Target and spring break. A sunny, 75-degree day and Walmart’s berry display. Disney’s MyMagic and a churro stand. At first glance, the correlation of these seemingly disparate items is unclear. But look a little closer—as any good product professional will—and the relationships become crystal clear.
These giants of the industry represent just three examples of companies that are successfully leveraging data to make better decisions. And, ultimately, these decisions result in better products, better customer experiences, and—perhaps most important—better revenue.
Early adopters of big-data analytics have proven the benefits of leveraging information. Companies with the most advanced analytics capabilities outperform their competitors by wide margins and are twice as likely to be in the top quartile of financial performance within their industries, according to survey results from management consulting firm Bain & Company. They’re also five times as likely to make decisions faster than market peers and three times as likely to execute decisions as intended.
“For companies that do it right, data is a way to learn who customers are, what they want, and what motivates them to either stick around or go to a competitor,” said Philip Alexander, PMC-IV, CEO of Pragmatic Institute. “Insights-driven companies are using this constant feedback to build better products—and it’s giving them a competitive edge.”
The flourishing relationship between data and product development and delivery is the reason why Pragmatic Institute (formerly Pragmatic Marketing) acquired The Data Incubator (TDI) in early 2019, Alexander said.
“Here’s the reality: 80% of all customer data is wasted. It’s completely unused,” he said. “We made the decision to get into the data business to help product leaders get a better understanding of the rich repository of information available to them—and show them how to use that information to deliver better business results. But achieving this requires a fundamental shift in how product managers relate to and interact with data.”
Pulling Back the Layers
Ninety percent of the world’s data was created in the past two years, and that pace is accelerating. Gone are the days when gigabytes were enough to measure information. Today, we are creating zettabytes of data. Consider that:
- On average, people send about 500 million tweets per day
- Walmart processes 1 million customer transactions per hour
- Amazon sells 600 items per second—and in fractions of a second makes recommendations based on these purchases
All of this is behavioral data that reveals what consumers want. It represents a shift away from products that drive user behavior to user behavior that drives products, and it falls in line with Pragmatic Rule No. 1: An outside-in approach increases the likelihood of product success. It also offers new avenues for gathering information apart from traditional customer surveys.
To make this shift, everyone in the company—not just data scientists—must dig into and leverage behavioral data to answer key questions, make better decisions, and build products that consumers love. Data delivers new value by delivering deeper insights into customers, partners, and the business overall.
To see this value in action, dive into the Target, Disney, and Walmart scenarios. For many years, Target launched its swimwear collection in late spring and early summer. Then the retailer looked at the data and realized it was missing a key piece of insight: College kids plan and get ready for spring break in late winter and early spring. To position itself as a leader for this market segment, Target started selling its swimwear collection online in February—just in time for spring break.
In the case of Disney, the mass media and entertainment conglomerate wanted to answer one simple question: What would make the park experience more enjoyable? A team of researchers, analysts, and innovation consultants worked to uncover a laundry list of friction points, and this, in turn, resulted in a five-year, $1 billion undertaking. The result: the MyMagic program, which produced the iconic MagicBand wristband with RFID technology.
With the simple wave of a wrist, visitors can enter theme parks, unlock hotel-room doors, buy meals and merchandise, and skip the wait on rides. For its part, Disney has continued collecting data on visitors’ behaviors, allowing the company to see patterns. For example, a churro stand across the theme park may be seeing increased traffic around 8 p.m. As a result, Disney may move that stand closer to where evening foot traffic flows, thus driving convenience—and sales.
And Walmart has learned that knowing when it’s going to rain, snow or hail offers an opportunity to deliver hyper-focused advertising—but not just for obvious products like umbrellas or rain boots. Data patterns revealed something more inconspicuous: When the weather is clear and sunny with a high of around 75 degrees, blueberries, blackberries, and raspberries tend to sell well. But as the temperature creeps to 80 degrees, salads sell better.
“These companies are leveraging users’ behavioral data as a direct line of communication to inform business decisions,” Alexander said.
Peering into the Future
Headline after headline tells us that technology is replacing jobs. And yes, while some jobs will go away, it’s important to remember that new jobs will take their place. The challenge is acquiring the right skills to succeed in these new roles.
“AI can’t create, conceptualize or manage complex strategic planning. It can’t interact with empathy and compassion,” said Steve Johnson, PMC-VII, vice president of product at Pragmatic Institute. “This is why product managers will continue to be pivotal. But, to be successful, product people need to become data-savvy leaders.”
Product managers need to think about how data and AI can change their approach to work, Johnson asserted.
“A new kind of product manager is evolving—the data product manager,” he said. “The availability of data is determining how products behave and what new classes of products are available. Machine-learning models automatically adapt products to users’ preferences, make recommendations for next steps and then suggest future features and products. Data product managers understand this and incorporate it into their products.”
However, Johnson said, working with data at the foundational level of product development requires an understanding of how to leverage data modeling, data infrastructure, and statistical and machine learning. Thus, if the traditional product manager operates at the intersection of business, technology and market, the data product manager must add domain knowledge of data and data science.
“The data product manager understands that building products with data requires strategy,” he said. “What is your plan for how data will be generated, collected and consumed? How does this uniquely position you to win in your market? It isn’t enough to collect data and stash it in a data warehouse so you can analyze it later. If that’s what you do, all of your efforts will have been nothing more than an academic exercise.”
Along with an understanding of data and data science, data product managers:
- Have a plan for how data generated by a product will be used to improve that product—algorithmically or otherwise—over time, and why this creates a defensible moat to increase the product’s chances of long-term success
- Understand the technological infrastructure involved in building products and know the type of infrastructure needed to support products
- Take on the role of translating requirements among data scientists, engineers, designers, marketers, and other product managers
- Build product instrumentation and data storage into their acceptance criteria while collaborating with data scientists to ensure that data is accessible and usable for analysis and modeling
Finally, and perhaps most important, data product managers know that data and its models and outputs aren’t enough—they still must be product managers who tie these components back to the business model and their organization’s strategy.
“Machine-learning models that don’t align with the business model not only waste time and money, they also undermine the organization’s trust in machine learning,” Alexander said. “This is especially true in companies that are late to data science, are skeptical about the power of data science or have a very qualitative leadership approach.”
Looking Product in the Eye
Product managers sit on the front line in the battle to beat their industry competitors, and the battle is no different from a career perspective. It is crucial that tomorrow’s product managers use metrics and analytics to influence new product development.
Innovative products come from finding gaps—this has been and will continue to be true. It’s the “how” behind exposing those gaps that is changing, and that’s where big data and metrics come into the picture for exploring new user behavior, forms of churn, and triggers for bottom-line costs. All of this will reinforce the business’ value proposition, gain traction with stakeholders, and optimize existing products. Remember, though, to measure the right metrics. What you choose to measure and analyze depends on your company’s size and industry—not to mention your product.
“Most successful products are built on a foundation of data,” Alexander said. “And innovation isn’t born from nothing; it’s fueled by market insights and it’s measurable. But understanding those insights goes beyond comprehending the results of experiments or reading dashboards. It requires a deep appreciation of what is possible and what will soon be possible by taking full advantage of the flow of data and applying it to our daily work.”