AI, Data and Predictive Analytics: A look into Nike’s Formula for Growth

Building a Data-Centric Organization the Nike Way

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Elizabeth Mixson
11/23/2020

Upon its founding in 1961, Nike not only revolutionized footwear by making athletic shoes fashionable but, as one of the early adopters of outsourcing, inspired millions of other businesses to follow suit.

Almost 60 years later and Nike is still a world-class innovator not only in shoe technology and sports science but, increasingly, in predictive analytics and artificial intelligence as well.

In between the emergence of e-commerce giants such as Amazon and direct-to-consumer (DTC) brands, the retail landscape has become increasingly competitive and tempestuous. While many brick-and-mortar retailers and legacy fashion brands struggle to stay afloat, Nike has grown steadily over the past 2 years due in large part from its successful transformation from a classic consumer goods business to a technology-driven DTC company.

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At the heart of this transition lies Nike’s ever-changing array of mobile apps. Nike’s main app offers wide-ranging sales support and houses its general loyalty program. Nike has also released workout & training apps, it’s SNKR app for shoes enthusiasts and the Nike Fit measurement tool. Though the primary purpose of these apps is, of course, to optimize the customer experience, they also help Nike collect a treasure trove of customer data.

For example, the Nike Fit app uses a combination of computer vision, data science, machine learning and artificial intelligence to cultivate a digital foot morphology based on 13 data points. Not only does this help the app make more informed product recommendations, Nike can also use this data to design better fitting shoes down the line. In addition, when a Nike App “member” walks into a Nike store, the app goes into in-store mode. The user can then scan items to learn more, request items be brought to them to try on and reserve items for purchase. All of this data is then used by Nike to provide the member with personalized product recommendations and content based on their past, real-world shopping behavior.

Further cementing Nike’s commitment to data-centricity is its acquisition of two predictive analytics companies: Zodiac and Celect, in 2018 and 2019, respectively. While both are AI-powered predictive analytics solutions, Zodiac is a marketing-focused tool that provides insights related to the “value of an individual customer to boost revenue and retention with the right marketing, recommendations and offers.” In other words, it allows Nike to crunch together data points from various apps (both their own and external such as Fitbit) to better understand customer habits and predict purchasing decisions. Nike has incorporated Zodiac into its app to facilitate personalized product recommendations and content.

On the other hand, Celect is a demand-sensing analytics platform that “provides proprietary insights that allow retailers to optimize inventory across an omnichannel environment through hyper-local demand predictions.”

Using inventory management tools such as Celect, Nike is able to more effectively determine what products to produce and where they should be sold. For example, during a recent earnings call, Nike’s CFO, Matt Friend stated, “Our new regional service center near Los Angeles went live this month and uses predictive modeling to anticipate consumer demand and ensure the product our consumers want is available and will arrive within one day to two days. We will achieve this level of service at a lower fulfillment cost over time.” He also mentioned that “predictive modeling tools, data-driven member personalization and inventory staging” were key to Nike’s efforts to boost operational efficiency.

Behind the scenes, Nike’s formidable enterprise analytics framework serves as the bedrock for this data analytics revolution. First and foremost, their analytics systems are fully integrated so they can easily connect predictive variables ranging from social media behavior to purchase patterns to better understand and anticipate customer behavior. Secondly, Nike has also successfully deployed self-service capabilities so that every member of its senior leadership team has access to a fully customized analytics dashboard that is tailored to their decision making needs. Furthermore, as Nike considers itself a creative company, they’ve invested heavily in data visualization tools to ensure insights are as easily digestible as possible.

Image sourced from "How Analytics Are Informing Change at Nike," https://ieondemand.com/presentations/how-analytics-are-informing-change-at-nike

Building the Data-Centric Organization

In our overview of “How Digital Transformation Unlocks Business Resilience” we define enterprise resilience as an “organization’s ability to not only react to change, but capitalize on it.” And that is exactly what Nike is doing with their approach to predictive analytics; seeking to disrupt themselves before any one else does.

We’ve said it before and we’ll say it again, all companies are increasingly becoming data companies. Why? Not only does it enable them to become more customer-centric, it helps them identify and capitalize on new revenue streams. In addition, at least for the foreseeable future, Artificial Intelligence (AI) and Machine Learning (ML) will require tremendous amounts of data, computing power and interconnectivity to function. By fortifying enterprise data and analytics systems now, you’re also laying the groundwork for future AI and ML initiatives, a must for surviving the next decade.

Nike is an interesting success story because, unlike some of the other companies we’ve covered, they’ve been around for decades and, historically speaking, have not always been on the forefront of digital technology. Nor have they always been the most profitable. In fact, Nike didn’t even really begin their full blown digital transformation until 2017. Like many companies out there, it's safe to assume that they too had to overcome incumbent challenges such as entrenched business processes, budget limitations or reluctant leadership.

With this in mind, we hope that those of you from the roughly 69% of companies still struggling to build a data-driven organization find inspiration in Nike’s story. Though the window is narrowing, there is still time to jumpstart your transformation into a data-centric organization and if Nike can “just do it,” so can you.