ordered an item from Jersey City, New Jersey, on Friday? Signals in customer data that allow a brand’s customer data store to successfully identify and merge the actions of a distinct customer is vital to machine learning and artificial intelligence. Companies should make intelligent investments in customer data platforms (CDPs) and integration of back-end systems to achieve the goal of a unified view of each customer.
Second, it is equally critical, if not more so, to ensure that your customer data is refreshed and updated on a regular basis. Email address lists, according to email validation service FreshAddress, decay at rates as high as 25 to 30 percent every year, and similar aging rates are seen for phone numbers critical to SMS messaging, location analysis, and other data points. This cuts you off from accurate customer data merging and key channels for effective, personalized offers and brand communication. It is essential that your data management strategy builds a solid data foundation that captures and recaptures current customer contact data.
How does the data scientist bring this together? By understanding the data and using its validity and recency to knit together an overall portrait of customer behavior, sentiment, and action. These insights can be generated to create customized content, offers, and calls to action as well as strategy support for back-end operations, including supply chain management, inventory, operational efficiency, and future projections. Ultimately the goal is to use customer data effectively as the foundation to every aspect of a brand’s operation.
While data science is a science, the instrumental factor is humanization. There’s a reason why a CRM agency’s secret weapon is the data scientist. This discipline allows marketers to answer core questions. Who to target? When to target? How to predict behavior? How to retain? Which channel? Don’t get lost in translation like the ill-fated Mars Orbiter. Or worse, leave money on the table by misjudging your data. Make use of your secret weapon—the data scientist.
Rekha Gibbons is chief operating officer at Raare Solutions. She evangelizes the customer journey to B2B2C companies seeking to increase sales and brand loyalty—for Gibbons, the secret sauce to increased conversion and loyalty is impeccable customer experience. She has nurtured this philosophy at companies like AIG, Agency.com, Jaguar, Land Rover, and Lindblad Expeditions. Gibbons is a sought-after speaker on topics such as marketing during crisis, pivoting customer journeys, and inspirational marketing. She lives in New Jersey with her husband and their three-legged COVID rescue dog, Sonny.