Nike – just Do it with Data science and Demand sensing

Nike has increased its focus on direct-to-customers strategy and cancelled its contract with Amazon to be able to collect its customer data. Through a series of acquisitions of data analytics and AI-based startups, it plans to use this data to understand customer journey, improve demand predictions at a hyper-local scale and enhance customer experience globally.

“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

Value creation

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.

Value capture

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.

Operating model

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.

Nike is cutting intermediaries to reach customers directly for data collection and analytics
Figure 1 : Nike is cutting intermediaries to get closer to customers ( access to data)

To make this happen, they have been systematically reducing their reliance on other retailers and increasing their footprint in the direct sales category-

  1. 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]
  2. 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.
  3. 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.
  4. 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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Student comments on Nike – just Do it with Data science and Demand sensing

  1. Hi Sneha, this post is fascinating. I think it is very interesting the the use of big data in the sports sector. In particular, I believe that Nike has a competitive advantage versus smaller players since it has high volume of data and cash for investing in the developing of new technologies. Other big competitors such as Adidas are pursuing similar actions ( I believe that data analytics will be an essential tool for surviving in that highly competitive market. As you highlight in the article for strengthening the analytics capabilities Nike should continue investing on recruiting data scientists to increase its capacity in data analytics. That could be challenging considering the competition for attracting talent with other traditional tech companies (e.g Google, Amazon, Apple).

    1. Thanks Andres! Completely agree! I got curious after reading your comment and went onto glassdoor to check what the range of salaries are. They are great, but lesser than other big tech companies. Indeed they will find it difficult to find the best talent. I think that is why they are acquiring to many data analytics startups alongside. Building a full fledged in house team must be difficult at this time, especially given how fast they have to ramp this initiative up!

  2. Great Post! I agree with Andres that Nike has a competitive advantage given its size, as smaller competitors may not be able to have good insights from a limited volume of data. I also wonder if going direct will mean losing data that may be interesting for Nike, such as the behavior of their customers on other verticals (clothing, tech, etc.) that partners may provide. Finally, I completely agree that they should expand into adjacent businesses such as sports tracking, health tracking, transforming in the go-to products/services when thinking about sports and health.

    1. Thanks Walter for your perspective. I believe Nike was not privy to the customer data from many of the partner retailers anyway. So it makes sense for them to get them out of the way and control the communication with the client directly. This allows them to control branding along with gathering data. Getting out of Amazon must be a big decision! But probably for the best for the future of Nike!

  3. I agree with you that to continue to utilize data to create a competitive advantage and increase customer retention, Nike will need to gather the right data. One interesting approach that they are taking is the Nike Plus membership program. This allows them to track all member purchases, online shopping habits and Nike app utilization. Nike has a tremendous challenge ahead to begin to gather this data from the physical channels that they are operating but are making strides to introduce tech to do this. To date, Nike has largely used this data for the consumer experience but I believe the real value lies in utilizing this data for internal operations like product design and planning.

    1. Indeed! I read about their new app- it, apparently, is super targeted to improve customer experience. They also said that they are using the app to track consumer shopping pattern and then predict demand so that they can have the right product at the right store at the right time – ultimately improving consumer experience. Their attempt is to use this data to improve hyperlocal demand and consumer experiences, while managing a global supply chain! Agreed, that there is more value to the data that just customer experience.

  4. I agree with Alli’s point above on the use of data for product design and planning. I think being able to fill demand is really only the start of how this data can be useful – so much time, money, and resources are currently invested in designing new products using traditional ways of trying to predict the market, demand, and preferences. However, if Nike can use their increased consolidation of data to automatically design products that fit certain demographics and needs, then they can create an even greater competitive advantage (on both the cost and revenue side – being able to cut down costs on design side and increase revenue by increasing demand). This creates almost the flywheel we’ve discussed in enabling them to capture more data from more consumers, more preferences, and become even better / more efficient at design, eventually separating themselves from peers who can’t catch up. It will be interesting to see how much they can execute on this concept and really make data a part of every aspect of their business.

    1. Great thought, Jesse! I did not think about how data can be their way to “flywheel” by utilizing it in an manner that can take them off! It is a great idea for them to acquire all these innovative startups in the data analytics, AI and ML space so that they can be fast in getting to the point where it becomes difficult for others to catch up! If they were to create their own infrastructure( although they also building their core strength too on the side by recruiting data scientists etc.) and relied on building it all by themselves, others would have entered the space faster – reducing their advantage of having more data.

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