“There’s a lot of talk about how much we need data, but actually we need the right data, and we use some serious analytics behind it to turn it into value creation.” — Hannah Jones, Nike
A laser-like-focus on collecting customer data to utilize it for predictive analysis is Nike’s new strategy. To achieve this, Nike is reaching its customers directly to have control over consumer data and use it to improve inventory management and enhance consumer experience. It is using technology to optimize its inventory across channels with hyper-localized demand predictions to ensure customers can find and purchase what they’re most interested in.
Nike’s latest retail and marketing strategy is largely driven by Big Data. Through its initiative – Nike Direct – the company is cutting out on intermediaries. Nike’s direct-to-consumer initiative contributed $10 billion in sales in FY18 and projected to increase by 60% by FY20.[i]
As many new entrants and startups disrupt the apparel industry, Nike is defending its business by investing heavily in data science to better understand the customer journey.
Video source: IBM website
Nike announced its Consumer Direct Offense model in mid-2017. With an objective to better serve its consumers personally and at scale, the Consumer Direct Offense was supported by Nike’s Triple Double strategy: 2X Innovation, 2X Speed and 2X Direct connections with consumers.[ii] Embracing innovation through data analytics and technology, Nike creates value for its customers by providing:
- Better consumer experience: Nike aims to learn from the customer data in order to proactively predict customer behavior and serve them with targeted offers, products and services.
- Increased services for customers: The Nike app is designed to deepen the company’s relationship with its customers. Through its Nike Plus rewards program, it provides a variety of services such as access to Nike sports experts and personalized workouts.
Through smart ways of using customer data, Nike has improved its customer acquisition and retention by identifying which customers to target and predicting the right time to target them. Further, data is allowing them to manage their local supply chain systems.
Nike is improving its local demand predictions, while operating at a global scale. The company’s ability to make predictions relies on the volume and quality of data that it collects. This is achievable only if Nike reaches out to its customers directly for both sales and marketing channels.
To make this happen, they have been systematically reducing their reliance on other retailers and increasing their footprint in the direct sales category-
- Flagship Nike stores: Nike has focused on improving technology at its flagship stores[iii] and reducing exposure to retail chains like Foot Locker. This helps them get quicker insights on consumer demand and control branding. [iv]
- Cancelled contract with Amazon: In late 2019 Nike stopped selling through Amazon. [v] This is in alignment with Nike’s strategy to get closer to its customers and collect useful user information.
- Startup acquisitions: Nike acquired a predictive analytics startup Celect, founded by MIT professors in 2013, to bolster its DTC strategy. Celect’s cloud-based platform, integrated with Nike’s mobile app and website will use data to optimize inventories with hyper-local demand prediction about what, when and how consumers will buy Nike products. [vi] Further, Nike also acquired consumer data analytics firm Zodiac and a computer-vision company Invertex to strengthen its artificial intelligence capabilities.
- Data scientists and machine learning engineers: Nike has been aggressively recruiting in-house data scientists to increase its capacity in data analytics and AI[vii]
Current challenges and opportunities
The primary challenge for Nike is speed with which it can collect data and then analyze it for valuable customer insights. Some competitors have also started to pay attention to collecting customer data e.g, Under Armour bought MyFitnessPal and was working with IBM Watson for data collection and analytics respectively.
Therefore, to keep its competitive advantage with data, Nike needs increased volume of relevant data. While it has been acquiring great tech startups for analytics, for the data it should think about acquiring startups in adjacent businesses. Some ideas could be fitness apps and travel planning companies as sale of Nike products could be closely related to fitness profiles of people and time around their travel.
Nike has a tremendous opportunity ahead of itself. By early start of predictive data analytics, Nike can leverage global data to predict local consumer demand, creating a data moat that will be hard to cross for new entrants.
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